## Problemset Title ---- ## [ PROJ ] < Problem set 3 > ## [ FILE ] < lastname_firstname_problemset3.R > ## [ AUTH ] < Your name / GitHub handle > ## [ INIT ] < Date you started this problem set > ## Overview ---- # 34 points # The aim of this problem set is to give you practice completing data management tasks associated # with filtering/isolating observations, sorting observations, and selecting variables. This can # be done using the `filter()`, `arrange()`, and `select()` functions from the `dplyr` package, # which is part of the `tidyverse`. # For the following questions, you'll be asked to complete the same task using `dplyr` functions # in multiple ways, with and without pipes. We want you to understand that there are several ways # to complete the same task, and we want you to practice completing it in different ways. # For the questions asking you to filter, we recommend that you write down the conditions the # question asks for on a scratch piece of paper before you write the code. This will help you # visualize the process before you write the code. # IMPORTANT: Your script is graded by running it from top to bottom. If any line produces an # error, every question after that line can lose points. Before submitting, restart R # (Session > Restart R) and run the whole script to make sure it finishes without errors. ## Question 1: Load and inspect `df_event` dataset ---- # Q1.1 ---- # 1 point # In the space below, complete the following: # 1. Load the `tidyverse` library using `library()`. # 2. Use the `load()` and `url()` functions to download the `df_event` dataframe from this url: # https://github.com/anyone-can-cook/rclass1/raw/master/data/recruiting/recruit_event_somevars.RData # Hint: `url()` goes inside `load()`, like this: load(url("paste_the_url_here")) # After it runs, `df_event` should appear in your Environment pane (18,680 rows, 32 columns). # Each row in `df_event` represents a recruiting visit # Q1.2 ---- # 2 points # Inspect the `df_event` dataframe in the space below: # a. Use `names()` to identify the column names in the dataframe # b. Use `typeof()` to show the data type of the `event_state` column # c. Use `str()` to show the structure of the `med_inc` column # d. Use `table()` to show the categorical values of the `event_type` column # Hint: Parts b-d ask about a single column. Use `$` to pull out a column from a # dataframe, e.g. df_event$column_name (df_event[["column_name"]] also works). # Assign each of the four lines of code above to its own object, named `q1_2a`, `q1_2b`, # `q1_2c`, and `q1_2d` (matching letters a-d above). # Note: `str()` prints its output to the console and does not return anything, so `q1_2c` # will show up as NULL in your Environment. That is expected! q1_2a <- q1_2b <- q1_2c <- q1_2d <- ## Question 2: Filtering/isolating observations ---- # Tidyverse functions can be done using multiple approaches with and without the "%>%" pipe # operator. Below is an example of using each method to obtain the total number of recruiting # visits to California from the `df_event` dataframe: # tidyverse filter() function without pipes nrow(filter(df_event, event_state == 'CA')) # tidyverse filter() function using pipes df_event %>% filter(event_state == 'CA') %>% nrow() # Your job is to create a filter that satisfies a few conditions at once. In the next # question you will do this in steps. Then you will put all the steps together into # one single `filter()` call. Later questions will ask you to perform multiple filters at once. # Tip: After each step, check how many rows your new dataframe has using `nrow()` or the # Environment pane. Each step should have fewer (or equal) rows than the step before it. # Q2.1a ---- # 1 point # Filter recruiting events in `df_event` by the University of Massachusetts-Amherst # (`univ_id`: `166629`). Assign this data to a new object named `q2_1a`. # Hint: `univ_id` is a numeric variable, so the value does not need quotes. q2_1a <- # Q2.1b ---- # 1 point # Filter recruiting events from the `q2_1a` dataframe you just created to get # any observations for events held at an OUT-OF-STATE high school (a high school in a # different state than the university). # Hint: Use the variables `event_state` (the state where the event was held) and # `instst` (the state where the university is located). An event is in-state when these # two variables are equal, and out-of-state when they are NOT equal. You can compare two # variables to each other directly, just like you compare a variable to a value. # Assign the new dataframe to a new object named `q2_1b`. q2_1b <- # Q2.1c ---- # 1 point # Filter recruiting events from the previous dataframe `q2_1b` to get observations # of any events at public high schools. Use the `event_type` variable for this. # Remember to inspect the `event_type` variable before filtering and make sure you are # using the right spelling, case, and data type (character variables require being in quotes!). # Your `table()` output from Q1.2 shows exactly how each category is spelled. # Assign the data to a new object named `q2_1c`. q2_1c <- # Q2.1d ---- # 1 point # Filter recruiting events from the `q2_1c` dataframe. Filter for observations # where the median household income of the zip code (`med_inc`) is greater than or equal # to $100,000. # Hint: Type numbers without commas or dollar signs (e.g., 100000, not $100,000). # Assign this to a new object named `q2_1d`. q2_1d <- # Q2.1e ---- # 1 point # Put all the previous steps together in a single `filter()` call without using %>% pipes. # Filter from `df_event` for all observations meeting the same criteria again: # 1. Visits from UMass-Amherst # 2. Out-of-state (in a different state than the institution) # 3. To public high schools # 4. Where median household income was $100,000 or higher. # Hint: Notice how many observations/rows were left in df `q2_1d`. Your new dataframe # should have the same number of rows. # Assign the data to a new object named `q2_1e`. # tidyverse filter() function without pipes q2_1e <- # Q2.2 ---- # 3 points # Do the exact same filtering as Q2.1e, but this time use `%>%` pipes. # It should have the same number of rows as `q2_1e`. # Assign the resulting dataframe (not the row count) to an object named `q2_2`. # tidyverse filter() function with pipes q2_2 <- # The following question will be in multiple parts again, like question 2.1. # The conditions can be confusing to address all at once, so you will filter for the # conditions given in single steps before putting all the code together. # Q2.3a ---- # 1 point # Without using `%>%` pipes, filter for observations of recruiting events # by the University of South Carolina-Columbia (`univ_id`: `218663`) # OR by the University of Alabama (`univ_id`: `100751`). # Hint: Separating conditions with a comma means AND, so writing # `univ_id == 218663, univ_id == 100751` asks for rows where `univ_id` is both values at # the same time, which is impossible, so you would get 0 rows. # Instead you need an OR condition, using either the `|` operator or the `%in%` operator. # Assign this to an object named `q2_3a`. q2_3a <- # Q2.3b ---- # 1 point # Filter the `q2_3a` dataframe to get observations that meet BOTH of these conditions: # 1. An in-state event (same state as the university). # 2. At a 2-year college (check the `event_type` categories for the exact spelling). # Hint: Separating conditions with a comma creates an AND condition. # You can also join two conditions together with the `&` symbol. Either works here. # Assign this to an object named `q2_3b`. q2_3b <- # Q2.3c ---- # 1 point # Go back to `q2_3a`, which was visits by South Carolina and Alabama. Without using `%>%` pipes, # filter `q2_3a` for observations that meet EITHER of these: # - in-state AND 2-year college (the same two conditions as Q2.3b), # - OR the event's zip code has a population greater than 10,000 (`pop_total`). # In words: keep events where USCC and Alabama went to in-state 2-year colleges, _or_ # where they went to locales with populations greater than 10,000 people. # Note the order of precedence: `&` is evaluated before `|`, just like multiplication is # evaluated before addition. To make your intent clear, you may wrap the AND part in # parentheses: (condition1 & condition2) | condition3 # Important: Use `&` (not a comma) for the AND part here, because a comma would apply # to the whole OR statement. # Once you're sure your code is correct, assign it to an object named `q2_3c`. # tidyverse filter() function without pipes q2_3c <- # Q2.4 ---- # 3 points # Do the exact same filtering as Q2.3c (starting from `df_event` or from `q2_3a`), # but this time use `%>%` pipes. It should have the same number of rows as `q2_3c`. # Assign it to an object named `q2_4`. # tidyverse filter() function with pipes q2_4 <- ## Question 3: Sorting observations ---- # Q3 ---- # 3 points # Create a new dataframe named `q3` that contains the events in `df_event` sorted by: # 1. Ascending `univ_id` # 2. Ascending `event_date` # 3. Ascending `event_state` # 4. Descending `pct_white_zip` # 5. Descending `med_inc` # all in that order, inside a single `arrange()` call. Use the tidyverse `arrange()` function. # Hint: `arrange()` sorts ascending by default. Wrap a variable in `desc()` to sort descending. # Do not use `select()` here; `q3` should keep all 32 columns. # You can preview the first 10 rows of the `q3` dataframe using `head()` if you wish to check your work. # tidyverse using arrange() q3 <- ## Question 4: Selecting variables ---- # Q4.1 ---- # 1 point # Create a new dataframe named `q4_1`. # Use the tidyverse `select()` function without `%>%` pipes to select the following columns # `univ_id`, `event_date`, `event_type`, `zip`, and `med_inc` from `df_event`, in that order. # tidyverse select() without pipes q4_1 <- # Q4.2 ---- # 1 point # Use the `names()` function to show what columns (variables) are present # in `q4_1`. Assign this line of code to an object named `q4_2`. q4_2 <- # Q4.3 ---- # 1 point # Do the exact same selection as Q4.1 but this time use `%>%` pipes. # Assign this to an object named `q4_3`. q4_3 <- # Q4.4 ---- # 1 point # Use the `names()` function to show what columns (variables) are present # in `q4_3`. Assign this code to an object named `q4_4`. q4_4 <- ## Question 5: Additional practice with `df_school_all` dataframe ---- # Q5.1 ---- # 1 point # Use the `load()` and `url()` functions to download the `df_school_all` dataframe from the url: # https://github.com/anyone-can-cook/rclass1/raw/master/data/recruiting/recruit_school_allvars.RData # Each row in `df_school_all` represents a high school (includes both public and private schools) # There are columns (e.g., `visits_by_100751`) indicating the number of times a university # visited that high school # The variable `total_visits` identifies the number of visits the high school received from # all (16) public research universities in this data collection sample. # Q5.2 ---- # 1 point # Use `table()` to show the categorical values of the `school_type` variable. # Assign this code to an object named `q5_2`. q5_2 <- # Q5.3 ---- # 1 point # Without using `%>%` pipes, use the tidyverse functions `arrange()` and `select()` to do the following: # 1. Sort `df_school_all` descending by `total_visits` # 2. Select the following variables, in this order: `name`, `state_code`, `city`, `school_type`, # `total_visits`, `med_inc`, `pct_white`, `pct_black`, `pct_hispanic`, `pct_asian`, `pct_amerindian` # Hint: Without pipes, you can nest one function inside the other. The inner function runs # first, and its output becomes the first argument of the outer function: # outer_function(inner_function(df, ...), ...) # Assign this dataframe to a new object named `q5_3`. q5_3 <- # Q5.4 ---- # 1 point # Get the first 10 rows of the `q5_3` dataframe using `head()`, which represents the top 10 # most visited schools by the 16 universities. # Hint: `head()` shows 6 rows by default. Use the `n` argument to change that. # Assign this data to a new object named `q5_4`. q5_4 <- # Q5.5 ---- # 1 point # Do the same selecting and arranging as Q5.3, this time using `%>%` pipes. # Assign this dataframe to an object named `q5_5`, which should have the exact same # dimensions as `q5_3`. q5_5 <- # Q5.6 ---- # 1 point # Get the top ten observations from `q5_5`, using `head()`. # Assign this dataframe to an object named `q5_6`. q5_6 <- # Q5.7 ---- # 1 point # Starting from `df_school_all`, get the top 10 most visited _public_ high schools in California. # Use `filter()` to keep only public high schools in California, then sort and select the # same way as Q5.3, and keep the first 10 rows. You may use pipes or not. # Hint: Check the exact values of `school_type` from Q5.2. California's `state_code` is "CA". # The result should have 10 rows and the same 11 columns as `q5_3`. # Assign this data to a new object named `q5_7`. q5_7 <- # Q5.8 ---- # 1 point # Do the same as Q5.7, but this time get the top 10 most visited _private_ high schools # in California. # Assign this data to a new object named `q5_8`. q5_8 <- ## Create a GitHub issue ---- # 2 points # Go to the class repository https://github.com/anyone-can-cook/rclass1_student_issues_f26 and create a new issue. # Refer to rclass1 student issues readme https://github.com/anyone-can-cook/rclass1_student_issues_f26/blob/main/README.md # for instructions on how to post questions or reflections. # You are also required to respond to at least one issue posted by another student. # Replace the empty quotes below with the URL to your new issue, as a character object. # Make sure the URL stays inside the quotes. Copy it from your browser's address bar, e.g.: # "https://github.com/anyone-can-cook/rclass1_student_issues_f26/issues/12" issue <- "" # Replace the empty quotes below with the URL to your response to another student's issue. # To get the link to your comment, click the "..." menu on your comment and choose "Copy link". # Make sure the URL stays inside the quotes. reply <- "" ## Submit problem set ---- # Use this naming convention "lastname_firstname_problemset#.R" for your R script # (e.g. jaquette_ozan_problemset3.R).