Challenge 8

Joining Data

library(tidyverse)
library(ggplot2)
library(dplyr)
library(tidyr)
library(readr)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Reading in the data

This dataset will look at SNL actor/actress gender over time.

actors <- read_csv("_data/snl_actors.csv")
casts <- read_csv("_data/snl_casts.csv")
seasons <- read_csv("_data/snl_seasons.csv")

tibble(actors, 10)

# A tibble: 2,306 × 5
   aid            url           type  gender   `10`
   <chr>          <chr>         <chr> <chr>   <dbl>
 1 Kate McKinnon  /Cast/?KaMc   cast  female     10
 2 Alex Moffat    /Cast/?AlMo   cast  male       10
 3 Ego Nwodim     /Cast/?EgNw   cast  unknown    10
 4 Chris Redd     /Cast/?ChRe   cast  male       10
 5 Kenan Thompson /Cast/?KeTh   cast  male       10
 6 Carey Mulligan /Guests/?3677 guest andy       10
 7 Marcus Mumford /Guests/?3679 guest male       10
 8 Aidy Bryant    /Cast/?AiBr   cast  female     10
 9 Steve Higgins  /Crew/?StHi   crew  male       10
10 Mikey Day      /Cast/?MiDa   cast  male       10
# … with 2,296 more rows

tibble(casts, 10)

# A tibble: 614 × 9
   aid                sid featured first…¹ last_…² updat…³ n_epi…⁴ seaso…⁵  `10`
   <chr>            <dbl> <lgl>      <dbl>   <dbl> <lgl>     <dbl>   <dbl> <dbl>
 1 A. Whitney Brown    11 TRUE      1.99e7      NA FALSE         8   0.444    10
 2 A. Whitney Brown    12 TRUE     NA           NA FALSE        20   1        10
 3 A. Whitney Brown    13 TRUE     NA           NA FALSE        13   1        10
 4 A. Whitney Brown    14 TRUE     NA           NA FALSE        20   1        10
 5 A. Whitney Brown    15 TRUE     NA           NA FALSE        20   1        10
 6 A. Whitney Brown    16 TRUE     NA           NA FALSE        20   1        10
 7 Alan Zweibel         5 TRUE      1.98e7      NA FALSE         5   0.25     10
 8 Sasheer Zamata      39 TRUE      2.01e7      NA FALSE        11   0.524    10
 9 Sasheer Zamata      40 TRUE     NA           NA FALSE        21   1        10
10 Sasheer Zamata      41 FALSE    NA           NA FALSE        21   1        10
# … with 604 more rows, and abbreviated variable names ¹​first_epid, ²​last_epid,
#   ³​update_anchor, ⁴​n_episodes, ⁵​season_fraction

tibble(seasons, 10)

# A tibble: 46 × 6
     sid  year first_epid last_epid n_episodes  `10`
   <dbl> <dbl>      <dbl>     <dbl>      <dbl> <dbl>
 1     1  1975   19751011  19760731         24    10
 2     2  1976   19760918  19770521         22    10
 3     3  1977   19770924  19780520         20    10
 4     4  1978   19781007  19790526         20    10
 5     5  1979   19791013  19800524         20    10
 6     6  1980   19801115  19810411         13    10
 7     7  1981   19811003  19820522         20    10
 8     8  1982   19820925  19830514         20    10
 9     9  1983   19831008  19840512         19    10
10    10  1984   19841006  19850413         17    10
# … with 36 more rows

In order to join these datasets I will have to join actors and casts by “aid” or actor ID and then join that with seasons through “sid” or season ID.

unique(actors$ "type")

[1] "cast"    "guest"   "crew"    "unknown"

As a way to filter out the potential skewing of data through guests starring or crew (though those would also be interesting to look at later) I will filter these out of our datasets.

casts_gender_count <- casts %>%
  left_join(filter(actors, type=="cast"), 
            by="aid") %>%
  count(sid, gender)
tibble(casts_gender_count)

# A tibble: 102 × 3
     sid gender     n
   <dbl> <chr>  <int>
 1     1 female     3
 2     1 male       6
 3     2 female     3
 4     2 male       5
 5     3 female     3
 6     3 male       6
 7     4 female     3
 8     4 male       6
 9     5 female     3
10     5 male      12
# … with 92 more rows

cast_prop_all <- casts_gender_count %>%
  group_by(sid) %>%
  mutate(prop=n/sum(n)) %>%
  ungroup () %>%
  select(-n) %>%
  pivot_wider(names_from = gender,
              values_from = prop) %>%
  mutate(across(everything(),~replace_na(.,0))) %>%
  pivot_longer(c(female, male, `NA`, unknown),
               values_to = "prop",
               names_to = "gender")
tibble(cast_prop_all)

# A tibble: 184 × 3
     sid gender   prop
   <dbl> <chr>   <dbl>
 1     1 female  0.333
 2     1 male    0.667
 3     1 NA      0    
 4     1 unknown 0    
 5     2 female  0.375
 6     2 male    0.625
 7     2 NA      0    
 8     2 unknown 0    
 9     3 female  0.333
10     3 male    0.667
# … with 174 more rows

Visualization