Analysis of 1,041 Terminated NSF Grants Totaling $613.26M

Representing 1,2993.5 years years of lost research time across four distinct patterns of termination

TidyTuesday
Data Visualization
R Programming
2025
Exploring patterns in NSF grants terminated in 2025 using K-means clustering to reveal four distinct groups based on funding amount, completion percentage, and remaining time, visualizing the impact on American scientific research.
Author

Steven Ponce

Published

May 4, 2025

Figure 1: Scatter plot showing analysis of 1,041 terminated NSF grants totaling $613.26M, representing 1293.5 years of lost research time. The visualization is divided into four quadrants showing different grant clusters: Early-stage Mega Grants (51 grants, 66% complete, 547 days left), Early-stage High-value Grants (382 grants, 81% complete, 236 days left), Late-stage High-value Grants (353 grants, 32% complete, 865 days left), and Late-stage Mid-value Grants (255 grants, 86% complete, 192 days left). Each cluster is represented in a different color (orange, teal, purple, and pink), with point size indicating days remaining. The y-axis shows the funding amount on a logarithmic scale, and the x-axis shows the percentage of grants completed.

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
  skimr,     # Compact and Flexible Summaries of Data
  scales,    # Scale Functions for Visualization
  glue,      # Interpreted String Literals
  here,      # A Simpler Way to Find Your Files
  cluster,   # "Finding Groups in Data": Cluster Analysis Extended Rousseeuw et al.
  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 
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2025, week = 18)

nsf_terminations_raw <- tt$nsf_terminations |> 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(nsf_terminations_raw)
skim(nsf_terminations_raw)
```

4. Tidy Data

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

### |-  tidy data ----
nsf_terminations <- nsf_terminations_raw |>
  mutate(
    # Calculate how long the grant had been running before termination
    days_active = as.numeric(termination_letter_date - nsf_startdate),
    months_active = days_active / 30.44,
    years_active = months_active / 12,

    # Calculate how much time was cut short
    days_remaining = as.numeric(nsf_expected_end_date - termination_letter_date),
    months_remaining = days_remaining / 30.44, # 365.25/12 = 30.4375. 365.25 days per year to account for leap years
    years_remaining = months_remaining / 12,

    # Calculate percentage of grant period completed
    grant_duration_days = as.numeric(nsf_expected_end_date - nsf_startdate),
    pct_completed = days_active / grant_duration_days * 100,

    # Round dollar amounts
    funding_millions = usaspending_obligated / 1000000,

    # Create date variables
    term_year = year(termination_letter_date),
    term_month = month(termination_letter_date),
    term_day = day(termination_letter_date),
  )

set.seed(42) # seed for reproducibility
k <- 4 # Number of clusters

# Prepare data for clustering
cluster_data <- nsf_terminations |>
  select(
    usaspending_obligated, days_active, days_remaining,
    pct_completed, in_cruz_list
  ) |>
  mutate(
    in_cruz_list = as.numeric(in_cruz_list),
    # Scale the data
    across(where(is.numeric), ~ scale(.x)[, 1]),
    # Fill in missing values with mean (0 after scaling)
    across(everything(), ~ ifelse(is.na(.x), 0, .x))
  )

# Perform k-means clustering
kmeans_result <- kmeans(cluster_data, centers = k, nstart = 25)

# Add cluster assignments back to the original data
nsf_clusters <- nsf_terminations |>
  mutate(
    # Numeric clusters for calculation
    cluster_num = kmeans_result$cluster,
    # Descriptive labels
    cluster = case_when(
      kmeans_result$cluster == 1 ~ "Early-stage High-value Grants",
      kmeans_result$cluster == 2 ~ "Late-stage High-value Grants",
      kmeans_result$cluster == 3 ~ "Early-stage Mega Grants",
      kmeans_result$cluster == 4 ~ "Late-stage Mid-value Grants",
      TRUE ~ paste("Cluster", kmeans_result$cluster)
    )
  )

# Create detailed summary of each cluster for the table and titles
cluster_summary <- nsf_clusters |>
  group_by(cluster, cluster_num) |>
  summarize(
    count = n(),
    pct_of_total = n() / nrow(nsf_clusters) * 100,
    total_funding = sum(usaspending_obligated, na.rm = TRUE),
    avg_funding = mean(usaspending_obligated, na.rm = TRUE),
    median_funding = median(usaspending_obligated, na.rm = TRUE),
    avg_days_active = mean(days_active, na.rm = TRUE),
    avg_days_remaining = mean(days_remaining, na.rm = TRUE),
    avg_pct_completed = mean(pct_completed, na.rm = TRUE),
    pct_cruz_list = mean(in_cruz_list == TRUE, na.rm = TRUE) * 100,
    .groups = "drop"
  ) |>
  # Format values for presentation
  mutate(
    # Formatted values for tables
    formatted_count = scales::comma(count),
    formatted_pct = paste0(round(pct_of_total, 1), "%"),
    formatted_total = scales::dollar(total_funding),
    formatted_avg = scales::dollar(avg_funding, accuracy = 1),
    formatted_median = scales::dollar(median_funding),
    formatted_days_remaining = round(avg_days_remaining, 0),
    formatted_pct_completed = paste0(round(avg_pct_completed), "%"),
    formatted_cruz = paste0(round(pct_cruz_list, 1), "%"),

    # Create concise strip labels with key stats
    # Format: Main title + count | Avg $ | Completion % | Days left
    concise_label = paste0(
      cluster, "\n",
      count, " grants | Avg: ", scales::dollar(avg_funding, scale = 1 / 1000), "K | ",
      round(avg_pct_completed), "% done | ",
      round(avg_days_remaining), " days left"
    )
  )

# Calculate overall totals for data-driven title
overall_stats <- nsf_clusters |>
  summarize(
    total_count = n(),
    total_funding = sum(usaspending_obligated, na.rm = TRUE),
    avg_funding = mean(usaspending_obligated, na.rm = TRUE),
    total_days_remaining = sum(days_remaining, na.rm = TRUE) / 365.25 # Convert to years
  ) |>
  mutate(
    # Format for title
    formatted_total = scales::dollar(total_funding, scale = 1 / 1000000, suffix = "M"),
    formatted_count = scales::comma(total_count),
    formatted_years = round(total_days_remaining, 1)
  )

# Create enhanced data for plotting with strip labels from summary
nsf_clusters_plot <- nsf_clusters |>
  left_join(
    cluster_summary |> select(cluster, concise_label),
    by = "cluster"
  )
```

5. Visualization Parameters

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

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = c(
    "Early-stage High-value Grants" = "#1CB3A6",
    "Late-stage High-value Grants" = "#5E60CE",
    "Early-stage Mega Grants" = "#FF8500",
    "Late-stage Mid-value Grants" = "#FF5A8C"
  )
)

### |-  custom facet ordering ----
### |-  Add ordering to existing cluster_summary ----
# First, add an ordering field to the existing data
cluster_summary <- cluster_summary |>
  mutate(
    # Create a new column for custom ordering
    cluster_order = case_when(
      cluster == "Early-stage Mega Grants" ~ 1,
      cluster == "Early-stage High-value Grants" ~ 2,
      cluster == "Late-stage High-value Grants" ~ 3,
      cluster == "Late-stage Mid-value Grants" ~ 4,
      TRUE ~ 5
    )
  )

### |-  Generate formatted strip labels with richtext ----
# Create richtext labels with different formatting for title and details
cluster_summary <- cluster_summary |>
    mutate(
        # Keep the ordering from before
        cluster_order = case_when(
            cluster == "Early-stage Mega Grants" ~ 1,
            cluster == "Early-stage High-value Grants" ~ 2,
            cluster == "Late-stage High-value Grants" ~ 3,
            cluster == "Late-stage Mid-value Grants" ~ 4,
            TRUE ~ 5
        ),
        
        # Create a two-part rich text label with different styling
        richtext_label = paste0(
            # First line (cluster name) - larger and bold
            "<span style='font-size:14pt; font-weight:bold;'>", 
            cluster, 
            "</span><br>",
            # Second line (details) - smaller and regular weight
            "<span style='font-size:8pt; font-weight:normal; color:gray40;'>",
            count, " grants | Avg: ", scales::dollar(avg_funding, scale = 1/1000), "K | ", 
            round(avg_pct_completed), "% done | ", 
            round(avg_days_remaining), " days left",
            "</span>"
        )
    )

# Update the plot data with formatted labels
nsf_clusters_plot <- nsf_clusters |>
    left_join(
        cluster_summary |> select(cluster, richtext_label, cluster_order),
        by = "cluster"
    ) |>
    # Convert to factor with custom ordering
    mutate(
        richtext_label = factor(richtext_label, 
                                levels = cluster_summary |> 
                                    arrange(cluster_order) |> 
                                    pull(richtext_label))
    )

### |-  titles and caption ----
title_text <- str_glue(
  "Analysis of ", overall_stats$formatted_count,
  " Terminated NSF Grants Totaling ", overall_stats$formatted_total
)
subtitle_text <- str_glue(
  "Representing ", overall_stats$formatted_years,
  " years of lost research time across four distinct patterns of termination"
)

# Create caption
caption_text <- create_social_caption(
  tt_year = 2025,
  tt_week = 18,
  source_text =  "Grant Watch NSF Terminations Dataset"
)

### |-  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 = element_text(color = colors$text, face = "bold", size = rel(0.8)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),

    # Grid elements
    panel.grid.minor = element_line(color = "gray80", linewidth = 0.05),
    panel.grid.major = element_line(color = "gray80", linewidth = 0.05),

    # Legend elements
    legend.position = "top",
    legend.direction = "horizontal",
    legend.title = element_text(family = fonts$text, size = rel(0.8), face = "bold"),
    legend.text = element_text(family = fonts$text, size = rel(0.7)),

    # Style facet labels
    strip.text = element_text(size = rel(0.75), face = "bold", 
                              color = colors$title, margin = margin(b = 8, t = 8)),

    # Add spacing
    panel.spacing = unit(1.2, "lines"),
    strip.background = element_rect(fill = "#e0e0e0", color = NA),

    # Plot margins
    plot.margin = margin(t = 20, r = 20, b = 20, l = 20),
  )
 )

# Set theme
theme_set(weekly_theme)
```

6. Plot

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

### |-  Plot  ----
p <- ggplot(
  nsf_clusters_plot,
  aes(x = pct_completed, y = usaspending_obligated, color = cluster, size = days_remaining)
  ) +
  # Geoms
  geom_point(alpha = 0.5) +
  # Scales
  scale_y_log10(labels = label_dollar()) +
  scale_color_manual(values = colors$palette) +
  # Improved size scale with meaningful breaks
  scale_size_continuous(
    breaks = c(100, 500, 1000, 1500),
    labels = c("100", "500", "1000", "1500"),
    range = c(0.5, 5)
  ) +
  # Facets with ordered layout
    facet_wrap(~ richtext_label, nrow = 2) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Percentage of Grant Completed",
    y = "Funding Amount (USD, log scale)",
    size = "Days Remaining",
  ) +
  # Legend
  guides(
    # Remove the color legend
    color = "none",
    size = guide_legend(
      title.position = "top",
      override.aes = list(
        alpha = 1,
        stroke = 1,
        fill = NA,
        color = "black"
      ),
      direction = "horizontal",
      keywidth = unit(1, "lines"),
      keyheight = unit(1, "lines"),
      title.hjust = 0.5,
      label.position = "bottom"
    )
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.8),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(0.85),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9),
      lineheight = 1.2,
      margin = margin(t = 5, b = 10)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$caption,
      color = colors$caption,
      hjust = 0.5,
      margin = margin(t = 10)
    ),
    legend.key = element_rect(fill = NA),
    strip.text = element_markdown(
        lineheight = 1.2,
        padding = 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 = 18, 
  width = 10,
  height = 10
)
```

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] camcorder_0.1.0 cluster_2.1.6   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] gtable_0.3.6       httr2_1.0.6        xfun_0.49          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    lifecycle_1.0.4    farver_2.1.2      
[17] compiler_4.4.0     textshaping_0.4.0  munsell_0.5.1      repr_1.1.7        
[21] codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1  yaml_2.3.10       
[25] crayon_1.5.3       pillar_1.9.0       magick_2.8.5       commonmark_1.9.2  
[29] tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4      labeling_0.4.3    
[33] rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0        
[37] colorspace_2.1-1   cli_3.6.4          magrittr_2.0.3     base64enc_0.1-3   
[41] utf8_1.2.4         withr_3.0.2        rappdirs_0.3.3     bit64_4.5.2       
[45] timechange_0.3.0   rmarkdown_2.29     tidytuesdayR_1.1.2 gitcreds_0.1.2    
[49] bit_4.5.0          ragg_1.3.3         hms_1.1.3          evaluate_1.0.1    
[53] knitr_1.49         markdown_1.13      rlang_1.1.6        gridtext_0.1.5    
[57] Rcpp_1.0.13-1      xml2_1.3.6         renv_1.0.3         vroom_1.6.5       
[61] svglite_2.1.3      rstudioapi_0.17.1  jsonlite_1.8.9     R6_2.5.1          
[65] systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in tt_2025_18.qmd.

For the full repository, click here.

10. References

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