AE 05: Tidying StatSci Majors

Suggested answers

Important

These are suggested answers. This document should be used as reference only, it’s not designed to be an exhaustive key.

Goal

Our ultimate goal in this application exercise is to make the following data visualization.

Line plot of numbers of Statistical Science majors over the years (2011 - 2021). Degree types represented are BS, BS2, AB, AB2. There is an increasing trend in BS degrees and somewhat steady trend in AB degrees.

Data

The data come from the Office of the University Registrar. They make the data available as a table that you can download as a PDF, but I’ve put the data exported in a CSV file for you. Let’s load that in.

library(tidyverse)

statsci <- read_csv("https://sta199-s24.github.io/data/statsci.csv")

And let’s take a look at the data.

statsci
# A tibble: 4 × 14
  degree   `2011` `2012` `2013` `2014` `2015` `2016` `2017` `2018` `2019` `2020`
  <chr>     <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
1 Statist…     NA      1     NA     NA      4      4      1     NA     NA      1
2 Statist…      2      2      4      1      3      6      3      4      4      1
3 Statist…      2      6      1     NA      5      6      6      8      8     17
4 Statist…      5      9      4     13     10     17     24     21     26     27
# ℹ 3 more variables: `2021` <dbl>, `2022` <dbl>, `2023` <dbl>

Pivoting

  • Demo: Pivot the statsci data frame longer such that each row represents a degree type / year combination and year and number of graduates for that year are columns in the data frame.
statsci |>
  pivot_longer(
    cols = -degree,
    names_to = "year",
    values_to = "n"
  )
# A tibble: 52 × 3
   degree                    year      n
   <chr>                     <chr> <dbl>
 1 Statistical Science (AB2) 2011     NA
 2 Statistical Science (AB2) 2012      1
 3 Statistical Science (AB2) 2013     NA
 4 Statistical Science (AB2) 2014     NA
 5 Statistical Science (AB2) 2015      4
 6 Statistical Science (AB2) 2016      4
 7 Statistical Science (AB2) 2017      1
 8 Statistical Science (AB2) 2018     NA
 9 Statistical Science (AB2) 2019     NA
10 Statistical Science (AB2) 2020      1
# ℹ 42 more rows
  • Question: What is the type of the year variable? Why? What should it be?

It’s a character (chr) variable since the information came from the columns of the original data frame and R cannot know that these character strings represent years. The variable type should be numeric.

  • Demo: Start over with pivoting, and this time also make sure year is a numerical variable in the resulting data frame.
statsci |>
  pivot_longer(
    cols = -degree,
    names_to = "year",
    names_transform = as.numeric,
    values_to = "n"
  )
# A tibble: 52 × 3
   degree                     year     n
   <chr>                     <dbl> <dbl>
 1 Statistical Science (AB2)  2011    NA
 2 Statistical Science (AB2)  2012     1
 3 Statistical Science (AB2)  2013    NA
 4 Statistical Science (AB2)  2014    NA
 5 Statistical Science (AB2)  2015     4
 6 Statistical Science (AB2)  2016     4
 7 Statistical Science (AB2)  2017     1
 8 Statistical Science (AB2)  2018    NA
 9 Statistical Science (AB2)  2019    NA
10 Statistical Science (AB2)  2020     1
# ℹ 42 more rows
  • Question: What does an NA mean in this context? Hint: The data come from the university registrar, and they have records on every single graduates, there shouldn’t be anything “unknown” to them about who graduated when.

NAs should actually be 0s.

  • Demo: Add on to your pipeline that you started with pivoting and convert NAs in n to 0s.
statsci |>
  pivot_longer(
    cols = -degree,
    names_to = "year",
    names_transform = as.numeric,
    values_to = "n"
  ) |>
  mutate(n = if_else(is.na(n), 0, n))
# A tibble: 52 × 3
   degree                     year     n
   <chr>                     <dbl> <dbl>
 1 Statistical Science (AB2)  2011     0
 2 Statistical Science (AB2)  2012     1
 3 Statistical Science (AB2)  2013     0
 4 Statistical Science (AB2)  2014     0
 5 Statistical Science (AB2)  2015     4
 6 Statistical Science (AB2)  2016     4
 7 Statistical Science (AB2)  2017     1
 8 Statistical Science (AB2)  2018     0
 9 Statistical Science (AB2)  2019     0
10 Statistical Science (AB2)  2020     1
# ℹ 42 more rows
  • Demo: In our plot the degree types are BS, BS2, AB, and AB2. This information is in our dataset, in the degree column, but this column also has additional characters we don’t need. Create a new column called degree_type with levels BS, BS2, AB, and AB2 (in this order) based on degree. Do this by adding on to your pipeline from earlier.
statsci |>
  pivot_longer(
    cols = -degree,
    names_to = "year",
    names_transform = as.numeric,
    values_to = "n"
  ) |>
  mutate(n = if_else(is.na(n), 0, n)) |>
  separate(degree, sep = " \\(", into = c("major", "degree_type")) |>
  mutate(
    degree_type = str_remove(degree_type, "\\)"),
    degree_type = fct_relevel(degree_type, "BS", "BS2", "AB", "AB2")
    )
# A tibble: 52 × 4
   major               degree_type  year     n
   <chr>               <fct>       <dbl> <dbl>
 1 Statistical Science AB2          2011     0
 2 Statistical Science AB2          2012     1
 3 Statistical Science AB2          2013     0
 4 Statistical Science AB2          2014     0
 5 Statistical Science AB2          2015     4
 6 Statistical Science AB2          2016     4
 7 Statistical Science AB2          2017     1
 8 Statistical Science AB2          2018     0
 9 Statistical Science AB2          2019     0
10 Statistical Science AB2          2020     1
# ℹ 42 more rows
  • Your turn (5 minutes): Now we start making our plot, but let’s not get too fancy right away. Create the following plot, which will serve as the “first draft” on the way to our Goal. Do this by adding on to your pipeline from earlier.

Line plot of numbers of Statistical Science majors over the years (2011 - 2021). Degree types represented are BS, BS2, AB, AB2. There is an increasing trend in BS degrees and somewhat steady trend in AB degrees.

statsci |>
  pivot_longer(
    cols = -degree,
    names_to = "year",
    names_transform = as.numeric,
    values_to = "n"
  ) |>
  mutate(n = if_else(is.na(n), 0, n)) |>
  separate(degree, sep = " \\(", into = c("major", "degree_type")) |>
  mutate(
    degree_type = str_remove(degree_type, "\\)"),
    degree_type = fct_relevel(degree_type, "BS", "BS2", "AB", "AB2")
    ) |>
  ggplot(aes(x = year, y = n, color = degree_type)) +
  geom_point() +
  geom_line()

  • Your turn (4 minutes): What aspects of the plot need to be updated to go from the draft you created above to the Goal plot at the beginning of this application exercise.

    • x-axis scale: need to go from 2011 to 2023 in increments of 2 years

    • line colors

    • axis labels: title, subtitle, x, y, caption

    • theme

    • legend position and border

  • Demo: Update x-axis scale such that the years displayed go from 2011 to 2023 in increments of 2 years. Do this by adding on to your pipeline from earlier.
statsci |>
  pivot_longer(
    cols = -degree,
    names_to = "year",
    names_transform = as.numeric,
    values_to = "n"
  ) |>
  mutate(n = if_else(is.na(n), 0, n)) |>
  separate(degree, sep = " \\(", into = c("major", "degree_type")) |>
  mutate(
    degree_type = str_remove(degree_type, "\\)"),
    degree_type = fct_relevel(degree_type, "BS", "BS2", "AB", "AB2")
    ) |>
  ggplot(aes(x = year, y = n, color = degree_type)) +
  geom_point() +
  geom_line() +
  scale_x_continuous(breaks = seq(2011, 2023, 2))

  • Demo: Update line colors using the following level / color assignments. Once again, do this by adding on to your pipeline from earlier.

    • “BS” = “cadetblue4”

    • “BS2” = “cadetblue3”

    • “AB” = “lightgoldenrod4”

    • “AB2” = “lightgoldenrod3”

statsci |>
  pivot_longer(
    cols = -degree,
    names_to = "year",
    names_transform = as.numeric,
    values_to = "n"
  ) |>
  mutate(n = if_else(is.na(n), 0, n)) |>
  separate(degree, sep = " \\(", into = c("major", "degree_type")) |>
  mutate(
    degree_type = str_remove(degree_type, "\\)"),
    degree_type = fct_relevel(degree_type, "BS", "BS2", "AB", "AB2")
    ) |>
  ggplot(aes(x = year, y = n, color = degree_type)) +
  geom_point() +
  geom_line() +
  scale_x_continuous(breaks = seq(2011, 2023, 2)) +
  scale_color_manual(
    values = c("BS" = "cadetblue4", 
               "BS2" = "cadetblue3", 
               "AB" = "lightgoldenrod4", 
               "AB2" = "lightgoldenrod3"))

  • Your turn (4 minutes): Update the plot labels (title, subtitle, x, y, and caption) and use theme_minimal(). Once again, do this by adding on to your pipeline from earlier.
statsci |>
  pivot_longer(
    cols = -degree,
    names_to = "year",
    names_transform = as.numeric,
    values_to = "n"
  ) |>
  mutate(n = if_else(is.na(n), 0, n)) |>
  separate(degree, sep = " \\(", into = c("major", "degree_type")) |>
  mutate(
    degree_type = str_remove(degree_type, "\\)"),
    degree_type = fct_relevel(degree_type, "BS", "BS2", "AB", "AB2")
    ) |>
  ggplot(aes(x = year, y = n, color = degree_type)) +
  geom_point() +
  geom_line() +
  scale_x_continuous(breaks = seq(2011, 2023, 2)) +
  scale_color_manual(
    values = c("BS" = "cadetblue4",
               "BS2" = "cadetblue3",
               "AB" = "lightgoldenrod4",
               "AB2" = "lightgoldenrod3")) +
  labs(
    x = "Graduation year",
    y = "Number of majors graduating",
    color = "Degree type",
    title = "Statistical Science majors over the years",
    subtitle = "Academic years 2011 - 2023",
    caption = "Source: Office of the University Registrar\nhttps://registrar.duke.edu/registration/enrollment-statistics"
  ) +
  theme_minimal()

  • Demo: Finally, adding to your pipeline you’ve developed so far, move the legend into the plot, make its background white, and its border gray. Set fig-width: 7 and fig-height: 5 for your plot in the chunk options.
statsci |>
  pivot_longer(
    cols = -degree,
    names_to = "year",
    names_transform = as.numeric,
    values_to = "n"
  ) |>
  mutate(n = if_else(is.na(n), 0, n)) |>
  separate(degree, sep = " \\(", into = c("major", "degree_type")) |>
  mutate(
    degree_type = str_remove(degree_type, "\\)"),
    degree_type = fct_relevel(degree_type, "BS", "BS2", "AB", "AB2")
    ) |>
  ggplot(aes(x = year, y = n, color = degree_type)) +
  geom_point() +
  geom_line() +
  scale_x_continuous(breaks = seq(2011, 2023, 2)) +
  scale_color_manual(
    values = c("BS" = "cadetblue4",
               "BS2" = "cadetblue3",
               "AB" = "lightgoldenrod4",
               "AB2" = "lightgoldenrod3")) +
  labs(
    x = "Graduation year",
    y = "Number of majors graduating",
    color = "Degree type",
    title = "Statistical Science majors over the years",
    subtitle = "Academic years 2011 - 2023",
    caption = "Source: Office of the University Registrar\nhttps://registrar.duke.edu/registration/enrollment-statistics"
  ) +
  theme_minimal() +
  theme(
    legend.position = c(0.2, 0.8),
    legend.background = element_rect(fill = "white", color = "gray")
    )