## Problemset Solutions ---- ## [ PROJ ] < Problem set 1 > ## [ FILE ] < solutions_problemset1.R > ## [ AUTH ] < Problem set 1 / @problemsetsolutions > ## [ INIT ] < 10/02/2026 > ## Overview ---- # Welcome to Introduction to Programming and Data Management using R! # This problem set is intended to give you some practice becoming familiar # with using R. In this problem set, we ask you to create an R project, # load and investigate an R data frame stored as a .csv file, apply some # basic functions to atomic vectors, and create a GitHub issue. # Question 1: Creating an R project & R script ---- # Q1.1 Create an R project ---- # 1 point # Create a folder where you want to save files associated with problem set 1. # Make sure to name the folder "ps1". # For instance, the file directory/structure could be # EDUC260a >> problem_sets >> problemset1. # 1 point # In RStudio, click on "File" >> "New Project" >> "Existing Directory" >> "Browse". # Browse to find and select your problem set 1 folder. # Click on "Create Project". # An R project file has the extension ".Rproj". # The name of the file should be "ps1.Rproj" (the name is based on the folder/directory name). # Warning: Creating a new R project can close any files currently open in R Studio # including this R script! Once you create the R project, you should re-open this `problemset1.R` file # inside the ps1 R project. You can check your current open project in the top right corner of Rstudio # (There should be a button with a pull-down menu that says "ps1") # Q1.2 Save this R script ---- # 1 point # If you have not already saved this file in the ps1 folder: # In RStudio, click on "File" >> "Save As ..." # Make sure to save this R script in the ps1 folder you just created # and put your name in the filename: "lastname_firstname_problemset1.R" (e.g. jaquette_ozan_problemset1.R) ## Confirm that Rproj and R script files were created # Q1.3 ---- # 1 point each # Let's look at our files and workding directory. # Run `getwd()` and then run `list.files()` below this comment. You should see a path to # the ps1 folder you created, and you should then see the .R script and the ps1.Rproj file. # (You might have other files saved here eventually. That's ok.) getwd() list.files() # Share your answer: Assign the outputs of the code above to two new objects named # `q1_3a` and `q1_3b`. Like this: q1_3a <- "/home/user/Documents/rclass/ps1" or q1_3a <- getwd(). # Either copy-pasting or using the functions works as long as we get two strings in both objects. q1_3a <- "/Users/jadasims/Desktop/educ260/problem_sets/ps1" #or q1_3a <- getwd() q1_3b <- c("calcgrades_ps1.R", "ps1_tests.R","recruit_ps1.csv","submissions") #or q1_3b <- list.files() ## Question 2: Load .csv file and then investigate the data frame ---- # Q2.1. ---- # 1 point # This question asks you to load a dataset using the `read.csv()` function # and a link to the dataset. You can load the data two ways: # 1) You can download a .csv file into your "ps1" folder (using R code `download.file` # or manually from a browser) and then use `read.csv()` to load from the file path. # 2) You can specify the URL inside the read.csv() function. # Remember to assign the csv data to an object named `df_recruiting` df_recruiting <- read.csv("https://raw.githubusercontent.com/anyone-can-cook/rclass1/refs/heads/master/data/recruiting/recruit_ps1.csv") # Q2.2. ---- # 1 point # How many rows and how many columns does `df_recruiting have`? # Assign both row and column values to an object named `q2_2` as a numeric vector # like this: `q2_2 <- c(x, y)`. # Note: Answering questions with numeric and character vectors is a big part of these assignments! #exploring data for answer nrow(df_recruiting) ncol(df_recruiting) #answering the question q2_2 <- c(6,6) # Q2.3. ---- # 1 point # Use the `typeof()` function to investigate the type of data frame `df_recruiting`. # Assign the output (with code, or copy and paste, or write it out, so long as it is literal output) # to an object named `q2_3`. #exploring data for answer typeof(df_recruiting) #answering the question q2_3 <- "list" #or q2_3 <- typeof(df_recruiting) # Q2.4. ---- # 1 point # Apply the `length()` function to the data frame `df_recruiting`. Assign this output # to an object named `q2_4` (Again, you may assign the code, or the output, so long as it is equal to the literal output). #exploring data for answer length(df_recruiting) #answering the question q2_4 <- 6 #or q2_4 <- length(df_recruiting) # Q2.4a # 1 point # `df_recruiting` has the same number of rows and columns, and this number is equal to # the answer you got from Q2.4. Which of these data frame dimensions, columns or rows, # does `length()` actually measure? Assign your answer as a character value to # an object named `q2_4a`. Don't forget to use quotes for word/character type answers! q2_4a <- "column" # Q2.5. ---- # 1 point # Use the `str()` function to investigate the structure of the data frame `df_recruiting` # and find the "class" of data structure that `df_recruiting` possesses. Corroborate with the # class() function. Now assign the data class (use the exact characters!) to an object named # `q2_5` as character. #exploring data for answer str(df_recruiting) class(df_recruiting) #answering the question q2_5 <- "data.frame" #or q2_5 <- class(df_recruiting) # Q2.6. ---- # 1 point # Use the `names` function to list the names of the elements (variables) within `df_recruiting`. # Assign these names to an object named `q2_6`. Again, you can assign with the code or type it out like `c("a","b"...)` # but it must match the output strings exactly. #exploring data for answer names(df_recruiting) #answering the question q2_6 <- c("state_code","school_type","ncessch","name","address","city") #or q2_6 <- names(df_recruiting) # Q2.7. ---- # 1 point total # Wrap the code from Q2.6 above, "names(data_frame_name)", inside the `typeof()` function. Then use the # `length()` function, and finally the `str()` function to inspect the data frame's names values three different ways. typeof(names(df_recruiting)) length(names(df_recruiting)) str(names(df_recruiting)) # Assign each of these three lines of code to three objects names `q2_7a`, `q2_7b`, and `q2_7c`. # Remember to assign them in the order they appear in the question! q2_7a <- typeof(names(df_recruiting)) q2_7b <- length(names(df_recruiting)) q2_7c <- str(names(df_recruiting)) # Question 3: Applying basic functions to atomic vectors ---- # Q3.1. ---- # 1 point # Create an atomic vector object named `age` with the following values: 3, 6, 41, 43. age <- c(3,6,41,43) # Q3.2. ---- # Apply the `typeof()`, `length()`, and `str()` functions to the object `age`. # When you know the type length and structure, assign the three answers to # an object named `q3_2` using `c()` in this order: # The type of `age` as a character. # The length of `age` as a number. # The class of `age` as a character. typeof(age) length(age) str(age) q3_2 <- c("double", 4, "numeric") # Q3.2a # 1 point # You stored the length of `age` as a number (without quotes) to `q3_2` in the question above. # Now inspect it within the q3_2 object. What type/class is it inside of the `q3_2` object? # Assign your answer in one word to an object named `q3_2a`. str(q3_2) q3_2a <- "character" # Q3.3. --- # 1 point # Apply the `sum()` function to `age`. What is the sum? Assign it to an object named # `q3_3`. sum(age) q3_3 <- 93 #or q3_3 <- sum(age) # Q3.4. --- # 1 point # This question is in three parts. You will assign those answers, in order, # as character elements in a vector as the final answer. # Run the `?sum` commnd. # 1) # Aside from `...`, what is the named argument in the sum() function? # 2) What is the default value of this argument? # 3) Yes or no: would different values of this argument change the output of `sum(age)`? # Assign your three answers to an object named `q3_4`. ?sum q3_4 <- c("na.rm", "false", "no") # Q3.5. ---- # 1 point # Create a new object `age2` with the following values: 3, 6, 41, 43, NA. # Then calculate the sum of `age2` using the argument `na.rm = FALSE` # and then calculate the sum using the argument `na.rm = TRUE`. age2 <- c(3,6,41,43,NA) sum(age2, na.rm = FALSE) sum(age2, na.rm = TRUE) # To an object named `q3_5` assign answers to the following as a character vector # (i.e. wrap your answers in quotes) in the order of the questions: # Are the two outputs of `sum(age2)` different? # What was the sum of values when `na.rm = FALSE`? # Does `na.rm` drop NA values before operating, or ignore NA values while operating? (choose "drop", or "ignore") q3_5 <- c("yes", "NA", "drop") # Q3.6. ---- # 1 point # Create a vector `tf` using the following code: `tf <- c(TRUE,FALSE,TRUE,FALSE,TRUE)`. # Then apply the `typeof()`, `length()`, and `str()` functions to the object `tf`. tf <- c(TRUE,FALSE,TRUE,FALSE,TRUE) typeof(tf) length(tf) str(tf) # Based on this output, what is the data type of `tf`? How many elements does it contain? # Assign both answers, in order and as characters, to an object named `q3_6`. q3_6 <- c("logical", "5") # Q3.7. ---- # 1 point # Apply the `sum()` function to the `tf` object, using the option to remove `NA` # values prior to calculation. sum(tf, na.rm= TRUE) # What numeric value do mathematical calculations in R assign to `TRUE` values? # What do they assign to `FALSE` values? # Assign answers as a numeric vector to an object named `q3_7`. q3_7 <- c(1,0) # Q3.8. ---- # 1 point each # Run the ?mean command and read the help file. In an object named `q3_8a` # assign a character vector with the three main argument names of the `mean()` function # in the order that they appear. # (do not include the generalized `...` argument) ?mean q3_8a <- c("x", "trim","na.rm") # Of the last two argument names for `mean()`, what are their respective # default argument values? Assign your answers as a character vector to an object named # `q3_8b`. q3_8b <- c("0", "FALSE") # When using a function, R requires you to type the values you assign to each # argument (unless you choose the default value). However, the argument names # are almost always optional. Even though it takes a bit more time, I usually # like typing in both argument names and argument values because it encourages # you to be more conscious about what values you are assigning to which argument, # especially when a function is unfamiliar. # Below, use the `mean()` function to calculate the mean of object `tf`. # In your function call, include the argument names (e.g. `x = `) and the argument value # for each corresponding argument name, even if it is the default. mean(tf) mean(x=tf, trim = 0, na.rm = FALSE) mean(tf,trim=0.5, na.rm = TRUE) mean(tf, 0, FALSE) # Then run the `mean` function again but without typing any argument names (e.g. `x = `), # only argument values. ## 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. # Create an object named `issue` below and assign it the URL to your new issue, as a character object. # Make sure the URL is in quotes. issue <- "https://github.com/anyone-can-cook/rclass1/blob/master/index.html" # Create an object named `reply` below and assign it the URL to your response to another issue, as a character object. # Make sure the URL is in quotes. reply <- "https://github.com/anyone-can-cook/rclass1/blob/master/index.html" ## Submit problem set on Canvas ---- # Use this naming convention "lastname_firstname_ps#" for your R script (e.g. jaquette_ozan_problemset2.R).