2019-07-30

Video Games Dataset

This week's data comes courtesy of Liza Wood via Steam Spy. She recently published a blog post on her data analysis of this video game data.

She was kind enough to provide a fairly clean dataset, and I have done some small additional clean up seen below.

There is time played, ownership, release date, publishing information, and for some a metascore! Lots of ways to slice and dice this data!

Warning

Please be advised that the average and median playtime is over the last two weeks, as such there are many many games where playtime is low or zero.

Get the data!

video_games <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-07-30/video_games.csv")

Data Dictionary

video_games.csv

variable	class	description
number	double	Game number
game	character	Game Title
release_date	character	Release date
price	double	US Dollars + Cents
owners	character	Estimated number of people owning this game.
developer	character	Group that developed the game
publisher	character	Group that published the game
average_playtime	double	Average playtime in minutes
median_playtime	double	Median playtime in minutes
metascore	double	Metascore rating

library(tidyverse)

# clean dataset from lizawood's github
url <- "https://raw.githubusercontent.com/lizawood/apps-and-games/master/PC_Games/PCgames_2004_2018_raw.csv"

# read in raw data
raw_df <- url %>% 
  read_csv() %>% 
  janitor::clean_names() 

# clean up some of the factors and playtime data
clean_df <- raw_df %>% 
  mutate(price = as.numeric(price),
         score_rank = word(score_rank_userscore_metascore, 1),
         average_playtime = word(playtime_median, 1),
         median_playtime = word(playtime_median, 2),
         median_playtime = str_remove(median_playtime, "\\("),
         median_playtime = str_remove(median_playtime, "\\)"),
         average_playtime = 60 * as.numeric(str_sub(average_playtime, 1, 2)) +
           as.numeric(str_sub(average_playtime, 4, 5)),
         median_playtime = 60 * as.numeric(str_sub(median_playtime, 1, 2)) +
           as.numeric(str_sub(median_playtime, 4, 5)),
         metascore = as.double(str_sub(score_rank_userscore_metascore, start = -4, end = -3))) %>% 
  select(-score_rank_userscore_metascore, -score_rank, -playtime_median) %>% 
  rename(publisher = publisher_s, developer = developer_s)