library(tidyverse)
graduation <- read_csv("https://jsuleiman.com/datasets/graduation.csv")Assignment 1
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.
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 |>
distinct(peer_institution) |>
pull() [1] "University of Southern Maine"
[2] "University of Michigan-Flint"
[3] "University of Arkansas at Little Rock"
[4] "Texas Woman's University"
[5] "Texas A & M University-Kingsville"
[6] "Salem State University"
[7] "North Carolina Central University"
[8] "Murray State University"
[9] "Fayetteville State University"
[10] "Chicago State University"
[11] "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.
top <-
graduation |>
filter(year == 2018) |>
slice_max(six_yr_grad_rate, n = 2) |>
select(peer_institution) |>
pull()Exercise 6
List the top two four_yr_grad_rate and the instution for each. Filter by the most recent year in the dataset.
top <-
graduation |>
filter(year == 2018) |>
slice_max(four_yr_grad_rate, n = 2) |>
select(peer_institution) |>
pull()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.
graduation |>
filter(year == 2018) |>
arrange(desc(six_yr_grad_rate)) |>
select(peer_institution, six_yr_grad_rate)# A tibble: 11 × 2
peer_institution six_yr_grad_rate
<chr> <dbl>
1 Salem State University 52
2 Murray State University 49
3 North Carolina Central University 43
4 California State University-Dominguez Hills 42
5 Texas Woman's University 38
6 University of Michigan-Flint 37
7 University of Southern Maine 34
8 Fayetteville State University 32
9 Texas A & M University-Kingsville 29
10 University of Arkansas at Little Rock 28
11 Chicago State University 14USM ranks 7th place.
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.
graduation |>
ggplot(aes(x = year, y = six_yr_grad_rate)) +
geom_line(aes(color = peer_institution)) +
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 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"))
# add your code here
top_bottom_usm |>
ggplot(aes(x = year, y = six_yr_grad_rate)) +
geom_line(aes(color = peer_institution)) +
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.
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"))
# make the lines in the prior line plot thicker.
top_bottom_usm |>
ggplot(aes(x = year, y = six_yr_grad_rate)) +
geom_line(aes(color = peer_institution), size = 1.5) +
theme_minimal()Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
#Describe: The first issue I faced was an error notification of “graduation” not found. This took me a while to figure out but I had to run all the previous exercises individually for it to recall the previous actions.
The second notification was after I rendered the document. I was warned about using the term “size” in my code, and to instead use “linewidth”, but it didn’t pop up as an action when I wrote it in the code so I kept copilot’s version.
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.
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 |>
ggplot(aes(x = year, y = six_yr_grad_rate)) +
geom_line(aes(color = peer_institution), size = 1.5) +
labs(
title = "USM VS. Top and Bottom Peer-Institutions' Graduation Rate",
y = "Six-Year Graduation %"
) +
theme_minimal()
Submission
To submit your assignment:
- Change the author name to your name in the YAML portion at the top of this document
- Render your document to html and publish it to RPubs.
- Submit the link to your Rpubs document in the Brightspace comments section for this assignment.
- Click on the “Add a File” button and upload your .qmd file for this assignment to Brightspace.