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) |>
filter(peer_institution != "University of Southern Maine")# A tibble: 10 × 1
peer_institution
<chr>
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 HillsExercise 5
List the top two six_yr_grad_rate and the institution for each. Filter by the most recent year in the dataset.
graduation |>
slice_max(six_yr_grad_rate, n = 2) |>
slice_max(year, n = 2) |>
select(six_yr_grad_rate, peer_institution, year)# A tibble: 2 × 3
six_yr_grad_rate peer_institution year
<dbl> <chr> <dbl>
1 54 Murray State University 2014
2 54 Murray State University 2012Exercise 6
List the top two four_yr_grad_rate and the instution for each. Filter by the most recent year in the dataset.
graduation |>
slice_max(four_yr_grad_rate, n = 2) |>
slice_max(year, n = 2) |>
select(four_yr_grad_rate, peer_institution, year)# A tibble: 2 × 3
four_yr_grad_rate peer_institution year
<dbl> <chr> <dbl>
1 37 Murray State University 2013
2 38 Murray State University 2012Exercise 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 |>
slice_max(year, n = 1) |>
arrange(desc(six_yr_grad_rate)) |>
select(six_yr_grad_rate, peer_institution, year)# A tibble: 11 × 3
six_yr_grad_rate peer_institution year
<dbl> <chr> <dbl>
1 52 Salem State University 2018
2 49 Murray State University 2018
3 43 North Carolina Central University 2018
4 42 California State University-Dominguez Hills 2018
5 38 Texas Woman's University 2018
6 37 University of Michigan-Flint 2018
7 34 University of Southern Maine 2018
8 32 Fayetteville State University 2018
9 29 Texas A & M University-Kingsville 2018
10 28 University of Arkansas at Little Rock 2018
11 14 Chicago State University 2018University of Southern Maine is ranked 7th, with a 34% rate.
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.
#! fig.cap: "Six Year Graduation Rate"
#| code-fold: true
graduation |>
ggplot(aes(x = year, y = six_yr_grad_rate)) +
geom_line(aes(color = peer_institution)) +
labs(title = "Six Year Graduation Rate",
x = "year",
y = "six_yr_grad_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.
#! fig.cap: "Six Year Graduation Rate"
#| code-fold: true
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)) +
labs(title = "Six Year Graduation Rate",
x = "year",
y = "six_yr_grad_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.
# 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)Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
The following is the warning which I was met with once creating the graph for Exercise 10: “Warning: Using size aesthetic for lines was deprecated in ggplot2 3.4.0. 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.
top_bottom_usm |>
ggplot(aes(x = year, y = six_yr_grad_rate)) +
geom_line(aes(color = peer_institution), size = 1) +
labs(title = "Six Year Graduation Rate from 2012 to 2018",
x = "Year",
y = "Six Year Graduation Rate") +
theme_minimal()
Submission
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