theme_set(theme_bw())

cols <- wes_palette(name = "IsleofDogs1")

Each project closes with a table summarising the R tools used. By visualising my most frequently used packages and functions I get a sense of where I may most benefit from going deeper and keeping abreast of the latest breaking changes.

I may also spot superseded functions e.g. spread and gather may now be replaced by pivot_wider and pivot_longer. Or an opportunity to switch a non-tidyverse package for a newer tidyverse (or ecosystem) alternative, e.g. for UpSetR I can now use ggupset which plays well with ggplot.

I’ll start by listing the paths to the html files in the project directory.

files <- list.files(
  path = "/Users/carl/R Projects/blogdown6/content/project/",
  pattern = "\\.html$", recursive = TRUE
) %>%
  str_c("/Users/carl/R Projects/blogdown6/content/project/", .) %>% 
  as_tibble() %>% 
  filter(!str_detect(value, "world|dt1|appfiles")) %>% 
  pull()

This enables me to extract the usage table for each project.

table_df <- map_dfr(files, function(x) {
  x %>%
    read_html() %>%
    html_nodes("#r-toolbox table") %>%
    html_table() %>%
    bind_rows()
}) %>% clean_names(replace = c("io" = ""))

A little “spring cleaning” is needed, and separation of tidyverse and non-tidyverse packages.

• tidyverse
• tidymodels
• tidyverts

tidyv <- tidyverse_packages()
tidyf <- fpp3_packages()
tidym <- tidymodels_packages()

tidy <- c(tidyv, tidyf, tidym) %>% unique()

tidy_df <- table_df %>%
  separate_rows(functn, sep = ";") %>%
  separate(functn, c("functn", "count"), literal("[")) %>%
  mutate(
    count = str_remove(count, "]") %>% as.integer(),
    functn = str_squish(functn)
  ) %>%
  group_by(package, functn) %>%
  summarise(count = sum(count)) %>%
  mutate(multiverse = case_when(
    package %in% tidy ~ "tidy",
    package %in% c("base", "graphics") ~ "base",
    TRUE ~ "special"
  ))

Then I can summarise usage and prepare for a faceted plot.

pack_df <- tidy_df %>%
  group_by(package, multiverse) %>%
  summarise(count = sum(count)) %>%
  ungroup() %>%
  mutate(name = "package")

fun_df <- tidy_df %>%
  group_by(functn, multiverse) %>%
  summarise(count = sum(count)) %>%
  ungroup() %>%
  mutate(name = "function")

n_url <- files %>% n_distinct()

packfun_df <- pack_df %>%
  bind_rows(fun_df) %>%
  group_by(name) %>%
  arrange(desc(count)) %>%
  mutate(
    packfun = coalesce(package, functn),
    name = fct_rev(name)
  ) 

Clearly “dplyr rules”! And mutate is slugging it out with library.

packfun_df %>%
  slice(1:20) %>% 
  ggplot(aes(reorder_within(packfun, count, name), count, fill = multiverse)) +
  geom_col() +
  geom_label(aes(label = count), size = 3, fill = "white") +
  facet_wrap(~name, ncol = 1, scales = "free", strip.position = "left") +
  scale_x_reordered() +
  scale_y_continuous(expand = expansion(mult = c(0, .15))) +
  scale_fill_manual(values = cols[c(2, 3, 1)]) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "bottom",
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    strip.background = element_rect(fill = cols[6]),
    strip.text = element_text(colour= "white")
  ) +
  labs(
    title = "Favourite Things",
    subtitle = glue("Most Frequent Usage Across {n_url} Projects"),
    x = NULL, y = NULL
  )

I’d also like a wordcloud. And thanks to blogdown, the updated visualisation is picked up as the new featured image for this project.

set.seed = 123

packfun_df %>%
  mutate(angle = 90 * sample(c(0, 1), n(), replace = TRUE, prob = c(60, 40))) %>%
  ggplot(aes(label = packfun, size = count, colour = multiverse, angle = angle)) +
  geom_text_wordcloud(eccentricity = .9, seed = 456) +
  scale_radius(range = c(0, 40), limits = c(0, NA)) +
  scale_colour_manual(values = cols[c(2:4)]) +
  theme_void() +
  theme(plot.background = element_rect(fill = cols[1]))