Preserving Public Health Knowledge: CDC Dataset Categories at Risk

Large repositories like NNDSS and NCHS contain essential health surveillance data being backed up

A treemap visualization for Day 4 of the #30DayChartChallenge 2025, comparing the sizes of CDC dataset categories being preserved by Archive.org amid concerns of data removal. The visualization highlights how two categories—NNDSS and NCHS—account for nearly 40% of all archived CDC datasets, underscoring what scientific resources are being protected from potential loss.

Author

Steven Ponce

Published

April 4, 2025

Figure 1: Treemap visualization titled ‘Preserving Public Health Knowledge: CDC Dataset Categories at Risk’ showing the distribution of archived CDC datasets by category. The largest categories are NNDSS (293 datasets, 23.3%) and NCHS (184 datasets, 14.6%), which together represent over a third of all preserved data. Other significant categories include Vaccinations (78 datasets), Public Health Surveillance (68 datasets), and 500 Cities & Places (57 datasets). Smaller categories include Policy, Funding, and Health Statistics. A color gradient from light orange to dark purple indicates dataset size from smallest to largest.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
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
  treemapify,     # Draw Treemaps in 'ggplot2'
  camcorder       # Record Your Plot History
)
})

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

tt <- tidytuesdayR::tt_load(2025, week = 6) 

cdc_datasets <- tt$cdc_datasets |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)

3. Examine the Data

Show code

glimpse(cdc_datasets)
skim(cdc_datasets)

4. Tidy Data

Show code

### |- Tidy ----
# Count datasets by category
category_counts <- cdc_datasets |>
  count(category) |>    
  arrange(desc(n)) |>
  filter(!is.na(category)) |>
  mutate(
    category = if_else(is.na(category), "Uncategorized", category),
    percent = n / sum(n) * 100,
    label = paste0(category, "\n", n, " (", round(percent, 1), "%)")
    )

5. Visualization Parameters

Show code

### |- plot aesthetics ---- 
colors <- get_theme_colors(palette =  colorRampPalette(c("#FFAF7B", "#D76D77", "#3A1C71", "black"))(30)
  )

### |-  titles and caption ----
# text
title_text    <- str_glue("Preserving Public Health Knowledge: CDC Dataset Categories at Risk") 
subtitle_text <- str_glue("Large repositories like NNDSS and NCHS contain essential health surveillance data being backed up")

# Create caption
caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 04,
  source_text =  "archive.org" 
)

### |-  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(
    # Text styling 
    plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
    plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
    
    # Axis elements
    axis.title.y = element_text(color = colors$text, size = rel(0.8),
                              hjust = 1, vjust = 0.5, angle = 90),
    axis.title.x = element_blank(),
    axis.text.x = element_text(color = colors$text, size = rel(0.7)),
    axis.text.y = ggtext::element_markdown(),
  
    # Grid elements
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
 
    # 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 = 15, b = 10, l = 15),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot ----
p <- ggplot(category_counts, aes(area = n, fill = n, label = label)) +
  # Geoms
  geom_treemap() +
  geom_treemap_text(
    colour = "white", 
    place = "centre", 
    grow = TRUE,
    min.size = 4,         
    fontface = "bold",    
    family = "sans"       
  ) +
  # Scales
  scale_fill_gradientn(
    colors = colors$palette, 
    trans = "log10",
    labels = comma_format(),  
    breaks = c(1, 10, 100, 300),  
    guide = guide_colorbar(
      direction = "vertical",
      barwidth = 1,
      barheight = 10,
      title.position = "top",
      title.hjust = 0.5
    )
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    fill = "Number of datasets",
    caption = caption_text
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.7),
      family = fonts$title,
      face  = "bold",
      color = colors$title,
      margin  = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size  = rel(1),
      family  = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.1,
      margin  = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
      size  = rel(.65),
      family  = fonts$caption,
      color = colors$caption,
      lineheight  = 0.65,
      hjust = 0.5,
      halign  = 0.5,
      margin  = margin(t = 10, b = 5)
    ),
  )

7. Save

Show code

### |-  plot image ----  

save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 04, 
  width = 10, 
  height = 8
  )

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] here_1.0.1       camcorder_0.1.0  treemapify_2.5.6 scales_1.3.0    
 [5] skimr_2.1.5      janitor_2.2.0    showtext_0.9-7   showtextdb_3.0  
 [9] sysfonts_0.8.9   ggtext_0.1.2     lubridate_1.9.3  forcats_1.0.0   
[13] stringr_1.5.1    dplyr_1.1.4      purrr_1.0.2      readr_2.1.5     
[17] tidyr_1.3.1      tibble_3.2.1     ggplot2_3.5.1    tidyverse_2.0.0 

loaded via a namespace (and not attached):
 [1] ggfittext_0.10.2   gtable_0.3.6       httr2_1.0.6        xfun_0.49         
 [5] htmlwidgets_1.6.4  gh_1.4.1           tzdb_0.4.0         vctrs_0.6.5       
 [9] tools_4.4.0        generics_0.1.3     parallel_4.4.0     curl_6.0.0        
[13] gifski_1.32.0-1    fansi_1.0.6        pacman_0.5.1       pkgconfig_2.0.3   
[17] lifecycle_1.0.4    farver_2.1.2       compiler_4.4.0     textshaping_0.4.0 
[21] munsell_0.5.1      repr_1.1.7         codetools_0.2-20   snakecase_0.11.1  
[25] htmltools_0.5.8.1  yaml_2.3.10        crayon_1.5.3       pillar_1.9.0      
[29] magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37     
[33] stringi_1.8.4      rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0     
[37] grid_4.4.0         colorspace_2.1-1   cli_3.6.3          magrittr_2.0.3    
[41] base64enc_0.1-3    utf8_1.2.4         withr_3.0.2        rappdirs_0.3.3    
[45] bit64_4.5.2        timechange_0.3.0   tidytuesdayR_1.1.2 rmarkdown_2.29    
[49] gitcreds_0.1.2     bit_4.5.0          ragg_1.3.3         hms_1.1.3         
[53] evaluate_1.0.1     knitr_1.49         markdown_1.13      rlang_1.1.4       
[57] gridtext_0.1.5     Rcpp_1.0.13-1      glue_1.8.0         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

Expand for GitHub Repo

The complete code for this analysis is available in 30dcc_2025_04.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:
- TidyTuesday 2025 week 06: CDC Datasets

--- title: "Preserving Public Health Knowledge: CDC Dataset Categories at Risk" subtitle: "Large repositories like NNDSS and NCHS contain essential health surveillance data being backed up" description: "A treemap visualization for Day 4 of the #30DayChartChallenge 2025, comparing the sizes of CDC dataset categories being preserved by Archive.org amid concerns of data removal. The visualization highlights how two categories—NNDSS and NCHS—account for nearly 40% of all archived CDC datasets, underscoring what scientific resources are being protected from potential loss." author: "Steven Ponce" date: "2025-04-04" categories: ["30DayChartChallenge", "Data Visualization", "R Programming", "2025"] tags: [ "treemap", "ggplot2", "public health", "data preservation", "CDC", "archive.org", "big-vs-small", "health surveillance", "data rescue", "NNDSS", "NCHS" ] image: "thumbnails/30dcc_2025_04.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" self-contained: true theme: light: [flatly, assets/styling/custom_styles.scss] dark: [darkly, assets/styling/custom_styles_dark.scss] 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/30DayChartChallenge/2025/30dcc_2025_04.html" # description: "Day 4 of #30DayChartChallenge: Visualizing the size disparities among CDC dataset categories being preserved by Archive.org. See which public health data collections are most at risk and which contain the most information." # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![Treemap visualization titled 'Preserving Public Health Knowledge: CDC Dataset Categories at Risk' showing the distribution of archived CDC datasets by category. The largest categories are NNDSS (293 datasets, 23.3%) and NCHS (184 datasets, 14.6%), which together represent over a third of all preserved data. Other significant categories include Vaccinations (78 datasets), Public Health Surveillance (68 datasets), and 500 Cities & Places (57 datasets). Smaller categories include Policy, Funding, and Health Statistics. A color gradient from light orange to dark purple indicates dataset size from smallest to largest.](30dcc_2025_04.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({ 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 treemapify, # Draw Treemaps in 'ggplot2' camcorder # Record Your Plot History ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 10, 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 ```{r} #| label: read #| include: true #| eval: true #| warning: false tt <- tidytuesdayR::tt_load(2025, week = 6) cdc_datasets <- tt$cdc_datasets |> clean_names() tidytuesdayR::readme(tt) rm(tt) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(cdc_datasets) skim(cdc_datasets) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- Tidy ---- # Count datasets by category category_counts <- cdc_datasets |> count(category) |> arrange(desc(n)) |> filter(!is.na(category)) |> mutate( category = if_else(is.na(category), "Uncategorized", category), percent = n / sum(n) * 100, label = paste0(category, "\n", n, " (", round(percent, 1), "%)") ) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors(palette = colorRampPalette(c("#FFAF7B", "#D76D77", "#3A1C71", "black"))(30) ) ### |- titles and caption ---- # text title_text <- str_glue("Preserving Public Health Knowledge: CDC Dataset Categories at Risk") subtitle_text <- str_glue("Large repositories like NNDSS and NCHS contain essential health surveillance data being backed up") # Create caption caption_text <- create_dcc_caption( dcc_year = 2025, dcc_day = 04, source_text = "archive.org" ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( # Text styling plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)), plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)), # Axis elements axis.title.y = element_text(color = colors$text, size = rel(0.8), hjust = 1, vjust = 0.5, angle = 90), axis.title.x = element_blank(), axis.text.x = element_text(color = colors$text, size = rel(0.7)), axis.text.y = ggtext::element_markdown(), # Grid elements panel.grid.major = element_blank(), panel.grid.minor = element_blank(), # 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 = 15, b = 10, l = 15), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- p <- ggplot(category_counts, aes(area = n, fill = n, label = label)) + # Geoms geom_treemap() + geom_treemap_text( colour = "white", place = "centre", grow = TRUE, min.size = 4, fontface = "bold", family = "sans" ) + # Scales scale_fill_gradientn( colors = colors$palette, trans = "log10", labels = comma_format(), breaks = c(1, 10, 100, 300), guide = guide_colorbar( direction = "vertical", barwidth = 1, barheight = 10, title.position = "top", title.hjust = 0.5 ) ) + # Labs labs( title = title_text, subtitle = subtitle_text, fill = "Number of datasets", caption = caption_text ) + # Theme theme( plot.title = element_text( size = rel(1.7), family = fonts$title, face = "bold", color = colors$title, margin = margin(t = 5, b = 5) ), plot.subtitle = element_markdown( size = rel(1), family = fonts$subtitle, color = colors$subtitle, lineheight = 1.1, margin = margin(t = 5, b = 20) ), plot.caption = element_markdown( size = rel(.65), family = fonts$caption, color = colors$caption, lineheight = 0.65, hjust = 0.5, halign = 0.5, margin = margin(t = 10, b = 5) ), ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot( p, type = "30daychartchallenge", year = 2025, day = 04, width = 10, height = 8 ) ``` #### 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 [`30dcc_2025_04.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/30dcc_2025_04.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) :::