Making more glamorous graphs in ggplot2

Redrawing my old ‘growth of R packages’ plot with ideas from William R. Chase’s ‘The Glamour of Graphics’.

NoteUpdated in 2026

Lightly edited from a 2020 post. The plot is re-rendered with current packages (size became linewidth in ggplot2). The data stop in February 2020, and the scraper is shown but not run, since the repository pages it parsed have changed.

Inspired by William R. Chase’s rstudio::conf 2020 talk “The Glamour of Graphics”, I decided to spruce up an old plot. It is the one I show my students every year to motivate them to learn R: the number of R packages available in various repositories over the years.

From William’s talk I took these lessons:

1. Left-align titles.
2. Use non-white backgrounds.
3. Skip axis labels if not necessary.
4. Use as few grid lines as possible.
5. Capture the legend in the title.

There is much more in the talk that I do not implement here, for example using professional fonts.

Libraries

library(tidyverse)
library(curl)
library(ggrepel)
library(scales)
library(ggtext)
library(glue)

Data

I have an ugly function that scrapes CRAN, R-Forge, Bioconductor and, later, also GitHub to count R packages (GitHub is a bit problematic, but hopefully a good proxy). The function repeats code that I should have encapsulated and parametrised. No one is perfect.

interrogate_repos <- function() {
  # CRAN
  cran_con <- curl::curl(url = 'https://cran.r-project.org/web/packages/index.html')
  cran <- paste0(readLines(con = cran_con, n = 20), collapse = '')
  cran_pkgs <- stringr::str_match(cran, 'repository features ([0-9]{1,}) available packages')[2]

  # R-Forge
  rforge_con <- curl::curl(url = 'https://r-forge.r-project.org')
  rforge <- paste0(readLines(con = rforge_con, n = 200), collapse = '')
  rforge_pkgs <- stringr::str_replace(stringr::str_match(rforge, 'Projects: <strong>([0-9]{1,},[0-9]{1,})</strong>')[2], ',', '')

  # Bioconductor
  bioconductor_con <- curl::curl(url = 'https://bioconductor.org')
  bioconductor <- paste0(readLines(con = bioconductor_con, n = 200), collapse = '')
  bioconductor_pkgs <- stringr::str_match(bioconductor, 'Software\">([0-9]{1,}).*software packages.')[2]

  # GitHub
  github_con <- curl::curl(url = 'https://github.com/search?l=R&q=R+package&type=Repositories')
  github <- paste0(readLines(con = github_con, n = 1200), collapse = '')
  github_pkgs <- stringr::str_replace(stringr::str_match(github, '([0-9]{1,},[0-9]{1,}) repository results')[2], ',', '')

  day <- format(Sys.time(), "%Y-%m-%d")
  result <- c(day, cran_pkgs, rforge_pkgs, bioconductor_pkgs, github_pkgs)
  names(result) <- c('date', 'CRAN', 'R-Forge', 'Bioconductor', 'GitHub')
  return(result)
}

From time to time I ran the following code to update my dataset:

prev_data <- read_csv('num_pkgs_data.csv')
today <- interrogate_repos()
if (last(prev_data$date) < today[1]) {
  prev_data <- rbind(prev_data, today)
  write_csv(x = prev_data, 'num_pkgs_data.csv')
}

The dataset looks like this:

prev_data <- read_csv('num_pkgs_data.csv')
prev_data

# A tibble: 10 × 5
   date        cran rforge bioconductor github
   <date>     <dbl>  <dbl>        <dbl>  <dbl>
 1 2011-05-17  2984    998          460     NA
 2 2011-11-22  3429   1182          516     NA
 3 2012-04-25  3745   1274          554     NA
 4 2013-07-11  4689   1584          671     NA
 5 2013-09-24  4846   1631          671     NA
 6 2016-08-31  9066   2024         1211     NA
 7 2017-03-22 10312   2048         1296     NA
 8 2017-10-20 11646   2063         1383     NA
 9 2018-11-11 13342   2086         1649     NA
10 2020-02-03 15330   2121         1823  28667

Data transformation

To plot this sensibly, I convert it from wide to long format:

data <- prev_data |>
  pivot_longer(cols = c(cran, rforge, bioconductor, github), names_to = 'repo') |>
  mutate(value = as.integer(value))

Plotting

I needed some cool colours. On my way home from rstudio::conf 2020 I watched Taika Waititi’s “Jojo Rabbit”, which I found visually appealing. I grabbed a screenshot and, with the Colormind tool, created two palettes from it.

jojo1 <- c('#c0b3aa', '#2b2117', '#9a8268', '#d3a677', '#e1d0ba')
jojo2 <- c('#221c17', '#947e66', '#da9869', '#e0c6a4', '#c1b3ab')

And now the most glamorous version of the plot, with the legend living in the title:

cols <- c('bioconductor' = jojo2[2], 'cran' = jojo2[3], 'github' = jojo2[1], 'rforge' = jojo2[5])

title <- glue('Log # of R packages available at',
              ' <span style="color:{cols[2]}">**CRAN**</span>,',
              ' <span style="color:{cols[4]}">**R-Forge**</span>,',
              ' <span style="color:{cols[1]}">**Bioconductor**</span>',
              ' and',
              ' <span style="color:{cols[3]}">**GitHub**</span>',
              ' over time.')

ggplot(data, mapping = aes(x = date, y = log(value), col = repo)) +
  geom_point(show.legend = FALSE) +
  geom_line(show.legend = FALSE) +
  geom_text_repel(aes(label = comma(value)), direction = 'y', vjust = 0.5, show.legend = FALSE) +
  scale_colour_manual(values = cols) +
  labs(title = title, x = '', y = '') +
  theme_minimal() +
  theme(panel.grid.major = element_line(colour = '#EEEEEE', linewidth = 0.25),
        plot.background = element_rect(fill = '#FFFDF8', colour = '#FFFDF8'),
        axis.title = element_text(colour = jojo2[5]),
        axis.text = element_text(colour = jojo2[5], size = 10),
        axis.title.y = element_blank(),
        plot.title = element_markdown(lineheight = 1.5, size = 12),
        plot.title.position = 'plot')