Distribution of College Attendance Rates by Selectivity Tier

A Comparison of Attendance Rates for U.S. Colleges Across Selectivity Levels

Attendance rates at U.S. colleges compared across selectivity tiers. Built in R.

Figure 1: A faceted bar plot displaying the distribution of college attendance rates based on selectivity tier. The x-axis is labeled “Attendance Rate,” and the y-axis is labeled “Frequency.” Six panels represent different college tiers: Ivy Plus, Highly Selective Private, Highly Selective Public, Other Elite Schools (Public and Private), Selective Private, and Selective Public. Each panel has a histogram in gray with colored bars highlighting specific sections, using different colors for each tier.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load

pacman::p_load(
  tidyverse,         # Easily Install and Load the 'Tidyverse'
  ggtext,            # Improved Text Rendering Support for 'ggplot2'
  showtext,          # Using Fonts More Easily in R Graphs
  janitor,           # Simple Tools for Examining and Cleaning Dirty Data
  skimr,             # Compact and Flexible Summaries of Data
  scales,            # Scale Functions for Visualization
  lubridate,         # Make Dealing with Dates a Little Easier
  MetBrewer,         # Color Palettes Inspired by Works at the Metropolitan Museum of Art
  MoMAColors,        # Color Palettes Inspired by Artwork at the Museum of Modern Art in New York City
  glue,              # Interpreted String Literals
  gghighlight        # Highlight Lines and Points in 'ggplot2'
 )  

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  =  7.77,
  height =  8,
  units  = "in",
  dpi    = 320
)

### |- resolution ----
showtext_opts(dpi = 320, regular.wt = 300, bold.wt = 800)
```

2. Read in the Data

```{r}
#| label: read

tt <-tidytuesdayR::tt_load(2024, week = 37) 

college_admissions <- tt$college_admissions |> clean_names() |> glimpse()

tidytuesdayR::readme(tt)
rm(tt)
```

3. Examing the Data

```{r}
#| label: examine

glimpse(college_admissions)
```

4. Tidy Data

```{r}
#| label: tidy

# Tidy
college_admissions_clean <- college_admissions  |> 
    filter(!is.na(attend)) |> 
    mutate(
        tier = str_to_title(tier),
        tier = case_when(
            tier == "Highly Selective Private" ~ "Highly Selective<br>Private",
            tier == "Highly Selective Public"  ~ "Highly Selective<br>Public",
            tier == "Other Elite Schools (Public And Private)" ~ "Other Elite Schools<br>(Public And Private)",
            TRUE ~ tier
        ),
        tier = factor(tier, 
                      levels = c("Ivy Plus", 
                                 "Highly Selective<br>Private", 
                                 "Highly Selective<br>Public", 
                                 "Other Elite Schools<br>(Public And Private)", 
                                 "Selective Private", 
                                 "Selective Public"))
    ) 
```

5. Visualization Parameters

```{r}
#| label: params

### |- plot aesthetics ----
bkg_col      <- colorspace::lighten('#f7f5e9', 0.05)    
title_col    <- "gray20"           
subtitle_col <- "gray20"     
caption_col  <- "gray30"   
text_col     <- "gray20"    
col_palette  <- MoMAColors::moma.colors(palette_name = "Panton", n = 6, type = 'discrete')

### |-  titles and caption ----
# icons
tt <- str_glue("#TidyTuesday: { 2024 } Week { 37 } &bull; Source: Opportunity Insights: College-Level Data for 139 Selective American Colleges<br>")
li <- str_glue("<span style='font-family:fa6-brands'>&#xf08c;</span>")
gh <- str_glue("<span style='font-family:fa6-brands'>&#xf09b;</span>")
mn <- str_glue("<span style='font-family:fa6-brands'>&#xf4f6;</span>")

# text
title_text    <- str_glue("Distribution of College Attendance Rates by Selectivity Tier")
subtitle_text <- str_glue("A Comparison of Attendance Rates for U.S. Colleges Across Selectivity Levels")
caption_text  <- str_glue("{tt} {li} stevenponce &bull; {mn} @sponce1(graphic.social) {gh} poncest &bull; #rstats #ggplot2")

### |-  fonts ----
font_add("fa6-brands", "fonts/6.4.2/Font Awesome 6 Brands-Regular-400.otf")
font_add_google("Oswald", regular.wt = 400, family = "title")
font_add_google("Merriweather Sans", regular.wt = 400, family = "subtitle")
font_add_google("Merriweather Sans", regular.wt = 400, family = "text")
font_add_google("Noto Sans", regular.wt = 400, family = "caption")
showtext_auto(enable = TRUE)

### |-  plot theme ----
theme_set(theme_minimal(base_size = 14, base_family = "text"))                

theme_update(
    plot.title.position   = "plot",
    plot.caption.position = "plot",
    legend.position       = 'plot',
    plot.background       = element_rect(fill = bkg_col, color = bkg_col),
    panel.background      = element_rect(fill = bkg_col, color = bkg_col),
    plot.margin           = margin(t = 10, r = 20, b = 10, l = 20),
    axis.title.x          = element_text(margin = margin(10, 0, 0, 0), size = rel(1.1), 
                                         color = text_col, family = "text", face = "bold", hjust = 0.5),
    axis.title.y          = element_text(margin = margin(0, 10, 0, 0), size = rel(1.1), 
                                         color = text_col, family = "text", face = "bold", hjust = 0.5),
    axis.text             = element_text(size = rel(0.8), color = text_col, family = "text"),
    axis.line.x           = element_line(color = "gray40", linewidth = .15),
    panel.grid.minor.y    = element_blank(),
    panel.grid.major.y    = element_line(linetype = "dotted", linewidth = 0.1, color = 'gray10'),
    panel.grid.minor.x    = element_blank(),
    panel.grid.major.x    = element_line(linetype = "dotted", linewidth = 0.1, color = 'gray10'),
    
    strip.text            = element_textbox(size     = rel(0.9),
                                            face     = 'bold',
                                            color    = text_col,
                                            hjust    = 0.5,
                                            halign   = 0.5,
                                            r        = unit(3, "pt"),
                                            width    = unit(6, "npc"),
                                            padding  = margin(2, 0, 2, 0),
                                            margin   = margin(3, 3, 3, 3),
                                            fill     = "transparent"),
    panel.spacing         = unit(1, 'lines')
)  
```

6. Plot

```{r}
#| label: plot

### |-  final plot ----  
p <- ggplot(college_admissions_clean, 
       aes(x = attend, fill = tier)) + 
    
    # Geoms
    geom_histogram(bins = 30, color = "gray10", linewidth = 0.2, alpha = 0.75) +
    gghighlight::gghighlight() +
    
    # Labs
    labs(
        x = "Attendance Rate",
        y = "Frequency",
        title    = title_text,
        subtitle = subtitle_text,
        caption  = caption_text
    ) +
    
    # Scales
    scale_x_continuous(breaks = pretty_breaks(n = 2)) +
    scale_y_continuous() +
    scale_fill_manual(values = col_palette) +
    coord_cartesian(clip = 'off') +
    
    # Facets
    facet_wrap(vars(tier)) +

    # Theme
    theme(
        plot.title = element_text(
            size = rel(1.5),
            family = "title",
            color = title_col,
            face = "bold",
            lineheight = 0.85,
            margin = margin(t = 5, b = 5)
        ),
        plot.subtitle = element_text(
            size = rel(0.95),
            family = "subtitle",
            color = title_col,
            lineheight = 1,
            margin = margin(t = 5, b = 15)
        ),
        plot.caption = element_markdown(
            size = rel(.5),
            family = "caption",
            color = caption_col,
            lineheight = 0.6,
            hjust = 0,
            halign = 0,
            margin = margin(t = 10, b = 5)
        )
    )
```

7. Save

```{r}
#| label: save

### |-  plot image ----  
ggsave(
  filename = here::here("data_visualizations/TidyTuesday/2024/tt_2024_37.png"),
  plot = p,
  width  =  7.77,
  height =  8,
  units  = "in",
  dpi    = 320
)

### |-  plot thumbnail----  
magick::image_read(here::here("data_visualizations/TidyTuesday/2024/tt_2024_37.png")) |> 
  magick::image_resize(geometry = "400") |> 
  magick::image_write(here::here("data_visualizations/TidyTuesday/2024/thumbnails/tt_2024_37.png"))
```

8. Session Info

```{r, eval=TRUE}
sessionInfo()
```

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

loaded via a namespace (and not attached):
 [1] htmlwidgets_1.6.4 compiler_4.4.0    fastmap_1.2.0     cli_3.6.4        
 [5] htmltools_0.5.8.1 tools_4.4.0       rstudioapi_0.17.1 yaml_2.3.10      
 [9] rmarkdown_2.29    knitr_1.49        jsonlite_1.8.9    xfun_0.49        
[13] digest_0.6.37     rlang_1.1.6       renv_1.0.3        evaluate_1.0.1