Health Funding Surges While Education Slips

Funder priorities, 2020 -> 2025

An arrow chart redesign avoiding the spaghetti graph problem, showing how non-profit funder support shifted across five categories over five years.

Author

Steven Ponce

Published

October 1, 2025

Original

This month’s Storytelling with Data challenge aims to avoid the spaghetti graph. Line graphs are generally a great way to show data over time. However, when there are too many series in a single chart, it can quickly become what I refer to as a “spaghetti graph”—a tangled mess of overlapping lines that’s hard to read. How would you redesign the following visual to avoid the spaghetti graph?

Additional information can be found HERE

Makeover

Figure 2: Arrow chart showing changes in non-profit funder support from 2020 to 2025. Health increased by 8% to 75%, while Arts & Culture grew by 23% to 43%. Education declined by 13% to 60%, Human Services fell by 5% to 55%, and Other dropped by 23% to 30%. A dashed vertical line marks the 2025 median at 55%.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

```{r}
#| label: load

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  =  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

```{r}
#| label: read

raw_data <- readxl::read_excel(
  here::here("data/SWDchallenge/2025/SWDchallenge OCT2025.xlsx"),
  range = "C7:I12") |> 
  clean_names()
```

3. Examine the Data

Show code

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

glimpse(raw_data)
```

4. Tidy Data

Show code

```{r}
#| label: tidy

arrow_data <- raw_data |>
  rename(category = x1) |>
  select(category, y2020 = x2020, y2025 = x2025) |>
  mutate(
    category = str_to_title(category),
    change = y2025 - y2020,
    change_pct = change,
    direction = if_else(change > 0, "Growth", "Decline")
  ) |>
  arrange(desc(y2025))

arrow_data <- arrow_data |>
  mutate(
    category = factor(category, levels = rev(category)),
    lab_2020 = label_percent(accuracy = 1)(y2020),
    lab_2025 = label_percent(accuracy = 1)(y2025),
    lab_delta = if_else(change >= 0,
      paste0("+", label_percent(accuracy = 1)(change)),
      label_percent(accuracy = 1)(change)
    ),
    x_mid = (y2020 + y2025) / 2,
    hjust_2020 = if_else(y2020 <= y2025, 1.15, -0.15),
    hjust_2025 = if_else(y2020 <= y2025, -0.15, 1.15)
  )

median_2025 <- median(arrow_data$y2025, na.rm = TRUE)
n_cats <- nrow(arrow_data)
max_x <- max(arrow_data$y2020, arrow_data$y2025, na.rm = TRUE)
```

5. Visualization Parameters

Show code

```{r}
#| label: params

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = c(
    `TRUE` = "#1976D2",   
    `FALSE` = "#F57C00"
  )
)

### |-  titles and caption ----
title_text <- str_glue("Health Funding Surges While Education Slips")
subtitle_text <- str_glue(
  "Funder priorities, 2020 -> 2025 (dashed = 2025 median)\n",
  "Percentages can exceed 100% because funders choose multiple categories • Data self-reported by funders"
  )

# Create caption
caption_text <- create_swd_caption(
  year = 2025,
  month = "Oct",
  source_text =  "Storytelling with Data: A Data Visualization Guide for Business Professionals"
)

### |-  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$title, color = colors$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$text, size = rel(0.8)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),
    
    # Grid elements
    panel.grid.minor = element_blank(),
    panel.grid.major = element_line(color = "grey95", linewidth = 0.1),
    

    # Legend elements
    legend.position = "plot",
    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

```{r}
#| label: plot

p <- ggplot(arrow_data, aes(y = category)) +

  # Geoms
  geom_vline(
    xintercept = median_2025, linetype = "dashed",
    color = "gray35", linewidth = 0.7
  ) +

  # median tag
  annotate(
    "label",
    x = median_2025, y = n_cats + 0.35,
    label = paste0("2025 median: ", label_percent(accuracy = 1)(median_2025)),
    size = 3.2, fontface = "bold",
    label.size = 0, fill = "white", alpha = 0.95, color = "gray15"
  ) +

  # arrows
  geom_segment(
    aes(x = y2020, xend = y2025, yend = category, color = change > 0),
    linewidth = 1.4, lineend = "round",
    arrow = arrow(length = unit(0.28, "cm"), type = "closed")
  ) +

  # arrows labels
  geom_text(aes(x = y2020, label = lab_2020, hjust = hjust_2020),
    size = 3.2, color = "gray35", fontface = "bold"
  ) +
  geom_text(aes(x = y2025, label = lab_2025, hjust = hjust_2025, color = change > 0),
    size = 3.2, fontface = "bold", show.legend = FALSE
  ) +
  geom_text(aes(x = x_mid, label = lab_delta, color = change > 0),
    vjust = -0.9, size = 3.3, fontface = "bold", show.legend = FALSE
  ) +

  # Scales
  scale_x_continuous(
    labels = label_percent(accuracy = 1),
    limits = c(0, max_x + 0.12),
    breaks = seq(0, 0.90, by = 0.10),
    expand = expansion(mult = c(0.01, 0.07))
  ) +
  scale_y_discrete() +
  scale_color_manual(values = colors$palette) +
  coord_cartesian(clip = "off") +

  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    x = "Percent of funders", y = NULL,
    caption = caption_text
  ) +

  # Theme
  theme(
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_line(color = "gray93", linewidth = 0.4),
    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(0.95),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9),
      lineheight = 1.2,
      margin = margin(t = 5, b = 10)
    ),
    plot.caption = element_markdown(
      size = rel(0.65),
      family = fonts$caption,
      color = colors$caption,
      hjust = 0.5,
      margin = margin(t = 10)
    )
  )
```

7. Save

Show code

```{r}
#| label: save

### |-  plot image ----  
save_plot(
  p, 
  type = 'swd', 
  year = 2025, 
  month = 10, 
  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      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         htmlwidgets_1.6.4 tzdb_0.5.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] gifski_1.32.0-1   fansi_1.0.6       pkgconfig_2.0.3   readxl_1.4.3     
[13] rematch_2.0.0     lifecycle_1.0.4   compiler_4.4.0    farver_2.1.2     
[17] textshaping_0.4.0 munsell_0.5.1     codetools_0.2-20  snakecase_0.11.1 
[21] htmltools_0.5.8.1 yaml_2.3.10       pillar_1.9.0      camcorder_0.1.0  
[25] magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1  digest_0.6.37    
[29] stringi_1.8.4     rsvg_2.6.1        rprojroot_2.0.4   fastmap_1.2.0    
[33] grid_4.4.0        colorspace_2.1-1  cli_3.6.4         magrittr_2.0.3   
[37] utf8_1.2.4        withr_3.0.2       timechange_0.3.0  rmarkdown_2.29   
[41] cellranger_1.1.0  ragg_1.3.3        hms_1.1.3         evaluate_1.0.1   
[45] knitr_1.49        markdown_1.13     rlang_1.1.6       gridtext_0.1.5   
[49] Rcpp_1.0.13-1     xml2_1.3.6        renv_1.0.3        svglite_2.1.3    
[53] rstudioapi_0.17.1 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 swd_2025_10.qmd. For the full repository, click here.

10. References

Expand for References

Data Sources:

Storytelling with Data: A Data Visualization Guide for Business Professionals, 10th Anniversary Edition : Storytelling with Data: A Data Visualization Guide for Business Professionals, 10th Anniversary Edition