R-Ladies chapters on meetup.com

Install and load packages

library(tidytext)
library(ggwordcloud)
library(dplyr)
library(tidyr)
library(ggplot2)
library(meetupr)

How many R-Ladies chapter are out there?

We can use the find_groups() function to search for groups with “r-ladies” in their name. Note that the function returns up to 200 results, so we will filter them afterwards.

meetup_groups <- find_groups("r-ladies")
meetup_groups

Since the search might return groups that are not actually R-Ladies chapters, we will filter them by name.

rladies <- meetup_groups |>
  filter(grepl("R-Ladies", name, ignore.case = TRUE))

rladies

Now, while searching for R-Ladies chapters is a nice way to get started, a more reliable way is to use the get_pro_groups() function, which retrieves all the official R-Ladies chapters. Since R-Ladies is a pro organization on meetup.com, we can use this function to get the list of all chapters that are part of the R-Ladies network.

rladies_pro <- get_pro_groups("rladies")

Growth

Let us now visualize the growth of R-Ladies chapters over time.

df <- rladies_pro |>
  mutate(
    dategroup = format(founded_date, "%Y-%m")
  ) |>
  group_by(dategroup) |>
  tally() |>
  mutate(
    cum_sum = cumsum(n)
  )

ggplot(
  data = df,
  aes(x = dategroup, y = cum_sum)
) +
  geom_bar(
    stat = "identity",
    color = "#88398A",
    fill = "#88398A"
  ) +
  theme_bw() +
  labs(
    title = "Growth of R-Ladies chapters",
    caption = "Data from meetup.com via the meetupR package",
    x = "",
    y = "Number of chapters"
  ) +
  theme(
    axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)
  ) +
  scale_x_discrete(
    breaks = df$dategroup[seq(1, length(df$dategroup), by = 2)]
  )

Chapter activity

Let us now extract the number of past events and the dates of last and next events, for each chapter.

rladies_events <- get_pro_events("rladies")

event_summary <- rladies_events |>
  group_by(group_name, group_urlname) |>
  summarise(
    n_past = ifelse("PAST" %in% status, length(status), 0),
    last_event = ifelse(
      "PAST" %in% status,
      format(max(date_time[status == "PAST"]), "%Y-%m-%d"),
      NA
    ),
    next_event = ifelse(
      "ACTIVE" %in% status,
      format(min(date_time[status == "ACTIVE"]), "%Y-%m-%d"),
      NA
    )
  )
event_summary

Which chapters have never had events and have not even planned one (and have been created more than 6 months ago)?

rladies <- rladies_pro |>
  rename(group_urlname = urlname) |>
  left_join(event_summary, by = "group_urlname")

rladies |>
  filter(
    n_past == 0,
    is.na(next_event),
    founded_date < Sys.Date() - 6 * 30
  ) |>
  arrange(founded_date)

Which chapters had no events in the past 6 months and have not even planned one?

rladies |>
  filter(
    last_event < as.POSIXct("2019-03-29"),
    is.na(next_event)
  ) |>
  arrange(last_event)

Wordcloud of events

Finally, let us create a wordcloud of the most common words in the event descriptions of all R-Ladies chapters.

# Strip html tags from event descriptions
strip_html <- function(x) {
  sapply(x, function(x) {
    gsub("<[^>]+>", "", x)
  })
}

#' custom function to create wordcloud data
rladies_wc <- function(data, column, n = 100) {
  data |>
    unnest_tokens(word, {{ column }}) |>
    anti_join(stop_words, by = "word") |>
    filter(
      !grepl("^[0-9]+$", word),
      nchar(word) > 2,
      !word %in% c("p", "br", "href", "http", "https", "class", "www", "com"),
      !word %in%
        c(
          "de",
          "la",
          "et",
          "les",
          "des",
          "le",
          "en",
          "un",
          "une",
          "du",
          "para",
          "con",
          "del",
          "el",
          "se",
          "y",
          "los",
          "es"
        )
    ) |>
    count(word, sort = TRUE) |>
    slice_max(n, n = n) |>

    ggplot(aes(
      label = word,
      size = n,
      color = n
    )) +
    geom_text_wordcloud() +
    scale_size_area(max_size = 12) +
    scale_color_gradient(
      low = "#88398A",
      high = "#562457"
    ) +
    theme_minimal()
}

rladies_wc(rladies_events, description) +
  labs(
    title = "Most common words in R-Ladies event descriptions",
    caption = "Data from meetup.com via the meetupr package"
  )