EDUC 152. Intro to quantitative research in education: Regression analysis
Fundamentals of causal inference and comparing two groups
1 Introduction
The theory, methods, and practice of forming judgments about the parameters of a population and the reliability of statistical relationships, typically on the basis of random sampling
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system
- said differently, the process of identifying the effect of an independent variable on an outcome of interest
1.1 Example research questions
We will use two different example research questions to explain causal inference concepts:
- What is the effect of participating in the Mexican American Studies (MAS) program (\(T_i=1\)) vs. not participating in MAS (\(T_i=0\)) on statewide standardized high school test score (\(Y\)) for students in the Tucson Unified School District (Cabrera, Milem, Jaquette, & Marx, 2014)?
- Cabrera, N. L., Milem, J. F., Jaquette, O., & Marx, R. (2014). Missing the (student achievement) forest for all the (political) trees: Empiricism and the Mexican American Studies controversy in Tucson. American Educational Research Journal, 51(6), 1084-1118.
- This study was an observational research design, rather than an experiment. Cannot use the data because I am not allowed to share, but good example for introducing causal effects
- Note. in Cabrera et al. (2014), we modeled the effect of MAS participation on probability of high school graduation and on statewide standardized test scores (in writing, reading, and math)
- in this lecture, we use \(Y=\) statewide standardized test score because easier to explain Rubin’s causal model using a continuous outcome variable
- What is the effect of having a “small” class size (\(T_i=1\)) vs. “large” class size (\(T_i=0\)) on reading test score (\(Y\)) for elementary school students?
- This research question is from the Tennessee Student Teacher Achievement Ratio (STAR) experiment
- We have data from this experiment
- LINK to information and data on the Tennessee STAR project
Note: I got the data from the AER package (code chunk not run)
#install.packages('AER')
library(AER)
data(STAR) # load data frameLINK of script to process STAR data
1.2 Libraries, data, functions
# uncomment below line to remove all objects
#rm(list = ls())
# Libraries
#install.packages('tidyverse') # if you haven't installed already
#install.packages('labelled') # if you haven't installed already
#install.packages('patchwork') # if you haven't installed already
library(tidyverse) # load tidyverse package
library(labelled) # load labelled package package
library(patchwork)
##########
########## RUN SCRIPT THAT CREATES USER DEFINED FUNCTIONS
##########
source(file = url('https://github.com/anyone-can-cook/educ152/raw/main/scripts/user_defined_functions/create_inference_functions.R'))
##########
########## IPEDS
##########
# Load ipeds dataset from course website url
load(file = url('https://github.com/anyone-can-cook/educ152/raw/main/data/ipeds/output_data/panel_data.RData'))
# Create ipeds data frame with fewer variables/observations
df_ipeds_pop <- panel_data %>%
# keep data from fall 2019
filter(year == 2019) %>%
# which universities to keep:
# 2015 carnegie classification: keep research universities (15,16,17) and master's universities (18,19,20)
filter(c15basic %in% c(15,16,17,18,19,20)) %>%
# which variables to keep
select(instnm,unitid,opeid6,opeid,control,c15basic,stabbr,city,zip,locale,obereg, # basic institutional characteristics
tuition6,fee6,tuition7,fee7, # avg tuition and fees for full-time grad, in-state and out-of-state
isprof3,ispfee3,osprof3,ospfee3, # avg tuition and fees for MD, in-state and out-of-state
isprof9,ispfee9,osprof9,ospfee9, # avg tuition and fees for Law, in-state and out-of-state
chg4ay3,chg7ay3,chg8ay3) %>% # [undergraduate] books+supplies; off-campus (not with family) room and board; off-campus (not with family) other expenses
# rename variables; syntax <new_name> = <old_name>
rename(region = obereg, # revion
tuit_grad_res = tuition6, fee_grad_res = fee6, tuit_grad_nres = tuition7, fee_grad_nres = fee7, # grad
tuit_md_res = isprof3, fee_md_res = ispfee3, tuit_md_nres = osprof3, fee_md_nres = ospfee3, # md
tuit_law_res = isprof9, fee_law_res = ispfee9, tuit_law_nres = osprof9, fee_law_nres = ospfee9, # law
books_supplies = chg4ay3, roomboard_off = chg7ay3, oth_expense_off = chg8ay3) %>% # [undergraduate] expenses
# create measures of tuition+fees
mutate(
tuitfee_grad_res = tuit_grad_res + fee_grad_res, # graduate, state resident
tuitfee_grad_nres = tuit_grad_nres + fee_grad_nres, # graduate, non-resident
tuitfee_md_res = tuit_md_res + fee_md_res, # MD, state resident
tuitfee_md_nres = tuit_md_nres + fee_md_nres, # MD, non-resident
tuitfee_law_res = tuit_law_res + fee_law_res, # Law, state resident
tuitfee_law_nres = tuit_law_nres + fee_law_nres) %>% # Law, non-resident
# create measures of cost-of-attendance (COA) as the sum of tuition, fees, book, living expenses
mutate(
coa_grad_res = tuit_grad_res + fee_grad_res + books_supplies + roomboard_off + oth_expense_off, # graduate, state resident
coa_grad_nres = tuit_grad_nres + fee_grad_nres + books_supplies + roomboard_off + oth_expense_off, # graduate, non-resident
coa_md_res = tuit_md_res + fee_md_res + books_supplies + roomboard_off + oth_expense_off, # MD, state resident
coa_md_nres = tuit_md_nres + fee_md_nres + books_supplies + roomboard_off + oth_expense_off, # MD, non-resident
coa_law_res = tuit_law_res + fee_law_res + books_supplies + roomboard_off + oth_expense_off, # Law, state resident
coa_law_nres = tuit_law_nres + fee_law_nres + books_supplies + roomboard_off + oth_expense_off) %>% # Law, non-resident
# keep only observations that have non-missing values for the variable coa_grad_res
# this does cause us to lose some interesting universities, but doing this will eliminate some needless complications with respect to learning core concepts about statistical inference
filter(!is.na(coa_grad_res))
# Add variable labels to the tuit+fees variables and coa variables
# tuition + fees variables
var_label(df_ipeds_pop[['tuitfee_grad_res']]) <- 'graduate, full-time, resident; avg tuition + required fees'
var_label(df_ipeds_pop[['tuitfee_grad_nres']]) <- 'graduate, full-time, non-resident; avg tuition + required fees'
var_label(df_ipeds_pop[['tuitfee_md_res']]) <- 'MD, full-time, state resident; avg tuition + required fees'
var_label(df_ipeds_pop[['tuitfee_md_nres']]) <- 'MD, full-time, non-resident; avg tuition + required fees'
var_label(df_ipeds_pop[['tuitfee_law_res']]) <- 'Law, full-time, state resident; avg tuition + required fees'
var_label(df_ipeds_pop[['tuitfee_law_nres']]) <- 'Law, full-time, non-resident; avg tuition + required fees'
# COA variables
var_label(df_ipeds_pop[['coa_grad_res']]) <- 'graduate, full-time, state resident COA; == tuition + fees + (ug) books/supplies + (ug) off-campus room and board + (ug) off-campus other expenses'
var_label(df_ipeds_pop[['coa_grad_nres']]) <- 'graduate, full-time, non-resident COA; == tuition + fees + (ug) books/supplies + (ug) off-campus room and board + (ug) off-campus other expenses'
var_label(df_ipeds_pop[['coa_md_res']]) <- 'MD, full-time, state resident COA; == tuition + fees + (ug) books/supplies + (ug) off-campus room and board + (ug) off-campus other expenses'
var_label(df_ipeds_pop[['coa_md_nres']]) <- 'MD, full-time, non-resident COA; == tuition + fees + (ug) books/supplies + (ug) off-campus room and board + (ug) off-campus other expenses'
var_label(df_ipeds_pop[['coa_law_res']]) <- 'Law, full-time, state resident COA; == tuition + fees + (ug) books/supplies + (ug) off-campus room and board + (ug) off-campus other expenses'
var_label(df_ipeds_pop[['coa_law_nres']]) <- 'Law, full-time, non-resident COA; == tuition + fees + (ug) books/supplies + (ug) off-campus room and board + (ug) off-campus other expenses'
df_ipeds_pop %>% glimpse()
#> Rows: 991
#> Columns: 38
#> $ instnm <chr> "Alabama A & M University", "University of Alabama at Birmingham", "Amridge University", "University of Alabama in Huntsville", "Alabama State University", "The University of Alabama", "Auburn University at Montgomery", "Auburn University", "Faulkner University", "Jacksonville State University", "University of West Alabama", "University of Montevallo", "University of North Alabama", "Samford University", "University of South Alabama", "Troy University", "Tuskegee University", "University of Alaska Anchorage", "University of Alaska Fairbanks", "University of Alaska Southeast", "Alaska Pacific University", "Arizona State University-Tempe", "University of Arizona", "Grand Canyon University", "Northern Arizona University", "Prescott College", "University of Arkansas at Little Rock", "University of Arkansas", "Arkansas State University-Main Campus", "Arkansas Tech University", "University of Arkansas at Monticello", "University of Central Arkansas", "Harding University", "Henderson State University", "John Brown University", "Southern Arkansas University Main Campus", "Azusa Pacific University", "Biola University", "California Baptist University", "California Institute of Technology", "California Lutheran University", "California Polytechnic State University-San Luis Obispo", "Alliant International University-San Diego", "California State University-Bakersfield", "California State University-Stanislaus", "California State University-San Bernardino", "California State Polytechnic University-Pomona", "California State University-Chico", "California State University-Dominguez Hills", "California State University-Fresno", "California State University-Fullerton", "California State University-East Bay", "California State University-Long Beach", "California State University-Los Angeles", "California State University-Northridge", "California State University-Sacramento", "University of California-Berkeley", "University of California-Davis", "University of California-Irvine", "University of California-Los Angeles", "University of California-Riverside", "University of California-San Diego", "University of California-Santa Barbara", "University of California-Santa Cruz", "Chapman University", "Concordia University-Irvine", "Dominican University of California", "Fresno Pacific University", "Holy Names University", "Humboldt State University", "University of La Verne", "La Sierra University", "The Master's University and Seminary", "Loyola Marymount University", "Mills College", "Mount Saint Mary's University", "National University", "Notre Dame de Namur University", "Hope International University", "University of the Pacific", "Pepperdine University", "Point Loma Nazarene University", "University of Redlands", "San Diego State University", "University of San Diego", "San Francisco State University", "University of San Francisco", "San Jose State University", "Santa Clara University", "Simpson University", "Saint Mary's College of California", "Sonoma State University", "Vanguard University of Southern California", "University of Southern California", "Walden University", "Woodbury University", "Adams State University", "University of Colorado Denver/Anschutz Medical Campus", "University of Colorado Colorado Springs", "University of Colorado Boulder", "Colorado Christian University", "Colorado School of Mines", "Colorado State University-Fort Collins", "Colorado Technical University-Colorado Springs", "University of Denver", "Metropolitan State University of Denver", "Naropa University", "University of Northern Colorado", "Regis University", "Colorado State University-Pueblo", "Western Colorado University", "Albertus Magnus College", "University of Bridgeport", "Central Connecticut State University", "University of Connecticut", "Eastern Connecticut State University", "Fairfield University", "University of Hartford", "University of New Haven", "Post University", "Quinnipiac University", "Sacred Heart University", "University of Saint Joseph", "Southern Connecticut State University", "Western Connecticut State University", "Delaware State University", "University of Delaware", "Wilmington University", "American University", "The Catholic University of America", "University of the District of Columbia", "Gallaudet University", "George Washington University", "Strayer University-District of Columbia", "Trinity Washington University", "Barry University", "Lynn University", "University of Central Florida", "Embry-Riddle Aeronautical University-Daytona Beach", "Florida Agricultural and Mechanical University", "Florida Atlantic University", "Florida Institute of Technology", "Florida International University", "Florida Southern College", "Florida State University", "University of Florida", "Full Sail University", "Jacksonville University", "Keiser University-Ft Lauderdale", "University of Miami", "University of North Florida", "Nova Southeastern University", "Palm Beach Atlantic University", "Rollins College", "Saint Leo University", "University of South Florida-Main Campus", "St. Thomas University", "Stetson University", "Southeastern University", "The University of Tampa", "The University of West Florida", "Albany State University", "Berry College", "Brenau University", "Clayton State University", "Columbus State University", "South University-Savannah", "Emory University", "Fort Valley State University", "Georgia Institute of Technology-Main Campus", "Georgia Southwestern State University", "Georgia College & State University", "Georgia Southern University", "Georgia State University", "University of Georgia", "Mercer University", "Piedmont College", "Savannah State University", "Thomas University", "Valdosta State University", "University of West Georgia", "Chaminade University of Honolulu", "University of Hawaii at Hilo", "University of Hawaii at Manoa", "Hawaii Pacific University", "Boise State University", "Idaho State University", "University of Idaho", "Northwest Nazarene University", "Aurora University", "Bradley University", "Chicago State University", "Columbia College Chicago", "Concordia University-Chicago", "DePaul University", "Eastern Illinois University", "Elmhurst College", "Governors State University", "Greenville University", "University of Illinois at Chicago", "Benedictine University", "Illinois Institute of Technology", "Illinois State University", "Judson University", "Lewis University", "Loyola University Chicago", "McKendree University", "National Louis University", "North Central College", "North Park University", "Northern Illinois University", "Northwestern University", "Northeastern Illinois University", "Olivet Nazarene University", "Robert Morris University Illinois", "Rockford University", "Roosevelt University", "Dominican University", "University of St Francis", "Saint Xavier University", "Southern Illinois University-Carbondale", "Southern Illinois University-Edwardsville", "Trinity International University-Illinois", "Western Illinois University", "Anderson University", "Ball State University", "Bethel University", "Butler University", "Calumet College of Saint Joseph", "University of Evansville", "Grace College and Theological Seminary", "Purdue University Fort Wayne", "Indiana University-Purdue University-Indianapolis", "University of Indianapolis", "University of Southern Indiana", "Indiana State University", "Indiana University-South Bend", "Indiana University-Bloomington", "Indiana University-Northwest", "Indiana University-Southeast", "Indiana University-East", "Marian University", "Indiana Wesleyan University-Marion", "University of Notre Dame", "University of Saint Francis-Fort Wayne", "Valparaiso University", "Drake University", "University of Dubuque", "Graceland University-Lamoni", "Iowa State University", "University of Iowa", "Maharishi University of Management", "Morningside College", "Mount Mercy University", "Ashford University", "University of Northern Iowa", "Saint Ambrose University", "Upper Iowa University", "William Penn University", "Baker University", "Emporia State University", "Fort Hays State University", "Friends University", "University of Kansas", "Newman University", "Kansas State University", "MidAmerica Nazarene University", "Pittsburg State University", "University of Saint Mary", "Southwestern College", "Washburn University", "Wichita State University", "Asbury University", "Bellarmine University", "Campbellsville University", "University of the Cumberlands", "Eastern Kentucky University", "University of Kentucky", "Lindsey Wilson College", "University of Louisville", "Midway University", "Morehead State University", "Murray State University", "Northern Kentucky University", "Spalding University", "Sullivan University", "Union College", "Western Kentucky University", "Grambling State University", "Louisiana State University and Agricultural & Mechanical College", "Louisiana State University-Shreveport", "Louisiana College", "Louisiana Tech University", "Loyola University New Orleans", "McNeese State University", "University of New Orleans", "Nicholls State University", "University of Louisiana at Monroe", "Northwestern State University of Louisiana", "Southeastern Louisiana University", "Southern University and A & M College", "Southern University at New Orleans", "University of Louisiana at Lafayette", "Tulane University of Louisiana", "Xavier University of Louisiana", "University of Maine", "University of New England", "Saint Joseph's College of Maine", "University of Southern Maine", "Thomas College", "University of Baltimore", "Bowie State University", "Washington Adventist University", "Coppin State University", "Frostburg State University", "Hood College", "Johns Hopkins University", "Loyola University Maryland", "University of Maryland Global Campus", "University of Maryland-Baltimore County", "University of Maryland-College Park", "University of Maryland Eastern Shore", "Morgan State University", "Mount St. Mary's University"…
#> $ unitid <dbl> 100654, 100663, 100690, 100706, 100724, 100751, 100830, 100858, 101189, 101480, 101587, 101709, 101879, 102049, 102094, 102368, 102377, 102553, 102614, 102632, 102669, 104151, 104179, 104717, 105330, 105589, 106245, 106397, 106458, 106467, 106485, 106704, 107044, 107071, 107141, 107983, 109785, 110097, 110361, 110404, 110413, 110422, 110468, 110486, 110495, 110510, 110529, 110538, 110547, 110556, 110565, 110574, 110583, 110592, 110608, 110617, 110635, 110644, 110653, 110662, 110671, 110680, 110705, 110714, 111948, 112075, 113698, 114813, 115728, 115755, 117140, 117627, 117751, 117946, 118888, 119173, 119605, 120184, 120537, 120883, 121150, 121309, 121691, 122409, 122436, 122597, 122612, 122755, 122931, 123457, 123554, 123572, 123651, 123961, 125231, 125897, 126182, 126562, 126580, 126614, 126669, 126775, 126818, 126827, 127060, 127565, 127653, 127741, 127918, 128106, 128391, 128498, 128744, 128771, 129020, 129215, 129242, 129525, 129941, 130183, 130226, 130253, 130314, 130493, 130776, 130934, 130943, 131113, 131159, 131283, 131399, 131450, 131469, 131803, 131876, 132471, 132657, 132903, 133553, 133650, 133669, 133881, 133951, 134079, 134097, 134130, 134237, 134945, 135081, 135726, 136172, 136215, 136330, 136950, 137032, 137351, 137476, 137546, 137564, 137847, 138354, 138716, 139144, 139199, 139311, 139366, 139579, 139658, 139719, 139755, 139764, 139861, 139931, 139940, 139959, 140447, 140818, 140960, 141167, 141264, 141334, 141486, 141565, 141574, 141644, 142115, 142276, 142285, 142461, 143118, 143358, 144005, 144281, 144351, 144740, 144892, 144962, 145336, 145372, 145600, 145619, 145725, 145813, 146339, 146612, 146719, 147013, 147536, 147660, 147679, 147703, 147767, 147776, 147828, 148335, 148405, 148487, 148496, 148584, 148627, 149222, 149231, 149514, 149772, 150066, 150136, 150145, 150163, 150172, 150534, 150677, 151102, 151111, 151263, 151306, 151324, 151342, 151351, 151360, 151379, 151388, 151786, 151801, 152080, 152336, 152600, 153269, 153278, 153366, 153603, 153658, 153861, 154004, 154013, 154022, 154095, 154235, 154493, 154590, 154688, 155025, 155061, 155089, 155317, 155335, 155399, 155520, 155681, 155812, 155900, 156082, 156125, 156213, 156286, 156365, 156541, 156620, 157085, 157216, 157289, 157377, 157386, 157401, 157447, 157757, 157793, 157863, 157951, 159009, 159391, 159416, 159568, 159647, 159656, 159717, 159939, 159966, 159993, 160038, 160612, 160621, 160630, 160658, 160755, 160904, 161253, 161457, 161518, 161554, 161563, 161873, 162007, 162210, 162283, 162584, 162760, 162928, 163046, 163204, 163268, 163286, 163338, 163453, 163462, 163578, 163851, 164076, 164173, 164447, 164492, 164562, 164632, 164739, 164924, 164988, 165015, 165024, 165167, 165334, 165529, 165644, 165662, 165699, 165820, 165866, 166391, 166452, 166513, 166629, 166638, 166850, 167358, 167394, 167729, 167783, 167899, 167987, 168005, 168148, 168227, 168254, 168263, 168421, 168430, 168740, 168786, 169248, 169479, 169716, 169798, 169910, 169983, 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188429, 188641, 189705, 190044, 190114, 190150, 190415, 190512, 190549, 190558, 190567, 190594, 190600, 190637, 190664, 190716, 190725, 190761, 191126, 191241, 191649, 191931, 191968, 192192, 192323, 192448, 192703, 192749, 192819, 192925, 193016, 193292, 193308, 193353, 193399, 193584, 193654, 193900, 193973, 194091, 194161, 194310, 194958, 195003, 195128, 195164, 195234, 195243, 195544, 195720, 195809, 196060, 196079, 196088, 196097, 196103, 196112, 196121, 196130, 196149, 196158, 196167, 196176, 196185, 196194, 196200, 196237, 196246, 196264, 196291, 196413, 196592, 197045, 197197, 197708, 197869, 198136, 198464, 198507, 198516, 198543, 198561, 198835, 198969, 199032, 199102, 199120, 199139, 199148, 199157, 199193, 199218, 199281, 199306, 199412, 199759, 199847, 199962, 199999, 200004, 200217, 200253, 200280, 200332, 200800, 201104, 201195, 201441, 201548, 201645, 201885, 202134, 202480, 202763, 203085, 203368, 203517, 203580, 203757, 203775, 204024, 204194, 204200, 204264, 204617, 204796, 204857, 204936, 205957, 206048, 206084, 206279, 206349, 206437, 206604, 206622, 206695, 206835, 206862, 206914, 206941, 207041, 207209, 207263, 207306, 207324, 207388, 207458, 207500, 207582, 207847, 207865, 207971, 208646, 208822, 209409, 209542, 209551, 209612, 209807, 209825, 210146, 210331, 210429, 210739, 210775, 211088, 211158, 211352, 211361, 211431, 211440, 211556, 211583, 211644, 211981, 212054, 212106, 212115, 212133, 212160, 212601, 212656, 212832, 212984, 213011, 213020, 213321, 213349, 213367, 213507, 213543, 213598, 213613, 213783, 213826, 213987, 214041, 214069, 214272, 214713, 214777, 215062, 215114, 215293, 215442, 215691, 215743, 215770, 215929, 215947, 216010, 216038, 216339, 216597, 216694, 216764, 216852, 216931, 217059, 217165, 217235, 217402, 217420, 217484, 217518, 217536, 217633, 217688, 217749, 217776, 217819, 217864, 217882, 217925, 217934, 217961, 218061, 218441, 218663, 218724, 218733, 218964, 219046, 219082, 219198, 219259, 219356, 219383, 219471, 219602, 219709, 219718, 219790, 219806, 219833, 219949, 219976, 220075, 220215, 220516, 220552, 220613, 220631, 220862, 220978, 221661, 221740, 221759, 221768, 221838, 221847, 221953, 221971, 222178, 222831, 223232, 224004, 224147, 224226, 224323, 224545, 224554, 225247, 225399, 225414, 225432, 225502, 225511, 225627, 226091, 226152, 226231, 226383, 226471, 226833, 227216, 227331, 227368, 227526, 227757, 227845, 227863, 227881, 228149, 228246, 228325, 228431, 228459, 228501, 228529, 228705, 228723, 228769, 228778, 228787, 228796, 228802, 228875, 229018, 229027, 229063, 229115, 229160, 229179, 229267, 229780, 229814, 230038, 230603, 230728, 230737, 230764, 230782, 230807, 230852, 230889, 230913, 230995, 231174, 231624, 231651, 231712, 232043, 232186, 232265, 232423, 232557, 232566, 232609, 232672, 232681, 232706, 232937, 232982, 233277, 233541, 233684, 233921, 234030, 234076, 234155, 234827, 235097, 235167, 235316, 235422, 236133, 236230, 236452, 236577, 236595, 236896, 236939, 236948, 237011, 237066, 237312, 237330, 237367, 237525, 237783, 237969, 238032, 238078, 238193, 238430, 238458, 238616, 238661, 238980, 239080, 239105, 239318, 239390, 239743, 240107, 240189, 240268, 240277, 240329, 240365, 240417, 240444, 240453, 240462, 240471, 240480, 240727, 240754, 241100, 241225, 241377, 241395, 241410, 241739, 242617, 242626, 242635, 242653, 242662, 243081, 243197, 243221, 243346, 243443, 243601, 243665, 243780, 245953, 251312, 262086, 363907, 366711, 367884, 377555, 377564, 407009, 409698, 413413, 420042, 420574, 433387, 433660, 437097, 438151, 438498, 438586, 441937, 442569, 443766, 443784, 445188, 445267, 446640, 448840, 448886, 449038, 449339, 450933, 451671, 453163, 454829, 458919, 459727, 460075, 475121, 476975, 479956, 480198, 480569, 482149, 482422, 482431, 482440, 482459, 482468, 482477, 482538, 482556, 482574, 482608, 482635, 482680, 483036, 483124, 484613, 484631, 484710, 484905, 486840, 486901, 487524, 490805
#> $ opeid6 <chr> "001002", "001052", "025034", "001055", "001005", "001051", "008310", "001009", "001003", "001020", "001024", "001004", "001016", "001036", "001057", "001047", "001050", "011462", "001063", "001065", "001061", "001081", "001083", "001074", "001082", "020653", "001101", "001108", "001090", "001089", "001085", "001092", "001097", "001098", "001100", "001107", "001117", "001122", "001125", "001131", "001133", "001143", "011117", "007993", "001157", "001142", "001144", "001146", "001141", "001147", "001137", "001138", "001139", "001140", "001153", "001150", "001312", "001313", "001314", "001315", "001316", "001317", "001320", "001321", "001164", "020705", "001196", "001253", "001183", "001149", "001216", "001215", "001220", "011649", "001238", "001243", "011460", "001179", "001252", "001329", "010149", "001262", "001322", "001151", "010395", "001154", "001325", "001155", "001326", "001291", "001302", "001156", "001293", "001328", "025042", "001343", "001345", "004508", "004509", "001370", "009401", "001348", "001350", "010148", "001371", "001360", "021175", "001349", "001363", "001365", "001372", "001374", "001416", "001378", "001417", "001425", "001385", "001422", "001397", "001401", "001402", "001403", "001409", "001406", "001380", "001428", "001431", "007948", "001434", "001437", "001441", "001443", "001444", "001459", "001460", "001466", "001505", "003954", "001479", "001480", "001481", "001469", "009635", "001488", "001489", "001535", "023621", "001495", "021519", "001536", "009841", "001509", "008849", "001515", "001526", "001537", "001468", "001531", "001521", "001538", "003955", "001544", "001554", "001556", "008976", "001561", "013039", "001564", "001566", "001569", "001573", "001602", "001572", "001574", "001598", "001580", "001588", "001590", "001555", "001599", "001601", "001605", "001611", "001610", "007279", "001616", "001620", "001626", "001624", "001634", "001641", "001694", "001665", "001666", "001671", "001674", "001676", "009145", "001684", "001776", "001767", "001691", "001692", "001700", "001707", "001710", "001722", "001733", "001734", "001735", "001737", "001739", "001693", "001741", "001746", "001748", "001749", "001750", "001664", "001768", "001758", "001759", "001772", "001780", "001785", "001786", "001787", "001788", "001834", "001795", "001800", "001828", "001813", "001804", "001808", "001807", "001816", "001809", "001815", "001817", "001811", "001821", "001822", "001840", "001832", "001842", "001860", "001891", "001866", "001869", "001892", "011113", "001879", "001880", "001881", "001890", "001889", "001893", "001900", "001903", "001927", "001915", "001918", "001948", "001939", "001928", "007032", "001926", "001943", "001940", "001949", "001950", "001952", "001954", "001959", "001962", "001963", "001989", "001972", "001999", "001975", "001976", "001977", "009275", "001960", "004619", "001988", "002002", "002006", "002010", "002013", "002007", "002008", "002016", "002017", "002015", "002005", "002020", "002021", "002024", "002025", "002026", "002031", "002029", "002032", "002053", "002050", "002051", "002054", "002052", "002102", "002062", "002067", "002068", "002072", "002076", "002077", "002078", "011644", "002105", "002103", "002106", "002083", "002086", "002065", "002091", "002099", "002107", "002114", "002117", "002118", "002122", "002124", "002128", "002130", "002133", "002183", "021829", "002139", "002143", "002145", "002146", "002148", "002184", "002185", "002158", "002160", "002161", "002221", "002222", "002120", "002199", "002140", "002188", "002208", "002211", "002210", "002218", "002219", "002225", "002226", "002189", "002233", "002190", "002238", "002239", "002243", "002249", "002323", "002259", "002260", "002262", "002266", "002268", "002279", "002282", "002325", "002290", "002292", "002326", "002327", "002301", "002307", "002314", "002316", "002318", "002329", "002330", "002334", "002336", "009058", "002347", "002354", "002360", "010374", "003969", "002388", "002367", "002371", "002377", "002380", "002343", "002345", "002342", "002375", "002394", "002396", "002397", "002403", "002410", "002440", "002422", "002424", "002415", "002423", "002441", "002447", "002449", "002454", "002456", "002461", "002463", "002464", "002479", "002480", "007540", "002516", "002518", "002519", "002495", "002496", "002498", "002499", "002506", "002502", "002512", "002501", "002503", "002520", "002521", "002525", "002530", "002532", "002536", "009743", "002539", "002541", "002542", "002551", "002554", "002555", "002565", "002559", "002540", "002566", "002569", "002568", "009192", "002575", "002579", "002580", "002589", "002590", "002591", "002586", "002598", "002599", "002607", "002610", "002607", "002608", "002609", "002613", "002622", "002616", "002617", "002621", "009344", "002628", "002629", "002629", "002629", "002638", "002632", "002600", "002639", "009345", "021922", "002642", "002625", "002651", "002653", "002654", "002663", "002657", "002650", "002664", "002666", "002668", "002681", "002699", "009769", "002707", "002711", "007273", "002687", "002698", "002688", "002689", "002693", "007022", "002690", "002712", "002808", "002713", "002866", "002722", "002732", "002737", "002739", "002744", "002748", "002751", "002758", "002760", "002765", "002777", "002772", "002775", "004799", "002778", "002703", "002779", "020662", "002785", "002788", "002782", "002790", "002791", "002805", "002806", "002810", "002817", "002705", "002832", "002825", "002821", "002823", "002835", "002836", "002837", "002838", "002851", "011678", "002841", "002842", "002843", "002844", "002845", "002846", "002847", "002848", "002850", "007109", "002849", "010286", "002853", "002882", "010142", "002883", "002899", "002903", "002906", "002913", "002923", "002926", "002927", "002928", "002929", "002941", "002946", "002948", "002905", "002974", "002975", "002976", "002950", "002972", "002984", "002954", "002955", "002957", "002963", "002978", "002985", "002986", "002981", "002992", "002994", "003005", "002997", "003123", "003012", "003014", "003018", "003023", "003024", "003125", "003032", "003127", "003045", "003048", "003050", "003051", "003066", "003069", "003072", "003077", "007085", "003033", "003084", "003035", "003090", "003100", "003110", "003036", "003121", "003131", "010923", "003134", "003135", "003078", "003144", "003145", "003151", "003149", "003150", "003152", "003154", "003157", "003161", "003163", "003165", "003170", "003166", "003184", "003985", "003179", "003181", "003185", "003193", "003194", "003208", "003210", "003223", "003212", "003216", "003224", "003219", "001339", "003209", "003986", "003233", "003235", "003315", "003241", "003316", "003303", "003242", "003244", "003245", "003318", "003252", "003256", "003258", "003320", "003259", "003321", "003266", "003267", "003270", "003275", "003276", "003277", "003282", "003322", "003287", "003288", "003289", "003290", "003323", "003324", "003296", "003297", "003325", "003247", "003988", "003329", "003329", "003378", "003351", "003379", "003357", "003360", "003366", "003367", "003384", "003362", "003326", "003327", "003371", "003388", "003391", "003328", "003313", "003394", "003399", "003402", "003404", "003406", "003407", "003414", "003410", "003411", "003418", "003419", "003421", "003422", "003428", "003423", "003425", "003429", "003430", "003431", "009226", "003441", "003448", "003451", "003446", "003456", "003459", "003463", "003465", "003466", "003471", "003469", "003474", "003478", "003479", "003480", "003536", "003481", "003482", "003485", "003486", "003487", "003492", "003496", "004938", "003500", "003502", "003509", "003510", "003518", "003529", "003530", "003531", "003522", "003523", "003527", "003528", "003537", "003541", "003545", "003557", "011161", "003560", "003651", "031703", "003565", "003571", "003576", "011711", "003612", "013231", "003652", "003578", "003581", "009651", "003584", "003586", "003588", "003592", "003594", "003598", "003599", "003630", "003604", "003621", "003654", "003606", "003623", "003613", "003616", "003624", "003615", "003625", "003631", "003639", "003632", "003656", "003658", "009741", "003661", "011163", "003636", "009930", "010115", "003642", "003644", "003645", "003646", "003647", "003663", "003665", "003670", "003678", "003677", "004027", "003675", "003680", "003681", "003684", "003686", "003688", "003692", "003696", "003705", "030913", "003706", "003708", "003749", "003714", "003721", "020530", "003719", "003720", "003723", "003746", "003724", "003765", "003728", "003732", "003737", "001459", "003754", "003735", "003745", "003764", "003771", "003775", "008155", "003778", "003777", "003783", "003785", "003794", "003788", "003790", "003799", "003800", "003798", "003802", "003804", "003818", "003810", "003812", "003815", "003820", "003830", "003827", "003831", "003832", "003837", "003838", "003842", "003848", "003854", "003861", "003863", "003868", "003869", "003850", "003911", "003926", "003917", "003899", "003919", "003920", "003915", "003895", "003896", "003921", "003923", "003924", "003932", "003935", "011941", "005022", "012525", "003936", "003936", "025875", "042542", "003939", "005026", "003940", "005029", "024824", "003944", "007108", "003941", "003937", "011719", "003946", "001825", "006942", "013039", "041618", "012525", "030113", "030375", "003798", "003798", "001081", "032603", "032673", "320988", "001081", "033394", "032553", "041103", "003674", "025412", "021136", "039803", "041223", "001459", "001459", "041271", "002453", "039483", "001537", "001081", "001459", "038193", "041215", "001537", "001459", "040803", "001459", "041425", "041331", "013039", "042087", "003329", "003225", "001469", "001579", "010727", "010727", "010727", "010727", "010727", "010727", "010727", "010727", "010727", "010727", "010727", "001585", "042295", "001081", "020988", "320988", "820988", "042421", "001577", "003511", "002043", "001827"
#> $ opeid <chr> "00100200", "00105200", "02503400", "00105500", "00100500", "00105100", "00831000", "00100900", "00100300", "00102000", "00102400", "00100400", "00101600", "00103600", "00105700", "00104700", "00105000", "01146200", "00106300", "00106500", "00106100", "00108100", "00108300", "00107400", "00108200", "02065300", "00110100", "00110800", "00109000", "00108900", "00108500", "00109200", "00109700", "00109800", "00110000", "00110700", "00111700", "00112200", "00112500", "00113100", "00113300", "00114300", "01111700", "00799300", "00115700", "00114200", "00114400", "00114600", "00114100", "00114700", "00113700", "00113800", "00113900", "00114000", "00115300", "00115000", "00131200", "00131300", "00131400", "00131500", "00131600", "00131700", "00132000", "00132100", "00116400", "02070500", "00119600", "00125300", "00118300", "00114900", "00121600", "00121500", "00122000", "01164900", "00123800", "00124300", "01146000", "00117900", "00125200", "00132900", "01014900", "00126200", "00132200", "00115100", "01039500", "00115400", "00132500", "00115500", "00132600", "00129100", "00130200", "00115600", "00129300", "00132800", "02504200", "00134300", "00134500", "00450800", "00450900", "00137000", "00940100", "00134800", "00135000", "01014800", "00137100", "00136000", "02117500", "00134900", "00136300", "00136500", "00137200", "00137400", "00141600", "00137800", "00141700", "00142500", "00138500", "00142200", "00139700", "00140100", "00140200", "00140300", "00140900", "00140600", "00138000", "00142800", "00143100", "00794800", "00143400", "00143700", "00144100", "00144300", "00144400", "00145900", "00146000", "00146600", "00150500", "00395400", "00147900", "00148000", "00148100", "00146900", "00963500", "00148800", "00148900", "00153500", "02362100", "00149500", "02151900", "00153600", "00984100", "00150900", "00884900", "00151500", "00152600", "00153700", "00146800", "00153100", "00152100", "00153800", "00395500", "00154400", "00155400", "00155600", "00897600", "00156100", "01303900", "00156400", "00156600", "00156900", "00157300", "00160200", "00157200", "00157400", "00159800", "00158000", "00158800", "00159000", "00155500", "00159900", "00160100", "00160500", "00161100", "00161000", "00727900", "00161600", "00162000", "00162600", "00162400", "00163400", "00164100", "00169400", "00166500", "00166600", "00167100", "00167400", "00167600", "00914500", "00168400", "00177600", "00176700", "00169100", "00169200", "00170000", "00170700", "00171000", "00172200", "00173300", "00173400", "00173500", "00173700", "00173900", "00169300", "00174100", "00174600", "00174800", "00174900", "00175000", "00166400", "00176800", "00175800", "00175900", "00177200", "00178000", "00178500", "00178600", "00178700", "00178800", "00183400", "00179500", "00180000", "00182800", "00181300", "00180400", "00180800", "00180700", "00181600", "00180900", "00181500", "00181700", "00181100", "00182100", "00182200", "00184000", "00183200", "00184200", "00186000", "00189100", "00186600", "00186900", "00189200", "01111300", "00187900", "00188000", "00188100", "00189000", "00188900", "00189300", "00190000", "00190300", "00192700", "00191500", "00191800", "00194800", "00193900", "00192800", "00703200", "00192600", "00194300", "00194000", "00194900", "00195000", "00195200", "00195400", "00195900", "00196200", "00196300", "00198900", "00197200", "00199900", "00197500", "00197600", "00197700", "00927500", "00196000", "00461900", "00198800", "00200200", "00200600", "00201000", "00201300", "00200700", "00200800", "00201600", "00201700", "00201500", "00200500", "00202000", "00202100", "00202400", "00202500", "00202600", "00203100", "00202900", "00203200", "00205300", "00205000", "00205100", "00205400", "00205200", "00210200", "00206200", "00206700", "00206800", "00207200", "00207600", "00207700", "00207800", "01164400", "00210500", "00210300", "00210600", "00208300", "00208600", "00206500", "00209100", "00209900", "00210700", "00211400", "00211700", "00211800", "00212200", "00212400", "00212800", "00213000", "00213300", "00218300", "02182900", "00213900", "00214300", "00214500", "00214600", "00214800", "00218400", "00218500", "00215800", "00216000", "00216100", "00222100", "00222200", "00212000", "00219900", "00214000", "00218800", "00220800", "00221100", "00221000", "00221800", "00221900", "00222500", "00222600", "00218900", "00223300", "00219000", "00223800", "00223900", "00224300", "00224900", "00232300", "00225900", "00226000", "00226200", "00226600", "00226800", "00227900", "00228200", "00232500", "00229000", "00229200", "00232600", "00232700", "00230100", "00230700", "00231400", "00231600", "00231800", "00232900", "00233000", "00233400", "00233600", "00905800", "00234700", "00235400", "00236000", "01037400", "00396900", "00238800", "00236700", "00237100", "00237700", "00238000", "00234300", "00234500", "00234200", "00237500", "00239400", "00239600", "00239700", "00240300", "00241000", "00244000", "00242200", "00242400", "00241500", "00242300", "00244100", "00244700", "00244900", "00245400", "00245600", "00246100", "00246300", "00246400", "00247900", "00248000", "00754000", "00251600", "00251800", "00251900", "00249500", "00249600", "00249800", "00249900", "00250600", "00250200", "00251200", "00250100", "00250300", "00252000", "00252100", "00252500", "00253000", "00253200", "00253600", "00974300", "00253900", "00254100", "00254200", "00255100", "00255400", "00255500", "00256500", "00255900", "00254000", "00256600", "00256900", "00256800", "00919200", "00257500", "00257900", "00258000", "00258900", "00259000", "00259100", "00258600", "00259800", "00259900", "00260700", "00261000", "00260701", "00260800", "00260900", "00261300", "00262200", "00261600", "00261700", "00262100", "00934400", "00262800", "00262901", "00262900", "00262902", "00263800", "00263200", "00260000", "00263900", "00934500", "02192200", "00264200", "00262500", "00265100", "00265300", "00265400", "00266300", "00265700", "00265000", "00266400", "00266600", "00266800", "00268100", "00269900", "00976900", "00270700", "00271100", "00727300", "00268700", "00269800", "00268800", "00268900", "00269300", "00702200", "00269000", "00271200", "00280800", "00271300", "00286600", "00272200", "00273200", "00273700", "00273900", "00274400", "00274800", "00275100", "00275800", "00276000", "00276500", "00277700", "00277200", "00277500", "00479900", "00277800", "00270300", "00277900", "02066200", "00278500", "00278800", "00278200", "00279000", "00279100", "00280500", "00280600", "00281000", "00281700", "00270500", "00283200", "00282500", "00282100", "00282300", "00283500", "00283600", "00283700", "00283800", "00285100", "01167800", "00284100", "00284200", "00284300", "00284400", "00284500", "00284600", "00284700", "00284800", "00285000", "00710900", "00284900", "01028600", "00285300", "00288200", "01014200", "00288300", "00289900", "00290300", "00290600", "00291300", "00292300", "00292600", "00292700", "00292800", "00292900", "00294100", "00294600", "00294800", "00290500", "00297400", "00297500", "00297600", "00295000", "00297200", "00298400", "00295400", "00295500", "00295700", "00296300", "00297800", "00298500", "00298600", "00298100", "00299200", "00299400", "00300500", "00299700", "00312300", "00301200", "00301400", "00301800", "00302300", "00302400", "00312500", "00303200", "00312700", "00304500", "00304800", "00305000", "00305100", "00306600", "00306900", "00307200", "00307700", "00708500", "00303300", "00308400", "00303500", "00309000", "00310000", "00311000", "00303600", "00312100", "00313100", "01092300", "00313400", "00313500", "00307800", "00314400", "00314500", "00315100", "00314900", "00315000", "00315200", "00315400", "00315700", "00316100", "00316300", "00316500", "00317000", "00316600", "00318400", "00398500", "00317900", "00318100", "00318500", "00319300", "00319400", "00320800", "00321000", "00322300", "00321200", "00321600", "00322400", "00321900", "00133900", "00320900", "00398600", "00323300", "00323500", "00331500", "00324100", "00331600", "00330300", "00324200", "00324400", "00324500", "00331800", "00325200", "00325600", "00325800", "00332000", "00325900", "00332100", "00326600", "00326700", "00327000", "00327500", "00327600", "00327700", "00328200", "00332200", "00328700", "00328800", "00328900", "00329000", "00332300", "00332400", "00329600", "00329700", "00332500", "00324700", "00398800", "00332920", "00332900", "00337800", "00335100", "00337900", "00335700", "00336000", "00336600", "00336700", "00338400", "00336200", "00332600", "00332700", "00337100", "00338800", "00339100", "00332800", "00331300", "00339400", "00339900", "00340200", "00340400", "00340600", "00340700", "00341400", "00341000", "00341100", "00341800", "00341900", "00342100", "00342200", "00342800", "00342300", "00342500", "00342900", "00343000", "00343100", "00922600", "00344100", "00344800", "00345100", "00344600", "00345600", "00345900", "00346300", "00346500", "00346600", "00347100", "00346900", "00347400", "00347800", "00347900", "00348000", "00353600", "00348100", "00348200", "00348500", "00348600", "00348700", "00349200", "00349600", "00493800", "00350000", "00350200", "00350900", "00351000", "00351800", "00352900", "00353000", "00353100", "00352200", "00352300", "00352700", "00352800", "00353700", "00354100", "00354500", "00355700", "01116100", "00356000", "00365100", "03170300", "00356500", "00357100", "00357600", "01171100", "00361200", "01323100", "00365200", "00357800", "00358100", "00965100", "00358400", "00358600", "00358800", "00359200", "00359400", "00359800", "00359900", "00363000", "00360400", "00362100", "00365400", "00360600", "00362300", "00361300", "00361600", "00362400", "00361500", "00362500", "00363100", "00363900", "00363200", "00365600", "00365800", "00974100", "00366100", "01116300", "00363600", "00993000", "01011500", "00364200", "00364400", "00364500", "00364600", "00364700", "00366300", "00366500", "00367000", "00367800", "00367700", "00402700", "00367500", "00368000",…
#> $ control <dbl+lbl> 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 3, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 3, 2, 1, 1, 1, 1, 2, 1, 1, 3, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 3, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 3, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1, 3, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 1, 1, 3, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 3, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 2, 3, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 3, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 1, 1, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 1, 1, 3, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 1, 1, 2, 2, 2, 1, 1, 2, 3, 2, 2, 1, 2, 1, 1, 1, 1, 3, 3, 1, 2, 1, 3, 2, 3, 3, 1, 3, 3, 3, 1, 2, 2, 1, 1, 3, 3, 3, 1, 3, 3, 3, 2, 2, 3, 1, 1, 2, 2, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 3, 3, 3, 1, 1, 2, 2, 1
#> $ c15basic <dbl+lbl> 18, 15, 20, 16, 19, 16, 18, 16, 20, 18, 18, 19, 18, 18, 16, 18, 20, 18, 16, 19, 20, 15, 15, 17, 16, 20, 17, 15, 18, 18, 20, 18, 18, 19, 19, 19, 17, 17, 18, 15, 18, 18, 17, 18, 18, 18, 18, 18, 18, 17, 17, 18, 18, 18, 18, 18, 15, 15, 15, 15, 15, 15, 15, 15, 18, 18, 19, 18, 19, 19, 17, 20, 20, 18, 18, 19, 18, 18, 19, 17, 17, 18, 18, 16, 17, 17, 17, 18, 18, 20, 18, 18, 20, 15, 17, 19, 18, 16, 18, 15, 19, 16, 15, 18, 16, 19, 18, 16, 18, 19, 20, 19, 18, 18, 15, 20, 18, 17, 18, 18, 18, 18, 18, 18, 19, 19, 15, 17, 16, 16, 20, 19, 15, 19, 18, 17, 18, 15, 18, 16, 16, 16, 15, 20, 15, 15, 18, 18, 18, 15, 18, 16, 18, 18, 18, 15, 18, 19, 19, 18, 17, 19, 20, 18, 19, 18, 19, 15, 20, 15, 19, 18, 17, 15, 15, 17, 18, 20, 20, 17, 17, 18, 20, 15, 18, 17, 17, 16, 18, 18, 19, 19, 19, 18, 17, 18, 19, 18, 20, 15, 17, 16, 16, 20, 18, 16, 18, 17, 19, 18, 16, 15, 18, 18, 18, 20, 18, 18, 18, 18, 16, 18, 17, 18, 19, 16, 20, 18, 20, 20, 20, 18, 16, 18, 18, 17, 19, 15, 19, 18, 20, 19, 18, 15, 19, 18, 18, 20, 19, 15, 15, 18, 19, 19, 18, 18, 18, 19, 20, 18, 18, 18, 18, 15, 19, 15, 19, 18, 19, 19, 19, 16, 20, 18, 18, 17, 18, 15, 18, 15, 20, 18, 18, 18, 17, 18, 19, 18, 18, 15, 19, 19, 17, 18, 18, 16, 19, 17, 18, 18, 18, 19, 16, 15, 20, 16, 18, 18, 18, 20, 18, 18, 19, 20, 18, 18, 15, 18, 18, 16, 15, 17, 17, 19, 18, 18, 18, 19, 17, 19, 19, 18, 18, 15, 15, 15, 18, 18, 17, 19, 20, 18, 18, 18, 18, 19, 17, 16, 15, 16, 19, 15, 20, 18, 18, 18, 16, 17, 15, 19, 19, 19, 16, 18, 17, 20, 16, 18, 18, 17, 19, 19, 18, 18, 18, 18, 15, 15, 16, 18, 18, 19, 17, 18, 19, 18, 15, 16, 18, 20, 18, 18, 18, 18, 18, 15, 18, 19, 20, 18, 18, 18, 17, 18, 19, 19, 19, 18, 18, 16, 15, 20, 20, 18, 16, 16, 18, 19, 18, 18, 19, 20, 18, 20, 17, 18, 15, 16, 16, 19, 18, 19, 18, 16, 19, 20, 18, 18, 15, 18, 18, 19, 16, 16, 18, 19, 18, 18, 18, 17, 19, 15, 20, 20, 19, 16, 16, 19, 19, 18, 18, 16, 20, 18, 18, 19, 19, 18, 20, 18, 19, 17, 18, 18, 18, 17, 16, 19, 18, 18, 15, 16, 18, 17, 19, 16, 18, 19, 18, 18, 19, 18, 20, 15, 16, 19, 19, 17, 19, 18, 17, 18, 15, 15, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 20, 20, 16, 17, 18, 18, 19, 18, 18, 19, 18, 18, 18, 18, 18, 18, 19, 20, 18, 16, 15, 18, 18, 18, 17, 18, 17, 18, 19, 18, 20, 18, 17, 17, 15, 16, 15, 15, 17, 19, 18, 18, 18, 19, 20, 18, 20, 18, 19, 20, 18, 19, 20, 15, 18, 18, 19, 16, 18, 18, 16, 20, 19, 19, 17, 20, 20, 20, 16, 15, 16, 16, 18, 15, 18, 18, 18, 19, 18, 16, 19, 19, 18, 18, 19, 16, 16, 16, 17, 18, 16, 19, 15, 15, 16, 16, 18, 20, 18, 16, 20, 19, 19, 16, 18, 19, 19, 19, 15, 16, 19, 19, 18, 16, 17, 18, 20, 17, 18, 18, 20, 18, 19, 18, 18, 19, 18, 20, 18, 16, 18, 15, 19, 19, 18, 16, 20, 18, 20, 15, 15, 18, 16, 19, 18, 20, 19, 18, 19, 18, 18, 18, 18, 18, 15, 18, 18, 18, 20, 16, 16, 19, 18, 18, 18, 19, 19, 18, 17, 17, 20, 18, 18, 20, 16, 19, 19, 20, 18, 19, 18, 19, 19, 18, 15, 15, 20, 15, 18, 19, 18, 18, 18, 19, 18, 18, 15, 17, 18, 18, 17, 18, 20, 19, 18, 18, 18, 16, 19, 19, 19, 19, 19, 18, 18, 18, 15, 19, 19, 19, 20, 20, 15, 18, 19, 18, 20, 20, 20, 20, 16, 19, 16, 18, 18, 18, 20, 19, 19, 19, 17, 17, 20, 18, 20, 19, 18, 16, 17, 19, 18, 15, 19, 17, 17, 19, 17, 18, 18, 16, 18, 17, 17, 18, 19, 16, 19, 19, 18, 20, 18, 15, 18, 17, 18, 19, 19, 19, 19, 15, 18, 17, 17, 15, 18, 18, 17, 18, 16, 20, 18, 16, 18, 18, 17, 15, 15, 15, 15, 16, 18, 16, 19, 16, 17, 15, 18, 17, 20, 18, 18, 16, 18, 16, 20, 15, 18, 18, 19, 19, 20, 18, 16, 16, 17, 20, 20, 15, 19, 18, 17, 19, 19, 19, 18, 18, 19, 16, 18, 17, 18, 15, 15, 15, 19, 18, 18, 19, 18, 20, 19, 19, 19, 17, 18, 19, 15, 15, 18, 20, 20, 20, 20, 18, 19, 20, 15, 19, 19, 17, 20, 18, 17, 18, 19, 16, 20, 19, 20, 18, 18, 19, 20, 18, 18, 18, 15, 15, 18, 19, 20, 16, 19, 20, 19, 20, 20, 17, 18, 19, 20, 19, 17, 20, 20, 19, 16, 18, 19, 17, 20, 15, 19, 20, 18, 20, 19, 20, 18, 18, 19, 19, 17, 20, 19, 18, 18, 17, 19, 18, 20, 20, 18, 19, 19, 16, 20, 20, 19, 17, 18, 18, 18, 20, 18, 17, 18, 20, 19, 18, 18, 18, 20, 18, 16, 19, 18, 19, 18, 18, 18, 19, 20, 18, 19, 18, 19, 18, 17, 17, 18, 19, 20, 17, 20, 19, 18
#> $ stabbr <chr+lbl> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AK", "AK", "AK", "AK", "AZ", "AZ", "AZ", "AZ", "AZ", "AR", "AR", "AR", "AR", "AR", "AR", "AR", "AR", "AR", "AR", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "MN", "CA", "CO", "CO", "CO", "CO", "CO", "CO", "CO", "CO", "CO", "CO", "CO", "CO", "CO", "CO", "CO", "CT", "CT", "CT", "CT", "CT", "CT", "CT", "CT", "CT", "CT", "CT", "CT", "CT", "CT", "DE", "DE", "DE", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "HI", "HI", "HI", "HI", "ID", "ID", "ID", "ID", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IA", "IA", "IA", "IA", "IA", "IA", "IA", "IA", "CA", "IA", "IA", "IA", "IA", "KS", "KS", "KS", "KS", "KS", "KS", "KS", "KS", "KS", "KS", "KS", "KS", "KS", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "LA", "LA", "LA", "LA", "LA", "LA", "LA", "LA", "LA", "LA", "LA", "LA", "LA", "LA", "LA", "LA", "LA", "ME", "ME", "ME", "ME", "ME", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MN", "MN", "MN", "MN", "MN", "MN", "MN", "MN", "MN", "MN", "MN", "MN", "MN", "MN", "MN", "MN", "MN", "MN", "MS", "MS", "MS", "MS", "MS", "MS", "MS", "MS", "MS", "MS", "MS", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MT", "MT", "MT", "NE", "NE", "NE", "NE", "NE", "NE", "NE", "NE", "NE", "NE", "NE", "NV", "NV", "NV", "NH", "NH", "NH", "NH", "NH", "NH", "NH", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NM", "NM", "NM", "NM", "NM", "NM", "NM", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "ND", "ND", "ND", "ND", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OK", "OK", "OK", "OK", "OK", "OK", "OK", "OK", "OK", "OK", "OK", "OK", "OK", "OK", "OK", "OK", "OR", "OR", "OR", "OR", "OR", "OR", "OR", "OR", "OR", "OR", "OR", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "RI", "RI", "RI", "RI", "RI", "RI", "RI", "SC", "SC", "SC", "SC", "SC", "SC", "SC", "SC", "SC", "SC", "SC", "SC", "SC", "SC", "SC", "SC", "SD", "SD", "SD", "SD", "SD", "SD", "SD", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "UT", "UT", "UT", "UT", "UT", "UT", "UT", "VT", "VT", "VT", "VT", "VT", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WV", "WV", "WV", "WV", "WV", "WV", "WV", "WV", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WY", "GU", "PR", "PR", "PR", "PR", "PR", "PR", "PR", "PR", "PR", "PR", "PR", "PR", "PR", "PR", "PR", "PR", "PR", "VI", "IN", "OK", "SC", "CA", "PR", "CA", "FL", "WA", "WA", "AZ", "CA", "MN", "HI", "AZ", "UT", "FL", "VA", "UT", "VA", "GA", "CA", "KS", "TN", "PA", "CA", "MO", "PA", "FL", "AZ", "FL", "WV", "AL", "FL", "NC", "CO", "GA", "CA", "CA", "GA", "CO", "PA", "OR", "FL", "GA", "AZ", "CA", "CO", "FL", "GA", "IL", "MO", "NJ", "OH", "PA", "TX", "GA", "TX", "AZ", "AZ", "CA", "NV", "TX", "GA", "TN", "ME", "IN"
#> $ city <chr> "Normal", "Birmingham", "Montgomery", "Huntsville", "Montgomery", "Tuscaloosa", "Montgomery", "Auburn", "Montgomery", "Jacksonville", "Livingston", "Montevallo", "Florence", "Birmingham", "Mobile", "Troy", "Tuskegee", "Anchorage", "Fairbanks", "Juneau", "Anchorage", "Tempe", "Tucson", "Phoenix", "Flagstaff", "Prescott", "Little Rock", "Fayetteville", "Jonesboro", "Russellville", "Monticello", "Conway", "Searcy", "Arkadelphia", "Siloam Springs", "Magnolia", "Azusa", "La Mirada", "Riverside", "Pasadena", "Thousand Oaks", "San Luis Obispo", "San Diego", "Bakersfield", "Turlock", "San Bernardino", "Pomona", "Chico", "Carson", "Fresno", "Fullerton", "Hayward", "Long Beach", "Los Angeles", "Northridge", "Sacramento", "Berkeley", "Davis", "Irvine", "Los Angeles", "Riverside", "La Jolla", "Santa Barbara", "Santa Cruz", "Orange", "Irvine", "San Rafael", "Fresno", "Oakland", "Arcata", "La Verne", "Riverside", "Santa Clarita", "Los Angeles", "Oakland", "Los Angeles", "La Jolla", "Belmont", "Fullerton", "Stockton", "Malibu", "San Diego", "Redlands", "San Diego", "San Diego", "San Francisco", "San Francisco", "San Jose", "Santa Clara", "Redding", "Moraga", "Rohnert Park", "Costa Mesa", "Los Angeles", "Minneapolis", "Burbank", "Alamosa", "Denver", "Colorado Springs", "Boulder", "Lakewood", "Golden", "Fort Collins", "Colorado Springs", "Denver", "Denver", "Boulder", "Greeley", "Denver", "Pueblo", "Gunnison", "New Haven", "Bridgeport", "New Britain", "Storrs", "Willimantic", "Fairfield", "West Hartford", "West Haven", "Waterbury", "Hamden", "Fairfield", "West Hartford", "New Haven", "Danbury", "Dover", "Newark", "New Castle", "Washington", "Washington", "Washington", "Washington", "Washington", "Washington", "Washington", "Miami", "Boca Raton", "Orlando", "Daytona Beach", "Tallahassee", "Boca Raton", "Melbourne", "Miami", "Lakeland", "Tallahassee", "Gainesville", "Winter Park", "Jacksonville", "Fort Lauderdale", "Coral Gables", "Jacksonville", "Fort Lauderdale", "West Palm Beach", "Winter Park", "Saint Leo", "Tampa", "Miami Gardens", "DeLand", "Lakeland", "Tampa", "Pensacola", "Albany", "Mount Berry", "Gainesville", "Morrow", "Columbus", "Savannah", "Atlanta", "Fort Valley", "Atlanta", "Americus", "Milledgeville", "Statesboro", "Atlanta", "Athens", "Macon", "Demorest", "Savannah", "Thomasville", "Valdosta", "Carrollton", "Honolulu", "Hilo", "Honolulu", "Honolulu", "Boise", "Pocatello", "Moscow", "Nampa", "Aurora", "Peoria", "Chicago", "Chicago", "River Forest", "Chicago", "Charleston", "Elmhurst", "University Park", "Greenville", "Chicago", "Lisle", "Chicago", "Normal", "Elgin", "Romeoville", "Chicago", "Lebanon", "Chicago", "Naperville", "Chicago", "Dekalb", "Evanston", "Chicago", "Bourbonnais", "Chicago", "Rockford", "Chicago", "River Forest", "Joliet", "Chicago", "Carbondale", "Edwardsville", "Deerfield", "Macomb", "Anderson", "Muncie", "Mishawaka", "Indianapolis", "Whiting", "Evansville", "Winona Lake", "Fort Wayne", "Indianapolis", "Indianapolis", "Evansville", "Terre Haute", "South Bend", "Bloomington", "Gary", "New Albany", "Richmond", "Indianapolis", "Marion", "Notre Dame", "Fort Wayne", "Valparaiso", "Des Moines", "Dubuque", "Lamoni", "Ames", "Iowa City", "Fairfield", "Sioux City", "Cedar Rapids", "San Diego", "Cedar Falls", "Davenport", "Fayette", "Oskaloosa", "Baldwin City", "Emporia", "Hays", "Wichita", "Lawrence", "Wichita", "Manhattan", "Olathe", "Pittsburg", "Leavenworth", "Winfield", "Topeka", "Wichita", "Wilmore", "Louisville", "Campbellsville", "Williamsburg", "Richmond", "Lexington", "Columbia", "Louisville", "Midway", "Morehead", "Murray", "Highland Heights", "Louisville", "Louisville", "Barbourville", "Bowling Green", "Grambling", "Baton Rouge", "Shreveport", "Pineville", "Ruston", "New Orleans", "Lake Charles", "New Orleans", "Thibodaux", "Monroe", "Natchitoches", "Hammond", "Baton Rouge", "New Orleans", "Lafayette", "New Orleans", "New Orleans", "Orono", "Biddeford", "Standish", "Portland", "Waterville", "Baltimore", "Bowie", "Takoma Park", "Baltimore", "Frostburg", "Frederick", "Baltimore", "Baltimore", "Adelphi", "Baltimore", "College Park", "Princess Anne", "Baltimore", "Emmitsburg", "Baltimore", "Salisbury", "Towson", "Stevenson", "Springfield", "Paxton", "Worcester", "Longmeadow", "Waltham", "Chestnut Hill", "Boston", "Waltham", "Bridgewater", "Boston", "Worcester", "Milton", "Quincy", "Boston", "Beverly", "Fitchburg", "Framingham", "Newton", "Cambridge", "Lowell", "Amherst", "Boston", "North Andover", "Boston", "Chicopee", "Salem", "Boston", "Springfield", "North Dartmouth", "Boston", "Medford", "Boston", "Springfield", "Westfield", "Worcester", "Worcester", "Berrien Springs", "Grand Rapids", "Mount Pleasant", "Grand Rapids", "Detroit", "Ypsilanti", "Big Rapids", "Flint", "Grand Rapids", "Allendale", "Southfield", "Livonia", "Ann Arbor", "East Lansing", "Houghton", "Dearborn", "Flint", "Marquette", "Rochester Hills", "University Center", "Adrian", "Spring Arbor", "Detroit", "Kalamazoo", "Minneapolis", "Bemidji", "Saint Paul", "Saint Paul", "Saint Paul", "Mankato", "Saint Paul", "Minneapolis", "Duluth", "Moorhead", "Saint Paul", "Saint Cloud", "Winona", "Duluth", "Saint Paul", "Saint Paul", "Marshall", "Winona", "Alcorn State", "Jackson", "Cleveland", "Jackson", "University", "Columbus", "Itta Bena", "Clinton", "Mississippi State", "Hattiesburg", "Hattiesburg", "Kansas City", "Warrensburg", "Columbia", "Springfield", "Springfield", "Saint Louis", "Jefferson City", "Saint Charles", "Saint Louis", "Columbia", "Kansas City", "Saint Louis", "Kirksville", "Maryville", "Parkville", "Kansas City", "Saint Louis", "Bolivar", "Columbia", "Cape Girardeau", "Springfield", "Saint Louis", "Saint Louis", "Fulton", "Billings", "Bozeman", "Missoula", "Bellevue", "Chadron", "Seward", "Omaha", "Kearney", "Omaha", "Lincoln", "Lincoln", "Peru", "Omaha", "Wayne", "Las Vegas", "Reno", "Incline Village", "Rindge", "Henniker", "Manchester", "Durham", "Keene", "Plymouth", "Nashua", "Caldwell", "Hackettstown", "Teaneck", "Lodi", "Madison", "Lakewood", "Glassboro", "Jersey City", "Union", "West Long Branch", "Montclair", "Newark", "Mahwah", "Lawrenceville", "Camden", "New Brunswick", "Newark", "Jersey City", "South Orange", "Morristown", "Hoboken", "Galloway", "Trenton", "Ewing", "Wayne", "Portales", "Las Vegas", "Socorro", "Albuquerque", "Las Cruces", "Hobbs", "Silver City", "Garden City", "Alfred", "Buffalo", "Potsdam", "New York", "New York", "Ithaca", "New York", "Brooklyn", "Staten Island", "New York", "New York", "New York", "Bronx", "Queens", "Buffalo", "Amherst", "Orangeburg", "New York", "Bronx", "Hempstead", "New Rochelle", "Ithaca", "Keuka Park", "Syracuse", "Brookville", "Riverdale", "Purchase", "Poughkeepsie", "Buffalo", "Dobbs Ferry", "Rockville Centre", "Bronx", "Newburgh", "Bronx", "Rochester", "New York", "New York", "Niagara University", "Old Westbury", "New York", "New York", "Rochester", "Rochester", "Troy", "Saint Bonaventure", "Albany", "Sparkill", "Brooklyn", "Rochester", "Queens", "Albany", "Vestal", "Buffalo", "Stony Brook", "Syracuse", "Utica", "Brockport", "Buffalo", "Cortland", "Fredonia", "Geneseo", "New Paltz", "Oneonta", "Oswego", "Potsdam", "Old Westbury", "Plattsburgh", "Saratoga Springs", "Throggs Neck", "Syracuse", "New York", "Utica", "Staten Island", "New York", "Boone", "Buies Creek", "Greenville", "Elizabeth City", "Elon", "Fayetteville", "Boiling Springs", "Hickory", "Fayetteville", "Montreat", "Greensboro", "Chapel Hill", "Charlotte", "Greensboro", "Durham", "Raleigh", "Wilmington", "Pembroke", "Misenheimer", "Charlotte", "Wake Forest", "Winston-Salem", "Wingate", "Winston-Salem", "Cullowhee", "Bismarck", "Minot", "Grand Forks", "Fargo", "Akron", "Ashland", "Berea", "Bowling Green", "Columbus", "Cleveland", "Cincinnati", "Cleveland", "Dayton", "Findlay", "Tiffin", "University Heights", "Kent", "Painesville", "Sylvania", "Canton", "Oxford", "Mount Vernon", "Cincinnati", "New Concord", "Columbus", "Columbus", "Athens", "Westerville", "Steubenville", "Tiffin", "Toledo", "Cincinnati", "Pepper Pike", "North Canton", "Dayton", "Cincinnati", "Youngstown", "Bartlesville", "Bethany", "Lawton", "Edmond", "Ada", "Langston", "Tahlequah", "Alva", "Edmond", "Stillwater", "Oklahoma City", "Norman", "Tulsa", "Durant", "Weatherford", "Tulsa", "La Grande", "Newberg", "Eugene", "Corvallis", "Eugene", "Forest Grove", "Portland", "Portland", "Ashland", "Salem", "Monmouth", "Center Valley", "Reading", "Glenside", "Bloomsburg", "Radnor", "California", "Pittsburgh", "Pittsburgh", "Pittsburgh", "Philadelphia", "Clarion", "Doylestown", "Philadelphia", "Pittsburgh", "East Stroudsburg", "Saint Davids", "Edinboro", "Erie", "Beaver Falls", "Gwynedd Valley", "Philadelphia", "Immaculata", "Indiana", "Wilkes-Barre", "Kutztown", "Philadelphia", "Annville", "Bethlehem", "Lincoln University", "Lock Haven", "Mansfield", "Scranton", "Erie", "Millersville", "Dallas", "Aston", "Middletown", "University Park", "Philadelphia", "Langhorne", "Pittsburgh", "Pittsburgh", "Rosemont", "Loretto", "Philadelphia", "Scranton", "Greensburg", "Shippensburg", "Slippery Rock", "Philadelphia", "Villanova", "Waynesburg", "West Chester", "Chester", "Wilkes-Barre", "York", "Smithfield", "Providence", "Providence", "Providence", "Kingston", "Bristol", "Newport", "Anderson", "Charleston", "Greenville", "Central", "Charleston", "Charleston", "Clemson", "Columbia", "Columbia", "Spartanburg", "Florence", "Tigerville", "Columbia", "Conway", "Orangeburg", "Rock Hill", "Spearfish", "Madison", "Yankton", "Aberdeen", "Brookings", "Sioux Falls", "Vermillion", "Clarksville", "Nashville", "McKenzie", "Dayton", "Jefferson City", "Memphis", "Lebanon", "Nashville", "Johnson City", "Henderson", "Bristol", "Knoxville", "Cleveland", "Harrogate", "Memphis", "Murfreesboro", "Collegedale", "Chattanooga", "Knoxville", "Martin", "Nashville", "Cookeville", "Greeneville", "Jackson", "Abilene", "San Angelo", "Waco", "Austin", "Corpus Christi…
#> $ zip <chr> "35762", "35294-0110", "36117-3553", "35899", "36104-0271", "35487-0100", "36117-3596", "36849", "36109-3390", "36265", "35470", "35115-6000", "35632-0001", "35229-2240", "36688-0002", "36082-0001", "36088-1920", "99508", "99775", "99801-8697", "99508", "85287", "85721-0066", "85017", "86011-4092", "86301", "72204", "72701", "72401", "72801-2222", "71656", "72035-0001", "72149-5615", "71999-0001", "72761", "71753", "91702-7000", "90639-0001", "92504-3297", "91125", "91360-2787", "93407", "92131", "93311-1099", "95382-0299", "92407-2393", "91768", "95929-0150", "90747-0005", "93740", "92831-3599", "94542", "90840-0115", "90032-8506", "91330", "95819-2694", "94720", "95616-8678", "92697", "90095-1405", "92521", "92093", "93106", "95064-1011", "92866", "92612-3203", "94901-2298", "93702-4709", "94619-1699", "95521-8299", "91750-4401", "92505-8247", "91321-1200", "90045-2659", "94613", "90049-1599", "92037-1011", "94002-1908", "92831-3199", "95211-0197", "90263", "92106-2899", "92373-0999", "92182-0001", "92110-2492", "94132", "94117-1080", "95192-0001", "95053", "96003-8606", "94575", "94928-3609", "92626", "90089", "55401", "91504-1052", "81101", "80217-3364", "80918", "80309-0017", "80226", "80401", "80523-0100", "80907-3812", "80208", "80217-3362", "80302-6697", "80639", "80221-1099", "81001-4901", "81231", "06511-1189", "06604-5620", "06050", "06269", "06226", "06824-5195", "06117-1599", "06516-1916", "06723", "06518", "06825-1000", "06117", "06515-1355", "06810-6826", "19901", "19716", "19720", "20016-8001", "20064", "20008", "20002-3695", "20052", "20005", "20017-1094", "33161-6695", "33431-5598", "32816", "32114-3900", "32307", "33431-0991", "32901-6975", "33199", "33801-5698", "32306-1037", "32611", "32792-7429", "32211-3394", "33309", "33146", "32224-7699", "33314-7796", "33401", "32789-4499", "33574-6665", "33620-9951", "33054-6459", "32723", "33801-6034", "33606-1490", "32514-5750", "31705", "30149", "30501-3668", "30260-0285", "31907-5645", "31406", "30322", "31030-4313", "30332-0530", "31709-4693", "31061", "30460-8126", "30303-3083", "30602", "31207", "30535-0010", "31404-5310", "31792", "31698", "30118-0001", "96816-1578", "96720-4091", "96822-2217", "96813", "83725", "83209", "83844-2282", "83686-5897", "60506-4892", "61625-0001", "60628-1598", "60605-1996", "60305-1499", "60604", "61920", "60126-3296", "60484", "62246", "60607", "60532-0900", "60616", "61790-1000", "60123-1498", "60446-2200", "60660", "62254-1291", "60603", "60540", "60625-4895", "60115-2828", "60208", "60625-4699", "60914-2271", "60605", "61108-2311", "60605-1394", "60305", "60435-6188", "60655", "62901-4512", "62026-1259", "60015-1283", "61455", "46012-3495", "47306", "46545-5591", "46208", "46394", "47722", "46590-1274", "46805-1499", "46202-5143", "46227-3697", "47712", "47809", "46615-7111", "47405-7000", "46408-1197", "47150", "47374-1289", "46222-1997", "46953-4974", "46556", "46808-3994", "46383", "50311-4505", "52001", "50140", "50011-2103", "52242-1316", "52557", "51106-1751", "52402-4797", "92123", "50614-0005", "52803-2898", "52142-1857", "52577", "66006-0065", "66801-5415", "67601-4099", "67213", "66045", "67213-2097", "66506", "66062-1899", "66762", "66048-5082", "67156-6224", "66621-0001", "67260-0124", "40390-1198", "40205-0671", "42718-2799", "40769", "40475-3102", "40506-0032", "42728-1223", "40292-0001", "40347-1120", "40351", "42071-3318", "41099", "40203-2188", "40205-3013", "40906-1499", "42101-1000", "71245", "70803-2750", "71115-2399", "71359", "71272", "70118-6143", "70605", "70148", "70310", "71209-0000", "71497-0002", "70402", "70813", "70126", "70503", "70118-5698", "70125-1098", "04469", "04005-9599", "04084-5236", "04103", "04901-5097", "21201-5720", "20715-9465", "20912", "21216-3698", "21532-1099", "21701", "21218-2688", "21210-2699", "20783-8010", "21250", "20742", "21853-1299", "21251-0001", "21727-7799", "21210-2476", "21801-6862", "21252-0001", "21153-0641", "01109", "01612-1198", "01609-1296", "01106", "02452-4705", "02467", "02215", "02454-9110", "02325", "02129", "01610-1477", "02186-2395", "02170-2999", "02116-4624", "01915", "01420-2697", "01701-9101", "02466", "02138-2790", "01854-5104", "01003", "02125-3393", "01845", "02115-5005", "01013", "01970", "02115-5898", "01109-3797", "02747-2300", "02108-3901", "02155-5555", "02115", "01119-2684", "01086-1630", "01609-2280", "01602-2597", "49104", "49506-1801", "48859", "49512-9396", "48221-3038", "48197", "49307-2251", "48504-6214", "49525-5897", "49401-9403", "48075-1058", "48150-1176", "48109", "48824-1046", "49931-1295", "48128-1491", "48502-1950", "49855-5301", "48309-4401", "48710", "49221", "49283-9799", "48202", "49008-5200", "55454", "56601", "55112", "55104-5494", "55104-1284", "56001", "55106-5000", "55455-0213", "55812", "56563", "55113-1598", "56301-4498", "55987-1399", "55811-4199", "55105-1078", "55105", "56258-5468", "55987-5838", "39096-7500", "39202", "38733", "39217", "38677-1848", "39701", "38941-1400", "39058", "39762", "39406-0001", "39401", "64145-1698", "64093", "65216", "65802-3791", "65802-2125", "63105-3098", "65101", "63301-1695", "63141-8698", "65211-1050", "64110", "63121-4400", "63501-4221", "64468-6001", "64152-3795", "64110-2561", "63103-2097", "65613-2596", "65215", "63701", "65897", "63130-4899", "63119-3194", "65251-2388", "59101-0298", "59717", "59812", "68005-3098", "69337-2690", "68434-1556", "68178", "68849-1212", "68182-0225", "68504-2794", "68588", "68421-0010", "68106", "68787", "89154", "89557", "89451", "03461-0060", "03242", "03106-1045", "03824-3547", "03435-1506", "03264-1595", "03060", "07006-6195", "07840", "07666", "07644", "07940", "08701-2697", "08028", "07305", "07083", "07764-1898", "07043-1624", "07102", "07430-1680", "08648-3099", "08102", "08901-1281", "07102", "07306-5997", "07079-2697", "07960", "07030-5991", "08205-9441", "08608", "08628-7718", "07470", "88130", "87701", "87801", "87106", "88003-8001", "88240", "88061", "11530-0701", "14802-1205", "14208-1098", "13699", "10006-1742", "10027", "14853", "10010", "11210", "10314", "10031", "10065", "10019", "10468", "11367", "14201-1084", "14226-3592", "10962-1210", "10001-5992", "10458", "11549", "10801-1890", "14850-7002", "14478", "13214-1301", "11548-1327", "10471-4098", "10577", "12601", "14214-2695", "10522", "11571-5002", "10468", "12550", "10471-1093", "14618-3790", "10011-8871", "10012-1091", "14109", "11568-8000", "10004", "10038-1598", "14624", "14623-5603", "12180", "14778-9800", "12203-1490", "10976-1050", "11205-3688", "14618-3597", "11439", "12222", "13850-6000", "14260-1660", "11794", "13210", "13502", "14420-2919", "14222", "13045-0900", "14063-1136", "14454-1465", "12561-2443", "13820-4015", "13126", "13676-2294", "11568-0210", "12901-2681", "12866-4391", "10465-4198", "13244", "10018", "13502-4892", "10301-4495", "10033-3299", "28608", "27506", "27858-4353", "27909", "27244-2010", "28301-4298", "28017-0997", "28601", "28311-1420", "28757-1267", "27411", "27599", "28223-0001", "27402-6170", "27707", "27695-7001", "28403-5963", "28372-1510", "28109-0960", "28274-0001", "27587-1889", "27106", "28174-0159", "27110-0001", "28723-9646", "58504", "58707", "58202-8193", "58108-6050", "44325-4702", "44805", "44017-2088", "43403", "43209-2394", "44106", "45221-0063", "44115-2214", "45469", "45840-3653", "44883-2462", "44118-4538", "44242-0001", "44077-3389", "43560-2898", "44709-3308", "45056", "43050-9500", "45233-1670", "43762", "43219", "43210", "45701-2979", "43081", "43952-1763", "44883", "43606-3390", "45206-1925", "44124", "44720-3396", "45435-0001", "45207-1092", "44555-0001", "74006-6299", "73008", "73505-6377", "73034", "74820-6999", "73050", "74464-2399", "73717-2799", "73013", "74078-1015", "73106-1402", "73019-3072", "74171", "74701-3347", "73096-3001", "74104-3189", "97850", "97132", "97401-3745", "97331", "97403", "97116", "97201", "97203-5798", "97520", "97317-9392", "97361-1394", "18034-9568", "19607-1799", "19038-3295", "17815", "19087-3698", "15419-1394", "15213-3165", "15213-3890", "15232", "19118-2693", "16214", "18901-2697", "19104", "15282", "18301-2999", "19087-3696", "16444-0001", "16541-0001", "15010-3599", "19437-0901", "19114-2009", "19345", "15705-1098", "18711-0801", "19530-0730", "19141-1199", "17003-1400", "18015", "19352-0999", "17745-2390", "16933", "18509-1598", "16546", "17551-0302", "18612-1098", "19014-1298", "17057-4846", "16802-1503", "19104-6303", "19047-2990", "15260", "15222-1984", "19010-1699", "15940", "19131-1395", "18510-4699", "15601", "17257-2299", "16057-1383", "19122-6096", "19085-1699", "15370-1257", "19383", "19013-5792", "18766", "17403-3651", "02917-1291", "02903-3703", "02918-0001", "02908", "02881", "02809-2921", "02840-4192", "29621-4035", "29406-9121", "29614-0001", "29630-1020", "29424-0001", "29409", "29634", "29203-1513", "29203", "29302-0006", "29506", "29688-1892", "29208", "29526", "29117-0001", "29733", "57799-9500", "57042-1799", "57078-3724", "57401-7198", "57007-1898", "57105-1699", "57069-2390", "37044", "37212-3757", "38201", "37321", "37760", "38104", "37087", "37204-3951", "37614", "38340-2399", "37620-2699", "37909-0000", "37311", "37752-9900", "38152", "37132", "37315-0370", "37403-2598", "37996", "38238", "37209-1561", "38505-0001", "37743", "38305-3697", "79699", "76909", "76798", "78726", "78412-5503", "75211-9299", "75062-4736", "75503", "75428-3011", "79698-0001", "77074-3298", "77058-1002", "77002-1014", "77901", "77204-2018", "78209", "77705", "78041-1900", "75602", "79407-2099", "76513-2599", "76308-2099", "76203-1277", "78207-4689", "78539-2999", "77446", "77005-1827", "78704-6489", "77006-4696", "77340", "78228", "75275-0221", "75165", "75961", "78666", "79832", "76401-0000", "78363", "77843-1248", "76013", "78705", "75080-3021", "79968-0691", "75799-6699", "76129", "79762-0001", "78249-1644", "77004", "79409-5005", "76105-1536", "76204", "78212-7200", "79072", "79016-0001", "84602", "84720", "84322-1400", "84058-59…
#> $ locale <dbl+lbl> 12, 12, 12, 12, 12, 12, 12, 13, 12, 23, 43, 21, 13, 21, 12, 33, 32, 11, 23, 33, 11, 12, 11, 11, 13, 13, 12, 13, 13, 33, 33, 13, 32, 32, 32, 33, 21, 21, 11, 12, 12, 23, 11, 11, 23, 12, 21, 13, 13, 11, 21, 21, 11, 11, 11, 11, 12, 23, 11, 11, 11, 11, 22, 13, 12, 11, 13, 11, 11, 33, 21, 11, 21, 11, 11, 11, 11, 21, 21, 11, 21, 11, 13, 11, 11, 11, 11, 11, 12, 13, 21, 21, 12, 11, 11, 11, 33, 11, 11, 12, 12, 21, 12, 11, 11, 11, 12, 12, 11, 12, 33, 12, 12, 21, 21, 31, 21, 12, 21, 22, 21, 21, 21, 12, 13, 13, 21, 21, 11, 11, 11, 11, 11, 11, 11, 21, 13, 21, 13, 12, 13, 13, 21, 12, 12, 12, 21, 11, 12, 13, 11, 21, 12, 21, 23, 11, 21, 13, 12, 11, 13, 13, 23, 13, 21, 12, 12, 11, 31, 11, 32, 32, 32, 11, 12, 12, 32, 12, 32, 13, 31, 11, 33, 11, 11, 12, 13, 32, 22, 21, 12, 11, 11, 21, 11, 32, 21, 41, 32, 11, 21, 11, 22, 12, 21, 11, 21, 11, 12, 11, 23, 13, 11, 23, 11, 12, 11, 21, 21, 11, 13, 21, 21, 33, 13, 13, 13, 11, 21, 12, 32, 11, 11, 11, 22, 13, 12, 13, 13, 21, 41, 11, 32, 21, 11, 21, 12, 13, 43, 13, 13, 33, 13, 12, 11, 13, 12, 43, 33, 31, 33, 33, 11, 13, 11, 13, 21, 32, 31, 32, 12, 11, 31, 11, 32, 33, 32, 11, 33, 11, 42, 33, 33, 21, 11, 11, 33, 13, 32, 12, 12, 23, 32, 11, 13, 11, 13, 13, 33, 13, 12, 11, 12, 11, 11, 23, 22, 41, 13, 41, 11, 21, 21, 11, 23, 13, 11, 11, 21, 21, 21, 31, 11, 31, 11, 23, 13, 41, 12, 41, 12, 21, 13, 13, 11, 13, 21, 11, 12, 21, 21, 11, 21, 22, 13, 13, 12, 21, 21, 11, 21, 11, 21, 21, 11, 12, 22, 11, 21, 11, 12, 21, 12, 12, 31, 12, 32, 21, 11, 21, 32, 13, 12, 21, 13, 13, 12, 13, 33, 13, 13, 33, 21, 22, 32, 23, 11, 13, 11, 33, 21, 11, 11, 13, 11, 11, 13, 22, 21, 13, 32, 13, 11, 11, 33, 32, 43, 12, 33, 12, 33, 33, 33, 21, 33, 13, 13, 11, 32, 12, 12, 12, 21, 13, 13, 21, 12, 11, 21, 33, 33, 21, 11, 11, 32, 12, 13, 12, 21, 21, 32, 12, 33, 13, 21, 33, 32, 11, 32, 11, 11, 11, 42, 11, 32, 12, 11, 32, 41, 42, 22, 23, 32, 33, 13, 21, 21, 21, 21, 21, 13, 21, 11, 21, 21, 21, 11, 21, 21, 13, 13, 11, 11, 21, 21, 21, 22, 13, 21, 21, 33, 33, 33, 11, 22, 41, 33, 21, 33, 11, 33, 11, 11, 13, 11, 11, 11, 11, 11, 11, 11, 11, 11, 21, 21, 11, 11, 21, 21, 23, 32, 21, 21, 11, 21, 21, 11, 21, 21, 11, 13, 11, 21, 11, 11, 21, 21, 11, 11, 21, 21, 13, 33, 13, 21, 11, 21, 11, 13, 22, 21, 21, 12, 22, 31, 11, 32, 32, 32, 21, 33, 32, 33, 21, 32, 23, 11, 12, 11, 13, 11, 11, 32, 31, 13, 32, 22, 12, 32, 13, 12, 21, 11, 13, 11, 11, 11, 11, 12, 32, 42, 11, 21, 12, 21, 12, 32, 41, 33, 13, 12, 12, 31, 21, 31, 21, 11, 11, 11, 12, 32, 32, 21, 21, 21, 21, 13, 31, 32, 21, 32, 11, 11, 32, 21, 13, 32, 11, 11, 21, 21, 21, 11, 13, 32, 21, 13, 21, 33, 42, 33, 33, 11, 32, 11, 22, 11, 32, 33, 11, 33, 31, 12, 13, 12, 21, 11, 11, 22, 12, 31, 41, 13, 21, 13, 21, 23, 11, 11, 11, 11, 33, 21, 11, 11, 13, 21, 31, 13, 21, 21, 11, 21, 32, 13, 31, 11, 23, 13, 21, 32, 32, 13, 13, 21, 21, 21, 21, 13, 11, 21, 11, 11, 21, 31, 11, 13, 21, 31, 32, 11, 21, 32, 21, 21, 13, 22, 21, 12, 12, 21, 21, 21, 21, 13, 12, 13, 21, 12, 12, 21, 12, 12, 13, 23, 41, 12, 13, 32, 13, 32, 32, 33, 33, 33, 12, 32, 12, 11, 32, 32, 23, 11, 31, 11, 13, 32, 13, 12, 13, 32, 11, 12, 21, 12, 12, 33, 11, 33, 32, 13, 12, 12, 12, 11, 11, 11, 12, 41, 32, 12, 11, 21, 11, 13, 11, 11, 12, 11, 13, 11, 23, 12, 12, 11, 13, 32, 11, 11, 11, 32, 11, 21, 21, 33, 13, 33, 33, 32, 12, 11, 11, 12, 11, 12, 11, 12, 11, 11, 11, 11, 12, 11, 33, 31, 12, 33, 13, 13, 12, 13, 12, 13, 42, 42, 32, 13, 23, 11, 12, 13, 21, 12, 13, 13, 33, 13, 13, 22, 12, 12, 12, 23, 13, 12, 13, 12, 23, 21, 32, 31, 22, 12, 41, 21, 21, 13, 11, 11, 23, 32, 11, 13, 21, 13, 32, 32, 13, 42, 33, 13, 13, 11, 21, 13, 21, 11, 41, 13, 11, 11, 11, 32, 13, 32, 13, 12, 13, 13, 32, 11, 11, 32, 32, 32, 33, 33, 12, 12, 12, 23, 12, 11, 13, 21, 13, 11, 22, 12, 13, 11, 21, 11, 21, 33, 13, 11, 21, 11, 12, 21, 21, 21, 12, 11, 22, 11, 11, 11, 21, 21, 12, 21, 21, 12, 22, 13, 11, 21, 41, 32, 13, 11, 11, 21, 31, 31, 21, 21, 11, 21, 21, 11, 12, 11, 41, 11, 13, 12, 11, 12, 21, 21, 21, 11, 11, 21, 11, 21, 12, 32, 41, 11, 11, 12, 21, 11, 21, 22, 13, 21
#> $ region <dbl+lbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 8, 8, 8, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 8, 8, 8, 7, 7, 7, 7, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 7, 7, 7, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 6, 6, 6, 6, 6, 6, 6, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 1, 1, 1, 1, 1, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 5, 5, 5, 5, 5, 5, 5, 5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 7, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 3, 6, 5, 8, 9, 8, 5, 8, 8, 6, 8, 4, 8, 6, 7, 5, 5, 7, 5, 5, 8, 4, 5, 2, 8, 4, 2, 5, 6, 5, 5, 5, 5, 5, 7, 5, 8, 8, 5, 7, 2, 8, 5, 5, 6, 8, 7, 5, 5, 3, 4, 2, 3, 2, 6, 5, 6, 6, 6, 8, 8, 6, 5, 5, 1, 3
#> $ tuit_grad_res <dbl> 10128, 8100, 11700, 10632, 7416, 10780, 7362, 9828, 10080, 9600, 6678, 10512, 6660, 17754, 7956, 7650, 19594, 11286, 10414, 11132, 11700, 11720, 11938, 10175, 10305, 21696, 5760, 7752, 4986, 5256, 6631, 5018, 13372, 5058, 14400, 5040, 17592, 11394, 12830, 52506, 19406, 7176, 24255, 7176, 7176, 7176, 7176, 7176, 7176, 7176, 7176, 7176, 7176, 7176, 7176, 7176, 11442, 11442, 11442, 11442, 11442, 11442, 11442, 11442, 34957, 7953, 17370, 14940, 18054, 7176, 14256, 13140, 10440, 18606, 34833, 11964, 15480, 17262, 13764, 48904, 34900, 11880, 22020, 7176, 27288, 7176, 25740, 7176, 25056, 13000, 31308, 7176, 15693, 46272, 12039, 38790, 4590, 6786, 13912, 11826, 13045, 16650, 10520, 13710, 51336, 8892, 22800, 10867, 14441, 5651, 16048, 20520, 18612, 7378, 16300, 7378, 18795, 14292, 16920, 7500, 18990, 30427, 14202, 7675, 7378, 5160, 34164, 8784, 30351, 48000, 9000, 18180, 31770, 14940, 14310, 17820, 13320, 6916, 17712, 8019, 5467, 22338, 8912, 16909, 9684, 10770, 14498, 14706, 27162, 37800, 8978, 19665, 10260, 20163, 7296, 8350, 10416, 16304, 12810, 11466, 7088, 3438, 12060, 12834, 5760, 6300, 15600, 42800, 3258, 14064, 3492, 5292, 4977, 7164, 8878, 15422, 10134, 3528, 11790, 4572, 4338, 24000, 11736, 15912, 20250, 7110, 7982, 7754, 10464, 12600, 16380, 5850, 1174, 8910, 19584, 7356, 31667, 6354, 8082, 13276, 18273, 29487, 7956, 27000, 14400, 18594, 9522, 11646, 15822, 13896, 8922, 56067, 7419, 13420, 18000, 12960, 20858, 20400, 13464, 16000, 11268, 7034, 17280, 7962, 9808, 8850, 8500, 12858, 18400, 14875, 7386, 5682, 8826, 37514, 9486, 7542, 7264, 9501, 7264, 7264, 7264, 21772, 13440, 54908, 9450, 11700, 11568, 13140, 17430, 9758, 10079, 28000, 5040, 10296, 12060, 9159, 17460, 21060, 9398, 10800, 6394, 3582, 11358, 9989, 10458, 10212, 13797, 6678, 11124, 11340, 9306, 5435, 9990, 11734, 12240, 5670, 9900, 12055, 8735, 13000, 6240, 10260, 8214, 11034, 12825, 14160, 9600, 12140, 5110, 9132, 6196, 7416, 6592, 14724, 5703, 6090, 5686, 6490, 5738, 6684, 5336, 6024, 5511, 54918, 6356, 8100, 23498, 9470, 7578, 26740, 13824, 7614, 11250, 6192, 7866, 8820, 55350, 17910, 8640, 11862, 13158, 7968, 8190, 13209, 10345, 8769, 7902, 8040, 11579, 10530, 12690, 14670, 38400, 30600, 54720, 51940, 1675, 14731, 46848, 16430, 10100, 30624, 12555, 3186, 1680, 10800, 18540, 14590, 14014, 18433, 29280, 24793, 14814, 2520, 21654, 32190, 16390, 39267, 52724, 37700, 25056, 1890, 28188, 3042, 27216, 10674, 16700, 14958, 29268, 18912, 7608, 13332, 13680, 17208, 21366, 15930, 23890, 18858, 20511, 12770, 11403, 9072, 18546, 14950, 12060, 7886, 16285, 16360, 13006, 7779, 11700, 8550, 9596, 7694, 8082, 17580, 17580, 8550, 8982, 7460, 9180, 13986, 24766, 15948, 7374, 7488, 5910, 10170, 7501, 8270, 8718, 7425, 7560, 11250, 8800, 8786, 9600, 6748, 7524, 8602, 8460, 9450, 13950, 7284, 18000, 10197, 9264, 9542, 12007, 9160, 6004, 6012, 15876, 20880, 5004, 8190, 5242, 5202, 54250, 13500, 6480, 5598, 5089, 4716, 10530, 4086, 8650, 16128, 6048, 5558, 5043, 5976, 8898, 17930, 4086, 5459, 5306, 18700, 16000, 14088, 18810, 14170, 9720, 9630, 11340, 17910, 16848, 22186, 19080, 22186, 15750, 12879, 13140, 15326, 27126, 12600, 20624, 17329, 20948, 17736, 17736, 17736, 21186, 23580, 18018, 36680, 13517, 12024, 14554, 13086, 7044, 5112, 7160, 5156, 4844, 22500, 3356, 41920, 37713, 14760, 33312, 28740, 47600, 29500, 11090, 11090, 11090, 11090, 11090, 11090, 11090, 11090, 17892, 23928, 17208, 11542, 34608, 25164, 21096, 21432, 20372, 16524, 22482, 18972, 17190, 15300, 21176, 16146, 21510, 15624, 15192, 18406, 17244, 46710, 33408, 14525, 23760, 12400, 31320, 14094, 47522, 13140, 13860, 14382, 18630, 19350, 17550, 28569, 11310, 11310, 11310, 11310, 11310, 11310, 11310, 11310, 11310, 11310, 11310, 11310, 11310, 11310, 11310, 11310, 11310, 11310, 11310, 38880, 12240, 21480, 21780, 34800, 4839, 13330, 4749, 3375, 16178, 3438, 9000, 10980, 38370, 13770, 4745, 10552, 4337, 5219, 4740, 9095, 4719, 4280, 12600, 13824, 5472, 38650, 11430, 3872, 4435, 16740, 8049, 10534, 6743, 8306, 6540, 13514, 8017, 12168, 46524, 12790, 10014, 17190, 16000, 12870, 16788, 11766, 12006, 13960, 10980, 13677, 8964, 12781, 10170, 10800, 11560, 9444, 595, 12600, 12600, 14047, 14652, 21132, 12870, 14298, 12060, 6058, 9540, 20772, 3564, 7000, 4298, 3273, 4500, 4775, 11250, 5531, 10620, 6583, 12960, 4716, 6084, 22896, 13572, 13845, 17400, 12933, 14958, 32768, 11718, 23868, 17028, 9730, 15300, 20520, 14670, 28683, 9288, 5985, 9288, 18766, 45037, 17766, 17040, 9288, 8304, 35181, 23580, 9288, 13050, 9288, 19170, 12042, 13749, 9000, 12780, 9288, 43786, 9288, 25060, 47980, 27000, 10106, 9288, 9288, 9888, 15570, 9288, 34530, 13860, 21792, 21960, 36254, 12762, 23530, 15858, 12510, 25000, 17334, 11580, 14652, 9288, 9288, 16956, 20506, 16080, 9288, 23830, 25104, 13212, 26832, 13734, 9660, 7722, 13734, 15768, 10890, 9651, 7500, 8162, 10170, 70, 10740, 10600, 9360, 8574, 8736, 10612, 10800, 13737, 10674, 9928, 15318, 8384, 5939, 6825, 5939, 8083, 7020, 6062, 8100, 22680, 11034, 9414, 9027, 12988, 6600, 18414, 8640, 10980, 19500, 27025, 13590, 18093, 9216, 8946, 11700, 8450, 11468, 9096, 10178, 10280, 7200, 9000, 22356, 4059, 32130, 10890, 3568, 18072, 33750, 4130, 3630, 12105, 10350, 7920, 7578, 6338, 8370, 17280, 6324, 3350, 18618, 8424, 16830, 4060, 5900, 16326, 5959, 4733, 48791, 28980, 20934, 5765, 17640, 40896, 10710, 5112, 6751, 3900, 4137, 3228, 6677, 8474, 11998, 13874, 5497, 5850, 30780, 4594, 5771, 5487, 5958, 14173, 4973, 42403, 13104, 4823, 7290, 7574, 5996, 9513, 7211, 7476, 14377, 14310, 19408, 10890, 17904, 12294, 10516, 15552, 7614, 14400, 12144, 12475, 10848, 7980, 6480, 10906, 13000, 8676, 18900, 8820, 10207, 8915, 21400, 14940, 13701, 12701, 16578, 8375, 9582, 11721, 8760, 16672, 9336, 19101, 30316, 15300, 30636, 19280, 22788, 11493, 18198, 10806, 12168, 16584, 8658, 7072, 7506, 15600, 12006, 10134, 9450, 9971, 10880, 11608, 11052, 17856, 11286, 10800, 21060, 22680, 14952, 6960, 8864, 8108, 8027, 7996, 8651, 7793, 7155, 10728, 10543, 7640, 7640, 8066, 4878, 5130, 6120, 6450, 5112, 5580, 5580, 2652, 3440, 3870, 3440, 3870, 3870, 15829, 3510, 3420, 2652, 4050, 2652, 6948, 9208, 11124, 15600, 11880, 5112, 7176, 13500, 18198, 18198, 11720, 7176, 14767, 16752, 11720, 7500, 6974, 7020, 15616, 13368, 20860, 7176, 4200, 14940, 14940, 11442, 5940, 7200, 8350, 11720, 14940, 6615, 3780, 8350, 14940, 3900, 14940, 9000, 7800, 13800, 9000, 21682, 12960, 16128, 5504, 17072, 17072, 17072, 17072, 17072, 17072, 17072, 17072, 17072, 17072, 17072, 4140, 4405, 6384, 16752, 16752, 16752, 4704, 5328, 8025, 15990, 5868
#> $ fee_grad_res <dbl> 1414, 0, 1300, 826, 2740, 0, 766, 1676, 1450, 1000, 190, 1404, 540, 610, 200, 856, 3020, 1474, 1842, 1476, 410, 888, 1333, 899, 816, 590, 1834, 1089, 1436, 1481, 2496, 1521, 520, 1329, 0, 1542, 250, 0, 730, 2031, 150, 4201, 360, 1677, 1800, 1210, 1654, 2064, 1204, 847, 1182, 1258, 1092, 1026, 1235, 1626, 2745, 2156, 1907, 1511, 2142, 2013, 2112, 2366, 244, 0, 480, 60, 526, 2122, 180, 930, 500, 297, 1537, 170, 0, 100, 660, 470, 120, 0, 172, 1768, 474, 1524, 70, 2110, 0, 140, 0, 2138, 0, 835, 500, 1500, 2721, 1283, 0, 1730, 984, 2412, 2351, 611, 303, 1719, 670, 2027, 0, 1607, 1323, 184, 2590, 4978, 2756, 5922, 95, 545, 230, 200, 740, 0, 882, 5326, 5380, 350, 792, 50, 762, 640, 860, 526, 54, 195, 0, 90, 100, 1956, 1444, 1857, 1226, 250, 2194, 250, 1869, 1967, 3442, 0, 1200, 1108, 1879, 1541, 580, 0, 0, 2078, 210, 0, 620, 80, 1974, 1870, 0, 420, 1474, 1870, 219, 842, 1708, 2024, 1436, 2022, 2092, 2128, 2290, 300, 200, 1778, 700, 2212, 2024, 0, 448, 902, 350, 2536, 1944, 2122, 285, 0, 280, 2506, 1146, 270, 390, 2788, 300, 1928, 0, 3290, 1136, 1450, 1797, 500, 150, 904, 0, 450, 0, 180, 2358, 500, 1325, 0, 0, 100, 313, 484, 375, 450, 4558, 2142, 250, 3841, 300, 662, 320, 0, 820, 1166, 0, 671, 1121, 1010, 560, 200, 632, 1372, 632, 632, 632, 0, 0, 472, 990, 520, 250, 0, 150, 1232, 1587, 530, 0, 70, 750, 1273, 280, 0, 0, 594, 2183, 774, 0, 1056, 546, 1065, 612, 1644, 0, 240, 110, 1497, 0, 0, 450, 300, 550, 1349, 165, 196, 150, 252, 900, 384, 0, 900, 50, 0, 2306, 3903, 1153, 400, 3011, 1516, 2417, 3018, 2362, 2749, 3948, 2142, 5186, 2836, 4637, 2040, 1165, 1247, 836, 0, 924, 980, 1580, 2498, 275, 1584, 2710, 250, 2206, 60, 270, 2520, 1635, 2194, 1467, 0, 370, 1944, 2628, 0, 60, 0, 140, 0, 780, 100, 782, 120, 9722, 0, 40, 0, 690, 640, 0, 2556, 10130, 160, 150, 570, 2195, 455, 880, 292, 200, 6035, 164, 370, 525, 210, 934, 0, 0, 4608, 60, 2754, 948, 120, 450, 930, 0, 100, 0, 0, 790, 0, 600, 0, 328, 0, 248, 800, 454, 767, 0, 350, 386, 290, 1941, 923, 0, 952, 0, 0, 0, 780, 722, 1641, 1504, 990, 0, 1112, 0, 1320, 225, 460, 816, 744, 1380, 450, 170, 175, 110, 100, 20, 880, 110, 110, 300, 0, 915, 0, 126, 520, 450, 1242, 100, 0, 1214, 1464, 0, 324, 1019, 0, 560, 654, 335, 675, 717, 1048, 500, 0, 100, 1448, 1517, 1894, 675, 1266, 0, 1770, 1333, 1580, 50, 1599, 0, 0, 1220, 699, 625, 465, 1200, 140, 0, 2095, 2052, 720, 315, 470, 216, 924, 970, 924, 968, 2908, 48, 2150, 712, 540, 3204, 1396, 450, 2409, 2247, 2231, 60, 770, 750, 1310, 1414, 0, 2529, 72, 0, 1878, 1310, 1300, 909, 0, 1604, 1500, 1010, 720, 900, 790, 2368, 522, 360, 432, 560, 312, 308, 442, 480, 522, 840, 125, 860, 920, 681, 970, 490, 0, 690, 150, 958, 570, 120, 100, 75, 660, 1100, 1100, 180, 0, 250, 1216, 2154, 0, 260, 500, 1080, 300, 296, 50, 0, 886, 80, 342, 270, 340, 2093, 2284, 2820, 2248, 1890, 1463, 982, 833, 1736, 1647, 946, 1432, 1187, 1180, 1441, 1298, 1802, 535, 488, 1308, 520, 550, 444, 300, 3137, 713, 2786, 2260, 0, 2292, 50, 0, 3100, 105, 3117, 1970, 3093, 2981, 2806, 2578, 2738, 2490, 0, 566, 640, 550, 0, 2540, 2979, 954, 1504, 1524, 1008, 1405, 0, 0, 1814, 220, 36, 1678, 134, 50, 900, 25, 0, 0, 918, 530, 200, 693, 0, 550, 0, 450, 865, 276, 350, 288, 400, 1762, 176, 330, 0, 0, 16, 420, 1995, 2668, 1026, 853, 842, 1588, 601, 639, 0, 2929, 2700, 1994, 1072, 306, 1236, 490, 1569, 135, 210, 1869, 2280, 247, 1500, 0, 1974, 200, 1848, 0, 293, 239, 2578, 320, 3631, 270, 872, 514, 230, 2891, 276, 840, 0, 2564, 214, 2958, 680, 0, 306, 290, 0, 3456, 1265, 1688, 570, 1140, 130, 1314, 3231, 1836, 1550, 460, 2642, 1760, 0, 992, 1034, 3928, 0, 950, 1620, 630, 500, 0, 50, 550, 2682, 3371, 890, 100, 0, 3052, 300, 1920, 0, 0, 0, 0, 819, 1780, 900, 120, 1100, 0, 600, 0, 14160, 150, 1196, 710, 0, 285, 776, 80, 400, 90, 1532, 500, 0, 721, 1275, 726, 1134, 0, 981, 1583, 830, 0, 130, 200, 407, 0, 0, 1919, 0, 0, 525, 200, 30, 1530, 1404, 0, 1820, 1912, 1520, 1108, 1278, 0, 22, 0, 2800, 3384, 0, 3252, 1100, 0, 1460, 2948, 1300, 2500, 1414, 1196, 1561, 1002, 1842, 1940, 2840, 0, 0, 1450, 2549, 1888, 840, 1169, 2896, 612, 150, 600, 2251, 1090, 6360, 960, 2161, 1913, 1216, 3078, 2473, 3496, 1774, 0, 0, 1247, 1728, 48, 1444, 1983, 1324, 2223, 2413, 2524, 334, 1218, 2059, 0, 764, 1007, 679, 1185, 850, 182, 0, 0, 0, 1650, 2106, 5924, 1400, 4392, 190, 3504, 35, 1128, 420, 2502, 212, 140, 2436, 396, 3870, 2921, 3428, 1265, 195, 2271, 2385, 2950, 3614, 1955, 983, 759, 424, 140, 352, 0, 0, 0, 825, 165, 1602, 1095, 1128, 0, 400, 500, 1850, 1222, 200, 350, 0, 260, 800, 0, 0, 120, 0, 0, 0, 0, 795, 520, 340, 0, 1010, 1479, 1575, 1368, 1234, 1365, 1452, 1507, 1205, 1442, 1420, 1411, 764, 200, 1183, 1112, 774, 810, 900, 732, 696, 706, 735, 735, 2573, 200, 200, 900, 1293, 900, 508, 784, 360, 219, 440, 1112, 1975, 500, 1020, 1269, 888, 1401, 466, 0, 888, 290, 1987, 90, 0, 150, 480, 1060, 600, 195, 195, 1765, 360, 0, 1876, 888, 195, 390, 135, 1689, 195, 240, 195, 0, 210, 219, 0, 504, 0, 0, 1940, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 1876, 1631, 208, 0, 0, 0, 1490, 2006, 650, 650, 376
#> $ tuit_grad_nres <dbl> 20160, 19188, 11700, 24430, 14832, 30250, 16542, 29484, 10080, 19200, 13356, 22464, 13320, 17754, 15912, 15300, 19594, 23738, 21904, 23414, 11700, 26160, 32065, 10175, 19522, 21696, 13050, 21032, 9972, 10512, 12511, 10037, 13372, 6318, 14400, 7920, 17592, 11394, 12830, 52506, 19406, 16680, 24255, 16680, 16680, 16680, 16680, 16680, 16680, 16680, 16680, 16680, 16680, 16680, 16680, 16680, 26544, 26544, 26544, 26544, 26544, 26544, 26544, 26544, 34957, 7953, 17370, 14940, 18054, 16680, 14256, 13140, 10440, 18606, 34833, 11964, 15480, 17262, 13764, 48904, 34900, 11880, 22020, 16680, 27288, 16680, 25740, 16680, 25056, 13000, 31308, 16680, 15693, 46272, 12039, 38790, 7830, 22590, 24438, 31284, 13045, 37350, 25791, 13710, 51336, 12607, 22800, 19620, 14441, 16803, 16048, 20520, 18612, 19008, 38212, 19008, 18795, 14292, 16920, 7500, 18990, 30427, 14202, 17677, 19008, 11376, 34164, 8784, 30351, 48000, 17316, 18180, 31770, 14940, 14310, 17820, 13320, 25759, 17712, 22108, 16695, 22338, 21393, 16909, 24116, 27335, 14498, 14706, 27162, 37800, 20517, 19665, 10260, 20163, 7296, 19048, 10416, 16304, 12810, 11466, 22166, 13716, 12060, 12834, 12411, 11394, 15600, 42800, 12096, 29140, 13806, 18846, 19890, 22662, 25186, 15422, 10134, 13068, 11790, 16470, 16830, 24000, 26568, 37392, 20250, 24030, 24278, 26990, 10464, 12600, 16380, 11340, 1174, 8910, 19584, 17654, 31667, 12708, 8082, 23850, 18273, 29487, 9233, 27000, 14400, 18594, 9522, 11646, 15822, 13896, 8922, 56067, 14839, 13420, 18000, 12960, 20858, 20400, 13464, 16000, 28170, 7034, 17280, 7962, 9808, 21954, 8500, 12858, 18400, 14875, 7386, 13710, 24144, 37514, 18528, 14814, 17113, 31932, 17113, 17113, 17113, 21772, 13440, 54908, 9450, 11700, 11568, 13140, 17430, 24720, 29026, 28000, 5040, 10296, 12060, 21375, 17460, 21060, 9398, 10800, 19888, 10206, 11358, 23951, 10458, 23050, 13797, 17510, 11124, 11340, 18788, 13348, 9990, 11734, 12240, 5670, 13950, 31215, 8735, 27114, 6240, 10260, 13500, 16974, 12825, 14160, 9600, 18340, 5110, 9132, 6196, 7416, 13333, 14724, 13740, 10660, 6779, 18590, 16526, 19162, 5336, 12965, 19239, 54918, 6356, 26388, 23498, 9470, 20538, 26740, 20034, 12762, 11250, 11394, 10080, 8820, 55350, 17910, 11862, 20376, 29250, 14784, 16092, 13209, 10345, 13599, 16344, 8040, 11579, 10530, 12690, 14670, 38400, 30600, 54720, 51940, 1675, 14731, 46848, 16430, 10100, 30624, 12555, 3186, 7050, 10800, 18540, 26370, 29529, 35980, 29280, 24793, 14814, 4140, 21654, 32190, 29578, 39267, 52724, 37700, 25056, 1890, 28188, 3042, 27216, 10674, 16700, 14958, 29268, 33600, 7608, 13332, 13680, 17208, 21366, 15930, 48204, 37056, 20511, 22750, 17069, 12528, 24648, 28430, 12060, 7886, 35274, 30432, 13006, 7779, 11700, 8550, 9596, 7694, 16164, 27204, 27204, 8550, 8982, 11334, 9180, 13986, 24766, 15948, 7374, 11286, 5910, 10170, 7501, 8270, 24989, 7425, 7560, 11250, 23840, 10786, 9600, 6748, 15048, 8602, 8460, 9450, 13950, 13536, 18000, 10197, 25363, 24636, 29477, 15862, 6004, 6012, 15876, 20880, 5004, 8190, 9796, 10476, 54250, 13500, 6480, 18611, 18765, 20064, 10530, 8172, 8650, 16128, 13296, 14440, 5043, 17280, 8898, 17930, 8172, 20510, 20357, 18700, 16000, 14088, 18810, 27810, 10620, 13680, 11340, 17910, 16848, 22186, 19080, 22186, 15750, 12879, 21052, 20288, 27126, 18432, 30540, 17329, 20948, 29232, 30144, 30144, 21186, 23580, 18018, 36680, 19708, 12024, 22467, 20412, 9342, 9336, 23585, 17407, 16834, 22500, 8730, 41920, 37713, 14760, 33312, 28740, 47600, 29500, 20520, 20520, 20520, 20520, 20520, 20520, 20520, 20520, 17892, 23928, 17208, 23586, 34608, 25164, 21096, 21432, 20372, 16524, 22482, 18972, 17190, 15300, 21176, 16146, 21510, 15624, 15192, 18406, 17244, 46710, 33408, 14525, 23760, 12400, 31320, 14094, 47522, 13140, 13860, 14382, 18630, 19350, 17550, 28569, 23100, 23100, 23100, 23100, 23100, 23100, 23100, 23100, 23100, 23100, 23100, 23100, 23100, 23100, 23100, 23100, 23100, 23100, 23100, 38880, 12240, 21480, 21780, 34800, 18271, 13330, 17898, 16437, 16178, 14503, 9000, 10980, 38370, 13770, 17545, 28278, 17771, 18937, 17694, 26421, 18548, 16344, 12600, 13824, 5472, 38650, 11430, 13987, 14842, 16740, 8049, 18434, 10115, 13812, 6540, 13514, 14009, 12168, 46524, 24532, 17134, 17190, 16000, 12870, 16788, 21952, 12006, 13960, 10980, 31414, 8964, 12781, 10170, 10800, 35144, 17436, 595, 12600, 12600, 24649, 14652, 21132, 12870, 24286, 12060, 9058, 9540, 20772, 10278, 17008, 10726, 8374, 9999, 9635, 11250, 21034, 10620, 21242, 12960, 10872, 12564, 22896, 17118, 13845, 17400, 24975, 26811, 32768, 17847, 23868, 20340, 9730, 23344, 20520, 14670, 28683, 13932, 5985, 13932, 18766, 45037, 17766, 17040, 10026, 8304, 35181, 23580, 13932, 13050, 13932, 19170, 12042, 13749, 9000, 12780, 13932, 43786, 13932, 25060, 47980, 27000, 17636, 13932, 13932, 9888, 15570, 13932, 34530, 13860, 28718, 37694, 36254, 12762, 39898, 15858, 12510, 25000, 17334, 11580, 14652, 13932, 13932, 23346, 20506, 16080, 13932, 23830, 25104, 13212, 26832, 13734, 9660, 15048, 26512, 15768, 10890, 9651, 7500, 8162, 10170, 70, 18390, 22050, 9360, 8574, 8736, 21224, 10800, 29760, 19746, 21038, 29506, 8384, 11088, 6825, 11088, 15541, 7020, 11656, 13086, 22680, 11034, 9414, 9027, 12988, 6600, 18414, 23760, 10980, 19500, 27025, 13590, 18093, 12672, 24768, 11700, 16514, 29656, 15136, 22642, 24800, 7200, 9000, 22356, 11655, 32130, 10890, 11661, 18072, 33750, 13141, 11232, 12105, 10350, 17568, 14274, 13934, 17746, 17280, 13920, 10946, 18618, 8424, 16830, 5230, 13496, 16326, 13555, 13693, 48791, 28980, 20934, 13361, 17640, 40896, 10710, 12708, 15191, 11370, 11733, 10824, 18700, 21206, 22966, 27796, 14766, 14328, 30780, 5220, 23300, 12868, 13428, 14173, 12569, 42403, 13104, 5668, 7290, 23784, 20327, 25391, 25455, 16656, 14377, 14310, 19408, 15858, 17904, 30960, 28305, 15552, 16722, 14400, 33456, 12475, 27888, 7980, 17910, 10906, 13000, 18396, 18900, 20872, 29851, 17441, 21400, 14940, 27614, 26039, 27770, 18627, 22449, 27214, 19980, 16672, 9336, 19101, 30316, 15300, 30636, 19280, 22788, 25248, 32439, 22431, 12168, 16584, 8658, 17038, 19840, 15600, 12006, 26154, 9450, 9971, 10880, 11608, 11052, 17856, 11286, 10800, 21060, 22680, 14952, 6960, 8864, 17797, 18061, 17551, 18637, 17107, 15465, 24054, 23775, 16771, 16771, 17706, 14598, 11700, 6120, 6450, 5112, 5580, 5580, 2652, 3440, 3870, 3440, 3870, 3870, 18014, 7020, 6840, 2652, 4050, 2652, 13230, 28010, 11124, 15600, 11880, 5112, 16680, 13500, 32439, 32439, 23544, 16680, 14767, 16752, 23544, 7500, 28170, 7020, 15616, 13368, 20860, 16680, 4200, 14940, 14940, 26544, 5940, 7200, 19048, 26160, 14940, 6615, 3780, 19048, 14940, 3900, 14940, 9000, 7800, 13800, 9000, 21682, 12960, 16128, 18772, 17072, 17072, 17072, 17072, 17072, 17072, 17072, 17072, 17072, 17072, 17072, 16488, 12000, 6384, 16752, 16752, 16752, 12174, 19188, 8025, 15990, 10874
#> $ fee_grad_nres <dbl> 1414, 0, 1300, 826, 2740, 0, 766, 1676, 1450, 1000, 190, 1404, 540, 610, 200, 856, 3020, 1973, 2289, 1953, 410, 888, 1333, 899, 774, 590, 1834, 1089, 1436, 1481, 2496, 1521, 520, 1329, 0, 1542, 250, 0, 730, 2031, 150, 4201, 360, 1677, 1800, 1210, 1654, 2064, 1204, 847, 1182, 1258, 1092, 1026, 1235, 1626, 2745, 2156, 1907, 1511, 2142, 2013, 2112, 2366, 244, 0, 480, 60, 526, 2122, 180, 930, 500, 297, 1537, 170, 0, 100, 660, 470, 120, 0, 172, 1768, 474, 1524, 70, 2110, 0, 140, 0, 2138, 0, 835, 500, 1500, 2721, 1283, 0, 1730, 984, 2412, 2351, 611, 303, 1719, 670, 2027, 0, 1607, 1323, 184, 2590, 6136, 2756, 7080, 95, 545, 230, 200, 740, 0, 882, 6314, 6538, 350, 792, 50, 762, 640, 860, 526, 54, 195, 0, 90, 100, 2898, 1444, 2561, 1787, 250, 2818, 250, 2591, 2795, 3442, 0, 1200, 1108, 2457, 1541, 580, 0, 0, 2078, 210, 0, 620, 80, 2728, 1870, 0, 420, 1474, 1870, 219, 842, 1708, 2024, 1436, 2022, 2092, 2128, 2290, 300, 200, 1778, 700, 2212, 2024, 0, 448, 902, 350, 2536, 1944, 2122, 285, 0, 280, 2506, 1146, 270, 390, 2788, 300, 1928, 0, 3290, 1136, 1450, 1797, 500, 150, 904, 0, 450, 0, 180, 2358, 500, 1325, 0, 0, 100, 313, 484, 375, 450, 4558, 2142, 250, 3841, 300, 662, 320, 0, 820, 1166, 0, 671, 1121, 1010, 560, 200, 632, 1372, 632, 632, 632, 0, 0, 472, 990, 520, 250, 0, 150, 1232, 1587, 530, 0, 70, 750, 1273, 280, 0, 0, 594, 2183, 774, 0, 1056, 546, 1065, 612, 1644, 0, 240, 110, 1497, 0, 0, 450, 300, 550, 1349, 165, 196, 150, 252, 900, 384, 0, 900, 50, 0, 11329, 20838, 13650, 400, 3011, 1516, 2417, 3018, 2362, 2749, 3948, 2142, 11632, 2836, 4637, 2040, 1165, 1247, 836, 0, 924, 980, 1580, 2519, 275, 1584, 2710, 250, 2206, 60, 270, 2520, 1635, 2194, 1467, 0, 370, 1944, 2628, 0, 60, 0, 140, 0, 780, 100, 782, 120, 9722, 0, 40, 0, 690, 640, 0, 2556, 10130, 160, 150, 570, 2195, 455, 880, 292, 200, 6035, 164, 370, 525, 210, 934, 0, 0, 4608, 60, 2754, 948, 120, 450, 930, 0, 100, 0, 0, 790, 0, 600, 0, 328, 0, 248, 800, 454, 767, 0, 350, 386, 290, 1941, 923, 0, 952, 0, 0, 0, 780, 722, 1641, 1504, 990, 0, 1112, 0, 1320, 225, 460, 816, 744, 1380, 450, 170, 1175, 110, 100, 20, 880, 110, 110, 300, 0, 915, 0, 126, 520, 450, 1242, 100, 0, 1214, 1464, 0, 324, 1019, 0, 560, 654, 335, 675, 717, 1048, 500, 0, 100, 1579, 1737, 1948, 675, 1266, 0, 1770, 1333, 1580, 50, 1599, 0, 0, 1220, 699, 625, 465, 1200, 140, 0, 2095, 2052, 720, 315, 470, 216, 924, 970, 924, 968, 2908, 48, 2150, 712, 540, 3204, 1396, 450, 2409, 2247, 2231, 60, 770, 750, 1310, 1414, 0, 2529, 72, 0, 1878, 1310, 1300, 909, 0, 1604, 1500, 1010, 720, 900, 790, 2368, 522, 360, 432, 560, 312, 308, 442, 480, 522, 840, 125, 860, 920, 681, 970, 490, 0, 690, 150, 958, 570, 120, 100, 75, 660, 1100, 1100, 180, 0, 250, 1216, 2154, 0, 260, 500, 1080, 300, 296, 50, 0, 886, 80, 342, 270, 340, 2093, 2284, 2820, 2248, 1890, 1463, 982, 833, 1736, 1647, 946, 1432, 1187, 1180, 1441, 1298, 1802, 535, 488, 1308, 520, 550, 444, 300, 3137, 713, 2786, 2260, 0, 2292, 50, 0, 3100, 105, 3117, 1970, 3093, 2981, 2806, 2578, 2738, 2490, 0, 566, 640, 550, 0, 2540, 2979, 954, 1504, 1524, 1008, 1405, 0, 0, 1814, 220, 36, 1678, 134, 50, 900, 25, 0, 0, 918, 530, 200, 693, 0, 550, 0, 450, 865, 276, 350, 288, 400, 1762, 176, 330, 0, 0, 16, 420, 1995, 2668, 1026, 853, 842, 1588, 601, 639, 0, 2929, 2700, 1994, 1072, 306, 1236, 490, 1569, 135, 210, 1869, 2280, 247, 1500, 0, 1974, 200, 1848, 0, 293, 239, 2794, 320, 4576, 270, 872, 514, 230, 3836, 276, 840, 0, 2780, 214, 3903, 680, 0, 306, 290, 0, 4401, 1265, 2368, 570, 1140, 130, 1452, 3447, 2052, 1550, 460, 2858, 1760, 0, 992, 1034, 3928, 0, 950, 1620, 630, 500, 0, 50, 550, 2898, 4283, 890, 100, 0, 3268, 300, 1920, 0, 0, 0, 0, 819, 1780, 900, 120, 1100, 0, 600, 0, 36522, 150, 1196, 710, 0, 285, 776, 80, 400, 90, 1532, 500, 0, 721, 1275, 726, 1134, 0, 981, 1583, 830, 0, 130, 200, 407, 0, 0, 1919, 0, 0, 525, 200, 30, 1530, 1404, 0, 1820, 2142, 1520, 1108, 1278, 0, 22, 0, 2800, 3384, 0, 3827, 1100, 0, 1460, 2948, 1300, 2500, 1414, 1196, 1561, 1002, 1842, 1940, 3900, 0, 0, 1450, 2549, 1888, 840, 1169, 2896, 612, 150, 600, 2251, 1090, 6360, 960, 2161, 1913, 1566, 3078, 2939, 3496, 1774, 0, 0, 1247, 1728, 48, 1444, 1983, 1397, 2223, 2413, 2524, 334, 1218, 2059, 0, 764, 1007, 684, 1185, 850, 182, 0, 0, 0, 1650, 2106, 6495, 1400, 4698, 190, 3504, 35, 1656, 420, 2502, 212, 140, 2436, 396, 3870, 3341, 3875, 1265, 195, 2875, 3045, 3632, 4367, 1955, 983, 759, 424, 140, 352, 0, 0, 0, 825, 165, 1602, 1095, 1128, 0, 400, 500, 2062, 1222, 200, 350, 0, 260, 800, 0, 0, 120, 0, 0, 0, 0, 795, 520, 340, 0, 1010, 1479, 1575, 1368, 1234, 1365, 1452, 1507, 1205, 1442, 1420, 1411, 764, 200, 1183, 1112, 774, 810, 900, 732, 696, 706, 735, 735, 2573, 200, 200, 900, 1293, 900, 508, 784, 360, 219, 440, 1112, 1975, 500, 1020, 1269, 888, 1401, 466, 0, 888, 290, 3046, 90, 0, 150, 480, 1060, 600, 195, 195, 1765, 360, 0, 1876, 888, 195, 390, 135, 1689, 195, 240, 195, 0, 210, 219, 0, 504, 0, 0, 1940, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 1876, 1631, 208, 0, 0, 0, 1490, 2006, 650, 650, 376
#> $ tuit_md_res <dbl> NA, 28978, NA, NA, NA, 28978, NA, NA, NA, NA, NA, NA, NA, NA, 31004, NA, NA, NA, NA, NA, NA, NA, 32602, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 34521, 34526, 34527, 36374, 35430, 34977, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 64538, NA, NA, NA, 40348, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 38585, NA, NA, NA, NA, NA, 59240, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 62060, NA, NA, NA, NA, 25491, NA, NA, 28111, NA, 32737, NA, 22408, 32744, NA, NA, NA, 45871, NA, 54580, NA, NA, NA, 29175, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 51000, NA, NA, NA, NA, NA, NA, NA, 42286, NA, NA, NA, NA, NA, NA, NA, 36672, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 30906, NA, NA, NA, NA, NA, 58630, NA, NA, NA, NA, NA, 62088, NA, NA, NA, NA, NA, NA, NA, NA, 33474, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 35503, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 36382, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37891, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 38518, NA, 40760, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 61148, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 54900, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 62872, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 63776, NA, NA, NA, NA, NA, NA, NA, 42880, NA, NA, NA, NA, NA, NA, NA, NA, NA, 39416, 31312, NA, NA, NA, NA, 56522, NA, NA, NA, 34039, NA, NA, NA, NA, NA, NA, NA, NA, 39402, 39402, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 31197, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 33675, 23926, NA, NA, NA, NA, NA, 55760, NA, NA, NA, NA, 62664, NA, NA, NA, NA, NA, NA, NA, NA, 58394, NA, NA, NA, NA, NA, NA, NA, 35651, 28810, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 40479, NA, NA, NA, NA, NA, NA, NA, NA, 41281, NA, NA, 61232, NA, NA, NA, NA, NA, NA, NA, NA, NA, 6534, NA, NA, NA, NA, NA, NA, NA, NA, 62980, NA, NA, NA, NA, 41600, NA, NA, NA, NA, NA, NA, NA, NA, NA, 51690, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 55645, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 43670, 43670, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20252, NA, NA, NA, NA, NA, NA, NA, NA, 32746, NA, NA, NA, NA, NA, NA, NA, NA, NA, 60000, NA, NA, NA, NA, NA, 32064, NA, NA, NA, NA, NA, NA, 65476, 30390, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 29680, NA, NA, NA, NA, 32550, NA, NA, NA, 36988, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 29053, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 59910, NA, 57684, NA, NA, NA, NA, NA, NA, NA, NA, 53406, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 42888, NA, NA, NA, NA, NA, NA, NA, NA, NA, 31787, NA, NA, NA, NA, NA, NA, NA, NA, 32834, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 16635, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 15566, NA, 20673, NA, NA, NA, 57500, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 40537, NA, NA, NA, NA, NA, NA, 37070, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 53113, 32227, 43828, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 38357, 36792, NA, NA, NA, NA, NA, 8472, NA, NA, 21762, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 36266, NA, NA, NA, NA, 14180, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37710, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 28926, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA
#> $ fee_md_res <dbl> NA, 0, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, 372, NA, NA, NA, NA, NA, NA, NA, 1333, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2745, 2156, 1907, 1511, 2142, 2013, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3065, NA, NA, NA, 223, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2910, NA, NA, NA, NA, NA, 970, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 140, NA, NA, NA, NA, 4189, NA, NA, 3523, NA, 6118, NA, 3203, 3913, NA, NA, NA, 1314, NA, 1195, NA, NA, NA, 4551, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1142, NA, NA, NA, NA, NA, NA, NA, 300, NA, NA, NA, NA, NA, NA, NA, 772, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3356, NA, NA, NA, NA, NA, 5164, NA, NA, NA, NA, NA, 828, NA, NA, NA, NA, NA, NA, NA, NA, 5468, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 348, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1387, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 844, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1524, NA, 196, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3984, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4909, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 794, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, 328, 0, NA, NA, NA, NA, 0, NA, NA, NA, 2220, NA, NA, NA, NA, NA, NA, NA, NA, 2074, 1238, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2600, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3036, 1640, NA, NA, NA, NA, NA, 654, NA, NA, NA, NA, 1500, NA, NA, NA, NA, NA, NA, NA, NA, 2280, NA, NA, NA, NA, NA, NA, NA, 1295, 5544, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2290, NA, NA, NA, NA, NA, NA, NA, NA, 2850, NA, NA, 2583, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1733, NA, NA, NA, NA, NA, NA, NA, NA, 2340, NA, NA, NA, NA, 156, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1500, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3495, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3050, 3824, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2902, NA, NA, NA, NA, NA, NA, NA, NA, 1949, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, 1524, NA, NA, NA, NA, NA, NA, 165, 1678, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 957, NA, NA, NA, NA, 2490, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 838, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5378, NA, 950, NA, NA, NA, NA, NA, NA, NA, NA, 890, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 600, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, 1253, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1663, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4157, NA, 0, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1246, NA, NA, NA, NA, NA, NA, 2834, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 943, 3142, 3002, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 766, 1095, NA, NA, NA, NA, NA, 15432, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1452, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3146, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA
#> $ tuit_md_nres <dbl> NA, 62714, NA, NA, NA, 62714, NA, NA, NA, NA, NA, NA, NA, NA, 62008, NA, NA, NA, NA, NA, NA, NA, 54180, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 46766, 46771, 46772, 48619, 47675, 47222, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 64538, NA, NA, NA, 66303, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72665, NA, NA, NA, NA, NA, 59240, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 62060, NA, NA, NA, NA, 51085, NA, NA, 62532, NA, 62737, NA, 55314, 60592, NA, NA, NA, 45871, NA, 61167, NA, NA, NA, 49355, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 51000, NA, NA, NA, NA, NA, NA, NA, 42286, NA, NA, NA, NA, NA, NA, NA, 71328, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 62358, NA, NA, NA, NA, NA, 58630, NA, NA, NA, NA, NA, 62088, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 60811, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 57157, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 67086, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 70849, NA, 61982, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 61148, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 54900, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 62872, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 63776, NA, NA, NA, NA, NA, NA, NA, 68515, NA, NA, NA, NA, NA, NA, NA, NA, NA, 58734, 58066, NA, NA, NA, NA, 56522, NA, NA, NA, 61414, NA, NA, NA, NA, NA, NA, NA, NA, 56547, 56547, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 73460, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 68578, 47852, NA, NA, NA, NA, NA, 55760, NA, NA, NA, NA, 62664, NA, NA, NA, NA, NA, NA, NA, NA, 58394, NA, NA, NA, NA, NA, NA, NA, 64651, 56574, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 64240, NA, NA, NA, NA, NA, NA, NA, NA, 63697, NA, NA, 61232, NA, NA, NA, NA, NA, NA, NA, NA, NA, 22061, NA, NA, NA, NA, NA, NA, NA, NA, 62980, NA, NA, NA, NA, 69260, NA, NA, NA, NA, NA, NA, NA, NA, NA, 51690, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 55645, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 65160, 65160, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20252, NA, NA, NA, NA, NA, NA, NA, NA, 60140, NA, NA, NA, NA, NA, NA, NA, NA, NA, 60000, NA, NA, NA, NA, NA, 59348, NA, NA, NA, NA, NA, NA, 65476, 48654, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 53458, NA, NA, NA, NA, 64050, NA, NA, NA, 55496, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 29053, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 59910, NA, 59930, NA, NA, NA, NA, NA, NA, NA, NA, 56628, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 87150, NA, NA, NA, NA, NA, NA, NA, NA, NA, 76173, NA, NA, NA, NA, NA, NA, NA, NA, 67813, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 29735, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 28666, NA, 34711, NA, NA, NA, 57500, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 76745, NA, NA, NA, NA, NA, NA, 64170, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 53113, 53966, 53952, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 66987, NA, NA, NA, NA, NA, 20856, NA, NA, 42768, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 50201, NA, NA, NA, NA, 14180, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 60000, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 57850, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA
#> $ fee_md_nres <dbl> NA, 0, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, 372, NA, NA, NA, NA, NA, NA, NA, 1333, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2745, 2156, 1907, 1511, 2142, 2013, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3065, NA, NA, NA, 223, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2910, NA, NA, NA, NA, NA, 970, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 140, NA, NA, NA, NA, 5469, NA, NA, 5244, NA, 7618, NA, 4848, 5306, NA, NA, NA, 1314, NA, 1195, NA, NA, NA, 5561, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1142, NA, NA, NA, NA, NA, NA, NA, 300, NA, NA, NA, NA, NA, NA, NA, 772, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3356, NA, NA, NA, NA, NA, 5164, NA, NA, NA, NA, NA, 828, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 348, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1387, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 844, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1524, NA, 196, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3984, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4909, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 794, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, 328, 0, NA, NA, NA, NA, 0, NA, NA, NA, 2220, NA, NA, NA, NA, NA, NA, NA, NA, 2074, 1238, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2600, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3036, 1640, NA, NA, NA, NA, NA, 654, NA, NA, NA, NA, 1500, NA, NA, NA, NA, NA, NA, NA, NA, 2280, NA, NA, NA, NA, NA, NA, NA, 1295, 5544, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2290, NA, NA, NA, NA, NA, NA, NA, NA, 2850, NA, NA, 2583, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1733, NA, NA, NA, NA, NA, NA, NA, NA, 2340, NA, NA, NA, NA, 156, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1500, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3495, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3050, 3824, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2902, NA, NA, NA, NA, NA, NA, NA, NA, 1949, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, 1524, NA, NA, NA, NA, NA, NA, 165, 1678, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 957, NA, NA, NA, NA, 2490, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 838, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5378, NA, 950, NA, NA, NA, NA, NA, NA, NA, NA, 890, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 600, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, 1253, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1663, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4157, NA, 0, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1246, NA, NA, NA, NA, NA, NA, 2834, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 943, 3802, 3694, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1095, NA, NA, NA, NA, NA, 35832, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1452, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3146, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA
#> $ tuit_law_res <dbl> NA, NA, NA, NA, NA, 23610, NA, NA, 1220, NA, NA, NA, NA, 40788, NA, NA, NA, NA, NA, NA, NA, NA, 24500, NA, NA, NA, 10681, 14650, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20836, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 49242, 47332, 45578, 45578, NA, NA, NA, NA, 54718, NA, NA, NA, NA, NA, 15140, NA, NA, 58470, NA, NA, NA, NA, NA, 53004, 57500, NA, NA, NA, 56230, NA, 50690, NA, 56280, NA, NA, NA, NA, 66306, NA, NA, NA, NA, NA, 29718, NA, NA, NA, NA, 52590, NA, NA, NA, NA, NA, NA, NA, NA, NA, 30354, NA, NA, NA, NA, NA, 49520, NA, NA, NA, NA, NA, NA, NA, 56684, 51400, NA, NA, 63205, NA, NA, 37280, NA, NA, NA, 9114, NA, NA, 17400, NA, 18071, 19139, NA, NA, NA, 53000, NA, 41790, NA, NA, NA, NA, 19950, 44468, NA, NA, NA, NA, NA, NA, NA, NA, NA, 58200, NA, NA, NA, NA, NA, 11826, 17604, 39184, NA, NA, NA, NA, NA, NA, NA, 22392, NA, NA, NA, 7754, NA, NA, NA, NA, NA, NA, 48670, NA, NA, NA, NA, 28800, NA, 48140, NA, NA, NA, 48570, NA, NA, NA, NA, 21970, 66506, NA, NA, NA, NA, NA, NA, NA, NA, 16410, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 21504, NA, NA, NA, NA, 35275, NA, NA, NA, NA, NA, 60240, NA, 40600, 42400, NA, NA, NA, 26181, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 22446, NA, NA, NA, NA, NA, NA, 22498, NA, NA, NA, NA, NA, NA, 23199, NA, 22434, NA, NA, NA, 21346, NA, NA, NA, NA, NA, 19750, NA, NA, NA, 43696, NA, NA, NA, NA, NA, NA, NA, NA, NA, 54310, NA, NA, NA, NA, 23190, NA, 30744, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 56940, 55700, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 52800, NA, NA, NA, NA, 14331, 49410, NA, NA, 42058, NA, NA, NA, NA, NA, NA, NA, 44790, NA, NA, NA, NA, NA, NA, NA, 61526, NA, NA, NA, NA, NA, NA, NA, NA, NA, 31667, NA, NA, NA, NA, NA, NA, NA, NA, 43704, NA, NA, NA, NA, NA, NA, 42298, NA, NA, NA, NA, NA, NA, NA, 16870, NA, NA, 27096, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20232, 18435, NA, NA, NA, NA, NA, 42540, NA, NA, NA, NA, 58800, NA, NA, NA, NA, 10848, NA, NA, NA, 39724, NA, NA, NA, 11850, NA, NA, NA, 25900, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 25804, NA, 25804, NA, 54848, NA, NA, NA, NA, NA, NA, NA, NA, NA, 6874, NA, NA, NA, NA, NA, NA, NA, NA, 69896, 67748, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 61770, 60254, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 66036, NA, NA, NA, 49000, NA, NA, NA, NA, NA, NA, NA, NA, 61150, NA, NA, 25410, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 51500, 50860, NA, NA, 61738, NA, 42600, NA, NA, 46140, NA, NA, NA, NA, NA, NA, 21141, NA, NA, 13444, NA, NA, NA, NA, NA, NA, 46268, NA, NA, NA, NA, NA, 14054, NA, 21376, NA, NA, NA, 1300, 53500, 22332, 19214, 35003, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 30584, NA, NA, NA, NA, 20135, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 30885, 15720, NA, NA, NA, 25338, NA, NA, NA, NA, 35604, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1540, 46124, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 49896, 63610, NA, 34962, NA, NA, NA, NA, NA, NA, NA, NA, 26980, 45993, NA, NA, 45730, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 21072, NA, NA, NA, NA, NA, NA, NA, NA, NA, 11620, NA, 45140, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37500, 17122, NA, NA, NA, 16696, NA, NA, NA, NA, NA, NA, NA, 55146, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 24912, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37830, 49404, NA, NA, NA, NA, NA, NA, 8262, NA, 34868, NA, NA, NA, NA, NA, NA, 18582, 18840, NA, NA, NA, NA, NA, 13450, NA, NA, NA, 27998, NA, NA, NA, NA, NA, NA, NA, 28723, 36000, NA, NA, 22701, NA, NA, 35582, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 60210, NA, NA, NA, NA, 40500, NA, NA, NA, NA, NA, 48330, NA, NA, 35955, NA, NA, NA, NA, NA, NA, NA, NA, 24228, NA, NA, NA, NA, NA, NA, NA, NA, 47550, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 24235, NA, NA, NA, NA, 14490, NA, NA, NA, NA, NA, 16000, NA, NA, NA, NA, NA, NA, NA, NA, 7500, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 27170, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 544, NA, NA, NA, NA
#> $ fee_law_res <dbl> NA, NA, NA, NA, NA, 0, NA, NA, 370, NA, NA, NA, NA, 550, NA, NA, NA, NA, NA, NA, NA, NA, 1333, NA, NA, NA, 3012, 1954, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 400, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2745, 2156, 1907, 1511, NA, NA, NA, NA, 381, NA, NA, NA, NA, NA, 1218, NA, NA, 870, NA, NA, NA, NA, NA, 430, 60, NA, NA, NA, 481, NA, 80, NA, 0, NA, NA, NA, NA, 885, NA, NA, NA, NA, NA, 2086, NA, NA, NA, NA, 332, NA, NA, NA, NA, NA, NA, NA, NA, NA, 934, NA, NA, NA, NA, NA, 920, NA, NA, NA, NA, NA, NA, NA, 1152, 820, NA, NA, 0, NA, NA, 150, NA, NA, NA, 1966, NA, NA, 3269, NA, 2623, 2665, NA, NA, NA, 1134, NA, 1350, NA, NA, NA, NA, 1145, 496, NA, NA, NA, NA, NA, NA, NA, NA, NA, 642, NA, NA, NA, NA, NA, 2242, 2290, 300, NA, NA, NA, NA, NA, NA, NA, 772, NA, NA, NA, 14506, NA, NA, NA, NA, NA, NA, 336, NA, NA, NA, NA, 3290, NA, 510, NA, NA, NA, 2130, NA, NA, NA, NA, 2558, 300, NA, NA, NA, NA, NA, NA, NA, NA, 5260, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1121, NA, NA, NA, NA, 1372, NA, NA, NA, NA, NA, 490, NA, 932, 62, NA, NA, NA, 1970, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1074, NA, NA, NA, NA, NA, NA, 70, NA, NA, NA, NA, NA, NA, 1524, NA, 196, NA, NA, NA, 384, NA, NA, NA, NA, NA, 3953, NA, NA, NA, 1710, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4300, NA, NA, NA, NA, 1348, NA, 2106, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 210, 1282, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 152, NA, NA, NA, NA, 400, 117, NA, NA, 2240, NA, NA, NA, NA, NA, NA, NA, 510, NA, NA, NA, NA, NA, NA, NA, 328, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2268, NA, NA, NA, NA, NA, NA, NA, NA, 2104, NA, NA, NA, NA, NA, NA, 370, NA, NA, NA, NA, NA, NA, NA, 110, NA, NA, 1840, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1299, 1552, NA, NA, NA, NA, NA, 654, NA, NA, NA, NA, 500, NA, NA, NA, NA, 2102, NA, NA, NA, 1825, NA, NA, NA, 3388, NA, NA, NA, 1202, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3371, NA, 3106, NA, 940, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1733, NA, NA, NA, NA, NA, NA, NA, NA, 2464, 522, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 882, 716, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2676, NA, NA, NA, 458, NA, NA, NA, NA, NA, NA, NA, NA, 400, NA, NA, 2870, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2578, 620, NA, NA, 670, NA, 1140, NA, NA, 0, NA, NA, NA, NA, NA, NA, 3339, NA, NA, 2752, NA, NA, NA, NA, NA, NA, 725, NA, NA, NA, NA, NA, 1524, NA, 2838, NA, NA, NA, 220, 174, 1678, 134, 260, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 865, NA, NA, NA, NA, 1855, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5470, 3281, NA, NA, NA, 490, NA, NA, NA, NA, 2282, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 420, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1084, 4996, NA, 950, NA, NA, NA, NA, NA, NA, NA, NA, 890, 555, NA, NA, 90, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1900, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3272, NA, 830, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 200, 1976, NA, NA, NA, 3472, NA, NA, NA, NA, NA, NA, NA, 3127, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1002, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1020, 6360, NA, NA, NA, NA, NA, NA, 22992, NA, 0, NA, NA, NA, NA, NA, NA, 1836, 4314, NA, NA, NA, NA, NA, 0, NA, NA, NA, 1246, NA, NA, NA, NA, NA, NA, NA, 6277, 1400, NA, NA, 2653, NA, NA, 1863, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2990, NA, NA, NA, NA, 165, NA, NA, NA, NA, NA, 66, NA, NA, 1095, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1452, NA, NA, NA, NA, 1411, NA, NA, NA, NA, NA, 848, NA, NA, NA, NA, NA, NA, NA, NA, 200, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 888, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 738, NA, NA, NA, NA
#> $ tuit_law_nres <dbl> NA, NA, NA, NA, NA, 43060, NA, NA, 1220, NA, NA, NA, NA, 40788, NA, NA, NA, NA, NA, NA, NA, NA, 29000, NA, NA, NA, 23436, 35284, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20836, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 52571, 56435, 51641, 51641, NA, NA, NA, NA, 54718, NA, NA, NA, NA, NA, 15140, NA, NA, 58470, NA, NA, NA, NA, NA, 53004, 57500, NA, NA, NA, 56230, NA, 50690, NA, 56280, NA, NA, NA, NA, 66306, NA, NA, NA, NA, NA, 36504, NA, NA, NA, NA, 52590, NA, NA, NA, NA, NA, NA, NA, NA, NA, 51786, NA, NA, NA, NA, NA, 49520, NA, NA, NA, NA, NA, NA, NA, 56684, 51400, NA, NA, 63205, NA, NA, 37280, NA, NA, NA, 23789, NA, NA, 30528, NA, 37130, 34602, NA, NA, NA, 53000, NA, 41790, NA, NA, NA, NA, 19950, 44468, NA, NA, NA, NA, NA, NA, NA, NA, NA, 58200, NA, NA, NA, NA, NA, 26532, 36362, 39184, NA, NA, NA, NA, NA, NA, NA, 45816, NA, NA, NA, 26990, NA, NA, NA, NA, NA, NA, 48670, NA, NA, NA, NA, 36000, NA, 48140, NA, NA, NA, 48570, NA, NA, NA, NA, 21970, 66506, NA, NA, NA, NA, NA, NA, NA, NA, 24615, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 36552, NA, NA, NA, NA, 55500, NA, NA, NA, NA, NA, 60240, NA, 40600, 42400, NA, NA, NA, 45796, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 28689, NA, NA, NA, NA, NA, NA, 35126, NA, NA, NA, NA, NA, NA, 48828, NA, 41844, NA, NA, NA, 34528, NA, NA, NA, NA, NA, 19750, NA, NA, NA, 43696, NA, NA, NA, NA, NA, NA, NA, NA, NA, 54310, NA, NA, NA, NA, 34680, NA, 45852, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 56940, 55700, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 52800, NA, NA, NA, NA, 18785, 49410, NA, NA, 42058, NA, NA, NA, NA, NA, NA, NA, 44790, NA, NA, NA, NA, NA, NA, NA, 64526, NA, NA, NA, NA, NA, NA, NA, NA, NA, 34740, NA, NA, NA, NA, NA, NA, NA, NA, 52560, NA, NA, NA, NA, NA, NA, 42298, NA, NA, NA, NA, NA, NA, NA, 36936, NA, NA, 27096, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 38484, 36393, NA, NA, NA, NA, NA, 42540, NA, NA, NA, NA, 58800, NA, NA, NA, NA, 31308, NA, NA, NA, 39724, NA, NA, NA, 31620, NA, NA, NA, 37800, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 39114, NA, 39114, NA, 54848, NA, NA, NA, NA, NA, NA, NA, NA, NA, 22061, NA, NA, NA, NA, NA, NA, NA, NA, 69896, 67748, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 61770, 60254, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 66036, NA, NA, NA, 49000, NA, NA, NA, NA, NA, NA, NA, NA, 61150, NA, NA, 29500, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 51500, 50860, NA, NA, 61738, NA, 42600, NA, NA, 46140, NA, NA, NA, NA, NA, NA, 38287, NA, NA, 36116, NA, NA, NA, NA, NA, NA, 46268, NA, NA, NA, NA, NA, 42163, NA, 21376, NA, NA, NA, 1300, 53500, 27332, 19283, 35003, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 45536, NA, NA, NA, NA, 31103, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 30885, 27555, NA, NA, NA, 25338, NA, NA, NA, NA, 44820, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1540, 46124, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 49896, 63610, NA, 44996, NA, NA, NA, NA, NA, NA, NA, NA, 41354, 45993, NA, NA, 45730, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 52080, NA, NA, NA, NA, NA, NA, NA, NA, NA, 30966, NA, 45140, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37500, 23904, NA, NA, NA, 35140, NA, NA, NA, NA, NA, NA, NA, 55146, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 36720, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37830, 49404, NA, NA, NA, NA, NA, NA, 23333, NA, 51774, NA, NA, NA, NA, NA, NA, 25992, 30390, NA, NA, NA, NA, NA, 13450, NA, NA, NA, 36933, NA, NA, NA, NA, NA, NA, NA, 37152, 36000, NA, NA, 38087, NA, NA, 35582, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 62528, NA, NA, NA, NA, 40500, NA, NA, NA, NA, NA, 48330, NA, NA, 47493, NA, NA, NA, NA, NA, NA, NA, NA, 40248, NA, NA, NA, NA, NA, NA, NA, NA, 47550, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 42932, NA, NA, NA, NA, 30900, NA, NA, NA, NA, NA, 16000, NA, NA, NA, NA, NA, NA, NA, NA, 15000, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 46414, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 993, NA, NA, NA, NA
#> $ fee_law_nres <dbl> NA, NA, NA, NA, NA, 0, NA, NA, 370, NA, NA, NA, NA, 550, NA, NA, NA, NA, NA, NA, NA, NA, 1333, NA, NA, NA, 3012, 1954, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 400, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2745, 2156, 1907, 1511, NA, NA, NA, NA, 381, NA, NA, NA, NA, NA, 1218, NA, NA, 870, NA, NA, NA, NA, NA, 430, 60, NA, NA, NA, 481, NA, 80, NA, 0, NA, NA, NA, NA, 885, NA, NA, NA, NA, NA, 2086, NA, NA, NA, NA, 332, NA, NA, NA, NA, NA, NA, NA, NA, NA, 934, NA, NA, NA, NA, NA, 920, NA, NA, NA, NA, NA, NA, NA, 1152, 820, NA, NA, 0, NA, NA, 150, NA, NA, NA, 2700, NA, NA, 3925, NA, 3575, 3438, NA, NA, NA, 1134, NA, 1350, NA, NA, NA, NA, 1145, 496, NA, NA, NA, NA, NA, NA, NA, NA, NA, 642, NA, NA, NA, NA, NA, 2242, 2290, 300, NA, NA, NA, NA, NA, NA, NA, 772, NA, NA, NA, 14506, NA, NA, NA, NA, NA, NA, 336, NA, NA, NA, NA, 3290, NA, 510, NA, NA, NA, 2130, NA, NA, NA, NA, 2558, 300, NA, NA, NA, NA, NA, NA, NA, NA, 5260, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1121, NA, NA, NA, NA, 1372, NA, NA, NA, NA, NA, 490, NA, 932, 62, NA, NA, NA, 1970, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1074, NA, NA, NA, NA, NA, NA, 70, NA, NA, NA, NA, NA, NA, 1524, NA, 196, NA, NA, NA, 384, NA, NA, NA, NA, NA, 19393, NA, NA, NA, 1710, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4300, NA, NA, NA, NA, 1348, NA, 2106, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 210, 1282, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 152, NA, NA, NA, NA, 400, 117, NA, NA, 2240, NA, NA, NA, NA, NA, NA, NA, 510, NA, NA, NA, NA, NA, NA, NA, 328, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2268, NA, NA, NA, NA, NA, NA, NA, NA, 2104, NA, NA, NA, NA, NA, NA, 370, NA, NA, NA, NA, NA, NA, NA, 110, NA, NA, 1840, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1299, 1552, NA, NA, NA, NA, NA, 654, NA, NA, NA, NA, 500, NA, NA, NA, NA, 2174, NA, NA, NA, 1825, NA, NA, NA, 3388, NA, NA, NA, 1202, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3371, NA, 3106, NA, 940, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1733, NA, NA, NA, NA, NA, NA, NA, NA, 2464, 522, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 882, 716, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2676, NA, NA, NA, 458, NA, NA, NA, NA, NA, NA, NA, NA, 400, NA, NA, 2870, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2578, 620, NA, NA, 670, NA, 1140, NA, NA, 0, NA, NA, NA, NA, NA, NA, 3339, NA, NA, 2752, NA, NA, NA, NA, NA, NA, 725, NA, NA, NA, NA, NA, 1524, NA, 2938, NA, NA, NA, 220, 174, 1678, 134, 260, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 865, NA, NA, NA, NA, 1855, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5470, 3281, NA, NA, NA, 490, NA, NA, NA, NA, 2282, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 420, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1084, 4996, NA, 950, NA, NA, NA, NA, NA, NA, NA, NA, 890, 555, NA, NA, 90, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1900, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3272, NA, 830, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 200, 1976, NA, NA, NA, 3702, NA, NA, NA, NA, NA, NA, NA, 3127, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1002, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1020, 6360, NA, NA, NA, NA, NA, NA, 15918, NA, 0, NA, NA, NA, NA, NA, NA, 1836, 4314, NA, NA, NA, NA, NA, 0, NA, NA, NA, 1246, NA, NA, NA, NA, NA, NA, NA, 6848, 1400, NA, NA, 2653, NA, NA, 1863, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3672, NA, NA, NA, NA, 165, NA, NA, NA, NA, NA, 66, NA, NA, 1095, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1452, NA, NA, NA, NA, 1411, NA, NA, NA, NA, NA, 848, NA, NA, NA, NA, NA, NA, NA, NA, 200, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 888, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 738, NA, NA, NA, NA
#> $ books_supplies <dbl> 1600, 1200, 900, 2120, 1600, 1000, 1200, 1200, 1800, 1204, 1200, 1020, 1500, 1000, 1300, 1300, 1812, 1440, 2000, 1400, 1200, 1171, 800, 800, 1000, 982, 1500, 1100, 1250, 1240, 1200, 1200, 1200, 2000, 800, 1600, 1972, 1970, 1970, 1428, 1971, 1941, 1972, 2058, 1700, 1971, 1800, 1970, 1970, 1972, 2058, 1972, 2058, 2058, 2058, 1970, 870, 1158, 1390, 1463, 1436, 1128, 1184, 1085, 1600, 1900, 1971, 1971, 1971, 1820, 1972, 1971, 1972, 1971, 1611, 2074, 1752, 2038, 1917, 1971, 1250, 1972, 1970, 1969, 1970, 2002, 1600, 2058, 1971, 1760, 1971, 1970, 1900, 1200, 900, 1971, 1800, 1800, 1800, 1200, 1000, 1500, 1200, 1416, 1200, 1400, 1256, 1200, 1800, 1800, 1300, 1000, 1500, 1050, 950, 800, 1150, 1102, 1000, 1200, 800, 1200, 1200, 1400, 1300, 2000, 1000, 1800, 800, 1000, 1280, 1600, 1400, 1350, 1060, 1500, 800, 1200, 1400, 1138, 1248, 1200, 1350, 1250, 1000, 850, 530, 1600, 2000, 1000, 1200, 1728, 1066, 716, 1056, 1100, 850, 1200, 1600, 1500, 1600, 1440, 1000, 1300, 2074, 1350, 1500, 1224, 1500, 800, 1200, 1500, 1200, 2000, 990, 1200, 1544, 1600, 2000, 1200, 1500, 1600, 1058, 1058, 1200, 1200, 1000, 1130, 1290, 1000, 1200, 1800, 1600, 1200, 1104, 150, 1200, 1000, 950, 1400, 1510, 1250, 802, 1270, 1500, 1200, 1000, 0, 1200, 1000, 1400, 1638, 2400, 1000, 1500, 1200, 1200, 1200, 800, 1200, 1100, 915, 1200, 1200, 1200, 1384, 1254, 1000, 1500, 1200, 1000, 1240, 1110, 1140, 1200, 1200, 1110, 1110, 1110, 1110, 1110, 1200, 1384, 1050, 1200, 1200, 1100, 950, 1400, 1041, 950, 1000, 1300, 1320, 1070, 900, 1320, 1560, 1278, 1200, 1000, 1000, 1500, 1212, 1268, 924, 1514, 1000, 2550, 1000, 1000, 1250, 1330, 752, 1200, 1400, 1200, 1000, 1000, 1200, 1200, 1440, 1260, 800, 1650, 1800, 1470, 1000, 1800, 1038, 1300, 1400, 1242, 1276, 1300, 1300, 1300, 1300, 1300, 1300, 1300, 1300, 1300, 1200, 1300, 1000, 1400, 1200, 600, 800, 1600, 2500, 1200, 800, 1500, 1248, 1250, 1250, 0, 1600, 1250, 2000, 2000, 1300, 1240, 1300, 1080, 1600, 1200, 1000, 1000, 1600, 1300, 1250, 1000, 1000, 800, 1500, 800, 1100, 0, 1150, 1238, 1200, 1200, 1000, 1500, 1200, 1000, 800, 1000, 1000, 1150, 1200, 1280, 1100, 1200, 1200, 1000, 1500, 1305, 1070, 1000, 1368, 1100, 930, 1000, 904, 1240, 1000, 859, 1100, 800, 740, 1536, 1360, 1048, 1108, 1200, 1300, 1000, 760, 784, 1059, 1000, 800, 1246, 987, 1200, 890, 1270, 2000, 690, 1042, 1500, 1000, 1200, 1000, 500, 1200, 1600, 1150, 1000, 1000, 1200, 900, 1586, 650, 1500, 1400, 1100, 1600, 2000, 1500, 1200, 1500, 1200, 1088, 1250, 1272, 1200, 1096, 1000, 2000, 1250, 1298, 1260, 862, 1000, 1000, 400, 1560, 1530, 1200, 1500, 2200, 631, 1288, 1126, 1000, 1300, 1550, 1450, 1100, 1350, 1800, 1000, 1200, 952, 1162, 1000, 1044, 1200, 848, 1200, 1250, 1300, 1600, 1300, 1000, 1200, 1000, 900, 1310, 1200, 2600, 900, 1230, 1300, 1230, 1350, 1850, 1200, 1000, 996, 1240, 2900, 1593, 1500, 1350, 1350, 1350, 1000, 1000, 1300, 1200, 1600, 1700, 1200, 1600, 950, 1144, 1150, 1158, 1240, 1200, 1600, 1020, 1300, 1000, 1446, 1000, 1294, 970, 1364, 1364, 1364, 1364, 1364, 1364, 1364, 1364, 1200, 1000, 1500, 1850, 1039, 1000, 1500, 1200, 1300, 1300, 2000, 1200, 800, 1125, 1119, 1620, 1470, 900, 1300, 1185, 1000, 1692, 752, 1250, 1104, 1000, 800, 1100, 1098, 1200, 800, 1200, 1250, 1000, 1100, 656, 1000, 1000, 1203, 900, 1200, 1400, 1016, 1038, 1000, 1200, 1000, 1500, 1200, 1000, 900, 2500, 1320, 1528, 1500, 1536, 822, 1400, 871, 1250, 700, 1600, 1490, 850, 900, 420, 1850, 1160, 1400, 1200, 1600, 972, 1250, 1000, 1500, 1082, 1126, 1505, 1500, 1200, 800, 1500, 2000, 1300, 1059, 1286, 1100, 1000, 800, 1000, 912, 1500, 727, 1600, 1200, 1200, 800, 1000, 1310, 1500, 1250, 1200, 1220, 1405, 1300, 1240, 1400, 1200, 1100, 1100, 1082, 962, 1330, 1200, 3000, 1180, 1216, 1200, 1128, 1584, 1300, 1100, 900, 1360, 1418, 1600, 1600, 780, 1380, 1600, 1200, 1120, 1500, 667, 1920, 1000, 1218, 1200, 1335, 950, 1050, 1200, 1178, 1050, 1263, 750, 999, 800, 1200, 1466, 1500, 1500, 1200, 1200, 1000, 1400, 1000, 1000, 1298, 1240, 1000, 1700, 1400, 1364, 1326, 1300, 1112, 1100, 1300, 1112, 2166, 1100, 1300, 1440, 1000, 1200, 1000, 1597, 1803, 2000, 1250, 1200, 1000, 1250, 1600, 1840, 1840, 1358, 1060, 755, 1200, 1500, 2000, 893, 1300, 1200, 1200, 1624, 1479, 1100, 1500, 1200, 1300, 1600, 1200, 1400, 1800, 1100, 1200, 1250, 900, 1450, 1200, 1400, 1200, 1060, 1236, 8000, 1436, 600, 1258, 1350, 1157, 2244, 1085, 1082, 2000, 1200, 1200, 1200, 1200, 1200, 1200, 950, 1200, 1550, 1400, 1200, 1250, 1600, 1250, 1500, 1500, 1276, 1300, 1520, 1298, 1600, 1400, 1600, 1260, 1100, 1400, 1598, 1400, 2900, 2300, 2964, 1600, 1250, 1200, 1264, 1274, 1080, 1350, 1040, 1400, 1317, 800, 1000, 1064, 1268, 1158, 1338, 1400, 1012, 1180, 1612, 1300, 1300, 1050, 1000, 1200, 1238, 1340, 1200, 1200, 1094, 1372, 1300, 800, 1324, 1202, 800, 1270, 1020, 1344, 1222, 1206, 700, 1200, 1632, 1292, 900, 1000, 1000, 1640, 1200, 2032, 1050, 1000, 1000, 1000, 872, 1600, 868, 724, 996, 1400, 1000, 1000, 600, 1000, 1500, 1200, 1000, 1000, 1304, 1000, 1278, 1100, 1056, 1372, 1350, 1200, 900, 1200, 1000, 2235, 1300, 1200, 1500, 1350, 1100, 2860, 1384, 1300, 900, 900, 900, 1158, 900, 870, 900, 1000, 900, 1200, 990, 960, 900, 954, 912, 1800, 1100, 1000, 1100, 1000, 2500, 950, 1300, 768, 700, 1126, 1800, 800, 1100, 800, 816, 1000, 1400, 1176, 1000, 580, 400, 800, 120, 1000, 418, 1150, 800, 630, 400, 300, 1200, 2050, 1240, 1504, 1272, 1735, 1735, 1700, 1171, 1171, 1171, 1718, 1171, 600, 2168, 2168, 1700, 2500, 1700, 1750, 1190, 1632, 1500, 1968, 1272, 2000, 1000, 900, 900, 1171, 1854, 432, 1360, 1171, 200, 1200, 240, 0, 0, 1200, 2002, 0, 1350, 1350, 1016, 1600, 1500, 1100, 1171, 1350, 0, 704, 1100, 1350, 1300, 1350, 1046, 1080, 1500, 768, 1840, 1680, 1200, 2000, 400, 400, 400, 400, 400, 400, 400, 400, 400, 400, 400, 1440, 1400, 1171, 1360, 1360, 1360, 1500, 1500, 1300, 1150, 1500
#> $ roomboard_off <dbl> 9240, 12307, 9600, 10400, 7320, 13636, 10296, 13600, 8200, 11367, 7200, 9810, 7830, 10800, 9178, 7586, 10055, 9922, 12050, 10872, 17200, 9684, 10350, 7800, 10646, 8162, 8811, 11330, 9108, 12281, 7524, 6843, 7170, 10000, 2500, 7188, 15086, 11514, 15082, 14631, 15084, 14706, 15080, 13215, 13216, 16189, 13215, 13216, 15083, 15084, 16189, 15084, 15056, 16189, 16188, 15084, 14771, 10588, 12861, 14303, 10986, 13681, 12818, 13216, 11206, 15000, 15084, 15084, 15084, 13120, 15084, 15084, 15084, 15084, 13883, 13952, 13408, 14194, 15084, 15084, 15670, 15084, 14670, 13533, 15084, 16189, 15410, 16189, 15084, 13000, 15084, 16188, 14250, 15912, 9119, 15084, 10242, 11799, 11080, 13050, 8910, 10142, 11799, 8517, 13437, 11799, 13978, 11204, 11798, 11800, 11799, 13940, 8776, 12528, 13258, 9708, 14710, 11610, 13382, 8000, 14760, 10462, 10866, 10910, 13452, 13494, 13208, 6000, 16096, 15260, 16781, 14928, 14300, 10917, 12180, 11400, 13035, 10010, 11746, 9696, 11716, 12880, 12584, 11860, 10780, 10220, 8400, 12490, 13620, 18238, 10938, 11430, 9565, 8610, 6840, 9920, 11764, 13868, 7476, 7000, 10062, 11140, 10344, 10218, 9999, 9588, 6843, 14972, 8000, 14830, 6616, 14054, 8326, 11240, 8392, 12036, 9700, 7762, 5320, 8950, 10340, 13554, 15302, 13366, 18700, 9760, 8280, 9080, 8900, 3858, 10940, 8724, 16000, 5850, 14736, 9596, 5500, 11638, 6842, 12479, 10672, 10800, 8172, 8000, 7000, 11808, 7500, 13500, 9420, 9720, 10880, 17019, 4212, 12550, 6894, 7750, 8600, 10549, 7200, 7578, 10622, 9881, 10800, 9800, 6000, 10534, 6844, 15540, 5600, 12000, 8000, 9620, 10000, 9324, 10600, 10800, 7932, 10830, 7932, 7932, 7932, 8361, 7032, 15640, 7640, 8900, 10848, 10210, 9100, 9149, 9424, 7400, 9840, 6106, 7687, 9160, 10026, 8706, 6224, 8250, 9408, 8210, 7980, 9690, 11096, 8098, 9450, 7515, 7060, 6843, 11132, 10552, 5605, 9420, 7300, 8281, 10476, 13210, 11000, 8530, 8600, 9896, 10390, 10046, 10920, 5791, 7500, 8432, 9656, 14510, 9656, 9072, 8874, 12936, 9656, 10030, 9656, 9656, 9656, 9656, 9800, 9656, 10990, 15774, 9656, 10966, 9700, 7200, 9956, 6500, 19350, 13746, 2226, 6498, 10682, 12066, 12235, 8010, 15430, 14422, 14434, 11136, 12902, 7500, 14081, 12360, 12846, 8400, 4730, 6012, 13128, 3500, 16960, 11550, 15720, 12500, 12576, 10500, 9170, 9850, 9870, 15750, 11592, 9800, 12604, 8000, 6725, 5650, 13004, 14100, 16320, 16930, 9814, 10564, 15200, 12930, 10000, 19585, 15086, 14526, 9730, 11453, 14774, 5400, 11050, 6222, 10328, 6425, 9926, 10046, 8722, 8400, 9630, 6416, 10540, 11450, 11996, 10522, 11004, 9048, 7200, 10774, 6876, 10411, 10990, 10260, 10382, 10567, 16820, 8660, 7260, 9200, 10592, 7472, 10832, 10358, 8374, 9560, 6170, 8900, 9350, 10088, 9536, 9300, 8278, 9098, 10374, 6000, 5908, 6167, 10734, 7900, 7815, 11035, 10436, 9600, 6272, 9920, 8962, 8384, 6290, 5202, 7998, 6994, 13160, 7159, 10508, 10626, 10218, 9012, 9022, 6400, 12235, 12600, 7884, 5000, 9267, 9128, 16900, 9448, 6842, 7420, 10300, 9966, 13536, 7820, 8850, 11274, 10452, 9920, 11048, 11830, 9088, 10548, 8270, 9998, 11960, 13892, 13900, 14000, 10218, 11942, 10318, 11350, 10218, 15000, 12080, 13716, 13800, 13716, 9882, 17961, 14574, 10971, 9238, 13068, 14600, 11427, 10020, 16434, 16434, 16434, 7500, 15222, 11578, 15770, 12496, 5103, 14048, 11164, 7496, 9954, 8624, 10090, 8862, 13200, 10568, 15350, 12718, 8000, 15580, 9600, 17955, 15246, 15577, 15577, 15577, 15577, 15577, 15577, 15577, 15577, 12652, 2900, 10000, 20514, 12438, 21083, 15736, 15570, 10320, 14120, 25000, 16870, 14520, 14620, 9662, 8622, 9436, 4200, 12028, 13380, 14230, 14500, 18684, 9400, 14592, 10000, 20618, 7598, 13540, 8200, 13120, 12986, 6000, 7300, 12650, 13750, 9994, 16068, 14134, 14151, 10180, 14025, 10262, 14220, 13100, 12830, 12906, 13928, 13640, 14290, 9768, 17000, 13720, 11600, 13256, 15910, 14938, 12400, 14575, 13283, 9508, 12190, 10030, 7524, 12685, 8070, 5800, 12510, 13077, 6244, 10284, 11526, 10985, 9264, 10227, 11359, 10897, 8208, 11658, 8964, 9336, 15018, 7000, 8943, 11170, 10966, 7144, 9444, 9078, 12291, 5500, 9300, 7694, 11360, 15614, 11668, 12882, 14050, 6250, 5418, 12232, 11706, 15180, 8214, 7500, 14124, 4500, 9000, 11590, 6370, 11700, 13332, 6974, 5400, 11550, 7320, 11508, 6450, 8850, 11592, 13310, 9700, 8470, 5400, 8312, 11494, 8290, 10246, 6842, 5000, 8550, 9150, 9236, 10994, 9765, 6791, 5652, 11032, 9852, 11650, 9700, 12906, 10312, 9108, 13611, 9316, 10166, 10616, 10260, 9856, 5100, 19498, 8996, 10000, 10794, 11902, 14162, 11862, 10524, 12668, 14620, 14241, 13088, 8179, 9180, 11540, 13530, 4000, 7184, 10272, 12620, 12616, 8352, 11626, 7500, 7500, 14160, 9828, 10197, 9308, 10000, 10330, 14009, 8530, 15096, 11884, 11884, 14406, 10320, 11814, 3150, 12820, 12948, 14840, 10200, 9706, 10260, 8868, 14484, 14444, 7620, 13174, 8190, 9000, 11540, 10000, 8000, 12200, 13160, 10486, 10300, 11180, 10268, 9500, 11904, 6000, 11550, 18972, 11414, 8000, 5532, 11260, 8211, 10890, 11239, 8848, 10000, 7776, 7142, 7231, 8146, 8925, 8726, 9130, 8608, 11542, 12520, 9198, 8417, 7508, 9143, 8200, 10500, 8242, 4500, 6000, 14787, 8325, 5000, 10175, 8976, 8450, 9500, 11482, 7996, 7612, 8850, 9340, 10100, 11065, 9130, 12020, 7509, 9948, 11430, 9000, 10461, 8875, 9230, 9800, 10218, 9228, 8254, 9231, 12284, 7632, 7217, 10750, 7940, 8782, 9310, 8684, 10044, 8198, 7571, 14140, 12774, 10090, 9552, 4000, 8700, 13968, 9012, 8820, 9558, 10712, 10569, 10400, 10290, 11812, 11044, 9496, 7918, 13200, 11673, 10071, 13712, 8670, 9654, 6993, 10674, 11000, 8582, 7766, 7500, 8616, 7614, 11862, 9900, 8950, 11222, 10370, 11020, 14352, 12496, 12926, 10845, 12424, 9000, 12264, 12285, 10938, 9234, 12076, 11450, 11250, 11500, 14454, 10844, 12090, 8995, 10570, 10917, 9342, 11506, 11950, 11544, 8100, 10500, 12363, 12330, 10170, 10440, 10876, 10516, 12285, 12531, 7680, 11648, 13296, 12036, 10170, 9300, 9762, 9730, 9580, 7480, 4000, 8580, 5000, 7120, 6517, 7026, 11170, 10125, 6084, 5100, 13200, 9768, 7254, 7822, 9295, 7670, 8216, 7170, 6465, 8260, 6944, 11558, 10792, 8380, 6576, 7428, 10437, 11169, 6470, 7524, 6007, 6785, 6785, 8000, 9806, 9806, 9806, 9806, 9806, 8400, 11161, 11161, 8000, 10300, 8000, 11374, 9050, 7752, 6843, 13408, 6007, 16189, 11448, 12636, 11748, 9684, 15040, 5265, 5250, 9684, 6000, 9672, 6435, 9503, 7000, 6461, 16188, 6200, 10917, 10917, 8595, 6880, 9150, 9920, 9684, 10917, 7480, 6844, 9920, 10917, 10488, 10917, 17654, 10800, 6843, 10488, 11884, 10728, 12880, 13128, 14084, 14084, 14084, 12068, 12068, 12068, 12068, 14084, 12068, 14084, 12068, 8660, 9136, 9684, 5250, 5250, 5250, 7745, 12947, 5948, 9900, 7800
#> $ oth_expense_off <dbl> 3090, 5555, 1600, 3994, 4228, 4600, 3850, 5904, 4800, 2753, 3505, 3180, 2800, 5350, 4916, 6766, 6480, 6002, 4250, 3716, 4350, 4412, 5800, 5700, 4720, 1444, 5670, 4330, 4566, 4125, 4986, 6317, 2806, 6318, 2850, 7495, 5274, 3716, 5274, 4311, 4374, 2835, 5692, 3283, 2904, 2982, 3004, 2796, 3304, 2982, 3283, 5274, 3282, 3283, 3282, 2982, 5359, 4856, 5184, 5126, 4792, 4760, 6045, 5442, 4570, 4800, 5274, 4779, 5274, 2540, 5344, 5631, 3996, 5274, 4178, 5874, 4688, 5400, 5274, 5274, 2500, 5274, 3718, 3742, 5274, 3291, 3800, 3283, 5274, 3990, 5254, 3282, 5150, 2152, 1127, 5274, 3069, 5769, 4660, 2556, 1118, 3083, 1404, 1453, 2940, 6141, 5140, 3068, 3730, 4870, 3069, 7282, 7372, 2215, 2850, 2497, 1730, 3301, 2580, 4250, 2500, 3150, 2144, 3924, 2250, 5888, 1800, 1800, 909, 3150, 5004, 5600, 1525, 2502, 2770, 6434, 7235, 4970, 4710, 6476, 8460, 3600, 5702, 2850, 5010, 3760, 6920, 2330, 8726, 3318, 4055, 9175, 7925, 2790, 2872, 4100, 5856, 3000, 2400, 4630, 2100, 4170, 4084, 4100, 2718, 4200, 6317, 2604, 500, 4060, 4948, 5406, 5804, 4896, 4650, 2778, 2890, 2450, 2200, 4690, 7294, 2413, 6788, 3213, 2240, 4318, 5921, 3306, 3720, 3520, 2240, 4451, 2856, 1700, 2364, 2484, 3645, 3000, 6410, 3628, 4364, 5368, 6941, 2000, 2618, 1600, 3250, 7320, 6262, 3200, 2090, 3306, 6309, 1000, 5181, 3900, 7700, 1372, 4120, 3258, 2941, 2906, 3020, 4112, 2800, 2688, 6316, 3050, 2350, 3010, 3200, 3417, 4064, 4348, 3300, 2824, 4064, 2770, 4064, 4064, 4064, 3964, 2700, 1950, 3000, 1620, 3700, 3790, 2904, 2430, 3306, 3800, 2800, 6564, 5195, 2296, 6594, 3286, 5404, 5100, 3518, 4246, 4000, 3134, 3964, 4080, 8380, 4546, 1970, 7420, 3600, 3270, 3510, 6688, 6408, 6063, 4600, 4358, 5243, 7420, 7000, 2914, 4570, 5364, 6540, 7769, 4030, 4080, 5130, 7686, 5130, 3730, 5031, 1966, 5130, 5280, 5202, 4880, 5130, 5130, 4200, 5130, 5130, 1110, 2130, 2200, 3650, 2700, 3000, 2800, 4150, 1600, 1411, 3686, 3058, 3262, 1118, 1690, 5850, 3952, 4202, 4050, 3565, 2300, 2410, 2700, 3350, 3300, 3610, 2000, 1800, 2400, 1230, 1900, 1950, 1500, 3544, 2500, 1050, 2650, 6192, 3240, 2000, 2500, 2380, 2500, 4000, 1924, 1400, 2972, 1450, 1800, 2916, 3660, 2550, 2250, 2072, 3000, 1536, 3750, 3300, 2672, 1200, 2200, 4400, 2484, 1080, 4014, 7594, 3150, 1603, 4200, 2360, 2040, 4490, 3510, 2454, 3040, 2378, 3276, 3978, 2626, 4888, 3443, 1664, 1780, 5329, 2500, 2500, 3000, 5410, 2000, 1200, 2530, 9996, 2434, 2320, 3486, 2760, 2600, 1700, 2346, 2516, 2350, 2930, 2640, 6840, 1000, 4700, 5233, 5100, 4115, 5360, 4544, 5358, 4346, 3417, 3250, 2450, 6528, 3200, 4464, 6338, 3342, 3575, 3837, 4952, 4552, 6318, 3800, 2575, 4300, 4535, 3644, 2000, 4080, 4708, 3855, 3592, 9380, 6318, 4640, 3738, 3948, 3069, 5270, 2440, 2800, 4496, 3688, 3500, 3320, 2944, 3654, 3080, 6380, 4370, 2000, 3200, 2250, 9532, 2651, 1750, 2330, 5102, 4200, 4614, 2856, 1780, 2856, 6600, 5328, 4708, 3580, 4266, 6830, 6300, 6614, 2500, 6041, 6041, 6041, 2900, 3700, 4908, 1050, 4059, 567, 3922, 2250, 4498, 3968, 5000, 5492, 4416, 2800, 4288, 4966, 2100, 1500, 2314, 3100, 5220, 2820, 5504, 5504, 5504, 5504, 5504, 5504, 5504, 5504, 3000, 1500, 3650, 2150, 2919, 2592, 1850, 2047, 3930, 1390, 2500, 2400, 1550, 2500, 1119, 3584, 9804, 5000, 2100, 2200, 1500, 2010, 2758, 5450, 4664, 6215, 2608, 3194, 946, 4960, 1700, 2460, 2900, 5300, 1500, 2160, 2120, 1000, 2805, 5336, 1050, 2500, 2868, 4278, 2838, 1222, 2509, 2300, 2402, 1600, 2026, 4300, 2750, 2284, 3600, 1774, 4924, 2951, 2447, 5400, 3480, 4000, 3630, 1350, 2600, 2387, 2700, 2850, 4861, 2460, 2300, 2750, 3968, 2604, 6605, 2717, 6146, 3296, 2000, 3595, 1565, 2400, 2800, 4389, 2679, 2600, 3550, 3424, 3400, 3120, 3728, 5860, 5261, 5088, 1900, 4950, 3470, 1750, 1305, 1500, 3500, 3162, 4268, 2884, 3700, 2530, 4600, 1800, 1468, 5198, 3014, 3074, 5900, 2060, 1500, 5298, 1200, 5000, 2694, 3042, 3200, 3484, 3934, 2000, 3920, 8718, 7352, 2800, 6358, 2500, 4555, 4650, 2750, 6317, 3840, 3800, 6858, 5900, 3111, 2050, 2700, 2694, 2466, 1800, 2502, 2840, 2697, 2000, 2496, 2304, 3600, 1100, 3370, 1700, 4924, 2082, 1400, 3500, 6274, 3270, 1800, 1200, 2200, 3252, 1467, 3300, 2620, 1150, 6370, 1368, 3874, 2080, 3594, 4290, 1000, 3082, 1065, 4415, 5624, 2596, 2000, 2261, 2810, 1000, 2900, 4788, 4788, 1946, 2010, 2686, 1980, 1518, 6000, 1347, 1872, 3000, 3750, 3782, 2438, 2300, 1310, 2520, 2358, 4200, 2550, 3500, 2059, 1600, 2200, 4290, 2674, 3400, 5190, 4000, 1600, 2085, 3923, 8089, 4054, 4510, 8144, 5400, 3716, 5670, 4386, 3938, 8000, 8260, 4500, 5724, 5138, 4200, 4580, 4155, 5795, 6317, 5025, 4620, 2700, 3108, 3097, 6770, 3500, 5385, 3000, 6760, 3753, 3805, 6060, 4699, 4212, 4250, 4050, 5666, 5518, 3900, 6350, 4808, 9240, 3272, 3280, 3900, 3140, 3593, 5373, 2920, 2037, 3938, 3176, 5970, 8394, 7200, 4178, 5064, 3650, 4336, 2900, 3262, 5090, 3030, 1950, 4726, 3300, 4400, 4207, 2650, 2526, 4762, 3866, 3000, 2600, 4856, 3674, 3590, 5172, 3147, 4913, 6308, 4744, 4392, 4476, 4232, 4958, 5064, 3630, 4924, 4866, 4420, 4725, 3708, 1400, 4256, 4854, 4808, 4800, 4052, 3430, 3574, 5060, 4719, 2346, 7586, 1750, 2700, 2722, 2041, 3303, 4294, 2470, 6318, 4500, 3982, 7265, 2418, 2050, 2860, 3520, 5380, 4009, 3000, 3300, 2600, 2502, 3150, 3760, 2962, 1675, 2922, 3360, 3525, 3622, 3821, 3457, 2808, 3000, 3189, 3600, 1944, 3542, 2733, 3672, 3436, 2900, 2944, 2000, 3087, 2000, 3565, 2750, 1200, 5935, 5332, 4998, 3100, 3310, 5680, 4100, 4840, 5034, 6510, 5074, 2500, 3170, 3396, 3616, 1972, 2700, 3206, 3120, 3396, 3544, 4550, 3284, 3040, 3366, 7016, 4401, 1911, 4839, 5201, 4800, 5490, 5490, 5490, 5490, 5490, 10500, 2453, 3251, 4800, 7500, 4800, 3322, 1670, 1600, 6317, 4688, 1911, 3282, 4401, 3789, 3309, 4412, 2958, 930, 2830, 4412, 1000, 3400, 500, 8777, 6300, 1364, 3282, 400, 2502, 2502, 4909, 5520, 1900, 4100, 4412, 2502, 2352, 4080, 4100, 2502, 1248, 2502, 3282, 3375, 6317, 1338, 3222, 2664, 3600, 1963, 6744, 6744, 6744, 6744, 6744, 6744, 6744, 6744, 6744, 6744, 6744, 4170, 1440, 4412, 2830, 2830, 2830, 4781, 6350, 4946, 2400, 6831
#> $ tuitfee_grad_res <dbl> 11542, 8100, 13000, 11458, 10156, 10780, 8128, 11504, 11530, 10600, 6868, 11916, 7200, 18364, 8156, 8506, 22614, 12760, 12256, 12608, 12110, 12608, 13271, 11074, 11121, 22286, 7594, 8841, 6422, 6737, 9127, 6539, 13892, 6387, 14400, 6582, 17842, 11394, 13560, 54537, 19556, 11377, 24615, 8853, 8976, 8386, 8830, 9240, 8380, 8023, 8358, 8434, 8268, 8202, 8411, 8802, 14187, 13598, 13349, 12953, 13584, 13455, 13554, 13808, 35201, 7953, 17850, 15000, 18580, 9298, 14436, 14070, 10940, 18903, 36370, 12134, 15480, 17362, 14424, 49374, 35020, 11880, 22192, 8944, 27762, 8700, 25810, 9286, 25056, 13140, 31308, 9314, 15693, 47107, 12539, 40290, 7311, 8069, 13912, 13556, 14029, 19062, 12871, 14321, 51639, 10611, 23470, 12894, 14441, 7258, 17371, 20704, 21202, 12356, 19056, 13300, 18890, 14837, 17150, 7700, 19730, 30427, 15084, 13001, 12758, 5510, 34956, 8834, 31113, 48640, 9860, 18706, 31824, 15135, 14310, 17910, 13420, 8872, 19156, 9876, 6693, 22588, 11106, 17159, 11553, 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#> $ tuitfee_grad_nres <dbl> 21574, 19188, 13000, 25256, 17572, 30250, 17308, 31160, 11530, 20200, 13546, 23868, 13860, 18364, 16112, 16156, 22614, 25711, 24193, 25367, 12110, 27048, 33398, 11074, 20296, 22286, 14884, 22121, 11408, 11993, 15007, 11558, 13892, 7647, 14400, 9462, 17842, 11394, 13560, 54537, 19556, 20881, 24615, 18357, 18480, 17890, 18334, 18744, 17884, 17527, 17862, 17938, 17772, 17706, 17915, 18306, 29289, 28700, 28451, 28055, 28686, 28557, 28656, 28910, 35201, 7953, 17850, 15000, 18580, 18802, 14436, 14070, 10940, 18903, 36370, 12134, 15480, 17362, 14424, 49374, 35020, 11880, 22192, 18448, 27762, 18204, 25810, 18790, 25056, 13140, 31308, 18818, 15693, 47107, 12539, 40290, 10551, 23873, 24438, 33014, 14029, 39762, 28142, 14321, 51639, 14326, 23470, 21647, 14441, 18410, 17371, 20704, 21202, 25144, 40968, 26088, 18890, 14837, 17150, 7700, 19730, 30427, 15084, 23991, 25546, 11726, 34956, 8834, 31113, 48640, 18176, 18706, 31824, 15135, 14310, 17910, 13420, 28657, 19156, 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15867, 28292, 16668, 11010, 10751, 7500, 8762, 10170, 36592, 18540, 23246, 10070, 8574, 9021, 22000, 10880, 30160, 19836, 22570, 30006, 8384, 11809, 8100, 11814, 16675, 7020, 12637, 14669, 23510, 11034, 9544, 9227, 13395, 6600, 18414, 25679, 10980, 19500, 27550, 13790, 18123, 14202, 26172, 11700, 18334, 31798, 16656, 23750, 26078, 7200, 9022, 22356, 14455, 35514, 10890, 15488, 19172, 33750, 14601, 14180, 13405, 12850, 18982, 15470, 15495, 18748, 19122, 15860, 14846, 18618, 8424, 18280, 7779, 15384, 17166, 14724, 16589, 49403, 29130, 21534, 15612, 18730, 47256, 11670, 14869, 17104, 12936, 14811, 13763, 22196, 22980, 22966, 27796, 16013, 16056, 30828, 6664, 25283, 14265, 15651, 16586, 15093, 42737, 14322, 7727, 7290, 24548, 21334, 26075, 26640, 17506, 14559, 14310, 19408, 15858, 19554, 33066, 34800, 16952, 21420, 14590, 36960, 12510, 29544, 8400, 20412, 11118, 13140, 20832, 19296, 24742, 33192, 21316, 22665, 15135, 30489, 29084, 31402, 22994, 24404, 28197, 20739, 17096, 9476, 19453, 30316, 15300, 30636, 20105, 22953, 26850, 33534, 23559, 12168, 16984, 9158, 19100, 21062, 15800, 12356, 26154, 9710, 10771, 10880, 11608, 11172, 17856, 11286, 10800, 21060, 23475, 15472, 7300, 8864, 18807, 19540, 19126, 20005, 18341, 16830, 25506, 25282, 17976, 18213, 19126, 16009, 12464, 6320, 7633, 6224, 6354, 6390, 3552, 4172, 4566, 4146, 4605, 4605, 20587, 7220, 7040, 3552, 5343, 3552, 13738, 28794, 11484, 15819, 12320, 6224, 18655, 14000, 33459, 33708, 24432, 18081, 15233, 16752, 24432, 7790, 31216, 7110, 15616, 13518, 21340, 17740, 4800, 15135, 15135, 28309, 6300, 7200, 20924, 27048, 15135, 7005, 3915, 20737, 15135, 4140, 15135, 9000, 8010, 14019, 9000, 22186, 12960, 16128, 20712, 17552, 17552, 17552, 17552, 17552, 17552, 17552, 17552, 17552, 17552, 17552, 18364, 13631, 6592, 16752, 16752, 16752, 13664, 21194, 8675, 16640, 11250
#> $ tuitfee_md_res <dbl> NA, 28978, NA, NA, NA, 28978, NA, NA, NA, NA, NA, NA, NA, NA, 31376, NA, NA, NA, NA, NA, NA, NA, 33935, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37266, 36682, 36434, 37885, 37572, 36990, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 67603, NA, NA, NA, 40571, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 41495, NA, NA, NA, NA, NA, 60210, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 62200, NA, NA, NA, NA, 29680, NA, NA, 31634, NA, 38855, NA, 25611, 36657, NA, NA, NA, 47185, NA, 55775, NA, NA, NA, 33726, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 52142, NA, NA, NA, NA, NA, NA, NA, 42586, NA, NA, NA, NA, NA, NA, NA, 37444, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 34262, NA, NA, NA, NA, NA, 63794, NA, NA, NA, NA, NA, 62916, NA, NA, NA, NA, NA, NA, NA, NA, 38942, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 35851, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37769, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 38735, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 40042, NA, 40956, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 65132, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 59809, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 62872, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 64570, NA, NA, NA, NA, NA, NA, NA, 42880, NA, NA, NA, NA, NA, NA, NA, NA, NA, 39744, 31312, NA, NA, NA, NA, 56522, NA, NA, NA, 36259, NA, NA, NA, NA, NA, NA, NA, NA, 41476, 40640, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 33797, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 36711, 25566, NA, NA, NA, NA, NA, 56414, NA, NA, NA, NA, 64164, NA, NA, NA, NA, NA, NA, NA, NA, 60674, NA, NA, NA, NA, NA, NA, NA, 36946, 34354, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 42769, NA, NA, NA, NA, NA, NA, NA, NA, 44131, NA, NA, 63815, NA, NA, NA, NA, NA, NA, NA, NA, NA, 8267, NA, NA, NA, NA, NA, NA, NA, NA, 65320, NA, NA, NA, NA, 41756, NA, NA, NA, NA, NA, NA, NA, NA, NA, 53190, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 59140, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 46720, 47494, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 23154, NA, NA, NA, NA, NA, NA, NA, NA, 34695, NA, NA, NA, NA, NA, NA, NA, NA, NA, 60000, NA, NA, NA, NA, NA, 33588, NA, NA, NA, NA, NA, NA, 65641, 32068, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 30637, NA, NA, NA, NA, 35040, NA, NA, NA, 36988, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 29891, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 65288, NA, 58634, NA, NA, NA, NA, NA, NA, NA, NA, 54296, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 43488, NA, NA, NA, NA, NA, NA, NA, NA, NA, 31787, NA, NA, NA, NA, NA, NA, NA, NA, 34087, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 18298, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 19723, NA, 20673, NA, NA, NA, 57500, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 41783, NA, NA, NA, NA, NA, NA, 39904, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 54056, 35369, 46830, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 39123, 37887, NA, NA, NA, NA, NA, 23904, NA, NA, 21762, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37718, NA, NA, NA, NA, 14180, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 40856, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 28926, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA
#> $ tuitfee_md_nres <dbl> NA, 62714, NA, NA, NA, 62714, NA, NA, NA, NA, NA, NA, NA, NA, 62380, NA, NA, NA, NA, NA, NA, NA, 55513, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 49511, 48927, 48679, 50130, 49817, 49235, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 67603, NA, NA, NA, 66526, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 75575, NA, NA, NA, NA, NA, 60210, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 62200, NA, NA, NA, NA, 56554, NA, NA, 67776, NA, 70355, NA, 60162, 65898, NA, NA, NA, 47185, NA, 62362, NA, NA, NA, 54916, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 52142, NA, NA, NA, NA, NA, NA, NA, 42586, NA, NA, NA, NA, NA, NA, NA, 72100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 65714, NA, NA, NA, NA, NA, 63794, NA, NA, NA, NA, NA, 62916, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 61159, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 58544, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 67930, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72373, NA, 62178, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 65132, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 59809, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 62872, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 64570, NA, NA, NA, NA, NA, NA, NA, 68515, NA, NA, NA, NA, NA, NA, NA, NA, NA, 59062, 58066, NA, NA, NA, NA, 56522, NA, NA, NA, 63634, NA, NA, NA, NA, NA, NA, NA, NA, 58621, 57785, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 76060, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 71614, 49492, NA, NA, NA, NA, NA, 56414, NA, NA, NA, NA, 64164, NA, NA, NA, NA, NA, NA, NA, NA, 60674, NA, NA, NA, NA, NA, NA, NA, 65946, 62118, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 66530, NA, NA, NA, NA, NA, NA, NA, NA, 66547, NA, NA, 63815, NA, NA, NA, NA, NA, NA, NA, NA, NA, 23794, NA, NA, NA, NA, NA, NA, NA, NA, 65320, NA, NA, NA, NA, 69416, NA, NA, NA, NA, NA, NA, NA, NA, NA, 53190, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 59140, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 68210, 68984, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 23154, NA, NA, NA, NA, NA, NA, NA, NA, 62089, NA, NA, NA, NA, NA, NA, NA, NA, NA, 60000, NA, NA, NA, NA, NA, 60872, NA, NA, NA, NA, NA, NA, 65641, 50332, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 54415, NA, NA, NA, NA, 66540, NA, NA, NA, 55496, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 29891, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 65288, NA, 60880, NA, NA, NA, NA, NA, NA, NA, NA, 57518, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 87750, NA, NA, NA, NA, NA, NA, NA, NA, NA, 76173, NA, NA, NA, NA, NA, NA, NA, NA, 69066, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 31398, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 32823, NA, 34711, NA, NA, NA, 57500, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 77991, NA, NA, NA, NA, NA, NA, 67004, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 54056, 57768, 57646, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 68082, NA, NA, NA, NA, NA, 56688, NA, NA, 42768, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 51653, NA, NA, NA, NA, 14180, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 63146, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 57850, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA
#> $ tuitfee_law_res <dbl> NA, NA, NA, NA, NA, 23610, NA, NA, 1590, NA, NA, NA, NA, 41338, NA, NA, NA, NA, NA, NA, NA, NA, 25833, NA, NA, NA, 13693, 16604, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 21236, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 51987, 49488, 47485, 47089, NA, NA, NA, NA, 55099, NA, NA, NA, NA, NA, 16358, NA, NA, 59340, NA, NA, NA, NA, NA, 53434, 57560, NA, NA, NA, 56711, NA, 50770, NA, 56280, NA, NA, NA, NA, 67191, NA, NA, NA, NA, NA, 31804, NA, NA, NA, NA, 52922, NA, NA, NA, NA, NA, NA, NA, NA, NA, 31288, NA, NA, NA, NA, NA, 50440, NA, NA, NA, NA, NA, NA, NA, 57836, 52220, NA, NA, 63205, NA, NA, 37430, NA, NA, NA, 11080, NA, NA, 20669, NA, 20694, 21804, NA, NA, NA, 54134, NA, 43140, NA, NA, NA, NA, 21095, 44964, NA, NA, NA, NA, NA, NA, NA, NA, NA, 58842, NA, NA, NA, NA, NA, 14068, 19894, 39484, NA, NA, NA, NA, NA, NA, NA, 23164, NA, NA, NA, 22260, NA, NA, NA, NA, NA, NA, 49006, NA, NA, NA, NA, 32090, NA, 48650, NA, NA, NA, 50700, NA, NA, NA, NA, 24528, 66806, NA, NA, NA, NA, NA, NA, NA, NA, 21670, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 22625, NA, NA, NA, NA, 36647, NA, NA, NA, NA, NA, 60730, NA, 41532, 42462, NA, NA, NA, 28151, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 23520, NA, NA, NA, NA, NA, NA, 22568, NA, NA, NA, NA, NA, NA, 24723, NA, 22630, NA, NA, NA, 21730, NA, NA, NA, NA, NA, 23703, NA, NA, NA, 45406, NA, NA, NA, NA, NA, NA, NA, NA, NA, 58610, NA, NA, NA, NA, 24538, NA, 32850, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 57150, 56982, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 52952, NA, NA, NA, NA, 14731, 49527, NA, NA, 44298, NA, NA, NA, NA, NA, NA, NA, 45300, NA, NA, NA, NA, NA, NA, NA, 61854, NA, NA, NA, NA, NA, NA, NA, NA, NA, 33935, NA, NA, NA, NA, NA, NA, NA, NA, 45808, NA, NA, NA, NA, NA, NA, 42668, NA, NA, NA, NA, NA, NA, NA, 16980, NA, NA, 28936, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 21531, 19987, NA, NA, NA, NA, NA, 43194, NA, NA, NA, NA, 59300, NA, NA, NA, NA, 12950, NA, NA, NA, 41549, NA, NA, NA, 15238, NA, NA, NA, 27102, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 29175, NA, 28910, NA, 55788, NA, NA, NA, NA, NA, NA, NA, NA, NA, 8607, NA, NA, NA, NA, NA, NA, NA, NA, 72360, 68270, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 62652, 60970, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 68712, NA, NA, NA, 49458, NA, NA, NA, NA, NA, NA, NA, NA, 61550, NA, NA, 28280, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 54078, 51480, NA, NA, 62408, NA, 43740, NA, NA, 46140, NA, NA, NA, NA, NA, NA, 24480, NA, NA, 16196, NA, NA, NA, NA, NA, NA, 46993, NA, NA, NA, NA, NA, 15578, NA, 24214, NA, NA, NA, 1520, 53674, 24010, 19348, 35263, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 31449, NA, NA, NA, NA, 21990, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 36355, 19001, NA, NA, NA, 25828, NA, NA, NA, NA, 37886, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1960, 46124, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 50980, 68606, NA, 35912, NA, NA, NA, NA, NA, NA, NA, NA, 27870, 46548, NA, NA, 45820, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 22972, NA, NA, NA, NA, NA, NA, NA, NA, NA, 14892, NA, 45970, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37700, 19098, NA, NA, NA, 20168, NA, NA, NA, NA, NA, NA, NA, 58273, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 25914, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 38850, 55764, NA, NA, NA, NA, NA, NA, 31254, NA, 34868, NA, NA, NA, NA, NA, NA, 20418, 23154, NA, NA, NA, NA, NA, 13450, NA, NA, NA, 29244, NA, NA, NA, NA, NA, NA, NA, 35000, 37400, NA, NA, 25354, NA, NA, 37445, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 63200, NA, NA, NA, NA, 40665, NA, NA, NA, NA, NA, 48396, NA, NA, 37050, NA, NA, NA, NA, NA, NA, NA, NA, 24228, NA, NA, NA, NA, NA, NA, NA, NA, 47550, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 25687, NA, NA, NA, NA, 15901, NA, NA, NA, NA, NA, 16848, NA, NA, NA, NA, NA, NA, NA, NA, 7700, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 28058, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1282, NA, NA, NA, NA
#> $ tuitfee_law_nres <dbl> NA, NA, NA, NA, NA, 43060, NA, NA, 1590, NA, NA, NA, NA, 41338, NA, NA, NA, NA, NA, NA, NA, NA, 30333, NA, NA, NA, 26448, 37238, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 21236, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 55316, 58591, 53548, 53152, NA, NA, NA, NA, 55099, NA, NA, NA, NA, NA, 16358, NA, NA, 59340, NA, NA, NA, NA, NA, 53434, 57560, NA, NA, NA, 56711, NA, 50770, NA, 56280, NA, NA, NA, NA, 67191, NA, NA, NA, NA, NA, 38590, NA, NA, NA, NA, 52922, NA, NA, NA, NA, NA, NA, NA, NA, NA, 52720, NA, NA, NA, NA, NA, 50440, NA, NA, NA, NA, NA, NA, NA, 57836, 52220, NA, NA, 63205, NA, NA, 37430, NA, NA, NA, 26489, NA, NA, 34453, NA, 40705, 38040, NA, NA, NA, 54134, NA, 43140, NA, NA, NA, NA, 21095, 44964, NA, NA, NA, NA, NA, NA, NA, NA, NA, 58842, NA, NA, NA, NA, NA, 28774, 38652, 39484, NA, NA, NA, NA, NA, NA, NA, 46588, NA, NA, NA, 41496, NA, NA, NA, NA, NA, NA, 49006, NA, NA, NA, NA, 39290, NA, 48650, NA, NA, NA, 50700, NA, NA, NA, NA, 24528, 66806, NA, NA, NA, NA, NA, NA, NA, NA, 29875, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37673, NA, NA, NA, NA, 56872, NA, NA, NA, NA, NA, 60730, NA, 41532, 42462, NA, NA, NA, 47766, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 29763, NA, NA, NA, NA, NA, NA, 35196, NA, NA, NA, NA, NA, NA, 50352, NA, 42040, NA, NA, NA, 34912, NA, NA, NA, NA, NA, 39143, NA, NA, NA, 45406, NA, NA, NA, NA, NA, NA, NA, NA, NA, 58610, NA, NA, NA, NA, 36028, NA, 47958, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 57150, 56982, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 52952, NA, NA, NA, NA, 19185, 49527, NA, NA, 44298, NA, NA, NA, NA, NA, NA, NA, 45300, NA, NA, NA, NA, NA, NA, NA, 64854, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37008, NA, NA, NA, NA, NA, NA, NA, NA, 54664, NA, NA, NA, NA, NA, NA, 42668, NA, NA, NA, NA, NA, NA, NA, 37046, NA, NA, 28936, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 39783, 37945, NA, NA, NA, NA, NA, 43194, NA, NA, NA, NA, 59300, NA, NA, NA, NA, 33482, NA, NA, NA, 41549, NA, NA, NA, 35008, NA, NA, NA, 39002, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 42485, NA, 42220, NA, 55788, NA, NA, NA, NA, NA, NA, NA, NA, NA, 23794, NA, NA, NA, NA, NA, NA, NA, NA, 72360, 68270, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 62652, 60970, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 68712, NA, NA, NA, 49458, NA, NA, NA, NA, NA, NA, NA, NA, 61550, NA, NA, 32370, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 54078, 51480, NA, NA, 62408, NA, 43740, NA, NA, 46140, NA, NA, NA, NA, NA, NA, 41626, NA, NA, 38868, NA, NA, NA, NA, NA, NA, 46993, NA, NA, NA, NA, NA, 43687, NA, 24314, NA, NA, NA, 1520, 53674, 29010, 19417, 35263, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 46401, NA, NA, NA, NA, 32958, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 36355, 30836, NA, NA, NA, 25828, NA, NA, NA, NA, 47102, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1960, 46124, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 50980, 68606, NA, 45946, NA, NA, NA, NA, NA, NA, NA, NA, 42244, 46548, NA, NA, 45820, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 53980, NA, NA, NA, NA, NA, NA, NA, NA, NA, 34238, NA, 45970, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37700, 25880, NA, NA, NA, 38842, NA, NA, NA, NA, NA, NA, NA, 58273, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37722, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 38850, 55764, NA, NA, NA, NA, NA, NA, 39251, NA, 51774, NA, NA, NA, NA, NA, NA, 27828, 34704, NA, NA, NA, NA, NA, 13450, NA, NA, NA, 38179, NA, NA, NA, NA, NA, NA, NA, 44000, 37400, NA, NA, 40740, NA, NA, 37445, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 66200, NA, NA, NA, NA, 40665, NA, NA, NA, NA, NA, 48396, NA, NA, 48588, NA, NA, NA, NA, NA, NA, NA, NA, 40248, NA, NA, NA, NA, NA, NA, NA, NA, 47550, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 44384, NA, NA, NA, NA, 32311, NA, NA, NA, NA, NA, 16848, NA, NA, NA, NA, NA, NA, NA, NA, 15200, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 47302, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1731, NA, NA, NA, NA
#> $ coa_grad_res <dbl> 25472, 27162, 25100, 27972, 23304, 30016, 23474, 32208, 26330, 25924, 18773, 25926, 19330, 35514, 23550, 24158, 40961, 30124, 30556, 28596, 34860, 27875, 30221, 25374, 27487, 32874, 23575, 25601, 21346, 24383, 22837, 20899, 25068, 24705, 20550, 22865, 40174, 28594, 35886, 74907, 40985, 30859, 47359, 27409, 26796, 29528, 26849, 27222, 28737, 28061, 29888, 30764, 28664, 29732, 29939, 28838, 35187, 30200, 32784, 33845, 30798, 33024, 33601, 33551, 52577, 29653, 40179, 36834, 40909, 26778, 36836, 36756, 31992, 41232, 56042, 34034, 35328, 38994, 36699, 71703, 54440, 34210, 42550, 28188, 50090, 30182, 46620, 30816, 47385, 31890, 53617, 30754, 36993, 66371, 23685, 62619, 22422, 27437, 31452, 30362, 25057, 33787, 27274, 25707, 69216, 29951, 43844, 28366, 31769, 25728, 33539, 42926, 38850, 28149, 36114, 26305, 36480, 30850, 34112, 21150, 37790, 45239, 29294, 29235, 29760, 26892, 50964, 18434, 48918, 68050, 32925, 40834, 49049, 29904, 30320, 37244, 34490, 25052, 37012, 27186, 28117, 40268, 30742, 33119, 28343, 27567, 33790, 31126, 52708, 61464, 27050, 43539, 29396, 32279, 18064, 25548, 29096, 34372, 24906, 24676, 22824, 22058, 27488, 28872, 22025, 23308, 30479, 62442, 14966, 35778, 17692, 28274, 22399, 27428, 25200, 31736, 24468, 17118, 22010, 21624, 25496, 41567, 35332, 34451, 42740, 24924, 25127, 23392, 24659, 20978, 31040, 23331, 22776, 17930, 38178, 22374, 42312, 23920, 22284, 34073, 35955, 48355, 25668, 38770, 25668, 34106, 21272, 32916, 32704, 27996, 25650, 78530, 21665, 27970, 31575, 25910, 38671, 34005, 25959, 28486, 30489, 22878, 32550, 26915, 20108, 24118, 23234, 32448, 28670, 32251, 19586, 20630, 25121, 53336, 25146, 22566, 21002, 25583, 21002, 21002, 21002, 35297, 24556, 74020, 22280, 23940, 27466, 28090, 30984, 23610, 25346, 40730, 18980, 24356, 26762, 22788, 35680, 34612, 22304, 25944, 22503, 17812, 24838, 25081, 27332, 24379, 33753, 21383, 22704, 26843, 25148, 22004, 20435, 28594, 27598, 21714, 26726, 31972, 26143, 30346, 23190, 24762, 25334, 27628, 31935, 30420, 22650, 25652, 24002, 36269, 23435, 22018, 24750, 32418, 24206, 25718, 24206, 25075, 25772, 24912, 25822, 24946, 27568, 75042, 20607, 23513, 39084, 20570, 22058, 37820, 40504, 27958, 16362, 18760, 25816, 25646, 72159, 28920, 30190, 34356, 34679, 27348, 28124, 24309, 28446, 27073, 27806, 21340, 21179, 19542, 28758, 22170, 58670, 45400, 74172, 67060, 28317, 29231, 57908, 30030, 26852, 51404, 27385, 19242, 27994, 22460, 30915, 23934, 31613, 36760, 48930, 44815, 28894, 23979, 40848, 48840, 30187, 63262, 71280, 57476, 39391, 21693, 45222, 14764, 44714, 20430, 29558, 27231, 48028, 33208, 18792, 27032, 27260, 26404, 38532, 32250, 39716, 33528, 35341, 27194, 24035, 23999, 31094, 30213, 26100, 21016, 35183, 31337, 33526, 21281, 25640, 21750, 22078, 19518, 31132, 33013, 30978, 23586, 18412, 21272, 21830, 28890, 38043, 29058, 20598, 20870, 26090, 18270, 19779, 21245, 25762, 21140, 22755, 29209, 25904, 24342, 20789, 21006, 21101, 24786, 19276, 20732, 29736, 20862, 36085, 22491, 27198, 27046, 29543, 23296, 19020, 18272, 34736, 38978, 16723, 20145, 20565, 20521, 76368, 33328, 21040, 20656, 22094, 21624, 29160, 20242, 20940, 33172, 23281, 21908, 20641, 23769, 22130, 32980, 17856, 23786, 23561, 36657, 35600, 31478, 39760, 31858, 24740, 25340, 28175, 40180, 34658, 40912, 36930, 40912, 34550, 40926, 33670, 33027, 42338, 34278, 47628, 38359, 35418, 43970, 43808, 43792, 32646, 44272, 36554, 56010, 33086, 19394, 36253, 28172, 19988, 22056, 23244, 23196, 20271, 39700, 21416, 64756, 54841, 25980, 53552, 43230, 74437, 49058, 33895, 33967, 34095, 33847, 33843, 33977, 34015, 34057, 35584, 29453, 33218, 36976, 51685, 50809, 40672, 40249, 36612, 33484, 52940, 40012, 34180, 33645, 33151, 30632, 43320, 26824, 30800, 35171, 34224, 66128, 57756, 30625, 44380, 30115, 56426, 26286, 63402, 27550, 29480, 31914, 28860, 33292, 33070, 45475, 26517, 31662, 32272, 33945, 25630, 30698, 26438, 31679, 29984, 28209, 28671, 30470, 29739, 29380, 25445, 36408, 30902, 27257, 30154, 59408, 33444, 38781, 40117, 55033, 21664, 31833, 22685, 15359, 32363, 16607, 19400, 27500, 60808, 23779, 22046, 27770, 23633, 21068, 25878, 26831, 25626, 19779, 27758, 28149, 17813, 58118, 23230, 21044, 22322, 32546, 21347, 25926, 21029, 26122, 16680, 30174, 23513, 30436, 65274, 32286, 27300, 34040, 25765, 21313, 33770, 27834, 33592, 26993, 23680, 32264, 19464, 25331, 24328, 23918, 28221, 27088, 15149, 21548, 29050, 29607, 28752, 34112, 25542, 30516, 29886, 20762, 24839, 32200, 18240, 29665, 22382, 18687, 19681, 14514, 25555, 23380, 26806, 26555, 29557, 16613, 21048, 41518, 29439, 28630, 31060, 31602, 31194, 44973, 30594, 36774, 32864, 23346, 31104, 34146, 25163, 51020, 25432, 19205, 29637, 34420, 62471, 34642, 35366, 29357, 26000, 53162, 40268, 24647, 25237, 28386, 37112, 18292, 28909, 22042, 31440, 28540, 58297, 28332, 35130, 60902, 43355, 27260, 30143, 25028, 24688, 29821, 29749, 47070, 33456, 41296, 41506, 57892, 26152, 39735, 23808, 28978, 46448, 34414, 25002, 29108, 27180, 26933, 36247, 38450, 26510, 29234, 35978, 41824, 28502, 41732, 25593, 24560, 25101, 31540, 30542, 27040, 27409, 22400, 23466, 19315, 30939, 45951, 28700, 23180, 23508, 27031, 24472, 29684, 30847, 24632, 31460, 33054, 21226, 20815, 22584, 20990, 23723, 21255, 22646, 29092, 42455, 26052, 21911, 21443, 26885, 23070, 33914, 25462, 19780, 33780, 47388, 27520, 30583, 27220, 24798, 25500, 25220, 32126, 25530, 25698, 29058, 24312, 29962, 37943, 20469, 52698, 22813, 21441, 37325, 46710, 19488, 20708, 26611, 29620, 29010, 26470, 21489, 25005, 36456, 21244, 17487, 34242, 22754, 31392, 18919, 22198, 31710, 20964, 20747, 67393, 45630, 37480, 22806, 27030, 59356, 31818, 21161, 21874, 21116, 22094, 22527, 28103, 26488, 28902, 30594, 22104, 21746, 49992, 22341, 23749, 27029, 22471, 32997, 19248, 55811, 30578, 21318, 20736, 22238, 20539, 21960, 24828, 24686, 29228, 28878, 37964, 24660, 38106, 30818, 32407, 32100, 30028, 27060, 35508, 30395, 27952, 26271, 24826, 25818, 28150, 27332, 40130, 29778, 29518, 25838, 37335, 29904, 29564, 33212, 35824, 26508, 23459, 27464, 26307, 34206, 24367, 34220, 44900, 29816, 47010, 37436, 33567, 29245, 36222, 28596, 26686, 30984, 22964, 21652, 22495, 26280, 22421, 22414, 17210, 24594, 23429, 24758, 27242, 32091, 24150, 20800, 39916, 39277, 30636, 21372, 21659, 20538, 21518, 21157, 18576, 20987, 19088, 28008, 27038, 21399, 20608, 20498, 20966, 22479, 21046, 21062, 15414, 19713, 20111, 18052, 20639, 21033, 20613, 21619, 21072, 37902, 19492, 20200, 18052, 25643, 18052, 23902, 21902, 22468, 30479, 32384, 15414, 30622, 30849, 36543, 35424, 27875, 28429, 21860, 26192, 27875, 14990, 23233, 14285, 33896, 26818, 30365, 29708, 11400, 29904, 29904, 27727, 20300, 19750, 25346, 27875, 29904, 16837, 15543, 25159, 29904, 17176, 29904, 30982, 23265, 28679, 21594, 39132, 28032, 33808, 24535, 38780, 38780, 38780, 36764, 36764, 36764, 36764, 38780, 36764, 38780, 36764, 20286, 18012, 21859, 26192, 26192, 26192, 20220, 28131, 20869, 30090, 22375
#> $ coa_grad_nres <dbl> 35504, 38250, 25100, 41770, 30720, 49486, 32654, 51864, 26330, 35524, 25451, 37878, 25990, 35514, 31506, 31808, 40961, 43075, 42493, 41355, 34860, 42315, 50348, 25374, 36662, 32874, 30865, 38881, 26332, 29639, 28717, 25918, 25068, 25965, 20550, 25745, 40174, 28594, 35886, 74907, 40985, 40363, 47359, 36913, 36300, 39032, 36353, 36726, 38241, 37565, 39392, 40268, 38168, 39236, 39443, 38342, 50289, 45302, 47886, 48947, 45900, 48126, 48703, 48653, 52577, 29653, 40179, 36834, 40909, 36282, 36836, 36756, 31992, 41232, 56042, 34034, 35328, 38994, 36699, 71703, 54440, 34210, 42550, 37692, 50090, 39686, 46620, 40320, 47385, 31890, 53617, 40258, 36993, 66371, 23685, 62619, 25662, 43241, 41978, 49820, 25057, 54487, 42545, 25707, 69216, 33666, 43844, 37119, 31769, 36880, 33539, 42926, 38850, 40937, 58026, 39093, 36480, 30850, 34112, 21150, 37790, 45239, 29294, 40225, 42548, 33108, 50964, 18434, 48918, 68050, 41241, 40834, 49049, 29904, 30320, 37244, 34490, 44837, 37012, 41979, 39906, 40268, 43847, 33119, 43497, 44960, 33790, 31126, 52708, 61464, 39167, 43539, 29396, 32279, 18064, 36246, 29096, 34372, 24906, 24676, 38656, 32336, 27488, 28872, 28676, 28402, 30479, 62442, 23804, 50854, 28006, 41828, 37312, 42926, 41508, 31736, 24468, 26658, 22010, 33522, 37988, 41567, 50164, 55931, 42740, 41844, 41423, 42628, 24659, 20978, 31040, 28821, 22776, 17930, 38178, 32672, 42312, 30274, 22284, 44647, 35955, 48355, 26945, 38770, 25668, 34106, 21272, 32916, 32704, 27996, 25650, 78530, 29085, 27970, 31575, 25910, 38671, 34005, 25959, 28486, 47391, 22878, 32550, 26915, 20108, 37222, 23234, 32448, 28670, 32251, 19586, 28658, 40439, 53336, 34188, 29838, 30851, 48014, 30851, 30851, 30851, 35297, 24556, 74020, 22280, 23940, 27466, 28090, 30984, 38572, 44293, 40730, 18980, 24356, 26762, 35004, 35680, 34612, 22304, 25944, 35997, 24436, 24838, 39043, 27332, 37217, 33753, 32215, 22704, 26843, 34630, 29917, 20435, 28594, 27598, 21714, 30776, 51132, 26143, 44460, 23190, 24762, 30620, 33568, 31935, 30420, 22650, 31852, 33025, 53204, 35932, 22018, 31491, 32418, 32243, 30288, 25299, 37175, 36560, 37390, 32268, 31887, 41296, 75042, 20607, 41801, 39084, 20570, 35018, 37820, 46714, 33127, 16362, 23962, 28030, 25646, 72159, 28920, 33412, 42870, 50771, 34164, 36026, 24309, 28446, 31903, 36248, 21340, 21179, 19542, 28758, 22170, 58670, 45400, 74172, 67060, 28317, 29231, 57908, 30030, 26852, 51404, 27385, 19242, 33364, 22460, 30915, 35714, 47128, 54307, 48930, 44815, 28894, 25599, 40848, 48840, 43375, 63262, 71280, 57476, 39391, 21693, 45222, 14764, 44714, 20430, 29558, 27231, 48028, 47896, 18792, 27032, 27260, 26404, 38532, 32250, 64030, 51726, 35341, 37174, 29701, 27455, 37196, 43693, 26100, 21016, 54172, 45409, 33526, 21281, 25640, 21750, 22078, 19518, 39214, 42637, 40602, 23586, 18412, 25146, 21830, 28890, 38043, 29058, 20598, 24668, 26090, 18270, 19779, 22245, 42033, 21140, 22755, 29209, 40944, 26342, 20789, 21006, 28625, 24786, 19276, 20732, 29736, 27114, 36085, 22491, 43297, 42140, 47013, 29998, 19020, 18272, 34736, 38978, 16723, 20145, 25119, 25795, 76368, 33328, 21040, 33800, 35990, 37026, 29160, 24328, 20940, 33172, 30529, 30790, 20641, 35073, 22130, 32980, 21942, 38837, 38612, 36657, 35600, 31478, 39760, 45498, 25640, 29390, 28175, 40180, 34658, 40912, 36930, 40912, 34550, 40926, 41582, 37989, 42338, 40110, 57544, 38359, 35418, 55466, 56216, 56200, 32646, 44272, 36554, 56010, 39277, 19394, 44166, 35498, 22286, 26280, 39669, 35447, 32261, 39700, 26790, 64756, 54841, 25980, 53552, 43230, 74437, 49058, 43325, 43397, 43525, 43277, 43273, 43407, 43445, 43487, 35584, 29453, 33218, 49020, 51685, 50809, 40672, 40249, 36612, 33484, 52940, 40012, 34180, 33645, 33151, 30632, 43320, 26824, 30800, 35171, 34224, 66128, 57756, 30625, 44380, 30115, 56426, 26286, 63402, 27550, 29480, 31914, 28860, 33292, 33070, 45475, 38307, 43452, 44062, 45735, 37420, 42488, 38228, 43469, 41774, 39999, 40461, 42260, 41529, 41170, 37235, 48198, 42692, 39047, 41944, 59408, 33444, 38781, 40117, 55033, 35096, 31833, 35834, 28421, 32363, 27672, 19400, 27500, 60808, 23779, 34846, 45496, 37067, 34786, 38832, 44157, 39455, 31843, 27758, 28149, 17813, 58118, 23230, 31159, 32729, 32546, 21347, 33826, 24401, 31628, 16680, 30174, 29505, 30436, 65274, 44028, 34420, 34040, 25765, 21313, 33770, 38020, 33592, 26993, 23680, 50001, 19464, 25331, 24328, 23918, 51805, 35080, 15149, 21548, 29050, 40209, 28752, 34112, 25542, 40504, 29886, 23762, 24839, 32200, 24954, 39673, 28810, 23788, 25180, 19374, 25555, 38883, 26806, 41214, 29557, 22769, 27528, 41518, 32985, 28630, 31060, 43644, 43047, 44973, 36723, 36774, 36176, 23346, 39148, 34146, 25163, 51020, 30292, 19205, 35226, 34420, 62471, 34642, 35366, 31040, 26000, 53162, 40268, 29507, 25237, 33975, 37112, 18292, 28909, 22042, 31440, 34129, 58297, 33656, 35130, 60902, 43355, 34928, 35003, 29888, 24688, 29821, 34609, 47070, 33456, 48222, 57240, 57892, 26152, 56103, 23808, 28978, 46448, 34414, 25002, 29108, 32040, 32489, 42637, 38450, 26510, 34094, 35978, 41824, 28502, 41732, 25593, 24560, 32427, 44318, 30542, 27040, 27409, 22400, 23466, 19315, 53301, 53601, 40150, 23180, 23508, 27031, 35084, 29684, 46870, 33704, 42570, 47242, 21226, 25964, 22584, 26139, 31181, 21255, 28240, 34078, 42455, 26052, 21911, 21443, 26885, 23070, 33914, 40582, 19780, 33780, 47388, 27520, 30583, 30676, 40620, 25500, 33284, 50544, 31570, 38162, 43578, 24312, 29962, 37943, 28065, 52698, 22813, 30109, 37325, 46710, 28499, 28310, 26611, 29620, 38658, 33166, 29085, 34381, 36456, 28840, 26143, 34242, 22754, 31392, 20089, 29794, 31710, 28560, 29707, 67393, 45630, 37480, 30402, 27030, 59356, 31818, 28757, 30314, 28936, 29690, 30589, 40126, 39220, 39870, 44516, 31373, 30224, 49992, 22967, 41278, 34483, 29941, 32997, 26844, 55811, 30578, 22163, 20736, 38448, 34870, 37843, 43072, 33866, 29228, 28878, 37964, 29628, 38106, 49484, 50767, 32100, 39442, 27060, 56820, 30395, 45520, 26271, 36256, 25818, 28150, 37052, 40130, 41830, 49582, 34811, 37335, 29904, 44081, 47210, 47698, 37513, 36326, 42957, 37527, 34206, 24367, 34220, 44900, 29816, 47010, 37436, 33567, 43000, 50463, 40221, 26686, 30984, 22964, 31830, 34829, 26280, 22421, 38434, 17210, 24594, 23429, 24758, 27242, 32091, 24150, 20800, 39916, 39277, 30636, 21372, 21659, 30227, 31552, 30712, 28562, 30301, 27398, 41334, 40270, 30530, 29739, 30138, 30686, 29049, 21046, 21062, 15414, 19713, 20111, 18052, 20639, 21033, 20613, 21619, 21072, 40087, 23002, 23620, 18052, 25643, 18052, 30184, 40704, 22468, 30479, 32384, 15414, 40126, 30849, 50784, 49665, 39699, 37933, 21860, 26192, 39699, 14990, 45488, 14285, 33896, 26818, 30365, 39212, 11400, 29904, 29904, 42829, 20300, 19750, 36044, 42315, 29904, 16837, 15543, 35857, 29904, 17176, 29904, 30982, 23265, 28679, 21594, 39132, 28032, 33808, 37803, 38780, 38780, 38780, 36764, 36764, 36764, 36764, 38780, 36764, 38780, 36764, 32634, 25607, 21859, 26192, 26192, 26192, 27690, 41991, 20869, 30090, 27381
#> $ coa_md_res <dbl> NA, 48040, NA, NA, NA, 48214, NA, NA, NA, NA, NA, NA, NA, NA, 46770, NA, NA, NA, NA, NA, NA, NA, 50885, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 58266, 53284, 55869, 58777, 54786, 56559, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 86867, NA, NA, NA, 59939, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 58553, NA, NA, NA, NA, NA, 78270, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 79425, NA, NA, NA, NA, 45860, NA, NA, 53058, NA, 58491, NA, 42401, 51487, NA, NA, NA, 69741, NA, 78108, NA, NA, NA, 48846, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 70942, NA, NA, NA, NA, NA, NA, NA, 58600, NA, NA, NA, NA, NA, NA, NA, 55081, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 51769, NA, NA, NA, NA, NA, 78402, NA, NA, NA, NA, NA, 84879, NA, NA, NA, NA, NA, NA, NA, NA, 53605, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 51025, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 51449, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 52771, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 58610, NA, 58106, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 83216, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 74412, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 81542, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 82192, NA, NA, NA, NA, NA, NA, NA, 55288, NA, NA, NA, NA, NA, NA, NA, NA, NA, 55242, 45982, NA, NA, NA, NA, 69070, NA, NA, NA, 53216, NA, NA, NA, NA, NA, NA, NA, NA, 55268, 52534, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 50731, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 53431, 41606, NA, NA, NA, NA, NA, 73858, NA, NA, NA, NA, 85782, NA, NA, NA, NA, NA, NA, NA, NA, 75948, NA, NA, NA, NA, NA, NA, NA, 54574, 51984, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 67908, NA, NA, NA, NA, NA, NA, NA, NA, 67956, NA, NA, 83737, NA, NA, NA, NA, NA, NA, NA, NA, NA, 25007, NA, NA, NA, NA, NA, NA, NA, NA, 89789, NA, NA, NA, NA, 64201, NA, NA, NA, NA, NA, NA, NA, NA, NA, 77865, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 81334, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 64862, 67881, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 38304, NA, NA, NA, NA, NA, NA, NA, NA, 49943, NA, NA, NA, NA, NA, NA, NA, NA, NA, 78918, NA, NA, NA, NA, NA, 47456, NA, NA, NA, NA, NA, NA, 84355, 49886, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 46433, NA, NA, NA, NA, 48838, NA, NA, NA, 53206, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 47032, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 82998, NA, 73889, NA, NA, NA, NA, NA, NA, NA, NA, 72697, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 60198, NA, NA, NA, NA, NA, NA, NA, NA, NA, 47390, NA, NA, NA, NA, NA, NA, NA, NA, 48990, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 32134, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37653, NA, 37577, NA, NA, NA, 76664, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 58215, NA, NA, NA, NA, NA, NA, 56322, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 67648, 53495, 63126, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 55273, 54816, NA, NA, NA, NA, NA, 37671, NA, NA, 34042, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 53546, NA, NA, NA, NA, 28857, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 60356, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 46017, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 14026, NA, NA, NA, NA
#> $ coa_md_nres <dbl> NA, 81776, NA, NA, NA, 81950, NA, NA, NA, NA, NA, NA, NA, NA, 77774, NA, NA, NA, NA, NA, NA, NA, 72463, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 70511, 65529, 68114, 71022, 67031, 68804, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 86867, NA, NA, NA, 85894, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 92633, NA, NA, NA, NA, NA, 78270, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 79425, NA, NA, NA, NA, 72734, NA, NA, 89200, NA, 89991, NA, 76952, 80728, NA, NA, NA, 69741, NA, 84695, NA, NA, NA, 70036, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 70942, NA, NA, NA, NA, NA, NA, NA, 58600, NA, NA, NA, NA, NA, NA, NA, 89737, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 83221, NA, NA, NA, NA, NA, 78402, NA, NA, NA, NA, NA, 84879, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 76333, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72224, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 81966, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 90941, NA, 79328, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 83216, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 74412, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 81542, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 82192, NA, NA, NA, NA, NA, NA, NA, 80923, NA, NA, NA, NA, NA, NA, NA, NA, NA, 74560, 72736, NA, NA, NA, NA, 69070, NA, NA, NA, 80591, NA, NA, NA, NA, NA, NA, NA, NA, 72413, 69679, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 92994, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 88334, 65532, NA, NA, NA, NA, NA, 73858, NA, NA, NA, NA, 85782, NA, NA, NA, NA, NA, NA, NA, NA, 75948, NA, NA, NA, NA, NA, NA, NA, 83574, 79748, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 91669, NA, NA, NA, NA, NA, NA, NA, NA, 90372, NA, NA, 83737, NA, NA, NA, NA, NA, NA, NA, NA, NA, 40534, NA, NA, NA, NA, NA, NA, NA, NA, 89789, NA, NA, NA, NA, 91861, NA, NA, NA, NA, NA, NA, NA, NA, NA, 77865, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 81334, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 86352, 89371, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 38304, NA, NA, NA, NA, NA, NA, NA, NA, 77337, NA, NA, NA, NA, NA, NA, NA, NA, NA, 78918, NA, NA, NA, NA, NA, 74740, NA, NA, NA, NA, NA, NA, 84355, 68150, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 70211, NA, NA, NA, NA, 80338, NA, NA, NA, 71714, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 47032, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 82998, NA, 76135, NA, NA, NA, NA, NA, NA, NA, NA, 75919, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 104460, NA, NA, NA, NA, NA, NA, NA, NA, NA, 91776, NA, NA, NA, NA, NA, NA, NA, NA, 83969, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 45234, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 50753, NA, 51615, NA, NA, NA, 76664, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 94423, NA, NA, NA, NA, NA, NA, 83422, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 67648, 75894, 73942, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 85011, NA, NA, NA, NA, NA, 70455, NA, NA, 55048, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 67481, NA, NA, NA, NA, 28857, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 82646, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 74941, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 14026, NA, NA, NA, NA
#> $ coa_law_res <dbl> NA, NA, NA, NA, NA, 42846, NA, NA, 16390, NA, NA, NA, NA, 58488, NA, NA, NA, NA, NA, NA, NA, NA, 42783, NA, NA, NA, 29674, 33364, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 43980, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72987, 66090, 66920, 67981, NA, NA, NA, NA, 72475, NA, NA, NA, NA, NA, 38758, NA, NA, 81669, NA, NA, NA, NA, NA, 75763, 76980, NA, NA, NA, 79039, NA, 71580, NA, 78609, NA, NA, NA, NA, 86455, NA, NA, NA, NA, NA, 48610, NA, NA, NA, NA, 70499, NA, NA, NA, NA, NA, NA, NA, NA, NA, 48346, NA, NA, NA, NA, NA, 68500, NA, NA, NA, NA, NA, NA, NA, 75641, 71630, NA, NA, 80430, NA, NA, 56764, NA, NA, NA, 28390, NA, NA, 40305, NA, 37484, 36634, NA, NA, NA, 76690, NA, 65473, NA, NA, NA, NA, 39565, 63032, NA, NA, NA, NA, NA, NA, NA, NA, NA, 77642, NA, NA, NA, NA, NA, 32204, 33926, 55498, NA, NA, NA, NA, NA, NA, NA, 40801, NA, NA, NA, 35776, NA, NA, NA, NA, NA, NA, 67210, NA, NA, NA, NA, 49597, NA, 66068, NA, NA, NA, 65308, NA, NA, NA, NA, 38898, 88769, NA, NA, NA, NA, NA, NA, NA, NA, 36333, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37799, NA, NA, NA, NA, 51357, NA, NA, NA, NA, NA, 79370, NA, 53252, 58110, NA, NA, NA, 41831, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37556, NA, NA, NA, NA, NA, NA, 38300, NA, NA, NA, NA, NA, NA, 43291, NA, 39780, NA, NA, NA, 37940, NA, NA, NA, NA, NA, 46937, NA, NA, NA, 61584, NA, NA, NA, NA, NA, NA, NA, NA, NA, 76694, NA, NA, NA, NA, 38094, NA, 57950, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 71850, 75652, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72682, NA, NA, NA, NA, 28003, 73312, NA, NA, 58633, NA, NA, NA, NA, NA, NA, NA, 64060, NA, NA, NA, NA, NA, NA, NA, 77352, NA, NA, NA, NA, NA, NA, NA, NA, NA, 50892, NA, NA, NA, NA, NA, NA, NA, NA, 59600, NA, NA, NA, NA, NA, NA, 55720, NA, NA, NA, NA, NA, NA, NA, 33914, NA, NA, 46015, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 38251, 36027, NA, NA, NA, NA, NA, 60638, NA, NA, NA, NA, 80918, NA, NA, NA, NA, 27964, NA, NA, NA, 56823, NA, NA, NA, 31432, NA, NA, NA, 44730, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 53000, NA, 52735, NA, 75710, NA, NA, NA, NA, NA, NA, NA, NA, NA, 25347, NA, NA, NA, NA, NA, NA, NA, NA, 96829, 87306, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 79048, 85645, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 90906, NA, NA, NA, 73484, NA, NA, NA, NA, NA, NA, NA, NA, 78116, NA, NA, 46422, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 73298, 72164, NA, NA, 82341, NA, 61530, NA, NA, 62325, NA, NA, NA, NA, NA, NA, 39728, NA, NA, 34528, NA, NA, NA, NA, NA, NA, 65911, NA, NA, NA, NA, NA, 29446, NA, 40625, NA, NA, NA, 19568, 72388, 41828, 36500, 52063, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 47245, NA, NA, NA, NA, 35788, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 49841, 36979, NA, NA, NA, 43960, NA, NA, NA, NA, 51842, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 19101, 62812, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 69492, 86316, NA, 51167, NA, NA, NA, NA, NA, NA, NA, NA, 46271, 64392, NA, NA, 57668, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 39682, NA, NA, NA, NA, NA, NA, NA, NA, NA, 30495, NA, 64915, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 50160, 35572, NA, NA, NA, 38914, NA, NA, NA, NA, NA, NA, NA, 75457, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 41547, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 47150, 67864, NA, NA, NA, NA, NA, NA, 49184, NA, 51772, NA, NA, NA, NA, NA, NA, 40636, 37444, NA, NA, NA, NA, NA, 26896, NA, NA, NA, 45676, NA, NA, NA, NA, NA, NA, NA, 50967, 52548, NA, NA, 45214, NA, NA, 55316, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 79496, NA, NA, NA, NA, 57775, NA, NA, NA, NA, NA, 65727, NA, NA, 53979, NA, NA, NA, NA, NA, NA, NA, NA, 36508, NA, NA, NA, NA, NA, NA, NA, NA, 66406, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 41515, NA, NA, NA, NA, 30578, NA, NA, NA, NA, NA, 30569, NA, NA, NA, NA, NA, NA, NA, NA, 24280, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 43325, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 15308, NA, NA, NA, NA
#> $ coa_law_nres <dbl> NA, NA, NA, NA, NA, 62296, NA, NA, 16390, NA, NA, NA, NA, 58488, NA, NA, NA, NA, NA, NA, NA, NA, 47283, NA, NA, NA, 42429, 53998, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 43980, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 76316, 75193, 72983, 74044, NA, NA, NA, NA, 72475, NA, NA, NA, NA, NA, 38758, NA, NA, 81669, NA, NA, NA, NA, NA, 75763, 76980, NA, NA, NA, 79039, NA, 71580, NA, 78609, NA, NA, NA, NA, 86455, NA, NA, NA, NA, NA, 55396, NA, NA, NA, NA, 70499, NA, NA, NA, NA, NA, NA, NA, NA, NA, 69778, NA, NA, NA, NA, NA, 68500, NA, NA, NA, NA, NA, NA, NA, 75641, 71630, NA, NA, 80430, NA, NA, 56764, NA, NA, NA, 43799, NA, NA, 54089, NA, 57495, 52870, NA, NA, NA, 76690, NA, 65473, NA, NA, NA, NA, 39565, 63032, NA, NA, NA, NA, NA, NA, NA, NA, NA, 77642, NA, NA, NA, NA, NA, 46910, 52684, 55498, NA, NA, NA, NA, NA, NA, NA, 64225, NA, NA, NA, 55012, NA, NA, NA, NA, NA, NA, 67210, NA, NA, NA, NA, 56797, NA, 66068, NA, NA, NA, 65308, NA, NA, NA, NA, 38898, 88769, NA, NA, NA, NA, NA, NA, NA, NA, 44538, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 52847, NA, NA, NA, NA, 71582, NA, NA, NA, NA, NA, 79370, NA, 53252, 58110, NA, NA, NA, 61446, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 43799, NA, NA, NA, NA, NA, NA, 50928, NA, NA, NA, NA, NA, NA, 68920, NA, 59190, NA, NA, NA, 51122, NA, NA, NA, NA, NA, 62377, NA, NA, NA, 61584, NA, NA, NA, NA, NA, NA, NA, NA, NA, 76694, NA, NA, NA, NA, 49584, NA, 73058, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 71850, 75652, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72682, NA, NA, NA, NA, 32457, 73312, NA, NA, 58633, NA, NA, NA, NA, NA, NA, NA, 64060, NA, NA, NA, NA, NA, NA, NA, 80352, NA, NA, NA, NA, NA, NA, NA, NA, NA, 53965, NA, NA, NA, NA, NA, NA, NA, NA, 68456, NA, NA, NA, NA, NA, NA, 55720, NA, NA, NA, NA, NA, NA, NA, 53980, NA, NA, 46015, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 56503, 53985, NA, NA, NA, NA, NA, 60638, NA, NA, NA, NA, 80918, NA, NA, NA, NA, 48496, NA, NA, NA, 56823, NA, NA, NA, 51202, NA, NA, NA, 56630, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 66310, NA, 66045, NA, 75710, NA, NA, NA, NA, NA, NA, NA, NA, NA, 40534, NA, NA, NA, NA, NA, NA, NA, NA, 96829, 87306, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 79048, 85645, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 90906, NA, NA, NA, 73484, NA, NA, NA, NA, NA, NA, NA, NA, 78116, NA, NA, 50512, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 73298, 72164, NA, NA, 82341, NA, 61530, NA, NA, 62325, NA, NA, NA, NA, NA, NA, 56874, NA, NA, 57200, NA, NA, NA, NA, NA, NA, 65911, NA, NA, NA, NA, NA, 57555, NA, 40725, NA, NA, NA, 19568, 72388, 46828, 36569, 52063, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 62197, NA, NA, NA, NA, 46756, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 49841, 48814, NA, NA, NA, 43960, NA, NA, NA, NA, 61058, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 19101, 62812, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 69492, 86316, NA, 61201, NA, NA, NA, NA, NA, NA, NA, NA, 60645, 64392, NA, NA, 57668, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 70690, NA, NA, NA, NA, NA, NA, NA, NA, NA, 49841, NA, 64915, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 50160, 42354, NA, NA, NA, 57588, NA, NA, NA, NA, NA, NA, NA, 75457, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 53355, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 47150, 67864, NA, NA, NA, NA, NA, NA, 57181, NA, 68678, NA, NA, NA, NA, NA, NA, 48046, 48994, NA, NA, NA, NA, NA, 26896, NA, NA, NA, 54611, NA, NA, NA, NA, NA, NA, NA, 59967, 52548, NA, NA, 60600, NA, NA, 55316, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 82496, NA, NA, NA, NA, 57775, NA, NA, NA, NA, NA, 65727, NA, NA, 65517, NA, NA, NA, NA, NA, NA, NA, NA, 52528, NA, NA, NA, NA, NA, NA, NA, NA, 66406, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 60212, NA, NA, NA, NA, 46988, NA, NA, NA, NA, NA, 30569, NA, NA, NA, NA, NA, NA, NA, NA, 31780, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 62569, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 15757, NA, NA, NA, NA
##########
########## Create data frame of generated variables, with each variable meant to represent the entire population
##########
num_obs <- 10000
# Generate normal distribution w/ custom mean and sd
set.seed(124)
norm_dist <- rnorm(n = num_obs, mean = 50, sd = 5)
# Generate right-skewed distribution
set.seed(124)
rskew_dist <- rbeta(n = num_obs, shape1 = 2, shape2 = 5)
# Generate left-skewed distribution
set.seed(124)
lskew_dist <- rbeta(n = num_obs, shape1 = 5, shape2 = 2)
# Generate standard normal distribution (default is mean = 0 and sd = 1)
set.seed(124)
stdnorm_dist <- rnorm(n = num_obs, mean = 0, sd = 1) # equivalent to rnorm(10)
# Create dataframe
df_generated_pop <- data.frame(norm_dist, rskew_dist, lskew_dist, stdnorm_dist)
# drop individual objects associated with each variable
rm(norm_dist,rskew_dist,lskew_dist,stdnorm_dist)
rm(num_obs)
##########
########## Create sample versions of generated population data frame and IPEDS population data frame
##########
# create sample version of our generated data
set.seed(124) # set seed so that everyone ends up with the same random sample
df_generated_sample <- df_generated_pop %>% sample_n(size = 200)
df_generated_sample %>% glimpse()
#> Rows: 200
#> Columns: 4
#> $ norm_dist <dbl> 42.70513, 50.24400, 61.29008, 45.47494, 44.74406, 47.99127, 55.04074, 49.69214, 50.38923, 39.19175, 51.07279, 54.43839, 53.62378, 41.83431, 53.60272, 53.29122, 51.63725, 52.84608, 51.38275, 41.76346, 43.36244, 48.05936, 54.19388, 53.09317, 46.09416, 55.18540, 47.30462, 53.61756, 49.37129, 51.65688, 59.97111, 53.45160, 56.22807, 58.01527, 36.07145, 49.59472, 49.14318, 49.77961, 52.82888, 45.69048, 49.32646, 42.21203, 53.29298, 62.49316, 44.84539, 47.13225, 46.81676, 49.06049, 53.55819, 55.73971, 53.49067, 50.02169, 52.30893, 43.86979, 43.91938, 52.52391, 47.13446, 50.44001, 49.13156, 55.47195, 49.43594, 53.83649, 48.88791, 46.58437, 51.80264, 47.76318, 54.92166, 52.77872, 41.86476, 56.20810, 45.88891, 50.99652, 47.37426, 52.78536, 51.50925, 51.46921, 51.09996, 42.20663, 40.50096, 54.86914, 49.79653, 44.44755, 50.01258, 50.81079, 49.92387, 55.03906, 48.14532, 51.19486, 46.43488, 55.09158, 42.22777, 48.19416, 48.41973, 48.49335, 53.25116, 50.22358, 48.85612, 45.39751, 51.70078, 49.40114, 49.81508, 38.89185, 55.78485, 44.18371, 56.53308, 51.35942, 48.59658, 49.74133, 47.80603, 42.29777, 45.05831, 47.75784, 48.34194, 47.35462, 41.42220, 56.67513, 46.53382, 45.68830, 50.18136, 50.64631, 53.57939, 47.56606, 48.84342, 52.70686, 56.90811, 43.38404, 49.45141, 53.56812, 54.75175, 58.36000, 45.63724, 45.51407, 48.90504, 53.92740, 45.23948, 61.00458, 48.21756, 47.92132, 52.03195, 58.05874, 37.77177, 53.14168, 43.97543, 48.27485, 45.54400, 56.32192, 51.10099, 45.99057, 40.34314, 54.78138, 50.43282, 49.74575, 55.38571, 50.75517, 46.41251, 45.67001, 50.61426, 47.18003, 43.94133, 49.42554, 54.22909, 49.16881, 52.90039, 51.40218, 50.02631, 41.30685, 47.07669, 55.21358, 55.69947, 55.43607, 50.24689, 55.80790, 47.17787, 56.12063, 48.89432, 52.69113, 49.24613, 55.70731, 59.27127, 52.84056, 43.99895, 44.97182, 50.88237, 52.82204, 47.83092, 41.25803, 58.63190, 40.74359, 47.45643, 51.66602, 53.31246, 53.50316, 49.66410, 48.62538, 40.64356, 43.06531, 45.35150, 49.07117, 45.95011, 43.68918
#> $ rskew_dist <dbl> 0.34451771, 0.31359906, 0.09375337, 0.05581678, 0.07445846, 0.30073040, 0.11378560, 0.16491116, 0.13776269, 0.21122786, 0.42166169, 0.50659353, 0.10523233, 0.37916146, 0.10309858, 0.03840800, 0.04266918, 0.14848578, 0.25606564, 0.02300487, 0.12163251, 0.12753151, 0.27866743, 0.25927965, 0.06452927, 0.40960334, 0.22663525, 0.18454281, 0.41744318, 0.37577638, 0.45178714, 0.11868932, 0.24789960, 0.41785766, 0.10818108, 0.10194045, 0.30258638, 0.10008212, 0.36912245, 0.14208359, 0.25795213, 0.21694231, 0.38570343, 0.34519116, 0.28787049, 0.53229942, 0.39366549, 0.36005312, 0.14176279, 0.38311790, 0.23082715, 0.51989070, 0.29717836, 0.41456648, 0.16882126, 0.16166155, 0.28794184, 0.24287424, 0.67952840, 0.35723832, 0.08462471, 0.63474209, 0.36377554, 0.14124243, 0.17004110, 0.23646472, 0.13642714, 0.11506730, 0.30446454, 0.22650372, 0.35521538, 0.34710094, 0.56771218, 0.17894184, 0.57104133, 0.35651879, 0.28583351, 0.30329232, 0.27334990, 0.13223347, 0.04536074, 0.54018390, 0.60301580, 0.04889720, 0.47365337, 0.18341073, 0.53947941, 0.17329835, 0.05631232, 0.52713998, 0.41434945, 0.18344548, 0.18331146, 0.08745563, 0.03697576, 0.06122208, 0.13436885, 0.64993218, 0.08587063, 0.08256558, 0.31087748, 0.30558599, 0.17749814, 0.19840925, 0.19278846, 0.23073872, 0.56422907, 0.10930243, 0.03480517, 0.16611344, 0.19937568, 0.12662522, 0.23545547, 0.47585407, 0.13082282, 0.30065451, 0.14454723, 0.28193342, 0.06026944, 0.53368530, 0.33654943, 0.17591854, 0.17097089, 0.50144283, 0.17947453, 0.21576142, 0.01285604, 0.38988708, 0.32640174, 0.31254058, 0.21241083, 0.19516630, 0.31972602, 0.27607159, 0.14289016, 0.33337844, 0.18734085, 0.32179101, 0.15249006, 0.19862691, 0.29666278, 0.21710765, 0.15559774, 0.29379274, 0.50648258, 0.20667525, 0.58727562, 0.16518038, 0.47890790, 0.57018306, 0.54808353, 0.06033227, 0.27391446, 0.61115642, 0.17768117, 0.69479604, 0.13830408, 0.26669225, 0.38706212, 0.21909775, 0.14703139, 0.46325013, 0.17828265, 0.30734715, 0.22227667, 0.09029895, 0.59331577, 0.24051906, 0.15877709, 0.15441401, 0.63871488, 0.23206352, 0.10759395, 0.02158398, 0.33221225, 0.31128480, 0.22879650, 0.13787491, 0.62737791, 0.36633272, 0.11165106, 0.28481807, 0.15171712, 0.09662778, 0.18458290, 0.44820517, 0.57401346, 0.08197392, 0.27120419, 0.05675496, 0.52984047, 0.31102941, 0.37136871, 0.33283260, 0.42751180, 0.23338304, 0.09035844, 0.43503391, 0.28810810, 0.07248912
#> $ lskew_dist <dbl> 0.6554823, 0.6864009, 0.9062466, 0.9441832, 0.9255415, 0.6992696, 0.8862144, 0.8350888, 0.8622373, 0.7887721, 0.5783383, 0.4934065, 0.8947677, 0.6208385, 0.8969014, 0.9615920, 0.9573308, 0.8515142, 0.7439344, 0.9769951, 0.8783675, 0.8724685, 0.7213326, 0.7407204, 0.9354707, 0.5903967, 0.7733648, 0.8154572, 0.5825568, 0.6242236, 0.5482129, 0.8813107, 0.7521004, 0.5821423, 0.8918189, 0.8980595, 0.6974136, 0.8999179, 0.6308775, 0.8579164, 0.7420479, 0.7830577, 0.6142966, 0.6548088, 0.7121295, 0.4677006, 0.6063345, 0.6399469, 0.8582372, 0.6168821, 0.7691728, 0.4801093, 0.7028216, 0.5854335, 0.8311787, 0.8383384, 0.7120582, 0.7571258, 0.3204716, 0.6427617, 0.9153753, 0.3652579, 0.6362245, 0.8587576, 0.8299589, 0.7635353, 0.8635729, 0.8849327, 0.6955355, 0.7734963, 0.6447846, 0.6528991, 0.4322878, 0.8210582, 0.4289587, 0.6434812, 0.7141665, 0.6967077, 0.7266501, 0.8677665, 0.9546393, 0.4598161, 0.3969842, 0.9511028, 0.5263466, 0.8165893, 0.4605206, 0.8267017, 0.9436877, 0.4728600, 0.5856505, 0.8165545, 0.8166885, 0.9125444, 0.9630242, 0.9387779, 0.8656311, 0.3500678, 0.9141294, 0.9174344, 0.6891225, 0.6944140, 0.8225019, 0.8015908, 0.8072115, 0.7692613, 0.4357709, 0.8906976, 0.9651948, 0.8338866, 0.8006243, 0.8733748, 0.7645445, 0.5241459, 0.8691772, 0.6993455, 0.8554528, 0.7180666, 0.9397306, 0.4663147, 0.6634506, 0.8240815, 0.8290291, 0.4985572, 0.8205255, 0.7842386, 0.9871440, 0.6101129, 0.6735983, 0.6874594, 0.7875892, 0.8048337, 0.6802740, 0.7239284, 0.8571098, 0.6666216, 0.8126592, 0.6782090, 0.8475099, 0.8013731, 0.7033372, 0.7828924, 0.8444023, 0.7062073, 0.4935174, 0.7933247, 0.4127244, 0.8348196, 0.5210921, 0.4298169, 0.4519165, 0.9396677, 0.7260855, 0.3888436, 0.8223188, 0.3052040, 0.8616959, 0.7333077, 0.6129379, 0.7809022, 0.8529686, 0.5367499, 0.8217174, 0.6926528, 0.7777233, 0.9097010, 0.4066842, 0.7594809, 0.8412229, 0.8455860, 0.3612851, 0.7679365, 0.8924061, 0.9784160, 0.6677878, 0.6887152, 0.7712035, 0.8621251, 0.3726221, 0.6336673, 0.8883489, 0.7151819, 0.8482829, 0.9033722, 0.8154171, 0.5517948, 0.4259865, 0.9180261, 0.7287958, 0.9432450, 0.4701595, 0.6889706, 0.6286313, 0.6671674, 0.5724882, 0.7666170, 0.9096416, 0.5649661, 0.7118919, 0.9275109
#> $ stdnorm_dist <dbl> -1.458973480, 0.048800972, 2.258015765, -0.905011638, -1.051188719, -0.401746030, 1.008148340, -0.061571475, 0.077846473, -2.161650065, 0.214558715, 0.887677696, 0.724755091, -1.633138924, 0.720543712, 0.658244829, 0.327449435, 0.569215178, 0.276550911, -1.647307037, -1.327511185, -0.388128582, 0.838775073, 0.618634581, -0.781168417, 1.037080561, -0.539076153, 0.723512002, -0.125742946, 0.331375107, 1.994221204, 0.690319287, 1.245614267, 1.603053457, -2.785710086, -0.081055017, -0.171364291, -0.044077298, 0.565775425, -0.861904286, -0.134708780, -1.557593629, 0.658595704, 2.498631481, -1.030922086, -0.573549688, -0.636647948, -0.187902287, 0.711638798, 1.147942839, 0.698134159, 0.004337686, 0.461785030, -1.226041894, -1.216123711, 0.504782944, -0.573107466, 0.088002987, -0.173688913, 1.094390119, -0.112811249, 0.767297423, -0.222418907, -0.683125187, 0.360528151, -0.447363198, 0.984331706, 0.555743578, -1.627048154, 1.241620667, -0.822217300, 0.199304827, -0.525147150, 0.557071552, 0.301850032, 0.293841101, 0.219991635, -1.558674519, -1.899807296, 0.973827316, -0.040694961, -1.110490589, 0.002516932, 0.162157135, -0.015226283, 1.007811901, -0.370935564, 0.238971253, -0.713024476, 1.018316185, -1.554445559, -0.361167675, -0.316053257, -0.301329540, 0.650231088, 0.044715579, -0.228776571, -0.920498185, 0.340155725, -0.119771574, -0.036984216, -2.221630639, 1.156969269, -1.163257567, 1.306615919, 0.271883695, -0.280684138, -0.051734016, -0.438793221, -1.540445909, -0.988338879, -0.448432695, -0.331611170, -0.529075050, -1.715559519, 1.335026881, -0.693236933, -0.862340835, 0.036271540, 0.129262273, 0.715877487, -0.486787855, -0.231315138, 0.541371450, 1.381622215, -1.323192908, -0.109717771, 0.713624908, 0.950349644, 1.671999266, -0.872552744, -0.897185149, -0.218991868, 0.785480375, -0.952103535, 2.200916158, -0.356487606, -0.415736576, 0.406389221, 1.611748618, -2.445645571, 0.628336588, -1.204914385, -0.345030070, -0.891200030, 1.264383388, 0.220198967, -0.801886455, -1.931372584, 0.956276224, 0.086564659, -0.050850953, 1.077142487, 0.151034618, -0.717497996, -0.865997119, 0.122852093, -0.563994528, -1.211733976, -0.114892889, 0.845818923, -0.166238218, 0.580077920, 0.280436573, 0.005261872, -1.738630353, -0.584661867, 1.042715974, 1.139893371, 1.087213715, 0.049378513, 1.161580364, -0.564426153, 1.224126176, -0.221136660, 0.538226020, -0.150774169, 1.141461982, 1.854254561, 0.568111685, -1.200209708, -1.005635370, 0.176474491, 0.564407365, -0.433816639, -1.748394173, 1.726380232, -1.851282607, -0.508714465, 0.333203647, 0.662492279, 0.700632630, -0.067180126, -0.274923393, -1.871287627, -1.386938658, -0.929700081, -0.185766662, -0.809978715, -1.262164021
# create sample version of our ipeds data
set.seed(124) # set seed so that everyone ends up with the same random sample
df_ipeds_sample <- df_ipeds_pop %>% sample_n(size = 200)
##########
########## STAR DATA
##########
# load star data
load(file = url('https://github.com/anyone-can-cook/educ152/raw/main/data/star/star_panel_data.RData'))
#df_star_panel %>% glimpse()
# create data frame for STAR experiment, keeping only kindergarten
df_stark <- df_star_panel %>%
# keep only kindergarten year
filter(grade ==1) %>%
# keep only observations with non-missing value for reading score
filter(!is.na(read)) %>%
# keep only observations with non-missing values for treatment assignment
filter(!is.na(star)) %>%
# drop observations where treatment status is regular+aide
filter(star !=3) %>%
# keep selected variables
select(id,grade,star,read,gender,ethnicity,lunch,school,degree,experience) %>%
# create a variable "treatment" that equals 1 if student receives treatment (small class) and equals 0 otherwise
mutate(
treatment = if_else(star==2,1,0)
)
df_stark %>% glimpse()
#> Rows: 3,745
#> Columns: 11
#> $ id <dbl+lbl> 943, 986, 1915, 2020, 2130, 3053, 3219, 3455, 3821, 4273, 4377, 4564, 4779, 5022, 5091, 5570, 6013, 6071, 6095, 6137, 6194, 6211, 6232, 6320, 6331, 6341, 6364, 6371, 6402, 6449, 6506, 6545, 6560, 6582, 6602, 6671, 6722, 6793, 6800, 6811, 6945, 6985, 7053, 7139, 7183, 7217, 7274, 7296, 7315, 7330, 7354, 7377, 7396, 7417, 7442, 7493, 7504, 7518, 7551, 7573, 7583, 7592, 7603, 7649, 7669, 7684, 7696, 7761, 7794, 7866, 7876, 7900, 7990, 8010, 8034, 8044, 8086, 8103, 8108, 8123, 8168, 8176, 8206, 8232, 8261, 8326, 8355, 8432, 8448, 8530, 8574, 8584, 8652, 8712, 8729, 8761, 8769, 8789, 8904, 8968, 8991, 9023, 9064, 9119, 9131, 9231, 9256, 9266, 9289, 9299, 9360, 9416, 9493, 9529, 9540, 9547, 9591, 9672, 9727, 9759, 9789, 9844, 9883, 9894, 10030, 10044, 10064, 10108, 10156, 10210, 10223, 10242, 10251, 10306, 10328, 10342, 10364, 10432, 10481, 10508, 10596, 10641, 10648, 10702, 10724, 10740, 10791, 10844, 10889, 10900, 10913, 10936, 10969, 10980, 10991, 11004, 11014, 11033, 11047, 11055, 11080, 11092, 11119, 11135, 11147, 11167, 11189, 11223, 11234, 11239, 11283, 11329, 11378, 11405, 11444, 11453, 11518, 11537, 11587, 7, 16, 42, 51, 86, 95, 105, 154, 205, 226, 242, 250, 339, 428, 434, 470, 538, 556, 682, 691, 722, 737, 791, 822, 863, 877, 891, 900, 908, 924, 930, 947, 955, 1023, 1035, 1048, 1053, 1092, 1144, 1155, 1175, 1254, 1266, 1320, 1368, 1380, 1402, 1414, 1422, 1498, 1508, 1548, 1577, 1634, 1641, 1678, 1697, 1714, 1835, 1932, 1942, 1956, 1964, 1976, 2051, 2162, 2179, 2262, 2308, 2324, 2331, 2366, 2450, 2513, 2556, 2564, 2575, 2601, 2610, 2665, 2751, 2762, 2820, 2830, 2844, 2864, 2900, 2973, 2984, 3041, 3049, 3062, 3078, 3121, 3130, 3142, 3167, 3200, 3223, 3240, 3262, 3322, 3331, 3351, 3400, 3486, 3498, 3519, 3577, 3599, 3632, 3643, 3664, 3718, 3731, 3763, 3794, 3807, 3941, 3955, 3973, 3988, 4097, 4109, 4132, 4182, 4242, 4299, 4348, 4361, 4461, 4485, 4507, 4543, 4585, 4606, 4616, 4654, 4665, 4695, 4709, 4762, 4830, 4850, 4873, 4938, 5031, 5069, 5120, 5129, 5141, 5194, 5229, 5271, 5281, 5327, 5346, 5397, 5429, 5528, 5550, 5574, 5592, 5642, 5721, 5729, 5762, 5797, 5898, 5919, 5929, 5941, 5953, 5976, 6003, 6009, 6014, 6015, 6016, 6020, 6026, 6027, 6030, 6037, 6040, 6044, 6046, 6047, 6048, 6054, 6058, 6061, 6069, 6070, 6074, 6075, 6079, 6080, 6081, 6084, 6085, 6088, 6089, 6092, 6097, 6101, 6103, 6114, 6116, 6121, 6125, 6127, 6131, 6136, 6138, 6143, 6147, 6149, 6150, 6152, 6153, 6158, 6167, 6169, 6174, 6176, 6182, 6183, 6187, 6191, 6196, 6198, 6202, 6204, 6206, 6208, 6213, 6217, 6219, 6223, 6234, 6235, 6236, 6240, 6243, 6245, 6247, 6249, 6250, 6255, 6257, 6262, 6264, 6265, 6268, 6271, 6273, 6274, 6275, 6278, 6282, 6287, 6288, 6290, 6292, 6294, 6296, 6297, 6299, 6301, 6304, 6314, 6316, 6322, 6327, 6328, 6329, 6333, 6334, 6336, 6339, 6340, 6343, 6346, 6352, 6366, 6369, 6373, 6375, 6382, 6383, 6384, 6386, 6388, 6390, 6391, 6393, 6394, 6398, 6400, 6406, 6408, 6414, 6419, 6421, 6431, 6433, 6437, 6443, 6447, 6450, 6452, 6453, 6454, 6458, 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2, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 2, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 2, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 1, 1, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 1,…
#> $ read <int> 447, 450, 448, 447, 431, 451, 478, 455, 430, 474, 424, 439, 437, 422, 424, 431, 456, 522, 416, 419, 418, 422, 414, 450, 458, 445, 439, 476, 467, 480, 465, 407, 487, 447, 430, 480, 440, 413, 422, 442, 410, 465, 436, 436, 428, 411, 436, 472, 443, 424, 428, 440, 433, 474, 414, 411, 422, 395, 407, 472, 402, 428, 413, 443, 403, 442, 443, 433, 422, 433, 440, 461, 445, 490, 451, 474, 400, 468, 386, 424, 453, 456, 451, 440, 445, 421, 408, 398, 450, 424, 400, 434, 425, 470, 472, 428, 436, 439, 434, 397, 445, 408, 428, 405, 447, 407, 434, 456, 418, 433, 427, 503, 442, 421, 431, 450, 428, 467, 413, 410, 453, 483, 411, 397, 421, 554, 411, 425, 458, 408, 407, 433, 436, 418, 443, 403, 419, 433, 503, 463, 413, 442, 393, 443, 410, 437, 472, 434, 427, 453, 418, 445, 463, 437, 411, 434, 433, 398, 463, 470, 411, 413, 395, 434, 492, 465, 448, 411, 518, 422, 470, 397, 390, 470, 580, 456, 403, 408, 443, 421, 442, 421, 450, 450, 410, 510, 418, 467, 436, 447, 468, 430, 442, 418, 485, 450, 408, 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425, 425, 434, 514, 425, 580, 451, 430, 442, 430, 437, 398, 443, 455, 419, 467, 427, 411, 453, 443, 400, 413, 442, 431, 388, 413, 419, 403, 397, 439, 424, 474, 514, 472, 402, 448, 413, 411, 439, 458, 397, 430, 483, 424, 397, 450, 447, 456, 455, 422, 405, 427, 416, 470, 416, 405, 382, 456, 497, 398, 456, 391, 445, 463, 428, 442, 472, 472, 397, 442, 439, 458, 414, 448, 434, 408, 458, 408, 474, 405, 440, 421, 468, 428, 478, 437, 451, 490, 440, 431, 470, 445, 443, 422, 500, 407, 428, 433, 413, 422, 425, 430, 460, 413, 545, 461, 413, 436, 447, 418, 413, 440, 443, 467, 439, 436, 458, 386, 442, 436, 403, 470, 461, 456, 408, 411, 405, 422, 413, 483, 428, 437, 431, 437, 424, 468, 465, 451, 419, 492, 443, 403, 431, 430, 424, 430, 478, 451, 434, 443, 439, 480, 443, 507, 442, 468, 391, 431, 436, 442, 440, 428, 425, 421, 413, 492, 456, 408, 467, 407, 421, 478, 436, 437, 391, 410, 442, 427, 411, 468, 416, 434, 419, 456, 386, 427, 408, 410, 414, 439, 398, 439, 422, 445, 474, 427, 437, 458, 467…
#> $ gender <dbl+lbl> 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 1, 1, 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1, 1,…
#> $ ethnicity <dbl+lbl> 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 2, 1, 2, 3, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 6, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 4, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 3, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, NA, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 3, 2, 2, 2, 1, 2, 2, 1, 1, 2, 1, 1, 2, 6, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 3, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 3, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 6, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 3, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 3, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 5, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 3, 1, 2, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1…
#> $ lunch <dbl+lbl> 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, NA, 2, 2, 2, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, NA, 1, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 1, 2, 1, NA, 1, 1, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 1, 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3, 2,…
#> $ degree <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 4, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 4, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 3, 1, 1, 1, 1, 4, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 4, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 4, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 4, 2, 2, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 3, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 3, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 3, 2, 1, 1, 4, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 4, 4, 1, 1, 1, 1, 1, 2, 3, 4, 1, 2, 1, 1, 1, 1, 2, 4, 1, 4, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 2, 1, 2, 1, 2, 4, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 3, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 4, 3, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 4, 4, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 4, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 3, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, 4, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 4, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 3, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 4, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 4, 2, 2, 4, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 4, 2, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 4, 2, 1, 4, 2, 2, 4, 2, 1, 4, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 4, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2, 1, 2, 2, 1, 4, 2, 2, 1, 2, 1, 1, 3, 2, 2, 2, 4, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 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1, 1,…
#> $ experience <int> 7, 21, 16, 5, 8, 3, 11, 10, 13, 0, 6, 13, 1, 8, 13, 13, 14, 8, 13, 0, 8, 15, 8, 10, 4, 7, 17, 12, 4, 0, 18, 4, 12, 14, 13, 14, 5, 5, 1, 5, 2, 2, 11, 13, 5, 0, 4, 5, 8, 2, 11, 14, 13, 3, 15, 20, 11, 20, 6, 11, 12, 6, 12, 18, 2, 11, 1, 17, 0, 13, 17, 20, 4, 15, 6, 10, 4, 16, 8, 6, 15, 10, 15, 8, 10, 3, 6, 6, 1, 13, 13, 9, 8, 6, 11, 8, 19, 8, 18, 3, 12, 5, 6, 8, 10, 8, 2, 11, 0, 14, 0, 7, 7, 0, 21, 0, 12, 12, 0, 1, 5, 3, 1, 2, 10, 14, 2, 1, 4, 11, 1, 0, 0, 3, 15, 3, 13, 3, 10, 11, 12, 11, 8, 16, 15, 10, 1, 9, 15, 8, 4, 4, 16, 13, 4, 9, 13, 9, 4, 12, 11, 1, 2, 11, 13, 14, 6, 11, 12, 6, 4, 9, 1, 10, 12, 8, 12, 15, 1, 20, 6, 9, 5, 0, 4, 14, 12, 2, 8, 6, 3, 15, 17, 14, 15, 4, 3, 11, 7, 10, 12, 9, 6, 1, 3, 1, 0, 8, 6, 11, 15, 10, 5, 9, 11, 5, 13, 24, 13, 7, 8, 5, 4, 9, 17, 8, 11, 15, 2, 8, 4, 0, 4, 12, 4, 3, 21, 5, 3, 12, 15, 7, 0, 4, 13, 2, 2, 4, 10, 17, 6, 24, 6, 2, 18, 8, 2, 12, 5, 5, 4, 8, 0, 13, 6, 9, 27, 19, 7, 3, 14, 1, 10, 6, 6, 9, 11, 16, 2, 15, 12, 10, 2, 15, 16, 12, 13, 8, 9, 0, 11, 3, 11, 3, 5, 20, 5, 1, 6, 0, 12, 12, 8, 14, 3, 10, 1, 2, 2, 1, 0, 4, 12, 16, 16, 3, 15, 12, 6, 0, 0, 1, 5, 20, 6, 24, 12, 1, 8, 13, 3, 14, 17, 24, 11, 11, 13, 14, 7, 4, 2, 21, 6, 16, 2, 12, 0, 1, 12, 0, 13, 3, 3, 1, 16, 13, 5, 12, 12, 8, 24, 14, 12, 11, 14, 1, 15, 1, 18, 11, 6, 12, 10, 7, 6, 12, 0, 19, 3, 5, 18, 8, 8, 12, 8, 8, 10, 10, 15, 6, 0, 6, 15, 20, 3, 11, 8, 10, 2, 5, 5, 14, 22, 11, 10, 20, 5, 9, 14, 16, 21, 18, 6, 9, 13, 0, 2, 8, 13, 11, 8, 8, 4, 4, 11, 15, 5, 5, 13, 11, 8, 5, 20, 3, 6, 15, 5, 4, 1, 13, 7, 7, 3, 11, 10, 0, 10, 17, 10, 15, 1, 16, 6, 13, 12, 4, 4, 11, 8, 0, 16, 10, 6, 3, 5, 11, 22, 9, 3, 15, 13, 13, 13, 6, 13, 8, 0, 8, 4, 15, 2, 9, 3, 1, 2, 13, 12, 6, 7, 13, 13, 0, 11, 3, 13, 1, 12, 8, 2, 8, 11, 12, 19, 7, 6, 0, 11, 8, 11, 5, 13, 11, 9, 10, 6, 2, 15, 11, 10, 14, 6, 6, 6, 5, 3, 8, 14, 8, 3, 1, 4, 4, 13, 21, 2, 17, 6, 1, 12, 5, 13, 20, 8, 13, 21, 13, 11, 8, 13, 13, 10, 4, 5, 12, 20, 13, 18, 3, 7, 8, 8, 10, 6, 6, 5, 1, 0, 9, 13, 2, 2, 11, 19, 8, 12, 8, 8, 6, 13, 8, 13, 5, 6, 14, 11, 11, 15, 8, 11, 9, 6, 10, 12, 19, 8, 10, 13, 12, 8, 8, 5, 0, 19, 10, 21, 6, 0, 11, 2, 3, 11, 4, 12, 4, 12, 3, 11, 2, 14, 8, 5, 8, 2, 0, 8, 11, 15, 10, 4, 8, 18, 15, 13, 0, 10, 3, 4, 11, 2, 4, 13, 8, 13, 5, 0, 12, 5, 1, 6, 8, 1, 10, 0, 3, 12, 0, 13, 14, 0, 14, 15, 14, 2, 13, 15, 21, 22, 21, 8, 8, 13, 12, 12, 11, 0, 2, 22, 10, 3, 4, 12, 3, 11, 11, 15, 6, 6, 3, 17, 0, 1, 14, 12, 9, 12, 1, 0, 10, 6, 11, 8, 0, 17, 16, 4, 5, 14, 0, 12, 3, 6, 8, 3, 6, 10, 12, 6, 20, 12, 15, 3, 5, 10, 1, 12, 12, 6, 13, 8, 8, 6, 13, 13, 22, 11, 11, 11, 8, 2, 15, 4, 8, 4, 11, 4, 13, 2, 20, 7, 6, 3, 6, 0, 12, 20, 8, 1, 4, 4, 6, 6, 13, 6, 8, 16, 8, 13, 9, 12, 11, 9, 8, 11, 5, 5, 3, 13, 5, 8, 22, 12, 13, 6, 1, 3, 11, 19, 13, 9, 10, 2, 6, 11, 6, 4, 3, 27, 20, 13, 10, 11, 0, 13, 10, 3, 3, 11, 8, 6, 6, 5, 0, 5, 13, 2, 8, 10, 8, 6, 2, 4, 3, 6, 6, 3, 16, 17, 17, 1, 6, 4, 10, 20, 4, 21, 11, 14, 1, 16, 2, 8, 4, 13, 8, 10, 6, 12, 7, 5, 12, 2, 21, 16, 2, 0, 5, 4, 14, 13, 8, 5, 13, 15, 15, 8, 12, 5, 8, 12, 6, 12, 13, 12, 8, 11, 27, 12, 11, 2, 7, 6, 1, 2, 11, 6, 21, 11, 12, 9, 13, 8, 5, 10, 15, 3, 13, 6, 1, 10, 6, 24, 1, 12, 8, 0, 4, 6, 6, 24, 13, 8, 4, 16, 12, 6, 4, 24, 4, 8, 11, 21, 1, 9, 6, 11, 1, 20, 9, 5, 14, 12, 10, 20, 1, 5, 8, 0, 5, 14, 4, 12, 14, 13, 1, 11, 17, 12, 12, 22, 11, 10, 15, 8, 4, 13, 20, 6, 8, 11, 13, 8, 13, 6, 11, 1, 3, 14, 13, 13, 6, 13, 5, 11, 20, 2, 12, 4, 22, 9, 8, 11, 22, 10, 19, 11, 12, 4, 16, 8, 24, 19, 7, 3, 13, 12, 9, 1, 12, 13, 6, 8, 3, 9, 24, 10, 6, 3, 1, 6, 6, 2, 6, 5, 13, 1, 1, 8, 12, 11, 9, 6, 7, 1, 11, 6, 12, 6, 11, 10, 16, 14, 9, 5, 13, 0, 8, 5, 2, 13, 14, 18, 18, 13, 9, 10, 0, 2, 2, 2, 4, 1, 5, 5, 10, 7, 13, 13, 13, 13, 1, 12, 21, 3, 12, 3, 7, 21, 12, 8, 11, 15, 2, 12, 16, 2, 11, 11, 5, 12, 6, 9, 3, 4, 8, 9, 13, 13, 4, 5, 11, 24, 8, 7, 15, 3, 13, 12, 16, 8, 1, 19, 10, 6, 17, 8, 10, 8, 9, 10, 5, 6, 14, 18, 11, 0, 13, 13, 10, 9, 24, 12, 0, 3, 10, 6, 3, 0, 8, 10, 6, 10, 6, 5, 11, 12, 8, 8, 4, 8, 10, 0, 12, 2, 6, 10, 6, 3, 10, 11, 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13, 8, 6, 19, 14, 13, 8, 8, 12, 20, 1, 6, 8, 6, 2, 3, 5, 8, 5, 2, 11, 24, 8, 5, 10, 21, 5, 1, 8, 10, 2, 15, 11, 17, 15, 11, 19, 15, 16, 6, 13, 5, 6, 8, 2, 3, 8, 10, 6, 15, 15, 8, 0, 1, 3, 15, 0, 6, 6, 6, 4, 12, 13, 1, 8, 14, 11, 8, 3, 13, 4, 4, 14, 1, 6, 1, 21, 3, 3, 6, 15, 2, 22, 15, 8, 15, 13, 6, 3, 1, 6, 12, 6, 11, 8, 5, 16, 14, 14, 13, 11, 2, 0, 8, 9, 16, 1, 6, 19, 8, 13, 19, 21, 14, 10, 8, 12, 21, 13, 12, 3, 21, 11, 10, 18, 8, 20, 0, 1, 1, 12, 10, 6, 8, 11, 6, 21, 18, 6, 2, 6, 13, 5, 6, 8, 8, 5, 19, 13, 12, 2, 9, 12, 12, 12, 15, 8, 14, 21, 6, 5, 2, 12, 10, 8, 2, 8, 3, 13, 13, 2, 3, 3, 16, 13, 13, 21, 8, 5, 5, 19, 2, 0, 13, 0, 7, 4, 12, 7, 12, 16, 20, 14, 8, 15, 6, 1, 1, 22, 13, 13, 2, 20, 10, 15, 7, 7, 12, 2, 15, 16, 1, 11, 12, 13, 6, 24, 1, 8, 1, 4, 7, 10, 2, 1, 5, 5, 12, 4, 0, 11, 15, 9, 2, 13, 15, 4, 15, 11, 5, 12, 11, 13, 8, 6, 4, 13, 0, 11, 12, 13, 13, 5, 15, 13, 3, 20, 2, 11, 1, 12, 14, 13, 17, 3, 4, 14, 13, 3, 9, 17, 13, 6, 15, 6, 7, 13, 3, 5, 12, 10, 10, 17, 4, 15, 7, 3, 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12, 3, 0, …
#> $ treatment <dbl> 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, …Investigate STAR data
- basically, we want to know if Kindergarten students randomly assigned to “small” class size (variable
treatmentequals1) have higher values of reading test score than students randomly assigned to “large” class size (variabletreatmentequals0)
# frequency count of treatment variable
#df_stark %>% count(star)
#df_stark %>% count(star) %>% as_factor()
df_stark %>% count(treatment)
#> # A tibble: 2 x 2
#> treatment n
#> <dbl> <int>
#> 1 0 2006
#> 2 1 1739
# calculate mean value of reading score separately for treated and nontreated
df_stark %>% group_by(treatment) %>% summarize(
mean_read = mean(read, na.rm = TRUE),
sd_read = sd(read, na.rm = TRUE)
)
#> # A tibble: 2 x 3
#> treatment mean_read sd_read
#> <dbl> <dbl> <dbl>
#> 1 0 435. 30.9
#> 2 1 441. 32.5
# t.test
#t.test(formula = read ~ treatment, mu = 0, data = df_stark)2 Fundamentals of causal inference
2.1 Overview of causal inference
Descriptive research questions
- Can investigate the magnitude of a problem (univariate):
- What percentage of high school graduates attend college?
- Investigate correlational relationship between variables (sometimes called “associational” relationships):
- Relationship between buying felt furniture pads and credit score?
- Relationship between avg. income at a high school and the number of off-campus recruiting visits by universities?
Causal research questions
- Want to know the “causal effect” of independent variable (X) on outcome (Y); If you change value of X, causal effect is the change in Y due to the change in X
- Have the form “what is effect of X on Y?” Examples:
- What is the effect of class size on math scores?
- What is effect of grant aid on graduation?
Example research question
What is the effect of participating in the Mexican American Studies (MAS) program (\(T_i=1\)) vs. not participating in MAS (\(T_i=0\)) on statewide standardized high school test score (\(Y\)) for students in the Tucson Unified School District (Cabrera et al., 2014)?
Counterfactuals
Actual and counterfactual (using the Mexican American Studies (MAS) example)
- Actual event
- actual treatment/control assignment and observed outcome
- e.g., person \(i\) participated in MAS (\(T_i =1\)) and graduated from high school (\(Y_i=1\))
- counterfactual
- for a person that received treatment, counterfactual is what outcome would have been if person had not received treatment
- e.g., if person \(i\) (who we know participated in MAS) had instead not participated in MAS (\(T_i =0\)), what would their value of high school graduation (\(Y\)) have been?
True causal effect of an intervention:
- Causal effect for person \(i\) who actually participated in the treatment
- The causal effect for person \(i\) is the observed “treated” outcome minus the counterfactual outcome had they not been treated
- Note: causal effect could be different for each person
- What is the problem with this approach to calculating causal effects?
- we only observe the actual treatment assignment and outcome
- we do not observe the counterfactual; it is counter to fact!
The primary challenge in program evaluation/causal inference research
Given that the true causal effect is observed outcome minus the counterfactual outcome, the primary challenge in program evaluation methods is finding a substitute for the counterfactual
- This substitute is called the “comparison group”
Creating comparison groups for the counterfactual
- treated vs. untreated research designs (cross-sectional)
- use “untreated” groups as “comparison group” for treated
- before vs. after research designs (longitudinal)
- use “outcome before treatment” groups as comparison group for “outcome after treatment”
- Some research designs use both cross-sectional and longitudinal variation (e.g., “difference-in-difference”)
Treated vs. untreated research designs
- Use “untreated” people as “comparison group” for treated
- We use non-participants to represent the counterfactual for participants (i.e., what would have happened to participants if they hadn’t participated)
- Example:
- What is effect of participation in MAS on HS graduation?
- Estimate of causal effect based on “cross-sectional” variation (also called “between” variation)
- Outcome measured at one point in time
- Uses “between” variation: variation in Y between treated and untreated at time outcome variable is measured
- Sample:
- People who participate in MAS people who do not participate in MAS
- This course will focus on treated vs. untreated designs
Before vs. after research designs
- Use “outcome before treatment” as comparison group for “outcome after treatment”
- Assumes outcome prior to treatment is counterfactual for outcome after treatment (i.e., what would have happened to participants if they hadn’t participated)
- Example
- What is the effect of participating in MAS (X) on days absent (Y)? Can answer this RQ using “treated vs. untreated” or “before vs. after” methods. Let’s focus on before vs. after
- Sample:
- Only students who participate in MAS
- Calculate causal effect from longitudinal (“within” )rather than cross-sectional (“between”) variation: - “within” variation: change over time in Y within each person - Must observe outcome before treatment after treatment
- Most of my research uses before vs. after designs because I study change in organizational behavior over time
Types of treated vs. untreated designs
- Random assignment experiment designs
- randomly assign each TUSD high school student to participate in MAS or not; and then observe their outcome (e.g., graduation, high school exit exam score)
- Observational (i.e., non-experimental) “selection on observables” designs
- These designs control for variables that affect outcome and treatment.
- Multivariate regression
- Matching estimators (e.g., propensity score matching)
- Observational “natural experiments” designs
- These designs utilize experimental variation in X in real world settings (e.g., access to school determined by lottery)
- Regression discontinuity
- Instrumental variables
Random assignment experiments: The “gold standard” treated vs. untreated designs
How experiments work:
- Randomly assign people to values of X (MAS or no MAS)
- group randomly assigned to “no MAS” serves as counterfactual to group assigned to MAS
- On average, “treated” group is identical to control group on variables (e.g., parental education, math achievement) that affect outcome (e.g., graduation)
- Only difference between “treated” and “control” group is participation in treatment
- Therefore, can say that difference in outcome between treatment and control is due to treatment
Experiments can only identify average causal effect
- If we knew the true counterfactual for each person, we could identify causal effect for each person
Observational treated vs. untreated designs
Observational (i.e., non-experimental) design
- People not randomly assigned to values of treatment (X)
- Rather, people self-select into treatment, or some other assignment mechanism
The problem with observational designs
- students with other characteristics (e.g., “motivation”) that affect the outcome of interest may be more/less likely to participate in the treatment (e.g., MAS)
- If students in the treatment tend to have higher values of outcome variable, hard to know whether this is because of participation in the treatment (i.e., causal) or because students who participated in MAS tended to be those who were more motivated about school (correlational)
Methods for observational data (e.g., multivariate regression) attempt to recreate experimental conditions
- Important to understand why experiments work so you can assess whether the observational method is recreating experimental conditions
2.2 Rubin’s causal model
Rubin’s causal model: The potential outcomes framework
- Rubin’s causal model (also called “the potential outcomes framework”) is the most important conceptual approach to understanding causal effects
- it lays out the logic of “true causal effect equals actual minus counterfactual” in a more rigorous way
Notation
- \(i=1,\ldots,n\) refers to “units” or “subjects” (e.g., students)
- \(Y_i\): actual observed outcome \(Y\) for unit \(i\)
- \(T_i\): actual observed treatment condition for unit \(i\). \(T_i=1\) is “treated”; \(T_i=0\) is “untreated” or “control”
Potential outcomes
A potential outcome is what the outcome would be for a unit if it received a given treatment condition
Each person \(i\) has two potential outcomes (using the Mexican American Studies (MAS) example)
- \(Y_i(1)\), the “treated potential outcome,” equals the outcome for unit \(i\) if it had received the treatment
- \(Y_i(0)\), the “untreated potential outcome,” equals the outcome for unit \(i\) if it had not received the treatment
The two potential outcomes, using the Mexican American Studies (MAS) example
- \(Y_i(1)\), the “treated potential outcome,” equals the outcome – statewide high school test score – for unit \(i\) if it had participated in MAS program
- \(Y_i(0)\), the “untreated potential outcome,” equals the outcome – statewide high school test score – for unit \(i\) if it had not participated in MAS program
How to think about potential outcomes
- for each person \(i\), imagine that the both the treated potential outcome \(Y_i(1)\) and the untreated potential outcome \(Y_i(0)\) already exist
- The observed value of the treatment variable \(T_i\) just determines which of the two potential outcomes we actually get to observe
Relationship between potential and observed outcomes, \(Y_i\)
- \(Y_i=T_i*Y_i(1)+(1-T_i)*Y_i(0)\)
- if subject is assigned to the treatment group (\(T_i=1\)) then the above equation simplifies to \(Y_i=Y_i(1)\) meaning that the outcome we actually observe is the treated potential outcome, \(Y_i(1)\)
- if subject is assigned to the control group (\(T_i=0\)) then the above equation simplifies to \(Y_i=Y_i(0)\) meaning that the outcome we actually observe is the untreated potential outcome, \(Y_i(0)\)
There are four combinations of potential outcome and treated assignment, two of which we actually observe and two of which we do not observe because they are counterfactuals
- Below, we will use this sort of notation: \(Y_i(1) \mid T_i=1\)
- means "the treated potential outcome for unit \(i\) given that unit \(i\) actually received the treatment (i.e., \(T_i=1\))
Four combinations of potential outcome and treated assignment
- \(Y_i(1) \mid T_i=1\) => treated potential outcome for those actually assigned to treatment (observed)
- For people actually assigned to the treatment, we get to observe their treated potential outcome
- \(Y_i(0) \mid T_i=1\) => untreated potential outcome for those actually assigned to treatment (not observed)
- For people actually assigned to the treatment, we do not get to observe their untreated potential outcome
- \(Y_i(1) \mid T_i=0\) => treated potential outcome for those actually assigned to control (not observed)
- For people actually assigned to the control, we do not get to observe their treated potential outcome
- \(Y_i(0) \mid T_i=0\) => untreated potential outcome for those actually assigned to control (observed)
- For people actually assigned to the control, we get to observe their untreated potential outcome
Below, we present these four combinations of potential outcome and treatment assignment in tabular form.
| Actually treated (T=1) | Actually not treated (T=0) | |
|---|---|---|
| \(Y_i(1)\) | \(Y_i(1) \mid T_i=1\) | \(Y_i(1) \mid T_i=0\) |
| \(Y_i(0)\) | \(Y_i(0) \mid T_i=1\) | \(Y_i(0) \mid T_i=0\) |
The Blue cells show notation for actual outcomes we get to observe (i.e., the observed outcome \(Y\) is the same as the potential outcome)
- e.g., “The treated potential outcome, for those who underwent treatment”
The Red cells are counterfactual outcomes, which we never get to see (i.e., the observed outcome \(Y\) is not the same as the potential outcome)
- We need counterfactual to calculate the “true” causal effect
- Need some substitute for the counterfactual to estimate causal effect
unit-level causal effect (“true” causal effect)
Unit causal effect: \(\tau_i = Y_i(1) - Y_i(0)\)
- in words: the causal effect of the treatment for unit \(i\) is the treated potential outcome (\(Y_i(1)\)) – outcome had they received the treatment – minus the untreated potential outcome (\(Y_i(0)\)) – outcome had they not received the treatment
- note that the unit causal effect differs for different subject
- in reality, we can never calculate the true unit causal effect because we can never observe both the treated potential outcome and the untreated potential outcome for a unit, \(i\)
Table of potential outcomes
But imagine we know treated \(Y_i(1)\) and untreated \(Y_i(0)\) potential outcomes for all \(i\)
| \(i\) | \(Y_i(1)\) Treated |
\(Y_i(0)\) Untreated |
\(\tau_i\) Unit effect |
|---|---|---|---|
| 1 | 65 | 60 | 5 |
| 2 | 30 | 35 | -5 |
| 3 | 55 | 60 | -5 |
| 4 | 25 | 30 | -5 |
| 5 | 50 | 50 | 0 |
| 6 | 80 | 70 | 10 |
| 7 | 45 | 45 | 0 |
| Average | 50 | 50 | 0 |
True causal effect for each \(i\)
- Unit treatment effect: for each person, compare their treated potential outcome to their untreated potential outcome
- \({UTE}_i=\tau_i=Y_i(1)-Y_i(0)\)
3 Experiments
Calculating the true (unit) causal effect of a treatment requires knowing the actual outcome and the counterfactual outcome (the potential outcome had the unit been assigned to a different treatment/control condition than what they actually received), for each unit \(i\)
- we can never do this
The most important/difficult part of causal inference research is creating a “comparison group” that is a good substitute for the counterfactual
An experiment (subjects randomly assigned to treatment vs. control) is the “gold standard” approach for creating a comparison group
- when we conduct an experiment, we cannot calculate the unit treatment effect (causal effect of the treatment that is different for each unit \(i\)), but we can calculate an “average treatment effect”
3.1 Expected values
Repeated random sampling
- Imagine we take an infinite number of samples of size N from the population
Expected value
- Expected value of a variable is the average value of a random variable based on an infinite number of samples
- expected value of discrete random variable X: \(E[X]=\sum xPr[X=x]\)
- \(Pr[X=x]\) is probability that \(X\) takes on the value \(x\), where summation is taken over all possible values of X
- Example of expected value of dice role, \(X\):
- \(E[X]=(1)(\frac{1}{6}) +(2)(\frac{1}{6})+(3)(\frac{1}{6})+(4)(\frac{1}{6})+(5)(\frac{1}{6})+(6)(\frac{1}{6})=3.5\)
3.1.1 Conditional expectations
Conditional expectations refer to subgroup averages
Using Tennesee STAR example: \(Y_i\)=achievement test score; \(T_i\)=treatment assignment (1=“small” class, treated; 0=“big” class, untreated); \(Z_i\)=parental education
- \(E[Y_i|T_i=1]\)
- Expected value of observed test score, given that student is assigned to the treatment (“small” class size)
- \(E[Y_i|Z_i >= baccalaureate\_degree"]\)
- Expected value of observed test score, given that parents level of education is baccalaureate degree or higher
- \(E[Y_i(1)|T_i=1]\)
- Expected value of treated potential outcome test score, given that student received the treatment (assigned to small class) [we get to observe this]
- \(E[Y_i(0)|T_i=1]\)
- Expected value of untreated potential outcome test score, given that student received the treatment [we do not get to observe this]
3.2 Random assignment
In an experiment, we randomly assign subjects to receive treatment or not (i.e., control)
Random assignment variable, \(T_i\)
- \(T_i\) is a variable whose value is randomly assigned (e.g., coin flip, or some random number generator)
- Value of \(T_i\) determines whether person \(i\) receives treatment
- e.g., student with \(T_i=1\) enroll in “small class” (treatment); units with \(T_i=0\) enroll in “big class” (control)
Potential outcomes and random assignment
- \(E[Y_i(1)|T_i=1]\)
- Treated potential outcome, given \(i\) assigned to treatment
- Observed?: Yes
- \(E[Y_i(1)|T_i=0]\)
- Treated potential outcome, given \(i\) not assigned to treatment
- Observed?: No
- \(E[Y_i(0)|T_i=1]\)
- Untreated potential outcome, given \(i\) assigned to treatment
- Observed?: No
- \(E[Y_i(0)|T_i=0]\)
- Untreated potential outcome, given \(i\) not assigned to treatment
- Observed?: Yes
3.2.1 Random assignment and repeated random sampling
Below table shows table of treated and untreated potential outcomes for 7 people, in the hypothetical case where we know both the treated and untreated potential outcome for each person
- Imagine that we take a random sample of 7 people from the population
| \(i\) | \(Y_i(1)\) Treated |
\(Y_i(0)\) Untreated |
\(\tau_i\) Treatment effect |
|---|---|---|---|
| 1 | 65 | 60 | 5 |
| 2 | 30 | 35 | -5 |
| 3 | 55 | 60 | -5 |
| 4 | 25 | 30 | -5 |
| 5 | 50 | 50 | 0 |
| 6 | 80 | 70 | 10 |
| 7 | 45 | 45 | 0 |
| Average | 50 | 50 | 0 |
Below equations show the the calculation of average treatment effect (ATE)
\(ATE \equiv \frac{1}{N} \sum_{i=1}^{N} \tau_i\)
\(ATE \equiv \frac{1}{N} \sum_{i=1}^{N} Y_i(1) - \frac{1}{N} \sum_{i=1}^{N} Y_i(0) = \frac{1}{N} \sum_{i=1}^{N} (Y_i(1)-Y_i(0)) = \frac{1}{N} \sum_{i=1}^{N} \tau_i\)
Now imagine that 3 of the 7 people are randomly assigned to receive the treatment (small class size)
- we randomly sample 7 people from the population; of those 7 people we randomly assign 3 people to receive the treatment
in the below table, bolded cells indicate cells we actually observe
- e.g., in the first row for unit \(i=1\) we observe the untreated outcome \(Y_i(0)=60\) (indicating that \(T_i=0\) for unit \(i=1\)), but we would not observe the treated outcome \(Y_i(1)=65\) because unit \(i=1\) was not assigned to the treatment
| \(i\) | \(Y_i(1)\) Treated |
\(Y_i(0)\) Untreated |
\(\tau_i\) Treatment effect |
|---|---|---|---|
| 1 | 65 | 60 | ? |
| 2 | 30 | 35 | ? |
| 3 | 55 | 60 | ? |
| 4 | 25 | 30 | ? |
| 5 | 50 | 50 | ? |
| 6 | 80 | 70 | ? |
| 7 | 45 | 45 | ? |
| Avg. observed | 41.66 | 53.75 | -12.08 |
| Avg. potential (unobserved) | 50 | 50 | 0 |
Now imagine that start over (re-sample) and randomly assign 3 of the 7 people people to receive the treatment
- we randomly sample 7 people from the population; of those 7 people we randomly assign 3 people to receive the treatment
- Note
- compared to previous table, what has changed is which 3 people (of the 7) receives the treatment; but we sample the same 7 people as before
- If we re-sampled in real life, we our random sample of 7 people would be a different set of people than the first time we randomly sampled; however, we keep it as the same 7 people because this meakes it easier to explain the concepts
| \(i\) | \(Y_i(1)\) Treated |
\(Y_i(0)\) Untreated |
\(\tau_i\) Treatment effect |
|---|---|---|---|
| 1 | 65 | 60 | 5 |
| 2 | 30 | 35 | -5 |
| 3 | 55 | 60 | -5 |
| 4 | 25 | 30 | -5 |
| 5 | 50 | 50 | 0 |
| 6 | 80 | 70 | 10 |
| 7 | 45 | 45 | 0 |
| Avg. observed | 56.67 | 47.5 | 9.17 |
| Avg. potential (unobserved) | 50 | 50 | 0 |
If we re-sampled an infinite number of times, and calculated the average of the “average observed treatment effect” it would equal the “average potential treatment effect”
- this is the exaxt same idea as: if we take an infinite number of random samples and calculate the sample mean \(\bar{Y}\) for each sample, the mean of all sample means would equal the population mean, \(\mu_Y\)
3.3 Why experiments work
Why are experiments viewed as the “gold standard” approach for estimating causal effects?
Recall from Rubin’s causal model:
- for each person \(i\), both the value of the treated potential outcome \(Y_i(1)\) and the value of the untreated potential outcome \(Y_i(0)\) already exist
- The observed value of the treatment variable \(T_i\) just determines which of the two potential outcomes we actually get to observe, \(Y_i\)
- If \(T_i=1\) then the observed outcome, \(Y_i\), is the treated potential outcome, \(Y_i(1)\)
- If \(T_i=0\) then the observed outcome, \(Y_i\), is the untreated potential outcome, \(Y_i(0)\)
In experiments, treatment status is randomly assigned (if \(T_i=1\) then treated; \(T_i=0\) then untreated)
- treatment assignment \(T_i\) does not affect the value of the either the treated potential outcome (\(Y_i(1)\)) or the untreated potential outcome (\(Y_i(0)\))
- rather, treatment assignment \(T_i\) just affects which potential outcome we observe
- If \(T_i=1\) then the observed outcome, \(Y_i\), is the treated potential outcome, \(Y_i(1)\)
- If \(T_i=0\) then the observed outcome, \(Y_i\), is the untreated potential outcome, \(Y_i(0)\)
Random assignment and expected values
- Every person has same probability of getting treatment (\(T_i=1\)); therefore, expected treated potential outcome among treated people (\(E[Y_i(1)|T_i=1]\)) is same as expected treated outcome for all people in sample (\(E[Y_i(1)]\)):
- \(E[Y_i(1)|T_i=1]=E[Y_i(1)]\)
- Every person has same probability of getting control (\(T_i=0\)); therefore, expected untreated potential outcome among untreated people (\(E[Y_i(0)|T_i=0]\)) is same as expected outcome for all people in sample (\(E[Y_i(0)]\)):
- \(E[Y_i(0)|T_i=0]=E[Y_i(0)]\)
Why experiments work (i.e., why they calculate unbiased estimates of causal effects
For any outcome of interest (e.g., reading achievement scores of 3rd graders), there are many variables – aside from the treatment (e.g., “small class size”) – that affect the value of the outcome
- e.g., parental education; household income; access to a quiet place to do homework; access to good nutrition; sleep
But because assignment to treatment is random:
- Assignment to treatment has no effect on value of the potential outcomes; it just affects which potential outcome is observed for each person
- Assignment to treatment has no relationship to characteristics (e.g., parental income) that affect value of potential outcomes
Why observational studies (people self-select into treatment) don’t calculate unbiased estimates of causal effects
For any outcome of interest (e.g., reading achievement scores of 3rd graders), there are many variables – aside from the treatment (e.g., “small class size”) – that affect the value of the outcome
- e.g., parental education; household income; access to a quiet place to do homework; access to good nutrition; sleep
When people self-select into the treatment (as opposed to treatment being randomly assigned), it is likely that people with certain characteristics are more likely to select into the treatment than others
- e.g., affluent parents are more likely to get their children into classes with small class size
So in observational research designs, the same characteristics (e.g., parental income) that determine the value of the dependent variable (student achievement score) also drive selection into the treatment (small class size)
- This makes it difficult to determine whether the higher outcome values for “treated” units are due to the treatment or due to other characteristics that are associated with both the treatment and the outcome
Calculating average treatment effects (ATE) for random assingment experiments
In a random assignment experiment, we can calculate the population average treatment effect (ATE) as follows
- \(ATE=\tau=E[Y_i(1)-Y_i(0)]= E[Y_i(1)] - E[Y_i(0)] = E[Y_i(1)|T_i=1]-E[Y_i(0)|T_i=0]= E[Y_i|T_i=1] - E[Y_i|T_i=0]\)
However, knowing expected values – e.g., \(E[Y_i|T_i=1]\), the expected value of \(Y_i\) amongst treated units – requires taking an infinite number of samples
- In real research, we are only able to randomly assign a single sample
- Using data from that single sample we calculate an estimate of the average treatment effect
- Using hypothesis testing methods we learned previously, we use our estimate of the average treatment effect to test hypotheses about the population treatment effect
Formula for the “difference in means” estimator of the average treatment effect
- \(\hat{ATE} = \hat{\tau} = \bar{Y}_{treatment} - \bar{Y}_{control}\)
- \(\hat{ATE} = \frac{\sum_{i=1}^{n_{t=1}} Y_{i,t=1}}{n_{t=1}} - \frac{\sum_{i=1}^{n_{t=0}} Y_{i,t=0}}{n_{t=0}}\), where:
- \(Y_{i,t=1}\): observed outcome for a unit \(i\) who received the treatment
- \(Y_{i,t=0}\): observed outcome for a unit \(i\) who received the control
- \(n_{t=1}\): number of units that received the treatment
- \(n_{t=0}\): number of units that received the control
Using data from Tenesse STAR experiment (data frame is df_stark), our estimate of ATE is the sample mean reading score for treated students (small class size) minus the sample mean reading score for untreated units (big class size)
- sample mean reading score for treated units = 440.55
- sample mean reading score for untreated units = 434.73
- difference between means estimate = 5.82
# number of observations in treatment and control
df_stark %>% count(treatment)
#> # A tibble: 2 x 2
#> treatment n
#> <dbl> <int>
#> 1 0 2006
#> 2 1 1739
# sample mean reading score for treated units
mean(df_stark$read[df_stark$treatment==1], na.rm = TRUE)
#> [1] 440.5474
# sample mean reading score for untreated unts
mean(df_stark$read[df_stark$treatment==0], na.rm = TRUE)
#> [1] 434.7323
# difference between means estimator
mean(df_stark$read[df_stark$treatment==1], na.rm = TRUE) - mean(df_stark$read[df_stark$treatment==0], na.rm = TRUE)
#> [1] 5.815138
# alternative approach to calculating mean value of reading score separately for treated and nontreated
df_stark %>% group_by(treatment) %>% summarize(
mean_read = mean(read, na.rm = TRUE),
sd_read = sd(read, na.rm = TRUE)
)
#> # A tibble: 2 x 3
#> treatment mean_read sd_read
#> <dbl> <dbl> <dbl>
#> 1 0 435. 30.9
#> 2 1 441. 32.54 Introduce user defined functions
We’ll use our “user defined functions” (loaded above) to visually explain concepts for hypothesis testing about population mean of two groups
- these functions have been updated/modified as of 4/15/2021
- try messing around with these functions on your own (e.g., try running them with a different variable and play around with function argument values)
plot_distribution() function plots the distribution of variables
- syntax and default values:
plot_distribution(data_vec1, data_vec2 = NULL, data_df = NULL, data_var = NULL, group_var = NULL, group_cat = NULL, pop_labels = NULL, show_group_hist = F, sampling_dist = F, plot_title = '')
- We can use this function to plot the distribution of a single variable, or to plot distribution of variable for two different groups (or two different variables)
# PLOTTING A SINGLE DISTRIBUTION
plot_distribution(data_df = df_ipeds_pop, data_var = 'tuitfee_grad_res')
# PLOTTING TWO DISTRIBUTIONS
plot_distribution(data_df = df_ipeds_pop, data_var = 'tuitfee_grad_res', group_var = 'control',group_cat = c(1, 2), pop_labels = c('Public', 'Private not-for-profit'))
get_sampling_distribution() function gets the sampling distribution of a variable (defaults = 1000 samples of sample size = 200)
- syntax and default values:
get_sampling_distribution(data_vec, num_samples = 1000, sample_size = 200)
- following revisions to the
plot_distribution()function, we usually will not call theget_sampling_distribution()function directly;- rather, a we can specify an optional argument within the
plot_distribution()to call theget_sampling_distribution() - the relevant argument within
plot_distribution()function issampling_distwhich has a default value ofFALSE
- rather, a we can specify an optional argument within the
# PLOT SAMPLING DISTRIBUTION, SINGLE POPULATION,
#(set sampling_dist= TRUE) to plot sampling distribution
plot_distribution(data_df = df_ipeds_pop, data_var = 'tuitfee_grad_res', sampling_dist = TRUE, plot_title = 'this is a sampling distribution')
#(set sampling_dist= FALSE) to plot distribution of the variable (not the sampling distribution)
plot_distribution(data_df = df_ipeds_pop, data_var = 'tuitfee_grad_res', sampling_dist = FALSE, plot_title = 'this is a variable distribution')
#plot_distribution(df_ipeds_pop$tuitfee_grad_res, sampling_dist = T) # this works too
# PLOT SAMPLING DISTRIBUTION OF TWO POPULATIONS
# IPEDS data: tuitfee_grad_res by control (control=1 minus control=2)
plot_distribution(data_df = df_ipeds_pop, data_var = 'tuitfee_grad_res', group_var = 'control', group_cat = c(2, 1), sampling_dist = T, pop_labels = c('private', 'public'))
# IPEDS data: tuitfee_grad_res by control (control=2 minus control=1)
plot_distribution(data_df = df_ipeds_pop, data_var = 'tuitfee_grad_res', group_var = 'control',group_cat = c(2, 1), sampling_dist = T, pop_labels = c('private', 'public'))
plot_t_distribution() function generates sampling distribution (with t-test values) assuming null hypothesis is correct
- syntax and default values:
plot_t_distribution(data_vec1, data_vec2 = NULL, data_df = NULL, data_var = NULL, group_var = NULL, group_cat = NULL, mu = 0, alpha = 0.05, alternative = 'two.sided', plot_title = '', shade_rejection = T, shade_pval = T, stacked = F)
# SINGLE POPULATION
# H0: population mean of coa_grad_res is $29,000
# Ha: population mean of coa_grad_res is NOT $29,000 (two-sided test)
# Verdict: Fail to reject H0
t.test(df_ipeds_sample$coa_grad_res, mu = 29000)
#>
#> One Sample t-test
#>
#> data: df_ipeds_sample$coa_grad_res
#> t = 1.2378, df = 199, p-value = 0.2173
#> alternative hypothesis: true mean is not equal to 29000
#> 95 percent confidence interval:
#> 28405.20 31600.29
#> sample estimates:
#> mean of x
#> 30002.74
plot_t_distribution(data_df = df_ipeds_sample, data_var = 'coa_grad_res', mu = 29000)
#plot_t_distribution(df_ipeds_sample$coa_grad_res, mu = 29000) # same as above
# TWO POPULATIONS
# H0: population mean of tuitfee_grad_res is equal for control=1 and control=2
# Ha: population means are NOT equal
# Verdict: Reject H0
t.test(formula = tuitfee_grad_res ~ control, data = df_ipeds_sample %>% filter(control %in% c(1,2)))
#>
#> Welch Two Sample t-test
#>
#> data: tuitfee_grad_res by control
#> t = -6.7041, df = 116.65, p-value = 0.0000000007599
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -11132.95 -6055.28
#> sample estimates:
#> mean in group 1 mean in group 2
#> 10227.46 18821.58
#t.test(formula = tuitfee_grad_res ~ control, data = df_ipeds_sample, subset = unclass(control) %in% c(1, 2))
# Here, the t-statistics line is off the grid
plot_t_distribution(data_df = df_ipeds_sample, data_var = 'tuitfee_grad_res',group_var = 'control', group_cat = c(1, 2), shade_pval = F)
#plot_t_distribution(data_df = df_ipeds_sample, data_var = 'tuitfee_grad_nres',group_var = 'control', group_cat = c(1, 2), shade_pval = F)
5 Hypothesis testing about two populations
This section explains how to test hypotheses about whether the population mean for one group is different from the population mean for another group
- The approach we introduce is the approach used to estimate causal effects in experiments where there is one treatment group and one control group
- Using data from Tennessee STAR, we will test whether kindergarten students assigned to small class size (treatment) have higher reading scores than kindergarten students assigned to big class size (control)
- The approach we introduce is also the approach used to compare population means of two groups in non experimental settings
- using a random sample of data from IPEDS (each observation is a university), we will test the null hypothesis that \(H_0\) that population mean value of a variable for one group is the same as that of another group (e.g., population mean grad-school tuition+fees at private universities is the same as population mean grad-school tuition+fees at public universities)
5.1 Steps in hypothesis testing about two populations
The steps in testing hypotheses about whether the population mean of a variable differs between two groups are the exact same as the steps for testing hypothesis about the population mean for a single group
- so don’t think of this as learning something new. think of this as applying something you already know, with a few modest variations
General steps in hypothesis testing:
- Hypothesis
- formally state your “null” and “alternative” hypothesis
- usually, you would decide on alpha level (rejection region) here
- Assumptions
- state assumptions that are relied upon by the statistical test you are using to test your hypothesis
- Test statistic
- Using some appropriate statistical analysis, calculate the “test statistic” necessary to test your hypothesis
- p-value (means probability value)
- calculate the probability of observing a test statistic as large or larger as the one you calculated
- Alpha level/rejection region and conclusion
- compare the p-value you observed to the alpha level and make a conclusion about your hypothesis test
5.2 Null and alternative hypothesis
Hypothesis about reading score of score of “small” vs. “big” class size
null hypothesis (\(H_0\))
- \(H_0: \mu_{Y_{{treated}}} = \mu_{Y_{{control}}}\)
- (in words): \(H_0:\) the population mean reading score for kindergarten students assigned to the treatment (small class size), \(\mu_{Y_{{treated}}}\), is the same as the population mean reading score for kindergarten students assigned to the control (big class size) (\(\mu_{Y_{{control}}}\))
Two-sided alternative hypothesis (\(H_a\))
- \(H_0: \mu_{Y_{{treated}}} \ne \mu_{Y_{{control}}}\)
- (in words): \(H_0:\) the population mean reading score for kindergarten students assigned to the treatment (small class size), \(\mu_{Y_{{treated}}}\), is different than the population mean reading score for kindergarten students assigned to the control (big class size) (\(\mu_{Y_{{control}}}\))
5.3 Conceptual understanding [MOST IMPORTANT!]
We will use IPEDS data to provide conceptual understanding of hypothesis tests about comparing two means.
- Why? because for IPEDS we have data on the entire population, which is useful for explaining concepts
- Key to conceputal understanding is realizing that hypothesis tests about comparing two means is conceptually the same as hypothesis tests about the value of a single population mean
Hypothesis about resident grad tuition+fees at private vs. public universities
note: for private universities, resident price is the same as non-resident price
null hypothesis (\(H_0\))
- \(H_0: \mu_{Y_{{priv}}} = \mu_{Y_{{pub}}}\)
- (in words): \(H_0:\) the population mean price of resident tuition+fees at private universities, \(\mu_{Y_{{priv}}}\), is the same as population mean price of resident tuition+fees at public universities (\(\mu_{Y_{{pub}}}\))
Two-sided alternative hypothesis (\(H_a\))
- \(H_0: \mu_{Y_{{priv}}} \ne \mu_{Y_{{pub}}}\)
- (in words): \(H_0:\) the population mean price of resident tuition+fees at private universities, \(\mu_{Y_{{priv}}}\), is not equal to the population mean price of resident tuition+fees at public universities (\(\mu_{Y_{{pub}}}\))
Since IPEDS has data on the whole population, we can plot the population distributiion, distribution of single sample, and the sampling distribution
- note: when testing hypothesis about a single population mean, we were concerned with the sampling distribution of the sample mean, \(\bar{Y}\)
- when testing hypothesis about whether two population means are the same/different, we are concerned with the sampling distribution of the difference between sample means, (\(\bar{Y}_{group2} - \bar{Y}_{group1}\))
plot_distribution(data_df = df_ipeds_pop, data_var = 'tuitfee_grad_res', group_var = 'control',group_cat = c(2, 1), sampling_dist = F, pop_labels = c('Private', 'Public'), plot_title = 'Population distribution') +
plot_distribution(data_df = df_ipeds_sample, data_var = 'tuitfee_grad_res', group_var = 'control',group_cat = c(2, 1), sampling_dist = F, pop_labels = c('Private', 'Public'), plot_title = 'Single sample distribution') + plot_distribution(data_df = df_ipeds_pop, data_var = 'tuitfee_grad_res', group_var = 'control',group_cat = c(2, 1), sampling_dist = T, pop_labels = c('Private', 'Public'), plot_title = 'Sampling distribution') +
plot_layout(ncol = 1)
Usually we cannot create the true sampling distribution because we do not have data on the entire population. So we test hypotheses about equality of two groups by creating the sampling distribution of the difference between sample means, (\(\bar{Y}_{group2} - \bar{Y}_{group1}\)) under the assumption that \(H_0\) is true
- that is, the sampling distribution under the assumption that \(\mu_{Y_{{group2}}} = \mu_{Y_{{group1}}}\) or – equivalently \(\mu_{Y_{{group2}}} - \mu_{Y_{{group1}}}=0\))
plot_distribution(data_df = df_ipeds_pop, data_var = 'tuitfee_grad_res', group_var = 'control',group_cat = c(2, 1), sampling_dist = F, pop_labels = c('Private', 'Public'), plot_title = 'Population distribution') +
plot_distribution(data_df = df_ipeds_sample, data_var = 'tuitfee_grad_res', group_var = 'control',group_cat = c(2, 1), sampling_dist = F, pop_labels = c('Private', 'Public'), plot_title = 'Single sample distribution') +
plot_t_distribution(data_df = df_ipeds_sample, data_var = 'tuitfee_grad_res',group_var = 'control', group_cat = c(2, 1), shade_pval = F, stacked = T) +
plot_layout(ncol = 1)
Just as in hypothesis testing about a single population mean, the value of the t-statistic shows how many standard errors the observed point estimate is away from the null population parameter (i.e., \(\mu_{Y_{{group2}}} - \mu_{Y_{{group1}}}==0\)).
- since the sampling distribution assuming \(H_0\) is normally distributed, it follows the “empirical rule”
- assuming \(H_0\) is true, 95% of point estimates should fall within 2 standard errors (i.e., \(-2<t<2\)) 99% of point estimates would fell within 3 standard errors ((i.e., \(-3<t<3\)))
- above, we got a t-statistic higher than 6. very unlikely to observe this if \(H_0\) is true. therefore, we conclude that \(H_0\) is not true.
5.4 Test statistic
Going back to the Tennessee STAR example, let’s restate \(H_0\) and \(H_a\)
null hypothesis (\(H_0\))
- \(H_0: \mu_{Y_{{treated}}} = \mu_{Y_{{control}}}\)
- (in words): \(H_0:\) the population mean reading score for kindergarten students assigned to the treatment (small class size), \(\mu_{Y_{{treated}}}\), is the same as the population mean reading score for kindergarten students assigned to the control (big class size) (\(\mu_{Y_{{control}}}\))
Two-sided alternative hypothesis (\(H_a\))
- \(H_0: \mu_{Y_{{treated}}} \ne \mu_{Y_{{control}}}\)
- (in words): \(H_0:\) the population mean reading score for kindergarten students assigned to the treatment (small class size), \(\mu_{Y_{{treated}}}\), is different than the population mean reading score for kindergarten students assigned to the control (big class size) (\(\mu_{Y_{{control}}}\))
Recall the general formula for (any) test statistic
\[ test\_statistic = \frac{sample\_estimate - value\_associated\_with\_H_0}{sample\_standard\_error}\]
Formula for test statistic about a single population mean
- \(t = \frac{\bar{Y} - \mu_{Y0}}{\hat{\sigma}_{\bar{Y}}}\)
- where:
- \(\hat{\sigma}_{Y}\) refers to sample standard deviation of variable \(Y\)
- \(n\) refers to sample size
- sample standard error of the sample mean \(= \hat{\sigma}_{\bar{Y}} = \frac{\hat{\sigma}_{Y}}{\sqrt{n}}\)
Formula for test statistic about two population means
\(t = \frac{(\bar{Y}_2 - \bar{Y}_1)- 0}{\hat{\sigma}_{\bar{Y}_2 - \bar{Y}_1}} = \frac{sample\_estimate - value\_associated\_with\_H_0}{sample\_standard\_error}\)
- \(\bar{Y}_2\) is sample mean of group 2
- \(\bar{Y}_1\) is sample mean of group 1
- \(\hat{\sigma}_{\bar{Y}_2 - \bar{Y}_1}\) is the sample standard error of \(\bar{Y}_2 - \bar{Y}_1\)
- Note: doesn’t matter which group is group 1 and which is group 2
- \(\hat{\sigma}_{\bar{Y}_2 - \bar{Y}_1}= \sqrt{\frac{\hat{\sigma}_{Y_1}^2}{n_1}+\frac{\hat{\sigma}_{Y_2}^2}{n_2}}\)
- \(n_1\) is the sample size of group 1
- \(n_2\) is the sample size of group 2
- \(\hat{\sigma}_{Y_1}^2\) is the sample standard deviation of group 1, squared
- \(\hat{\sigma}_{Y_2}^2\) is the sample standard deviation of group 2, squared
Calculating t-statistic for Tennessee STAR example, by hand
df_stark %>% group_by(treatment) %>%
summarize(
n = n(),
n_nonmiss = sum(!is.na(read)),
mean = mean(read, na.rm = TRUE),
sd = sd(read, na.rm = TRUE)
)
#> # A tibble: 2 x 5
#> treatment n n_nonmiss mean sd
#> <dbl> <int> <int> <dbl> <dbl>
#> 1 0 2006 2006 435. 30.9
#> 2 1 1739 1739 441. 32.5
# point estimate - H_0_value
(440.5474 - 434.7323) - 0
#> [1] 5.8151
# standard error
sqrt(30.93590^2/2006 + 32.49738^2/1739)
#> [1] 1.041333
# t-statistic = (point estimate - H_0_value)/(standard error)
(440.5474 - 434.7323)/sqrt(30.93590^2/2006 + 32.49738^2/1739)
#> [1] 5.584283Interpreting values
- point estimate =
5.815- the sample mean reading score for treated (small class) students is 5.815 larger than the sample mean reading score for untreated (large class) students
- standard error =
1.041- on average the point estimate \(\bar{Y}_{treated}-\bar{Y}_{control}\) a single random sample is 1.04 away from the mean of point estimates from an infinite number of random samples
- t-statistic =
5.584- under the assumption that \(H_0\) is true (i.e., \(\mu_{Y_{{treated}}} - \mu_{Y_{{control}}}=0\)) the observed point estimate is 5.584 standard errors away from the value associated with \(H_0\) (i.e.,
0)
- under the assumption that \(H_0\) is true (i.e., \(\mu_{Y_{{treated}}} - \mu_{Y_{{control}}}=0\)) the observed point estimate is 5.584 standard errors away from the value associated with \(H_0\) (i.e.,
Calculating t-statistic for Tennessee STAR example using t.test() function
t.test() function, using the “formula” method
- syntax:
t.test(formula, data, subset, mu=0, alternative = c("two.sided", "less", "greater"))
- selected arguments
formula: follows formformula = y_var ~ x_var- where
y_varis the outcome variable andx_varis the variable that defines the two groups you are comparing
- where
data: data frame that contains variables named informulaalternative: whether you want two-sided or one-sided alternative hypothesis (default istwo.sided)subset: subset of data to test (useful ifx_varhas more than two groups and you want to restrict test to those two groups)mu: value associated with null hypothesis (default is0)
#?t.test
t.test(formula = read ~ treatment, mu = 0, data = df_stark)
#>
#> Welch Two Sample t-test
#>
#> data: read by treatment
#> t = -5.5843, df = 3610.1, p-value = 0.00000002519
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -7.856798 -3.773477
#> sample estimates:
#> mean in group 0 mean in group 1
#> 434.7323 440.54745.5 p-value and conclusion
Definition of p-value (same as for hypothesis test about a single population mean)
- Under the assumption if null hypothesis is true, the p-value is the probability of observing a point estimate as far away from the null hypothesis value as the one we observed
Above, we observed a p-value of less than 0.01, indicating it would be very unlikely to observe the pooint estimate we observed (\(\bar{Y}_{treated}-\bar{Y}_{control}=\) 5.82) under the assumption that \(H_0\) is true
Below, we visually show the sampling distribution assuming \(H_0\) is true
- note that the blue dotted line associated with the t-statistic/p-value far exceeds the red dotted line, which is associated with the threshold for rejection of \(H_0\)
plot_t_distribution(data_df = df_stark, data_var = 'read',group_var = 'treatment', group_cat = c(1, 0), shade_pval = F)
Let’s use an alpha level of .05 to determine whether to reject \(H_0\)
Conclusion:
- The p-value of 0.00 is less than the alpha level of
.05. Therefore, we reject \(H_0\) and accept \(H_a\) - In words, the population mean reading score for kindergarten students assigned to the treatment (small class size), \(\mu_{Y_{{treated}}}\), is different than the population mean reading score for kindergarten students assigned to the control (big class size) (\(\mu_{Y_{{control}}}\))
- furthermore, we can say that the population mean reading score for students in the treatment is larger than the population mean reading score for students in the control group.
References
Cabrera, N. L., Milem, J. F., Jaquette, O., & Marx, R. (2014). Missing the (student achievement) forest for all the (political) trees: Empiricism and the mexican american studies controversy in tucson. American Educational Research Journal, 51(6), 1084–1118.