Citrus: The Only Effective Treatment for Scurvy (1757)

Fractions of patients showing improvement vs. continuing symptoms after treatment

A visualization exploring James Lind’s pioneering 1757 scurvy treatment trial, illustrating how citrus was the only effective remedy among six treatments tested. This diverging stacked bar chart highlights the fractions of patients showing improvement versus continuing symptoms, showcasing an early example of evidence-based medicine.

Figure 1: A diverging stacked bar chart comparing scurvy treatments from 1757. Only citrus shows 100% of patients with improvement (50% none, 50% mild symptoms), while all other treatments show moderate to severe symptoms. Dilute sulfuric acid shows 50% mild improvement. The chart demonstrates why citrus was the only effective treatment for scurvy.

Steps to Create this Graphic

1. Load Packages & Setup

## 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
  camcorder     # Record Your Plot History
)
})

### |- figure size ----
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

tt <- tidytuesdayR::tt_load(2023, week = 30) 

scurvy <- tt$scurvy |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)

3. Examine the Data

glimpse(scurvy)
skim(scurvy)

4. Tidy Data

### |- Tidy ----
# Define constants upfront
symptom_names <- c(
  gum_rot_d6 = "Gum Rot",
  skin_sores_d6 = "Skin Sores", 
  weakness_of_the_knees_d6 = "Knee Weakness",
  lassitude_d6 = "Lassitude"
)

treatment_names <- c(
  dilute_sulfuric_acid = "Dilute Sulfuric Acid",
  purgative_mixture = "Purgative Mixture",
  sea_water = "Sea Water"
)

# Treatment effectiveness order 
treatment_order <- c(
  "Citrus", "Cider", "Dilute Sulfuric Acid", 
  "Vinegar", "Sea Water", "Purgative Mixture"
)

# Process scurvy data 
complete_diverging_data <- scurvy |>
  # Convert Likert scales to ordered factors
  mutate(across(
    names(symptom_names),
    \(x) factor(
      case_when(
        str_detect(x, "0_none") ~ "None (0)",
        str_detect(x, "1_mild") ~ "Mild (1)",
        str_detect(x, "2_moderate") ~ "Moderate (2)",
        str_detect(x, "3_severe") ~ "Severe (3)"
      ),
      levels = c("None (0)", "Mild (1)", "Moderate (2)", "Severe (3)")
    )
  )) |>
  # Clean treatment names
  mutate(
    treatment_clean = case_when(
      treatment %in% names(treatment_names) ~ treatment_names[treatment],
      TRUE ~ str_to_title(treatment)
    )
  ) |>
  # Convert to long format
  pivot_longer(
    cols = names(symptom_names),
    names_to = "symptom",
    values_to = "severity"
  ) |>
  # Map symptom codes to readable names
  mutate(symptom = symptom_names[symptom]) |>
  # Calculate proportions
  group_by(treatment_clean, symptom) |>
  count(severity) |>
  mutate(proportion = n / sum(n)) |>
  ungroup() |>
  # Create diverging data structure
  mutate(
    position = if_else(
      severity %in% c("None (0)", "Mild (1)"), 
      "Improved", 
      "Problem"
    ),
    plot_proportion = if_else(position == "Improved", -proportion, proportion),
    severity_ordered = factor(
      severity, 
      levels = c("None (0)", "Mild (1)", "Moderate (2)", "Severe (3)")
    )
  ) |>
  # Ensure all combinations exist (handle missing values)
  complete(
    treatment_clean, 
    symptom, 
    severity_ordered,
    fill = list(proportion = 0, plot_proportion = 0, n = 0)
  ) |>
  # Recreate position for new rows
  mutate(
    position = case_when(
      severity_ordered %in% c("None (0)", "Mild (1)") ~ "Improved",
      severity_ordered %in% c("Moderate (2)", "Severe (3)") ~ "Problem",
      TRUE ~ NA_character_
    )
  )

5. Visualization Parameters

### |- plot aesthetics ---- 
colors <- get_theme_colors(palette = c(
  "None (0)" = "#1d4e89",       
  "Mild (1)" = "#4b86c5",      
  "Moderate (2)" = "#e8996f",  
  "Severe (3)" = "#d95d33"      
  ))  

### |-  titles and caption ----
# text
title_text    <- str_glue("Citrus: The Only Effective Treatment for Scurvy (1757)") 
subtitle_text <- str_glue("Fractions of patients showing improvement vs. continuing symptoms after treatment")

# Create caption
caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 01,
  source_text =  "{ medicaldata } 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(
    # 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$text, size = rel(0.8)),
    axis.text.x = element_text(color = colors$text, size = rel(0.7)),
    axis.text.y = element_text(color = colors$text, size = rel(0.75), face = "bold"),
    
    # Grid elements
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    
    # Legend elements
    legend.position = "top",
    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

### |-  Plot ----
p <- ggplot(
  complete_diverging_data |>
    filter(symptom == "Gum Rot") |>
    mutate(treatment_clean = factor(treatment_clean, levels = treatment_order))
) +
  # Geoms
  geom_col(
    aes(
      x = treatment_clean,
      y = plot_proportion,
      fill = severity_ordered
    ),
    position = "stack"
  ) +
  geom_text(
    aes(
      x = treatment_clean,
      y = plot_proportion,
      label = ifelse(proportion >= 0.05,
        scales::percent(abs(proportion),
          accuracy = 1
        ), ""
      )
    ),
    position = position_stack(vjust = 0.5),
    color = "white",
    size = 3.5,
    fontface = "bold"
  ) +
  # Scales
  scale_y_continuous(
    labels = function(x) paste0(abs(x) * 100, "%"),
    limits = c(-1, 1),
    breaks = seq(-1, 1, 0.25),
    minor_breaks = NULL
  ) +
  scale_fill_manual(
    values = colors$palette
  ) +
  coord_flip(clip = "off") +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    x = NULL,
    y = NULL,
    fill = "Severity Level",
    caption = caption_text
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.75),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(0.95),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.caption = element_markdown(
      size = rel(.65),
      family = fonts$caption,
      color = colors$caption,
      lineheight = 0.65,
      hjust = 0.5,
      halign = 0.5,
      margin = margin(t = 10, b = 5)
    ),
  )

7. Save

### |-  plot image ----  

save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 01, 
  width = 8, 
  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 scales_1.3.0    skimr_2.1.5    
 [5] janitor_2.2.0   showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1  
[13] dplyr_1.1.4     purrr_1.0.2     readr_2.1.5     tidyr_1.3.1    
[17] tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0

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_0.4.0         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        pacman_0.5.1       pkgconfig_2.0.3    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       magick_2.8.5      
[29] commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4     
[33] rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0        
[37] colorspace_2.1-1   cli_3.6.3          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_1.1.4        gridtext_0.1.5    
[57] Rcpp_1.0.13-1      glue_1.8.0         xml2_1.3.6         renv_1.0.3        
[61] vroom_1.6.5        svglite_2.1.3      rstudioapi_0.17.1  jsonlite_1.8.9    
[65] 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_01.qmd.

For the full repository, click here.

10. References

Expand for References

1. Data Sources:
   • TidyTuesday 2023 week 30 Scurvy: Scurvy