[4.5 points possible; 0.5 extra credit points]

Question 0 [0 points]

Require tidyverse and load the sc_debt.Rds data by assigning it to an object named df.

require() # Load tidyverse

## Loading required package:

df <- read_rds() # Load the dataset directly from github

## Error in read_rds(): could not find function "read_rds"

Question 1 [0.25 points]

Which school has the lowest admission rate (adm_rate) and which state is it in (stabbr)?

df %>% 
  arrange() %>% # Arrange by the admission rate
  select() # Select the school name, the admission rate, and the state

## Error in df %>% arrange() %>% select(): could not find function "%>%"

Write answer here

Question 2 [0.25 points]

Which are the top 10 schools by average SAT score (sat_avg)?

df %>%
  arrange() %>% # arrange by SAT scores in descending order
  select() %>% # Select the school name and SAT score
  print() # Print the first 12 rows (hint: there is a tie)

## Error in df %>% arrange() %>% select() %>% print(): could not find function "%>%"

Write answer here

Question 3 [0.25 points]

Create a new variable called adm_rate_pct which is the admissions rate multiplied by 100 to convert from a 0-to-1 decimal to a 0-to-100 percentage point.

df <- df %>% # Use the object assignment operator to overwrite the df object
  mutate() # Create the new variable adm_rate_pct

## Error in df %>% mutate(): could not find function "%>%"

Question 4 [0.25 points]

Calculate the average SAT score and median earnings of recent graduates by state.

df %>%
  group_by() %>% # Calculate state-by-state with group_by()
  summarise(sat_avg = , # Summarise the average SAT
            earn_avg = ) # Summarise the average earnings

## Error in df %>% group_by() %>% summarise(sat_avg = , earn_avg = ): could not find function "%>%"

Extra Credit [0.5 points]

Plot the average SAT score (x-axis) against the median earnings of recent graduates (y-axis) by school, and add the line of best fit. What relationship do you observe? Why do you think this relationship exists?

# INSERT CODE HERE

Write answer here

Question 5 [1.5 points]

Research Question: Do students who graduate from smaller schools (i.e., schools with smaller student bodies) make more money in their future careers? Before looking at the data, write out what you think the answer is, and explain why you think so.

Write a few sentences here.

Based on this research question, what is the outcome / dependent / \(Y\) variable and what is the explanatory / independent / \(X\) variable? Create the scatterplot of the data based on this answer, along with a line of best fit. Is your answer to the research question supported?

df %>%
  ggplot(aes(x = , # Put the explanatory variable on the x-axis
             y = )) +  # Put the outcome variable on the y-axis
  geom_point() + # Create a scatterplot
  geom_smooth() + # Add line of best fit
  labs(title = '', # give the plot meaningful labels to help the viewer understand it
       x = '',
       y = '')

## Error in df %>% ggplot(aes(x = , y = )): could not find function "%>%"

Write a few sentences here.

Question 6 [1 points]

Does this relationship change by whether the school is a research university? Using the filter() function, create two versions of the plot, one for research universities and the other for non-research universities.

df %>%
  filter() %>% # Filter to non-research universities
  ggplot(aes(x = , # Put the explanatory variable on the x-axis
             y = )) +  # Put the outcome variable on the y-axis
  geom_point() + # Create a scatterplot
  geom_smooth() + # Add line of best fit
  labs(title = '', # give the plot meaningful labels to help the viewer understand it
       subtitle = '', 
       x = '',
       y = '')

## Error in df %>% filter() %>% ggplot(aes(x = , y = )): could not find function "%>%"

df %>%
  filter() %>% # Filter to research universities
  ggplot(aes(x = , # Put the explanatory variable on the x-axis
             y = )) +  # Put the outcome variable on the y-axis
  geom_point() + # Create a scatterplot
  geom_smooth() + # Add line of best fit
  labs(title = '', # give the plot meaningful labels to help the viewer understand it
       subtitle = '', 
       x = '',
       y = '')

## Error in df %>% filter() %>% ggplot(aes(x = , y = )): could not find function "%>%"

Question 7 [1 point]

Instead of creating two separate plots, color the points by whether the school is a research university. To do this, you first need to modify the research_u variable to be categorical (it is currently stored as numeric). To do this, use the mutate command with ifelse() to create a new variable called research_u_cat which is either “Research” if research_u is equal to 1, and “Non-Research” otherwise.

df <- df %>%
  mutate(research_u_cat = ifelse()) # Create a labeled version of the research_u variable

## Error in df %>% mutate(research_u_cat = ifelse()): could not find function "%>%"

df %>%
  ggplot(aes(x = , # Put the explanatory variable on the x-axis
             y = , # Put the outcome variable on the y-axis
             color = )) + # Color the points by the new variable you created above
  geom_point() + # Create a scatterplot
  geom_smooth() + # Add line of best fit
  labs(title = '', # give the plot meaningful labels to help the viewer understand it
       x = '',
       color = '',
       y = '')

## Error in df %>% ggplot(aes(x = , y = , color = )): could not find function "%>%"

[2.5 points; +0.5 extra credit point available]

For part 2 of this problem set, we will be using the MI2020_ExitPoll.Rds file from the course github page.

Question 8 [0 points]

Require an additional package called labelled (remember to install.packages("labelled") if you don’t have it yet) and load the MI2020_ExitPoll.Rds data to an object called MI_raw. (Tip: use the read_rds() function.)

require()

## Loading required package:

MI_raw <- read_rds('') 

## Error in read_rds(""): could not find function "read_rds"

What is the unit of analysis in this dataset? How many variables does it have? How many observations?

Write answer here

Question 9 [0.5 points]

This has too much information that we don’t care about. Create a new object called MI_clean that contains only the following variables:

• AGE10
• SEX
• PARTYID
• EDUC18
• PRSMI20
• QLT20
• LGBT
• BRNAGAIN
• LATINOS
• QRACEAI
• WEIGHT

and then list which of these variables contain missing data recorded as NA. How many respondents were not asked certain questions?

MI_clean <- MI_raw %>% 
  select() # Select the requested variables

## Error in MI_raw %>% select(): could not find function "%>%"

summary() # Identify which have missing data recorded as NA

## Error in summary.default(): argument "object" is missing, with no default

Write answer here

Question 10 [0.5 points]

Are there unit non-response data in the PRSMI20 variable? If so, how are they recorded? What about the PARTYID variable? How many people refused to answer both of these questions?

MI_clean %>%
  count() # Tip: use count() function to look at your variables.

## Error in MI_clean %>% count(): could not find function "%>%"

Write answer here.

Question 11 [0.5 points]

Let’s create a new variable called preschoice that converts PRSMI20 to a character. To do this, install the labelled package if you haven’t already, then use the to_character() function from the labelled package. Now count() the number of respondents who reported voting for each candidate. How many respondents voted for candidate Trump in 2020? How many respondents refused to tell us who they voted for?

MI_clean <- MI_clean %>%
  mutate(preschoice = ) # Convert to character

## Error in MI_clean %>% mutate(preschoice = ): could not find function "%>%"

MI_clean %>%
  count()

## Error in MI_clean %>% count(): could not find function "%>%"

Write answer here

Question 12 [1 point]

What proportion of women supported Trump?

# Women Trump supporters
MI_clean %>%
  drop_na() %>% # Drop any missing values for preschoice
  filter() %>% # Filter to only women
  count() %>% # Count the number of women who supported each candidate
  mutate(share = ) # Calculate the proportion of women who supported Trump

## Error in MI_clean %>% drop_na() %>% filter() %>% count() %>% mutate(share = ): could not find function "%>%"

# Alternative approach
MI_clean %>%
  drop_na() %>% # Drop any missing values for preschoice
  mutate(trumpSupp = ifelse()) %>% # Create "dummy" variable for whether the person voted for Trump or not that is either 1 (they voted for Trump) or 0
  group_by() %>% # Group by gender
  summarise(share = mean(trumpSupp)) # Calculate proportion who supported Trump

## Error in MI_clean %>% drop_na() %>% mutate(trumpSupp = ifelse()) %>% group_by() %>% : could not find function "%>%"

Write answer here.

Extra Credit [0.5 point]

Among women, which age group sees the highest support for Trump? To answer, you will need to calculate the proportion of women who supported Trump by age-group to determine which age-group had the highest Trump support among women. You will need to clean the AGE10 variable before completing this problem, just like we did with the PRSMI20 variable. Call the new variable “Age”. HINT: to make your life easier (and not write a 10-level nested ifelse() function), try asking ChatGPT for help with this prompt: “I have a labelled variable in R that I want to convert to text. How can I do this?”

# Insert code here.

Write answer here