Genre Ratings From 1917-2016, Excluding Multi Genre.

Loading Librarys and selecting data set.

library(tidyverse)
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library("dslabs")
data(package="dslabs")
list.files(system.file("script", package = "dslabs"))
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data("movielens")

Selecting columns that will be used and arranging by year.

movieratings <- movielens |>
  select("year","genres", "rating") |>
  arrange(year)

Filtering for the genres that are not multi genre.

movielens_f <- movieratings |>
  filter (genres%in%c("Action","Adventure","Animation","Children","Comedy","Crime","Documentary","Drama","Fantasy","Film-Noir","Horror","Musical","Mystery","Romance","Sci-Fi","Thriller","War","Western"))

Grouping by year and genre and getting the mean rating.

movielens_hm <- movielens_f |>
  group_by(year,genres) |>
  summarize(mean_rating = mean(rating))
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.

Heat Map

movielens_hm |>
  ggplot(aes(year, genres,  fill = mean_rating)) +
  geom_tile(color = "black") +
  scale_fill_gradient(low = "red", high = "green", name = "Average Rating") +
  theme_minimal() +
  labs(title = "Genre Ratings From 1917-2016, Excluding Multi Genre",
       caption = "Source: GroupLens Research lab", #searched up source for movielens data set in dslabs library in R
       x = "Year",
       y = "Genre")
## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_tile()`).

movielens_hm
## # A tibble: 454 × 3
## # Groups:   year [98]
##     year genres mean_rating
##    <int> <fct>        <dbl>
##  1  1917 Comedy        4.25
##  2  1918 Comedy        4.25
##  3  1921 Comedy        4.38
##  4  1922 Comedy        4.25
##  5  1922 Horror        3.81
##  6  1923 Comedy        4.25
##  7  1924 Comedy        4.5 
##  8  1924 Drama         3.83
##  9  1925 Comedy        4   
## 10  1926 Drama         3.5 
## # ℹ 444 more rows

Paragraph

This visualization was made with the movielens data set from library dslabs which is about movie ratings. I chose to make a heat map on the mean ratings of genres that are not multi genre. First I selected the columns that I was going to use which where year, genres, and rating. Next I filtered for the genres that are not multi genre. Then grouped by year and genre and got the mean ratings. Lastly created a heat map where the x axis shows the year, the y axsis shows the genre, and the colors go from red(low rating) to green (high rating). I found it interesting to see that comedy and drama had ratings for the most amount of years and that mystery only has ratings for 2 years even though the ratings where high.