Assignment 1

Author

Anna StPierre

We will be using USM peer graduation rate comparison data from the UMS Dashboard.

Exercises

There are eleven exercises in this assignment.

Exercise 1

Load the tidyverse family of packages and read the graduation.csv file into a tibble called graduation. Suppress the message generated from loading tidyverse. The file is available at https://jsuleiman.com/datasets/graduation.csv

Exercise 1 has already been completed for you. It won’t be for future assignments.

library(tidyverse)
graduation <- read_csv("https://jsuleiman.com/datasets/graduation.csv")

Exercise 2

glimpse() the graduation data in the code cell below.

glimpse(graduation)

Rows: 77
Columns: 5
$ year              <dbl> 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2012, 2013…
$ peer_institution  <chr> "University of Southern Maine", "University of South…
$ four_yr_grad_rate <dbl> 17, 10, 9, 10, 13, 13, 14, 11, 9, 10, 9, 9, 11, 13, …
$ retention         <dbl> 64, 67, 65, 64, 63, 68, 70, 74, 80, 72, 72, 68, 77, …
$ six_yr_grad_rate  <chr> "3500.00%", "3300.00%", "3000.00%", "3400.00%", "320…

Describe any potential issues with sixyrgradrate in your narrative for this exercise.

Exercise 3

Modify graduation so the data type for sixyrgradrate is a dbl format. Overwrite the existing tibble.

Exercise 3 has already been completed for you since we haven’t fully covered how to do this. The mutate() function should look familiar but parse_number() is new.

graduation <- graduation |> 
  mutate(six_yr_grad_rate = parse_number(six_yr_grad_rate)/100)
glimpse(graduation)

Rows: 77
Columns: 5
$ year              <dbl> 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2012, 2013…
$ peer_institution  <chr> "University of Southern Maine", "University of South…
$ four_yr_grad_rate <dbl> 17, 10, 9, 10, 13, 13, 14, 11, 9, 10, 9, 9, 11, 13, …
$ retention         <dbl> 64, 67, 65, 64, 63, 68, 70, 74, 80, 72, 72, 68, 77, …
$ six_yr_grad_rate  <dbl> 35, 33, 30, 34, 32, 33, 34, 39, 37, 36, 38, 34, 38, …

Exercise 4

List the peer schools for University of Southern Maine without duplicates and without listing University of Southern Maine. For these exercises, if there is no code chunk (like this one) position your cursor in the whitespace above the next exercise heading and select Insert -> Executable Cell -> R

If there is no whitespace, position your cursor to the left of the E in Exercise and hit enter to create a blank line.

graduation |>

filter(peer_institution != “University of Southern Maine”) |> distinct(peer_institution) peer_institution 1 University of Michigan-Flint 2 University of Arkansas at Little Rock 3 Texas Woman’s University 4 Texas A & M University-Kingsville 5 Salem State University 6 North Carolina Central University 7 Murray State University 8 Fayetteville State University 9 Chicago State University 10 California State University-Dominguez Hills

Exercise 5

List the top two six_yr_grad_rate and the instution for each. Filter by the most recent year in the dataset.

recent_year <- max(graduation$year)

graduation |> filter(year == recent_year) |> arrange(desc(six_yr_grad_rate)) |> slice_head(n = 2) |> select(peer_institution, six_yr_grad_rate)

peer_institution six_yr_grad_rate 1 Salem State University 5200.00% 2 Murray State University 4900.00% ### Exercise 6

List the top two four_yr_grad_rate and the instution for each. Filter by the most recent year in the dataset.

graduation |> filter(year == recent_year) |> arrange(desc(four_yr_grad_rate)) |> slice_head(n = 2) |> select(peer_institution, four_yr_grad_rate) Salem State University 28 2 Murray State University 25 ### Exercise 7

Use the arrange function arrange(desc(six_yr_grad_rate)) to show the six_yr_grad_rate for the most recent year in the dataset? In the narrative below the code, mention where USM ranks in the list. ranking <- graduation |> filter(year == recent_year) |> arrange(desc(six_yr_grad_rate)) |> mutate(rank = row_number())

usm_rank <- ranking |> filter(peer_institution == “University of Southern Maine”) |> select(peer_institution, rank)

ranking year peer_institution four_yr_grad_rate retention 1 2018 Salem State University 28 74 2 2018 Murray State University 25 79 3 2018 North Carolina Central University 20 82 4 2018 California State University-Dominguez Hills 5 78 5 2018 Texas Woman’s University 21 73 6 2018 University of Michigan-Flint 13 72 7 2018 University of Southern Maine 14 70 8 2018 Fayetteville State University 17 69 9 2018 Texas A & M University-Kingsville 15 67 10 2018 University of Arkansas at Little Rock 12 68 11 2018 Chicago State University 3 60 six_yr_grad_rate rank 1 5200.00% 1 2 4900.00% 2 3 4300.00% 3 4 4200.00% 4 5 3800.00% 5 6 3700.00% 6 7 3400.00% 7 8 3200.00% 8 9 2900.00% 9 10 2800.00% 10 11 1400.00% 11 ### Exercise 8

Using ggplot() and geom_line() create a line plot of six_yr_grad_rate over time. The x-axis should be year. The line should be colored by peer_institution. Make sure only the graph displays, not the code. library(ggplot2)

ggplot(graduation, aes(x = year, y = six_yr_grad_rate, color = peer_institution)) + geom_line() + labs(title = “Six-Year Graduation Rate Over Time”, x = “Year”, y = “Six-Year Graduation Rate”) + theme_minimal() ### Exercise 9

There are too many institutions on the list. The code below will filter the list to only show the top and bottom two institutions along with USM. It creates a new tibble called top_bottom_usm. Use top_bottom_usm to create a line plot of six_yr_grad_rate over time. The x-axis should be year. The line should be colored by peer_institution. Make sure only the graph displays, not the code.

top <-
  graduation |> 
  filter(year == 2018) |>
  slice_max(six_yr_grad_rate, n = 2) |> 
  select(peer_institution) |> 
  pull()

bottom <-
  graduation |> 
  filter(year == 2018) |>
  slice_min(six_yr_grad_rate, n = 2) |> 
  select(peer_institution) |> 
  pull()

top_bottom_usm <-
  graduation |>
  filter(peer_institution %in% c(top, bottom, "University of Southern Maine"))
top_bottom_usm <- graduation |>
  filter(peer_institution %in% c(top, bottom, "University of Southern Maine"))

ggplot(top_bottom_usm, aes(x = year, y = six_yr_grad_rate, color = peer_institution)) +
  geom_line() +
  labs(title = "Comparison of Six-Year Graduation Rates", x = "Year", y = "Six-Year Graduation Rate") +
  theme_minimal()

Exercise 10

The lines are a bit thin. With Copilot enabled, create a code chunk that adds a comment line that reads: # make the lines in the prior line plot thicker. Describe what happened in the narrative below the code. If a warning is generated, describe that as well.

ggplot(top_bottom_usm, aes(x = year, y = six_yr_grad_rate, color = peer_institution)) +
  geom_line(size = 1.5) +  # Adjusting line thickness
  labs(title = "Comparison of Six-Year Graduation Rates", x = "Year", y = "Six-Year Graduation Rate") +
  theme_minimal()

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

Exercise 11

Take the graph from the prior exercise, add a title, and provide a better label for the y-axis. Use a minimal theme and if the code generates a warning either fix it or suppress the warning. ggplot(top_bottom_usm, aes(x = year, y = six_yr_grad_rate, color = peer_institution)) + geom_line(size = 1.5) + labs(title = “Six-Year Graduation Rate Trends for Selected Institutions”, x = “Year”, y = “Graduation Rate (%)”) + theme_minimal()

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