I wasn’t planning to spend a lot of time on this, as I don’t really know much or care much about comic books, and life calls in lots of ways! But I wanted to keep practicing my tidy skills, and the hour I spent working on these data paid off!
I thought it might be interesting to see how often some of the issues focused on characters experiencing different actions, broken down by Bechdel Test status. I tried geom_hist
and geom_bar
, but because of the count default, I wasn’t really seeing a good comparison (there are a lot more issues which pass the Bechdel Test than don’t).
Through some sleuthing, I was able to find the stat_count
function which uses y=..prop..
in aes()
to determine the y-axis. I also messed with the colors a bit. And though there aren’t any important results with respect to the comic books, I was able to create the exact plots I wanted.
Also, shout out to Malcolm Barrett (https://twitter.com/malco_barrett) who is doing so many great things in the #rstats community!! Thanks, Malcolm.
comic_bechdel <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-30/comic_bechdel.csv')
character_visualization <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-30/character_visualization.csv')
characters <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-30/characters.csv')
xmen_bechdel <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-30/xmen_bechdel.csv')
covers <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-30/covers.csv')
issue_collaborators <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-30/issue_collaborators.csv')
locations <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-30/locations.csv')
xmen <- full_join(xmen_bechdel, characters, by = "issue")
temp <- xmen %>%
filter(!is.na(character)) %>%
group_by(issue, pass_bechdel) %>%
summarize(tornclothes = sum(clothing_torn), dead = sum(declared_dead), quits = sum(quits_team),
kiss = sum(!is.na(kiss_with_which_character)),
hug = sum(!is.na(hugging_with_which_character)),
date = sum(!is.na(on_a_date_with_which_character)),
dance = sum(!is.na(dancing_with_which_character)))
temp %>% group_by(pass_bechdel) %>% summarise(n())
## # A tibble: 2 x 2
## pass_bechdel `n()`
## <chr> <int>
## 1 no 42
## 2 yes 148
temp %>%
ggplot() +
stat_count(aes(x=tornclothes, fill = pass_bechdel, group = pass_bechdel,
y = ..prop..), position = "dodge") +
scale_fill_manual(values = c("steelblue", "darkgoldenrod2" ))
temp %>%
ggplot() +
stat_count(aes(x=dead, fill = pass_bechdel, group = pass_bechdel,
y = ..prop..), position = "dodge") +
scale_fill_manual(values = c("steelblue", "darkgoldenrod2" ))
temp %>%
ggplot() +
stat_count(aes(x=quits, fill = pass_bechdel, group = pass_bechdel,
y = ..prop..), position = "dodge") +
scale_fill_manual(values = c("steelblue", "darkgoldenrod2" ))
temp %>%
ggplot() +
stat_count(aes(x=kiss, fill = pass_bechdel, group = pass_bechdel,
y = ..prop..), position = "dodge") +
scale_fill_manual(values = c("steelblue", "darkgoldenrod2" ))
temp %>%
ggplot() +
stat_count(aes(x=hug, fill = pass_bechdel, group = pass_bechdel,
y = ..prop..), position = "dodge") +
scale_fill_manual(values = c("steelblue", "darkgoldenrod2" ))
temp %>%
ggplot() +
stat_count(aes(x=date, fill = pass_bechdel, group = pass_bechdel,
y = ..prop..), position = "dodge") +
scale_fill_manual(values = c("steelblue", "darkgoldenrod2" ))
temp %>%
ggplot() +
stat_count(aes(x=dance, fill = pass_bechdel, group = pass_bechdel,
y = ..prop..), position = "dodge") +
scale_fill_manual(values = c("steelblue", "darkgoldenrod2" ))