Diversity in Federal Judicial Appointments Has Increased Over Time

Number of appointments by gender across racial groups, 1960-present (note: different scales)

TidyTuesday
2025
Visualization showing the growth of diversity in federal judicial appointments from 1960-present. Analysis reveals significant increases in female representation across all racial groups, demonstrating progress toward a more diverse federal judiciary.
Author

Steven Ponce

Published

June 3, 2025

Figure 1: Stacked bar chart showing federal judicial appointments 1960-present by race and gender. Demonstrates significant increases in both racial diversity and female representation across all groups, with White appointments showing the largest absolute numbers but all groups showing growing gender diversity over time.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
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
  scales,        # Scale Functions for Visualization
  glue           # Interpreted String Literals
  )})

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

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2025, week = 23)

appointments_raw <- tt$judges_appointments |> clean_names()
people_raw <- tt$judges_people |> clean_names()

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

3. Examine the Data

Show code
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(appointments_raw)
skimr::skim(appointments_raw)

glimpse(people_raw)
skimr::skim(people_raw)
```

4. Tidy Data

Show code
```{r}
#| label: tidy
#| warning: false

### |-  tidy data ----
# Joining the datasets first
judges_data <- appointments_raw |>
  left_join(people_raw, by = "judge_id") |>
  mutate(
    nomination_date = mdy(nomination_date),
    commission_date = mdy(commission_date),
    senate_confirmation_date = mdy(senate_confirmation_date),
    nomination_year = year(nomination_date)
  )

# Prepare the data
plot_data <- judges_data |>
  filter(
    !is.na(nomination_year), !is.na(gender), !is.na(race),
    nomination_year >= 1960
  ) |>
  mutate(
    race_simplified = case_when(
      str_detect(race, "White") ~ "White",
      str_detect(race, "Black|African") ~ "Black",
      str_detect(race, "Hispanic|Latino") ~ "Hispanic",
      str_detect(race, "Asian") ~ "Asian",
      TRUE ~ "Other"
    ),
    decade = floor(nomination_year / 10) * 10
  ) |>
  count(decade, gender, race_simplified) |>
  group_by(decade, race_simplified) |>
  mutate(
    total = sum(n),
    female_count = ifelse(gender == "F", n, 0),
    female_label_y = ifelse(gender == "F", total - n / 2, NA),
    race_simplified = factor(race_simplified, 
                             levels = c("White", "Black", "Asian", "Hispanic", "Other"))
  ) |>
  ungroup()
```

5. Visualization Parameters

Show code
```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
colors <- get_theme_colors(
    palette = c("F" = "#8E44AD", "M" = "#2C3E50")
)

### |-  titles and caption ----
title_text <- str_glue("Diversity in Federal Judicial Appointments Has Increased Over Time")

subtitle_text <- str_glue("Number of appointments by gender across racial groups, 1960-present (note: different scales)")

caption_text <- create_social_caption(
  tt_year = 2025,
  tt_week = 23,
  source_text =  "Web site of the Federal Judicial Center, via the historydata R package"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Axis elements
    axis.text = element_text(color = colors$text, size = rel(0.7)),
    axis.title.x = element_text(color = colors$text, face = "bold", size = rel(0.8), margin = margin(t = 15)),
    axis.title.y = element_text(color = colors$text, face = "bold", size = rel(0.8), margin = margin(r = 10)),
    
    # Grid elements
    panel.grid.major.y = element_line(color = 'gray50', linewidth = 0.05),
    panel.grid.minor = element_blank(), 
    panel.grid.major.x = element_blank(),

    # Legend elements
    # legend.position = "plot",
    legend.title = element_text(family = fonts$tsubtitle, color = colors$text, size = rel(0.8), face = "bold"),
    legend.text = element_text(family = fonts$tsubtitle, color = colors$text, size = rel(0.7)),

    # Plot margins
    plot.margin = margin(t = 15, r = 15, b = 15, l = 15),
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code
```{r}
#| label: plot
#| warning: false

# Final plot -----
p <- plot_data |>
  ggplot(aes(x = decade, y = n, fill = gender)) +
  # Geoms
  geom_bar(stat = "identity", position = "stack", alpha = 0.85) +
  geom_text(aes(y = total + (total * 0.08), label = total),
    position = position_identity(),
    size = 3.2,
    color = "black",
    fontface = "bold",
    family = "Arial",
    data = plot_data |> filter(gender == "F", total > 3)
  ) +
  geom_text(aes(y = female_label_y, label = female_count),
    position = position_identity(),
    size = 3,
    color = "white", 
    fontface = "bold",
    family = "Arial",
    data = plot_data |> filter(gender == "F", female_count > 3)
  ) +
    
  # Scales
  scale_fill_manual(
    values = colors$palette,
    labels = c("F" = "Female", "M" = "Male")
  ) +
  scale_x_continuous(
    breaks = seq(1960, 2020, 20),
    labels = c("1960s", "1980s", "2000s", "2020+")
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Decade",
    y = "",
    fill = "Gender",
  ) +
  # Facets
  facet_wrap(~race_simplified, scales = "free_y", ncol = 3) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.4),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_text(
      size = rel(0.75),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9),
      lineheight = 1.2,
      margin = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
      size = rel(0.55),
      family = fonts$caption,
      color = colors$caption,
      hjust = 0.5,
      margin = margin(t = 10)
    ),
    strip.text = element_text(size = rel(0.78), face = "bold"),
    panel.spacing = unit(1.1, "lines"),

    # Legend
    legend.position = "inside",
    legend.position.inside = c(0.82, 0.15),
    legend.title = element_text(face = "bold", size = 10),
    legend.text = element_text(size = 9),
    legend.background = element_rect(fill = NA, color = "gray90", linewidth = 0.5),
    legend.margin = margin(5, 5, 5, 5),
  )
```

7. Save

Show code
```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 23, 
  width = 8,
  height = 8
)
```

8. Session Info

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     

other attached packages:
 [1] here_1.0.1      glue_1.8.0      scales_1.3.0    janitor_2.2.0  
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[13] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[17] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          httr2_1.0.6        htmlwidgets_1.6.4 
 [5] gh_1.4.1           tzdb_0.5.0         vctrs_0.6.5        tools_4.4.0       
 [9] generics_0.1.3     parallel_4.4.0     curl_6.0.0         gifski_1.32.0-1   
[13] fansi_1.0.6        pkgconfig_2.0.3    skimr_2.1.5        lifecycle_1.0.4   
[17] farver_2.1.2       compiler_4.4.0     textshaping_0.4.0  munsell_0.5.1     
[21] repr_1.1.7         codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1 
[25] yaml_2.3.10        crayon_1.5.3       pillar_1.9.0       camcorder_0.1.0   
[29] magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37     
[33] stringi_1.8.4      labeling_0.4.3     rsvg_2.6.1         rprojroot_2.0.4   
[37] fastmap_1.2.0      grid_4.4.0         colorspace_2.1-1   cli_3.6.4         
[41] magrittr_2.0.3     base64enc_0.1-3    utf8_1.2.4         withr_3.0.2       
[45] rappdirs_0.3.3     bit64_4.5.2        timechange_0.3.0   rmarkdown_2.29    
[49] tidytuesdayR_1.1.2 gitcreds_0.1.2     bit_4.5.0          ragg_1.3.3        
[53] hms_1.1.3          evaluate_1.0.1     knitr_1.49         markdown_1.13     
[57] rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6        
[61] renv_1.0.3         vroom_1.6.5        svglite_2.1.3      rstudioapi_0.17.1 
[65] jsonlite_1.8.9     R6_2.5.1           systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in tt_2025_23.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
  • TidyTuesday 2025 Week 23: [U.S. Judges and the historydata R package)](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-06-10
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