Load the Data and Check Duplicates

library(tidyverse)
library(lubridate)
library(kableExtra)
library(ggridges)


# there were complete duplicated rows
dat <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-22/horror_movies.csv") %>%
  distinct(.) # removes complete dups

# check duplicates
dup_title <- dat %>%
  filter(duplicated(title) | duplicated(title, fromLast = TRUE)) %>%
  arrange(title)

# examined they seem different movies even though same title
dup_title %>%
  filter(duplicated(plot))

## # A tibble: 0 x 12
## # … with 12 variables: title <chr>, genres <chr>, release_date <chr>,
## #   release_country <chr>, movie_rating <chr>, review_rating <dbl>,
## #   movie_run_time <chr>, plot <chr>, cast <chr>, language <chr>,
## #   filming_locations <chr>, budget <chr>

dup_title %>%
  filter(duplicated(release_date)| duplicated(release_date, fromLast = TRUE))

## # A tibble: 2 x 12
##   title genres release_date release_country movie_rating review_rating
##   <chr> <chr>  <chr>        <chr>           <chr>                <dbl>
## 1 The … Comed… 21-Jul-15    USA             <NA>                   5.2
## 2 The … Comed… 21-Jul-15    USA             NOT RATED              3.6
## # … with 6 more variables: movie_run_time <chr>, plot <chr>, cast <chr>,
## #   language <chr>, filming_locations <chr>, budget <chr>

# The Jokesters seems to be a duplicate but with different rating and run time
# Deleting it for now
dat <- dat %>%
  filter(title != "The Jokesters (2015)")

Genres

The genre column looked extremely messy so some data munging fun. Each film can be categorized into multiple genres.

dat_long <- dat %>% 
  separate_rows(genres, sep = "\\|") %>% # long format
  mutate(genres = str_trim(genres))  

# Just to check - looks okay - just 1 movie with no genre
table(dat_long$genres, useNA = "ifany")

## 
##     Action      Adult  Adventure  Animation  Biography     Comedy 
##        335          1        115         39          4        511 
##      Crime      Drama     Family    Fantasy    History     Horror 
##        120        529         11        229          6       3309 
##      Music    Musical    Mystery Reality-TV    Romance     Sci-Fi 
##          5         13        453          1         99        308 
##      Sport   Thriller        War    Western       <NA> 
##          4       1369         14         15          1

dat_long <- dat_long %>%
  mutate(genres = fct_infreq(fct_lump(genres, n = 8))) # Factor keeping 8 most frequent categories and lumping the rest to Other and order the factor by frequency

genre_count <- dat_long %>%
  filter(!is.na(genres)) %>%
  group_by(genres) %>%
  summarize(n = n()) %>%
  ungroup() 

kable(genre_count, format = "html", table.attr = "style = \"color: white;\"") %>%
  kable_styling(bootstrap_options = "striped", full_width = F)

genre_count %>%
  ggplot(aes(x = genres, y = n, fill = genres)) +
  geom_bar(stat = "identity") + 
  scale_y_continuous(labels = scales::comma) +  # y axis to have commas 
  scale_fill_brewer(palette ="BuPu", direction = -1) + # reverse order the palette
  theme_light() + 
  labs(x = "", y = "Number of Films") + 
  theme(legend.position = "none")

Review Rating by Release Year

Some of the years are dmy format, some just have the years. I am extracting the year and filling in any that didn’t parse with the year value from the original release_date column. No missing values for year :)

date_dat <- dat %>%
  mutate(date = dmy(release_date),
         yr = year(date),
         yr = ifelse(is.na(yr), release_date, yr))

table(is.na(date_dat$yr))

## 
## FALSE 
##  3310

table(is.na(date_dat$review_rating))

## 
## FALSE  TRUE 
##  3058   252

date_dat %>%
  select(release_date, date, yr) %>%
  filter(is.na(date)) %>%
  head()

## # A tibble: 6 x 3
##   release_date date       yr   
##   <chr>        <date>     <chr>
## 1 2017         NA         2017 
## 2 2013         NA         2013 
## 3 2012         NA         2012 
## 4 2013         NA         2013 
## 5 2017         NA         2017 
## 6 2017         NA         2017

date_dat %>%  
  ggplot(aes(x = yr, y = review_rating, fill = yr)) +
  geom_boxplot(alpha = .5) + 
  labs(x = "Release Year", y = "Review Rating") + 
  theme_light()  + 
  theme(legend.position = "none")

Looks like there is a slight increase in ratings for newer films.

And here is a ridgeline plot :)

date_dat %>%  
  ggplot(aes(y = yr, x = review_rating, fill = yr)) +
  geom_density_ridges(alpha = .5) + 
  labs(y = "Release Year", x = "Review Rating") +
  theme_light() + 
  theme(legend.position = "none")