CDC Dataset Purge: A Timeline of Public Health Data Removal

Analysis of CDC datasets backed up on archive.org during the Trump administration’s data purge

TidyTuesday

2025

A data visualization analysis exploring CDC dataset archival patterns from 2016 to 2025. The analysis reveals changing accessibility patterns and a significant increase in archival activity beginning in 2020, with a focus on public health data preservation.

Author

Steven Ponce

Published

February 8, 2025

Figure 1: A two-panel visualization of CDC dataset archival patterns. Top panel: Bar chart showing distribution of 1,257 datasets by access level - majority are either Unspecified (51.5%) or Public Access (47.9%). Bottom panel: Area chart showing cumulative growth of archived datasets from 2016 to 2025, with a notable increase in archival activity starting 2020, marked by a vertical dashed line.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

## 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
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    patchwork,      # The Composer of Plots
    camcorder       # Record Your Plot History 
    )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  10,
    height =  10,
    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

tt <- tidytuesdayR::tt_load(2025, week = 06) 

cdc <- tt$cdc_datasets |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)

3. Examine the Data

Show code

glimpse(cdc)
skim(cdc)

4. Tidy Data

Show code

# P1. Distribution plot  
distribution <- cdc |>
  mutate(
    access_category = case_when(
      public_access_level %in% c("public", "public domain") ~ "Public Access",
      public_access_level == "restricted public" ~ "Restricted Access",
      public_access_level == "non-public" ~ "No Public Access",
      TRUE ~ "Unspecified"
    )
  ) |>
  count(access_category) |>
  mutate(
    pct = n / sum(n) * 100,
    label = sprintf("%.1f%%\n(n=%d)", pct, n),
    # Conditional hjust and color based on count
    hjust = ifelse(n > 200, 1.2, -0.2),
    label_color = ifelse(n > 200, "white", "gray30")
  )

# P2. Timeline plot
timeline_data <- cdc |>
  mutate(
    access_category = case_when(
      public_access_level %in% c("public", "public domain") ~ "Public Access",
      public_access_level == "restricted public" ~ "Restricted Access",
      public_access_level == "non-public" ~ "No Public Access",
      TRUE ~ "Unspecified"
    ),
    issued = as.Date(issued)
  ) |>
  filter(!is.na(issued)) |>
  arrange(issued) |>
  group_by(access_category) |>
  mutate(cumulative = row_number()) |>
  ungroup()

# Find the inflection point around 2020
inflection_date <- as.Date("2020-04-01")

5. Visualization Parameters

Show code

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(
    palette = c(
        "Public Access"     = "#3498DB",      
        "Restricted Access" = "#E67E22",   
        "No Public Access"  = "#C0392B",    
        "Unspecified"       = "#7F8C8D" 
    )
)

### |-  titles and caption ----
title_text <- str_glue("CDC Dataset Purge: A Timeline of Public Health Data Removal")
subtitle_text <- str_glue("Analysis of CDC datasets backed up on archive.org during the Trump administration's data purge")

# Create caption
caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 06,
    source_text = "archive.org/details/20250128-cdc-datasets"
)

### |-  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.title = element_text(color = colors$text, size = rel(0.8)),
        axis.text = element_text(color = colors$text, size = rel(0.7)),
        
        # Grid elements
        panel.grid.minor = element_blank(),
        panel.grid.major = element_line(color = "grey80", linewidth = 0.1),
        
        # Legend elements
        legend.position = "right",
        legend.title = element_text(family = fonts$text, size = rel(0.8)),
        legend.text = element_text(family = fonts$text, size = rel(0.7)),
        
        # Plot margins 
        plot.margin = margin(t = 10, r = 10, b = 10, l = 10),

    )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot ----

# P1. Distribution plot 
p1 <- ggplot(distribution,
             aes(x = reorder(access_category, n), 
                 y = n, 
                 fill = access_category)
) +
    
    # Geoms
    geom_col() +
    geom_text(
        aes(label = label,
            hjust = hjust,
            color = label_color),
        size = 3.5,
        fontface = "bold",
        family = fonts$text
    ) +
    coord_flip() +
    
    # Scales
    scale_fill_manual(values = colors$palette) +
    scale_color_identity() +  
    scale_x_discrete() +
    scale_y_continuous(
        expand = expansion(mult = c(0, 0.25)) 
    ) +
    
    # Labs
    labs(
        title = "Distribution of Dataset Access Levels",
        x = NULL,
        y = "Number of Datasets"
    ) +
    
    # Theme
    theme(
        legend.position = "none",
        panel.grid.major.y = element_blank(),
        
        plot.title = element_text(
            size   = rel(1.3),
            family = fonts$title,
            face   = "bold",
            color  = colors$title,
            lineheight = 1.1,
            margin = margin(t = 5, b = 5)
        )
    )

# P2. Timeline plot with enhancements
p2 <- ggplot(timeline_data,
             aes(x = issued, 
                 y = cumulative, 
                 fill = access_category)
) +
    # Rectangle
    annotate(
        "rect",
        xmin = inflection_date,
        xmax = max(timeline_data$issued),
        ymin = 0,
        ymax = max(timeline_data$cumulative),
        fill = "gray85",
        alpha = 0.3
    ) +
    
    # Geoms
    geom_area(alpha = 0.8) +
    geom_line(color = "white", linewidth = 0.5) +
    geom_vline(
        xintercept = inflection_date, 
        linetype = "dashed", 
        color = colors$caption,
        alpha = 0.7
    ) +
    # Note
    annotate(
        "text", 
        x = inflection_date,
        y = max(timeline_data$cumulative) * 0.9,
        label = "Notable increase in\narchival activity",
        hjust = -0.1,
        size = 3.5,
        color = colors$caption,
        family = fonts$caption
    ) +
    
    # Scales
    scale_fill_manual(values = colors$palette) +
    scale_x_date(
        date_breaks = "1 year",
        date_labels = "%Y",
    ) +
    scale_y_continuous(
        expand = expansion(mult = c(0, 0.1)),
        breaks = function(x) seq(0, max(x), by = 50)
    ) +
    
    # Labs
    labs(
        title = "Cumulative Growth of Archived Datasets",
        subtitle = sprintf("Note: Only %d out of %d datasets (%.1f%%) have valid dates",
                           sum(!is.na(cdc$issued)),
                           nrow(cdc),
                           100 * sum(!is.na(cdc$issued)) / nrow(cdc)),
        x = "Year",
        y = "Number of Datasets"
    ) +
    
    # Theme
    theme(
        legend.position = "top",
        legend.title = element_blank(),
        legend.spacing.x = unit(0.2, "cm"),
        panel.grid.minor = element_blank(),
        panel.grid.major.x = element_blank(),
        
        plot.title = element_text(
            size = rel(1.3),
            family = fonts$title,
            face = "bold",
            color = colors$title,
            lineheight = 1.1,
            margin = margin(t = 5, b = 5)
        ),
        plot.subtitle = element_text(
            size = rel(0.9),
            family = fonts$caption,
            color = colors$caption,
            margin = margin(b = 10)
        ),
        axis.title = element_text(
            size = rel(0.9),
            family = fonts$caption,
            color = colors$caption
        ),
    )

# Combine plots 
combined_plot <- (p1 / p2) +
    plot_layout(heights = c(0.8, 1.2)) 

combined_plot <- combined_plot +
    plot_annotation(
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text, 
        theme = theme(
            plot.title = element_text(
                size   = rel(1.75),
                family = fonts$title,
                face   = "bold",
                color  = colors$title,
                lineheight = 1.1,
                margin = margin(t = 5, b = 5)
            ),   
            plot.subtitle = element_text(
                size   = rel(0.9),
                family = fonts$subtitle,
                color  = colors$subtitle,
                lineheight = 1.2,
                margin = margin(t = 5, b = 5)
            ), 
            plot.caption = element_markdown(
                size   = rel(0.6),
                family = fonts$caption,
                color  = colors$caption,
                hjust  = 0.5,
                margin = margin(t = 10)
            ),
            plot.margin = margin(t = 20, r = 10, b = 20, l = 10),
        )
    )

7. Save

Show code

### |-  plot image ----  

save_plot_patchwork(
  plot = combined_plot, 
  type = "tidytuesday", 
  year = 2025, 
  week = 6, 
  width = 10,
  height = 10
)

8. Session Info

Expand for 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] camcorder_0.1.0 patchwork_1.3.0 here_1.0.1      glue_1.8.0     
 [5] scales_1.3.0    skimr_2.1.5     janitor_2.2.0   showtext_0.9-7 
 [9] showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3
[13] forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2    
[17] readr_2.1.5     tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1  
[21] tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   farver_2.1.2       fastmap_1.2.0      gh_1.4.1          
 [5] digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4    rsvg_2.6.1        
 [9] magrittr_2.0.3     compiler_4.4.0     rlang_1.1.4        tools_4.4.0       
[13] utf8_1.2.4         yaml_2.3.10        knitr_1.49         labeling_0.4.3    
[17] htmlwidgets_1.6.4  bit_4.5.0          curl_6.0.0         xml2_1.3.6        
[21] repr_1.1.7         tidytuesdayR_1.1.2 withr_3.0.2        grid_4.4.0        
[25] fansi_1.0.6        colorspace_2.1-1   gitcreds_0.1.2     cli_3.6.3         
[29] rmarkdown_2.29     crayon_1.5.3       generics_0.1.3     rstudioapi_0.17.1 
[33] tzdb_0.4.0         commonmark_1.9.2   parallel_4.4.0     ggplotify_0.1.2   
[37] base64enc_0.1-3    vctrs_0.6.5        yulab.utils_0.1.8  jsonlite_1.8.9    
[41] gridGraphics_0.5-1 hms_1.1.3          bit64_4.5.2        systemfonts_1.1.0 
[45] magick_2.8.5       gifski_1.32.0-1    codetools_0.2-20   stringi_1.8.4     
[49] gtable_0.3.6       munsell_0.5.1      pillar_1.9.0       rappdirs_0.3.3    
[53] htmltools_0.5.8.1  R6_2.5.1           httr2_1.0.6        rprojroot_2.0.4   
[57] vroom_1.6.5        evaluate_1.0.1     markdown_1.13      gridtext_0.1.5    
[61] snakecase_0.11.1   renv_1.0.3         Rcpp_1.0.13-1      svglite_2.1.3     
[65] xfun_0.49          fs_1.6.5           pkgconfig_2.0.3

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in tt_2025_06.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:
- TidyTuesday 2025 Week 06: CDC Datasets

--- title: "CDC Dataset Purge: A Timeline of Public Health Data Removal" subtitle: "Analysis of CDC datasets backed up on archive.org during the Trump administration's data purge" description: "A data visualization analysis exploring CDC dataset archival patterns from 2016 to 2025. The analysis reveals changing accessibility patterns and a significant increase in archival activity beginning in 2020, with a focus on public health data preservation." author: "Steven Ponce" date: "2025-02-08" categories: ["TidyTuesday", "2025"] tags: [ "CDC", "public health", "data accessibility", "data preservation", "archive.org", "ggplot2", "time series", "data analysis", "health data", "data visualization", "R", "tidyverse" ] image: "thumbnails/tt_2025_06.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true # filters: # - social-share # share: # permalink: "https://stevenponce.netlify.app/data_visualizations/TidyTuesday/2025/tt_2025_06.html" # description: "Exploring patterns in CDC dataset archival activity: A visual analysis of public health data accessibility and preservation efforts from 2016-2025. #TidyTuesday #rstats #DataViz" # # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![A two-panel visualization of CDC dataset archival patterns. Top panel: Bar chart showing distribution of 1,257 datasets by access level - majority are either Unspecified (51.5%) or Public Access (47.9%). Bottom panel: Area chart showing cumulative growth of archived datasets from 2016 to 2025, with a notable increase in archival activity starting 2020, marked by a vertical dashed line.](tt_2025_06.png){#fig-1} ### <mark> **Steps to Create this Graphic** </mark> #### 1. Load Packages & Setup ```{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 skimr, # Compact and Flexible Summaries of Data scales, # Scale Functions for Visualization glue, # Interpreted String Literals here, # A Simpler Way to Find Your Files patchwork, # The Composer of Plots camcorder # Record Your Plot History ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 10, height = 10, 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 ```{r} #| label: read #| include: true #| eval: true #| warning: false tt <- tidytuesdayR::tt_load(2025, week = 06) cdc <- tt$cdc_datasets |> clean_names() tidytuesdayR::readme(tt) rm(tt) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(cdc) skim(cdc) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false # P1. Distribution plot distribution <- cdc |> mutate( access_category = case_when( public_access_level %in% c("public", "public domain") ~ "Public Access", public_access_level == "restricted public" ~ "Restricted Access", public_access_level == "non-public" ~ "No Public Access", TRUE ~ "Unspecified" ) ) |> count(access_category) |> mutate( pct = n / sum(n) * 100, label = sprintf("%.1f%%\n(n=%d)", pct, n), # Conditional hjust and color based on count hjust = ifelse(n > 200, 1.2, -0.2), label_color = ifelse(n > 200, "white", "gray30") ) # P2. Timeline plot timeline_data <- cdc |> mutate( access_category = case_when( public_access_level %in% c("public", "public domain") ~ "Public Access", public_access_level == "restricted public" ~ "Restricted Access", public_access_level == "non-public" ~ "No Public Access", TRUE ~ "Unspecified" ), issued = as.Date(issued) ) |> filter(!is.na(issued)) |> arrange(issued) |> group_by(access_category) |> mutate(cumulative = row_number()) |> ungroup() # Find the inflection point around 2020 inflection_date <- as.Date("2020-04-01") ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- # Get base colors with custom palette colors <- get_theme_colors( palette = c( "Public Access" = "#3498DB", "Restricted Access" = "#E67E22", "No Public Access" = "#C0392B", "Unspecified" = "#7F8C8D" ) ) ### |- titles and caption ---- title_text <- str_glue("CDC Dataset Purge: A Timeline of Public Health Data Removal") subtitle_text <- str_glue("Analysis of CDC datasets backed up on archive.org during the Trump administration's data purge") # Create caption caption_text <- create_social_caption( tt_year = 2025, tt_week = 06, source_text = "archive.org/details/20250128-cdc-datasets" ) ### |- 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.title = element_text(color = colors$text, size = rel(0.8)), axis.text = element_text(color = colors$text, size = rel(0.7)), # Grid elements panel.grid.minor = element_blank(), panel.grid.major = element_line(color = "grey80", linewidth = 0.1), # Legend elements legend.position = "right", legend.title = element_text(family = fonts$text, size = rel(0.8)), legend.text = element_text(family = fonts$text, size = rel(0.7)), # Plot margins plot.margin = margin(t = 10, r = 10, b = 10, l = 10), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- # P1. Distribution plot p1 <- ggplot(distribution, aes(x = reorder(access_category, n), y = n, fill = access_category) ) + # Geoms geom_col() + geom_text( aes(label = label, hjust = hjust, color = label_color), size = 3.5, fontface = "bold", family = fonts$text ) + coord_flip() + # Scales scale_fill_manual(values = colors$palette) + scale_color_identity() + scale_x_discrete() + scale_y_continuous( expand = expansion(mult = c(0, 0.25)) ) + # Labs labs( title = "Distribution of Dataset Access Levels", x = NULL, y = "Number of Datasets" ) + # Theme theme( legend.position = "none", panel.grid.major.y = element_blank(), plot.title = element_text( size = rel(1.3), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ) ) # P2. Timeline plot with enhancements p2 <- ggplot(timeline_data, aes(x = issued, y = cumulative, fill = access_category) ) + # Rectangle annotate( "rect", xmin = inflection_date, xmax = max(timeline_data$issued), ymin = 0, ymax = max(timeline_data$cumulative), fill = "gray85", alpha = 0.3 ) + # Geoms geom_area(alpha = 0.8) + geom_line(color = "white", linewidth = 0.5) + geom_vline( xintercept = inflection_date, linetype = "dashed", color = colors$caption, alpha = 0.7 ) + # Note annotate( "text", x = inflection_date, y = max(timeline_data$cumulative) * 0.9, label = "Notable increase in\narchival activity", hjust = -0.1, size = 3.5, color = colors$caption, family = fonts$caption ) + # Scales scale_fill_manual(values = colors$palette) + scale_x_date( date_breaks = "1 year", date_labels = "%Y", ) + scale_y_continuous( expand = expansion(mult = c(0, 0.1)), breaks = function(x) seq(0, max(x), by = 50) ) + # Labs labs( title = "Cumulative Growth of Archived Datasets", subtitle = sprintf("Note: Only %d out of %d datasets (%.1f%%) have valid dates", sum(!is.na(cdc$issued)), nrow(cdc), 100 * sum(!is.na(cdc$issued)) / nrow(cdc)), x = "Year", y = "Number of Datasets" ) + # Theme theme( legend.position = "top", legend.title = element_blank(), legend.spacing.x = unit(0.2, "cm"), panel.grid.minor = element_blank(), panel.grid.major.x = element_blank(), plot.title = element_text( size = rel(1.3), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.9), family = fonts$caption, color = colors$caption, margin = margin(b = 10) ), axis.title = element_text( size = rel(0.9), family = fonts$caption, color = colors$caption ), ) # Combine plots combined_plot <- (p1 / p2) + plot_layout(heights = c(0.8, 1.2)) combined_plot <- combined_plot + plot_annotation( title = title_text, subtitle = subtitle_text, caption = caption_text, theme = theme( plot.title = element_text( size = rel(1.75), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.9), family = fonts$subtitle, color = colors$subtitle, lineheight = 1.2, margin = margin(t = 5, b = 5) ), plot.caption = element_markdown( size = rel(0.6), family = fonts$caption, color = colors$caption, hjust = 0.5, margin = margin(t = 10) ), plot.margin = margin(t = 20, r = 10, b = 20, l = 10), ) ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot_patchwork( plot = combined_plot, type = "tidytuesday", year = 2025, week = 6, width = 10, height = 10 ) ``` #### 8. Session Info ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### 9. GitHub Repository ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in [`tt_2025_06.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/tt_2025_06.qmd). For the full repository, [click here](https://github.com/poncest/personal-website/). ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data Sources: - TidyTuesday 2025 Week 06: [CDC Datasets](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-02-11) :::