Gender Inequality in Film
Nhi Hin
2021-03-16
Last updated: 2021-12-06
Checks: 7 0
Knit directory: TidyTuesday/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | a751071 | Nhi Hin | 2021-03-23 | Build site. |
| Rmd | 66ebe88 | Nhi Hin | 2021-03-23 | wflow_publish(“analysis/gender.Rmd”) |
| html | 2144705 | Nhi Hin | 2021-03-16 | Build site. |
| html | b0b17f3 | Nhi Hin | 2021-03-16 | Build site. |
| html | 4d5c860 | Nhi Hin | 2021-03-16 | Build site. |
| Rmd | d1f85dc | Nhi Hin | 2021-03-16 | add gender film analysis |
Import Data
- Import in data as follows:
tuesdata <- tidytuesdayR::tt_load(2021, week = 11)--- Compiling #TidyTuesday Information for 2021-03-09 ------- There are 2 files available ------ Starting Download ---
Downloading file 1 of 2: `raw_bechdel.csv`
Downloading file 2 of 2: `movies.csv`--- Download complete ---movies <- tuesdata$movies
raw_bechdel <- tuesdata$raw_bechdel- Preview data:
raw_bechdel# A tibble: 8,839 x 5
year id imdb_id title rating
<dbl> <dbl> <chr> <chr> <dbl>
1 1888 8040 0392728 Roundhay Garden Scene 0
2 1892 5433 0000003 Pauvre Pierrot 0
3 1895 6200 0132134 The Execution of Mary, Queen of Scots 0
4 1895 5444 0000014 Tables Turned on the Gardener 0
5 1896 5406 0000131 Une nuit terrible 0
6 1896 5445 0223341 La fee aux choux 0
7 1896 6199 0000012 The Arrival of a Train 0
8 1896 4982 0000091 The House of the Devil 0
9 1897 9328 0000041 Bataille de neige 0
10 1898 4978 0135696 Four Heads Are Better Than One 0
# … with 8,829 more rowsmovies# A tibble: 1,794 x 34
year imdb title test clean_test binary budget domgross intgross code
<dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
1 2013 tt171… 21 &… notalk notalk FAIL 1.3 e7 25682380 42195766 2013…
2 2012 tt134… Dredd 3D ok-di… ok PASS 4.5 e7 13414714 40868994 2012…
3 2013 tt202… 12 Year… notal… notalk FAIL 2 e7 53107035 1586070… 2013…
4 2013 tt127… 2 Guns notalk notalk FAIL 6.1 e7 75612460 1324930… 2013…
5 2013 tt045… 42 men men FAIL 4 e7 95020213 95020213 2013…
6 2013 tt133… 47 Ronin men men FAIL 2.25e8 38362475 1458038… 2013…
7 2013 tt160… A Good … notalk notalk FAIL 9.2 e7 67349198 3042491… 2013…
8 2013 tt219… About T… ok-di… ok PASS 1.2 e7 15323921 87324746 2013…
9 2013 tt181… Admissi… ok ok PASS 1.3 e7 18007317 18007317 2013…
10 2013 tt181… After E… notalk notalk FAIL 1.3 e8 60522097 2443731… 2013…
# … with 1,784 more rows, and 24 more variables: budget_2013 <dbl>,
# domgross_2013 <chr>, intgross_2013 <chr>, period_code <dbl>,
# decade_code <dbl>, imdb_id <chr>, plot <chr>, rated <chr>, response <lgl>,
# language <chr>, country <chr>, writer <chr>, metascore <dbl>,
# imdb_rating <dbl>, director <chr>, released <chr>, actors <chr>,
# genre <chr>, awards <chr>, runtime <chr>, type <chr>, poster <chr>,
# imdb_votes <dbl>, error <lgl>Analysis Part 1 - The Bechdel Test Over Time
- Let’s try to reproduce the following plot from the FiveThirtyEight article.
- The scoring seems to be coming from the
test(orclean_test) in themoviesdata.
movies$clean_test %>% unique()[1] "notalk" "ok" "men" "nowomen" "dubious"- The histogram below shows that each year, there are more movies.
movies %>%
ggplot(aes(x = year, fill = clean_test)) +
geom_bar() +
ggtitle("The Bechdel Test Over Time")
| Version | Author | Date |
|---|---|---|
| 4d5c860 | Nhi Hin | 2021-03-16 |
- In the FiveThirtyEight plot, they bin the years, and show the values as percentages rather than exact values. Let’s try to do this. First, we set the
breaksaccordingly.
# Adapted from https://www.jdatalab.com/data_science_and_data_mining/2017/01/30/data-binning-plot.html
# set up cut-off values
breaks <- c(1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2013)
# specify interval/bin labels
tags <- c("[1970-1974)",
"[1975-1979)",
"[1980-1984)",
"[1985-1989)",
"[1990-1994)",
"[1995-1999)",
"[2000-2004)",
"[2005-2009)",
"[2010-2013)")
# bucketing values into bins
group_tags <- cut(movies$year,
breaks=breaks,
include.lowest=TRUE,
right=FALSE,
labels=tags)
# inspect bins
summary(group_tags)[1970-1974) [1975-1979) [1980-1984) [1985-1989) [1990-1994) [1995-1999)
21 33 58 67 90 247
[2000-2004) [2005-2009) [2010-2013)
352 488 438 # order factor
movies$year_bin <- factor(group_tags,
levels = tags,
ordered = TRUE)- I’m also going to reorder the
clean_testso that it matches the FiveThirtyEight plot.
movies$clean_test %<>% factor(levels = c("ok", "dubious", "men", "notalk", "nowomen"))- Redo the bar plot for the bins, and change position to fill in order to make it show percentages (see https://www.r-graph-gallery.com/48-grouped-barplot-with-ggplot2.html).
movies %>%
ggplot(aes(x=year_bin, fill = forcats::fct_rev(clean_test))) +
geom_bar(position = "fill", color = "white")+
scale_x_discrete(labels = c("[1970-1974)" = "1970-74",
"[1975-1979)" = "",
"[1980-1984)" = "1980-84",
"[1985-1989)" = "",
"[1990-1994)" = "1990-94",
"[1995-1999)" = "",
"[2000-2004)" = "2000-04",
"[2005-2009)" = "",
"[2010-2013)" = "2010-13")) +
labs(x = "Year", y = "Percentage (%)", fill = "") +
scale_y_continuous(breaks = c(0, 0.25, 0.5, 0.75, 1),
labels = c("0", "25", "50", "75", "100")) +
ggtitle("The Bechdel Test Over Time",
subtitle = "How women are represented in movies") +
scale_fill_manual(values = c("#ff2600", "#ff937f", "#ffcac0", "#6ab2d5", "#008fd5"),
labels = c("ok"="Passes Bechdel Test",
"dubious"="Dubious",
"men"="Women only talk about men",
"notalk"="Women don't talk to each other",
"nowomen"="Fewer than two women"))
Analysis Part 2 - Median Budget for Films
- Also trying to recreate this plot:
The
budget_2013variable makes it simple as this contains dollars which have already been adjusted to be in 2013 dollars as the FiveThirtyEight plot shows.We will prepare a summarised data.frame that contains the median of
budget_2013across all films in each category of the Bechdel Test result stored inclean_test.
movies_summary <- movies %>%
dplyr::group_by(clean_test) %>%
dplyr::summarise(median_budget_2013 = median(budget_2013)) %>%
dplyr::mutate(label = case_when(
clean_test == "ok" ~ "Passes Bechdel Test",
clean_test == "dubious" ~ "Dubious",
clean_test == "men" ~ "Women only talk about men",
clean_test == "notalk" ~ "Women don't talk to each other",
clean_test == "nowomen" ~ "Fewer than two women"
)) %>%
dplyr::mutate(median_budget_2013_M = median_budget_2013 / 1e6)- The bar plot can now be plotted as follows:
movies_summary %>%
ggplot(aes(x = median_budget_2013_M, y = label)) +
geom_bar(stat = "identity", fill = "cornflowerblue") +
labs(x = "millions of dollars", y = "") +
ggtitle("Median Budget for Films Since 1990",
subtitle = "2013 dollars")
| Version | Author | Date |
|---|---|---|
| a751071 | Nhi Hin | 2021-03-23 |
Analysis Part 3 - Sankey Plot (Incomplete)
- This part of the analysis comes from Jodi, who showcased a very interesting and effective use of Sankey plots in the Slack channel.
nodes <- movies$clean_test %>%
unique() %>%
as.data.frame %>%
set_colnames("name") %>%
tibble::rowid_to_column("node")
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] htmlwidgets_1.5.2 networkD3_0.4 scales_1.1.1 ggplot2_3.3.3
[5] magrittr_2.0.1 dplyr_1.0.5 tidytuesdayR_1.0.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.23 bslib_0.2.4 purrr_0.3.4
[5] colorspace_2.0-0 vctrs_0.3.7 generics_0.1.0 htmltools_0.5.1.1
[9] usethis_1.6.3 yaml_2.2.1 utf8_1.1.4 rlang_0.4.10
[13] jquerylib_0.1.3 later_1.1.0.1 pillar_1.6.0 withr_2.3.0
[17] glue_1.4.2 DBI_1.1.0 selectr_0.4-2 readxl_1.3.1
[21] lifecycle_1.0.0 stringr_1.4.0 munsell_0.5.0 gtable_0.3.0
[25] cellranger_1.1.0 rvest_1.0.0 evaluate_0.14 forcats_0.5.1
[29] labeling_0.4.2 knitr_1.30 httpuv_1.5.4 curl_4.3
[33] fansi_0.4.1 Rcpp_1.0.5 readr_1.4.0 promises_1.1.1
[37] jsonlite_1.7.2 farver_2.0.3 fs_1.5.0 hms_1.0.0
[41] digest_0.6.27 stringi_1.5.3 rprojroot_2.0.2 grid_4.0.3
[45] cli_3.0.1 tools_4.0.3 sass_0.3.1 tibble_3.1.1
[49] crayon_1.4.1 whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.1
[53] xml2_1.3.2 lubridate_1.7.10 assertthat_0.2.1 rmarkdown_2.8
[57] httr_1.4.2 rstudioapi_0.13 R6_2.5.0 igraph_1.2.6
[61] git2r_0.27.1 compiler_4.0.3