This is an extension of the tidytuesday assignment you have already done. Complete the questions below, using the screencast you chose for the tidytuesday assigment.
library(tidyverse) theme_set(theme_light())
horror_movies_raw <- readr::read_csv(“https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-22/horror_movies.csv”)
This data set a a compilation of 3,328 horror movies and ratings. Variables in play are title, genres, relase_date, relase_country, movie_rating, review_rating, movie_run_time, plot, cast, launguage, filming_location and budget. Their are lots of variables on this set but most of them are not signifacant. In my Data set the varibles used will be genre, launguage and budget in dollars.
Hint: One graph of your choice.
horror_movies <- horror_movies_raw%>% arrange(desc(review_rating)) %>% extract(title, “year”, “\((\d\d\d\d)\)$”, remove = FALSE, convert = TRUE) %>% mutate(budget = parse_number(budget)) %>% separate(plot, c(“director”, “cast_sentence”, “plot”), extra = “merge”, sep = “\.”, fill = “right”) %>% distinct(title, .keep_all = TRUE)
This graph is a reprensentation of movie budget and the amount of movies that have that budget. Further down the road this graph leads David to the question “do higher budget movies end up higher rated”. He found using review_rating and budget that their was little to no correlation. ## Hide the messages, but display the code and its results on the webpage.