library(tidyverse)
library(httr)
library(jsonlite)
library(stringi)607 Final Project
Approach
For this project I will be collecting data on video game sales as well as video game reviews and seeing how factors like game sales and genre affect video game reviews.
I will limit the scope to finding a correlation/relationship between the sales a videogame has and the reviews it gets from Rawg.io
Collecting Data
To start this project we need to first collect the data.
I have chosen two data sources one is a csv from https://www.kaggle.com/datasets/volodymyrpivoshenko/video-game-sales-dataset
The other is using an api of a gaming review website rawg.io. This is a link to their api documentation https://api.rawg.io/docs/#tag/developers
First I will collect the data from the csv. This will serve as our base data that we will use to query the api.
url <- "https://raw.githubusercontent.com/DylanGoldJ/607-Final-Project/refs/heads/main/video_games_sales.csv"
df <- read_csv(
file = url,
col_names = TRUE
)
head(df, 5)# A tibble: 5 × 11
Rank Name Platform Year Genre Publisher NA_Sales EU_Sales JP_Sales
<dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 1 Wii Sports Wii 2006 Spor… Nintendo 41.5 29.0 3.77
2 2 Super Mario B… NES 1985 Plat… Nintendo 29.1 3.58 6.81
3 3 Mario Kart Wii Wii 2008 Raci… Nintendo 15.8 12.9 3.79
4 4 Wii Sports Re… Wii 2009 Spor… Nintendo 15.8 11.0 3.28
5 5 Pokemon Red/P… GB 1996 Role… Nintendo 11.3 8.89 10.2
# ℹ 2 more variables: Other_Sales <dbl>, Global_Sales <dbl>Now I also need to collect data from the api.
I will start with a simple example.
First I will load in my key
api_key <- Sys.getenv("RAWGIO_API_KEY") # Load api key from envrionment.Because the api has way more total games than our data set we will query the data set for each game individually.
First I will try to get Wii Sports game info from the api.
api_url <- "https://api.rawg.io/api/games"
response <- GET(
api_url,
query = list(
key = api_key, # Api key
search = "Wii Sports" # Wii sports as our testing value.
)
)
status_code(response)[1] 200We got a sucessful response. Now I will extract the data from the response
data <- fromJSON(content(response, "text", encoding = "UTF-8"))
game_review <- data$results
head(game_review) slug name playtime
1 wii-sports Wii Sports 55
2 wii-sports-resort Wii Sports Resort 2
3 pokepark-wii-pikachus-adventure PokePark Wii: Pikachu's Adventure 0
4 wii-sports-itch Wii sports (itch) 0
5 wii-sports-club Wii Sports Club 21
6 e-sport-manager E-Sport Manager 1
platforms
1 11, Wii, wii
2 11, Wii, wii
3 11, Wii, wii
4 171, Web, web
5 10, Wii U, wii-u
6 4, 7, PC, Nintendo Switch, pc, nintendo-switch
stores released tba
1 6, Nintendo Store, nintendo 2006-11-19 FALSE
2 6, Nintendo Store, nintendo 2009-07-26 FALSE
3 6, Nintendo Store, nintendo 2010-11-02 FALSE
4 9, itch.io, itch 2023-04-09 FALSE
5 6, Nintendo Store, nintendo 2013-10-30 FALSE
6 1, 6, Steam, Nintendo Store, steam, nintendo 2018-06-16 FALSE
background_image
1 https://media.rawg.io/media/games/173/1739bdc5c33e85a0fa54b499a173690b.jpg
2 https://media.rawg.io/media/screenshots/472/472c6954864d52a7681e7c8ef3d3a94b.jpg
3 https://media.rawg.io/media/games/d15/d15da3cfeb0ab883caed37134b5c5c34.jpg
4 https://media.rawg.io/media/screenshots/031/031b17a241ec3d1036c26b2c1872d816.jpg
5 https://media.rawg.io/media/screenshots/650/650b76388f99fb913be50e0e3937d524.jpg
6 https://media.rawg.io/media/screenshots/2ad/2ad379f560aa6daf4344f106dc754f0b.jpg
rating rating_top
1 4.21 4
2 4.22 4
3 4.00 4
4 0.00 0
5 0.00 0
6 0.00 0
ratings
1 4, 5, 3, 1, recommended, exceptional, meh, skip, 146, 94, 23, 5, 54.48, 35.07, 8.58, 1.87
2 4, 5, 3, 1, recommended, exceptional, meh, skip, 75, 54, 11, 4, 52.08, 37.5, 7.64, 2.78
3 4, 5, 3, 1, recommended, exceptional, meh, skip, 10, 6, 3, 1, 50, 30, 15, 5
4 NULL
5 3, 4, meh, recommended, 3, 1, 75, 25
6 1, 5, skip, exceptional, 2, 1, 66.67, 33.33
ratings_count reviews_text_count added added_by_status.yet
1 264 4 489 12
2 142 2 298 8
3 19 1 56 5
4 0 0 0 NA
5 4 0 13 3
6 2 0 74 2
added_by_status.owned added_by_status.beaten added_by_status.toplay
1 89 270 3
2 55 146 7
3 11 27 6
4 NA NA NA
5 5 2 2
6 69 1 1
added_by_status.dropped added_by_status.playing metacritic suggestions_count
1 109 6 76 177
2 75 7 80 479
3 6 1 62 220
4 NA NA NA 0
5 1 NA NA 282
6 1 NA NA 344
updated id score clip
1 2026-02-21T19:47:50 25057 72.34808 NA
2 2026-02-21T19:49:27 26002 37.27656 NA
3 2024-11-27T23:36:39 26544 22.37658 NA
4 2023-04-10T07:38:39 954422 22.276562 NA
5 2023-08-11T12:57:49 27444 22.276562 NA
6 2020-06-03T15:50:59 51478 21.282118 NA
tags
1 37796, 37797, exclusive, true exclusive, exclusive, true-exclusive, eng, eng, 4491, 3981, https://media.rawg.io/media/games/b7d/b7d3f1715fa8381a4e780173a197a615.jpg, https://media.rawg.io/media/games/d30/d30ef0c7dd4878161b1f781e297ae6a0.jpg
2 37796, 37797, exclusive, true exclusive, exclusive, true-exclusive, eng, eng, 4491, 3981, https://media.rawg.io/media/games/b7d/b7d3f1715fa8381a4e780173a197a615.jpg, https://media.rawg.io/media/games/d30/d30ef0c7dd4878161b1f781e297ae6a0.jpg
3 115, 5816, 1484, 1863, 731, Controller, console, skill, challenge, NES, controller, console, skill, challenge, nes, eng, eng, eng, eng, eng, 15017, 1390, 3533, 12773, 1337, https://media.rawg.io/media/games/04a/04a7e7e185fb51493bdcbe1693a8b3dc.jpg, https://media.rawg.io/media/games/075/0753492cda7ee3c9bd4a3ca673fd0c8c.jpg, https://media.rawg.io/media/screenshots/ec6/ec6eda0d3d08d023793486b253534c27.jpg, https://media.rawg.io/media/games/eb1/eb1ff1ffdab179ff7f0987d0266d4fe5.jpg, https://media.rawg.io/media/screenshots/ada/ada90672691bd75745ee88cd43d49657.jpg
4 NULL
5 37796, 37797, exclusive, true exclusive, exclusive, true-exclusive, eng, eng, 4491, 3980, https://media.rawg.io/media/games/f24/f2493ea338fe7bd3c7d73750a85a0959.jpeg, https://media.rawg.io/media/games/4be/4be6a6ad0364751a96229c56bf69be59.jpg
6 31, 42396, 42417, 42398, 42400, 42421, 42399, 42413, 42444, 42562, 42460, 42576, 42531, 42551, 42631, 42521, 42588, 169, 42474, Singleplayer, Для одного игрока, Экшен, Инди, Атмосфера, Стратегия, Казуальная игра, Симулятор, Песочница, Для всей семьи, Реализм, Изометрия, Спортивная игра, Менеджмент, Управление ресурсами, Экономика, Киберспорт, MOBA, Ячеечный инвентарь, singleplayer, dlia-odnogo-igroka, ekshen, indi-2, atmosfera, strategiia, kazualnaia-igra, simuliator, pesochnitsa, dlia-vsei-semi, realizm, izometriia, sportivnaia-igra, menedzhment, upravlenie-resursami, ekonomika, kibersport, moba, iacheechnyi-inventar, eng, rus, rus, rus, rus, rus, rus, rus, rus, rus, rus, rus, rus, rus, rus, rus, rus, eng, rus, 250833, 70700, 50554, 68749, 6083, 24813, 54922, 26133, 5742, 10884, 9134, 2843, 4672, 5452, 4114, 2475, 967, 712, 309, https://media.rawg.io/media/games/587/587588c64afbff80e6f444eb2e46f9da.jpg, https://media.rawg.io/media/games/b8c/b8c243eaa0fbac8115e0cdccac3f91dc.jpg, https://media.rawg.io/media/games/8cc/8cce7c0e99dcc43d66c8efd42f9d03e3.jpg, https://media.rawg.io/media/games/e04/e04963f3ac4c4fa83a1dc0b9231e50db.jpg, https://media.rawg.io/media/games/8a0/8a02f84a5916ede2f923b88d5f8217ba.jpg, https://media.rawg.io/media/games/879/879c930f9c6787c920153fa2df452eb3.jpg, https://media.rawg.io/media/games/852/8522935d8ab27b610a254b52de0da212.jpg, https://media.rawg.io/media/games/651/651ae84f2d5e36206aad90976a453329.jpg, https://media.rawg.io/media/games/713/713269608dc8f2f40f5a670a14b2de94.jpg, https://media.rawg.io/media/games/9fb/9fbf956a16249def7625ab5dc3d09515.jpg, https://media.rawg.io/media/games/ccd/ccd40e8f86c0ae10a082b610d31d4475.jpg, https://media.rawg.io/media/games/963/9639183ff27251b0b686acaa6aac0297.jpg, https://media.rawg.io/media/games/a1c/a1cea552040aecf9414548e209f9c0d8.jpg, https://media.rawg.io/media/games/476/4767c380895fd35a4f1b59016dc45967.jpg, https://media.rawg.io/media/screenshots/02b/02b2367620982ae26d04ed51d5c35505_apw2ZVh.jpg, https://media.rawg.io/media/games/6bc/6bc79f5bc023b1e6938f6eaf9926f073.jpg, https://media.rawg.io/media/screenshots/54f/54fcf1a626faa92afa3f5d2834dbc5ce.jpg, https://media.rawg.io/media/screenshots/713/71368ff253871f4453a8aa90f86ed262.jpg, https://media.rawg.io/media/screenshots/8d2/8d223357b0c68bb17af037b791e107df.jpg
esrb_rating.id esrb_rating.name esrb_rating.slug esrb_rating.name_en
1 NA <NA> <NA> <NA>
2 1 Everyone everyone Everyone
3 1 Everyone everyone Everyone
4 NA <NA> <NA> <NA>
5 NA <NA> <NA> <NA>
6 NA <NA> <NA> <NA>
esrb_rating.name_ru user_game reviews_count saturated_color dominant_color
1 <NA> NA 268 0f0f0f 0f0f0f
2 Для всех NA 144 0f0f0f 0f0f0f
3 Для всех NA 20 0f0f0f 0f0f0f
4 <NA> NA 0 0f0f0f 0f0f0f
5 <NA> NA 4 0f0f0f 0f0f0f
6 <NA> NA 3 0f0f0f 0f0f0f
short_screenshots
1 -1, 248457, 248458, 248459, 248460, 786231, 786232, https://media.rawg.io/media/games/173/1739bdc5c33e85a0fa54b499a173690b.jpg, https://media.rawg.io/media/screenshots/d4c/d4cafc5b5fa5b09cedb6c453b4027796.jpg, https://media.rawg.io/media/screenshots/197/1971ea633720a92f79a7c7f0b2bf407f.jpg, https://media.rawg.io/media/screenshots/502/50277d7dcc8350491131185a77c776d1.jpg, https://media.rawg.io/media/screenshots/3a0/3a0610755754b6ad7befc022ad89016a.jpg, https://media.rawg.io/media/screenshots/51d/51db9229b2f0351cef0f8d29f260e2a4.jpg, https://media.rawg.io/media/screenshots/c0e/c0ee5ca366d5e754db18d4ba73263f72.jpg
2 -1, 3882985, 252124, 252125, 252126, 252127, 252128, https://media.rawg.io/media/screenshots/472/472c6954864d52a7681e7c8ef3d3a94b.jpg, https://media.rawg.io/media/screenshots/044/044e38975ee7fc59b595933c265353c4.jpg, https://media.rawg.io/media/screenshots/58f/58f857964b72b04ced97df53229a4b0f.jpg, https://media.rawg.io/media/screenshots/37b/37b1f533d0396eaa108eeaff58d3f035.jpg, https://media.rawg.io/media/screenshots/142/1423190d03f47b78d1c941b53a208694.jpg, https://media.rawg.io/media/screenshots/374/374c393671900cdbb68969561b092270.jpg, https://media.rawg.io/media/screenshots/ea2/ea2471a5c85f88224d89aee02ab61418.jpg
3 -1, 265834, 265835, 265836, 265837, 265838, 799037, https://media.rawg.io/media/games/d15/d15da3cfeb0ab883caed37134b5c5c34.jpg, https://media.rawg.io/media/screenshots/0c1/0c180ea0ac0771b968b578a4a64cf5bb.jpg, https://media.rawg.io/media/screenshots/90e/90ebd6fa665d199f4e80c8ae308f6930.jpg, https://media.rawg.io/media/screenshots/7bf/7bfbf1e8b302d9dbb38d7991dae27ebd.jpg, https://media.rawg.io/media/screenshots/826/826da2e6fef9de471efbd26cc5c33640.jpg, https://media.rawg.io/media/screenshots/043/04359615bdc819bcc28c380421afd720.jpg, https://media.rawg.io/media/screenshots/cc8/cc8e21bb0430327ed3a048e0ebdd3de8.jpg
4 -1, 3861999, https://media.rawg.io/media/screenshots/031/031b17a241ec3d1036c26b2c1872d816.jpg, https://media.rawg.io/media/screenshots/031/031b17a241ec3d1036c26b2c1872d816.jpg
5 -1, 263466, 263467, 263468, 263469, 263470, 263471, https://media.rawg.io/media/screenshots/650/650b76388f99fb913be50e0e3937d524.jpg, https://media.rawg.io/media/screenshots/9fb/9fbd7f4b4eb73eb0692b5d5719e06a52.jpg, https://media.rawg.io/media/screenshots/207/207b5204ff3e88ca1c728f46e0aebd66.jpg, https://media.rawg.io/media/screenshots/62c/62c71d49624d5f96b5d38ddaaa299e7f.jpg, https://media.rawg.io/media/screenshots/991/99165daedd777649f6944208ec48cd91.jpg, https://media.rawg.io/media/screenshots/ed1/ed1f9d0420f3d62abf3aed844bf9dd7c.jpg, https://media.rawg.io/media/screenshots/b90/b90711abc86414099d36ad3e10523fdb.jpg
6 -1, 714772, 714773, 714774, 714775, 714776, 714777, https://media.rawg.io/media/screenshots/2ad/2ad379f560aa6daf4344f106dc754f0b.jpg, https://media.rawg.io/media/screenshots/ce6/ce6c1655b9987b605c888691eddb6e87.jpg, https://media.rawg.io/media/screenshots/e1f/e1fc73b6f1934bf2afe8d81139c2429b.jpg, https://media.rawg.io/media/screenshots/385/3855f52eb3e12a5220e2cb1a5872cfc7.jpg, https://media.rawg.io/media/screenshots/af2/af2f8d8bd600dc33a5db7697ec82e340.jpg, https://media.rawg.io/media/screenshots/124/1242cb5d796fe5945cba0e93b703fde9.jpg, https://media.rawg.io/media/screenshots/994/99474e6b6908115f4e03b594c80e0da1.jpg
parent_platforms
1 7, Nintendo, nintendo
2 7, Nintendo, nintendo
3 7, Nintendo, nintendo
4 14, Web, web
5 7, Nintendo, nintendo
6 1, 7, PC, Nintendo, pc, nintendo
genres
1 15, Sports, sports
2 15, Sports, sports
3 19, 3, 4, Family, Adventure, Action, family, adventure, action
4 NULL
5 NULL
6 4, 10, 14, 15, 51, Action, Strategy, Simulation, Sports, Indie, action, strategy, simulation, sports, indie
community_rating
1 NA
2 NA
3 NA
4 0
5 0
6 0We can see that we were given a list of games that match the search query. The top match Wii Sports is exactly what we searched for.
We can also see that the second game from this search is also the 4th ranked game on our games sold data frame.
There will be times when we get multiple games from a search query that are in our games sold data frame.
To minimize the number of requests (I have only 20,000 this month) I will try to fill in this data as well from each search.
This was a challenge I had, making sure to not waste my limited requests. Initially my plan was to search each game one at a time. While I may have had enough requests to do this it would be a lot slower as well as waste requests that I may need for the future.
Because the search was not an exact match, we can get values that we did not search directly which helps maintain our query limit.
First I will make a function that takes the values from the request for the game and binds them to the original data frame with a left join.
game_data <- df # Copy of df to work on
# Make new columns in game_data
# This function will take the sold df and the reviews df and fill in columns for the game_data df based on the df_reviews.
combine_data <- function(df, df_reviews){
# Select columns from reviews that we want to add.
head(df_reviews)
df_reviews <- df_reviews %>% select( # Select different rating to use, `rating` seems to be the main one.
Name = name,
rating,
rating_top,
metacritic,
score,
ratings_count
)
combined <- left_join(df, df_reviews, by = "Name")
return(combined)
}
combined_wii <- combine_data(game_data, game_review)
head(combined_wii)# A tibble: 6 × 16
Rank Name Platform Year Genre Publisher NA_Sales EU_Sales JP_Sales
<dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 1 Wii Sports Wii 2006 Spor… Nintendo 41.5 29.0 3.77
2 2 Super Mario B… NES 1985 Plat… Nintendo 29.1 3.58 6.81
3 3 Mario Kart Wii Wii 2008 Raci… Nintendo 15.8 12.9 3.79
4 4 Wii Sports Re… Wii 2009 Spor… Nintendo 15.8 11.0 3.28
5 5 Pokemon Red/P… GB 1996 Role… Nintendo 11.3 8.89 10.2
6 6 Tetris GB 1989 Puzz… Nintendo 23.2 2.26 4.22
# ℹ 7 more variables: Other_Sales <dbl>, Global_Sales <dbl>, rating <dbl>,
# rating_top <int>, metacritic <int>, score <chr>, ratings_count <int>We can see that there are several columns that got filled in below is some of the values
head(combined_wii %>% filter(!is.na(rating))) # Check rows with a rating# A tibble: 6 × 16
Rank Name Platform Year Genre Publisher NA_Sales EU_Sales JP_Sales
<dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 1 Wii Sports Wii 2006 Spor… Nintendo 41.5 29.0 3.77
2 3 Mario Kart Wii Wii 2008 Raci… Nintendo 15.8 12.9 3.79
3 4 Wii Sports Re… Wii 2009 Spor… Nintendo 15.8 11.0 3.28
4 8 Wii Play Wii 2006 Misc Nintendo 14.0 9.2 2.93
5 9 New Super Mar… Wii 2009 Plat… Nintendo 14.6 7.06 4.7
6 14 Wii Fit Wii 2007 Spor… Nintendo 8.94 8.03 3.6
# ℹ 7 more variables: Other_Sales <dbl>, Global_Sales <dbl>, rating <dbl>,
# rating_top <int>, metacritic <int>, score <chr>, ratings_count <int>Now I will make a function that can get the api request data given the search term.
It will return the data frame with the data or null if the request is bad
search_api <- function(query){
api_url <- "https://api.rawg.io/api/games"
response <- GET(
api_url,
query = list(
key = api_key, # Api key
search = query # Wii sports as our testing value.
)
)
if (status_code(response) == 200){
data <- fromJSON(content(response, "text", encoding = "UTF-8"))
game_review <- data$results
return(game_review)
}
return(NULL) # If we have a bad response return null
}I will test this function on a popular series Call of Duty. I will search for a specific game “Call of Duty: Black Ops”
cod_response_df <- search_api("Call of Duty: Black Ops")
head(cod_response_df) slug name playtime
1 call-of-duty-black-ops Call of Duty: Black Ops 9
2 call-of-duty-black-ops-6 Call of Duty: Black Ops 6 0
3 call-of-duty-black-ops-7 Call of Duty: Black Ops 7 0
4 cod-black-ops-ii Call of Duty: Black Ops II 5
5 cod-black-ops-iii Call of Duty: Black Ops III 16
6 call-of-duty-black-ops-cold-war Call of Duty: Black Ops Cold War 11
platforms
1 4, 1, 3, 9, 5, 14, 16, 11, PC, Xbox One, iOS, Nintendo DS, macOS, Xbox 360, PlayStation 3, Wii, pc, xbox-one, ios, nintendo-ds, macos, xbox360, playstation3, wii
2 4, 187, 186, PC, PlayStation 5, Xbox Series S/X, pc, playstation5, xbox-series-x
3 4, 187, 186, PC, PlayStation 5, Xbox Series S/X, pc, playstation5, xbox-series-x
4 4, 1, 14, 16, 10, PC, Xbox One, Xbox 360, PlayStation 3, Wii U, pc, xbox-one, xbox360, playstation3, wii-u
5 4, 1, 18, 14, 16, PC, Xbox One, PlayStation 4, Xbox 360, PlayStation 3, pc, xbox-one, playstation4, xbox360, playstation3
6 4, 187, 1, 18, 186, PC, PlayStation 5, Xbox One, PlayStation 4, Xbox Series S/X, pc, playstation5, xbox-one, playstation4, xbox-series-x
stores
1 1, 2, 4, 6, 7, Steam, Xbox Store, App Store, Nintendo Store, Xbox 360 Store, steam, xbox-store, apple-appstore, nintendo, xbox360
2 1, Steam, steam
3 1, Steam, steam
4 1, 2, 6, 7, Steam, Xbox Store, Nintendo Store, Xbox 360 Store, steam, xbox-store, nintendo, xbox360
5 1, 3, 2, 7, Steam, PlayStation Store, Xbox Store, Xbox 360 Store, steam, playstation-store, xbox-store, xbox360
6 1, 3, 2, Steam, PlayStation Store, Xbox Store, steam, playstation-store, xbox-store
released tba
1 2010-11-09 FALSE
2 2024-10-24 FALSE
3 2025-11-13 FALSE
4 2012-11-13 FALSE
5 2015-11-06 FALSE
6 2020-11-13 FALSE
background_image
1 https://media.rawg.io/media/games/410/41033a495ce8f7fd4b0934bdb975f12a.jpg
2 https://media.rawg.io/media/games/e89/e89f9a409bce5d36873f65ea9659c36e.jpg
3 https://media.rawg.io/media/games/4ca/4cabe38446288beb37fc73b2fe047b08.jpg
4 https://media.rawg.io/media/games/8ee/8eed88e297441ef9202b5d1d35d7d86f.jpg
5 https://media.rawg.io/media/games/fd6/fd6a1eecd3ec0f875f1924f3656b7dd9.jpg
6 https://media.rawg.io/media/games/6b1/6b14dc4cc1785e396580c69165e55d2d.jpg
rating rating_top
1 4.05 4
2 3.86 4
3 1.63 1
4 3.96 4
5 3.27 3
6 3.80 4
ratings
1 4, 5, 3, 1, recommended, exceptional, meh, skip, 1058, 537, 227, 69, 55.95, 28.4, 12, 3.65
2 4, 3, 5, 1, recommended, meh, exceptional, skip, 76, 23, 13, 2, 66.67, 20.18, 11.4, 1.75
3 1, 3, skip, meh, 13, 6, 68.42, 31.58
4 4, 5, 3, 1, recommended, exceptional, meh, skip, 783, 356, 255, 53, 54.11, 24.6, 17.62, 3.66
5 3, 4, 1, 5, meh, recommended, skip, exceptional, 602, 568, 212, 135, 39.68, 37.44, 13.97, 8.9
6 4, 5, 3, 1, recommended, exceptional, meh, skip, 251, 58, 57, 26, 64.03, 14.8, 14.54, 6.63
ratings_count reviews_text_count added added_by_status.yet
1 1872 13 6982 131
2 112 1 321 30
3 19 0 65 18
4 1435 5 6154 113
5 1504 6 7264 231
6 378 9 2585 159
added_by_status.owned added_by_status.beaten added_by_status.toplay
1 4036 2391 113
2 46 122 78
3 15 6 13
4 3875 1703 120
5 5178 1016 165
6 1647 409 231
added_by_status.dropped added_by_status.playing metacritic suggestions_count
1 286 25 81 708
2 26 19 NA 422
3 7 6 NA 230
4 321 22 74 625
5 610 64 73 571
6 100 39 76 471
updated id score clip
1 2026-05-02T06:56:24 865 109.19273 NA
2 2026-05-02T06:57:45 983212 73.12896 NA
3 2026-04-12T18:34:57 1007995 73.12896 NA
4 2026-05-02T06:58:21 14446 72.19404 NA
5 2026-05-02T06:58:39 906 70.66635 NA
6 2026-05-02T06:58:00 481910 69.86627 NA
tags
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story-rich, otkrytyi-mir, open-world, cooperative, shuter, ot-pervogo-litsa, first-person, fps, shuter-ot-pervogo-litsa, online-co-op, miaso, gore, nasilie, violent, pvp, cross-platform-multiplayer, igrok-protiv-igroka, war, in-app-purchases, voina, captions-available, realizm, realistic, voennye-deistviia, military, online-pvp, pve, romance, setevoi-kooperativ, sovmestnaia-kampaniia, co-op-campaign, igrok-protiv-ii, kapitalizm, capitalism, romantika, eng, rus, rus, eng, eng, eng, rus, rus, eng, eng, rus, eng, eng, rus, rus, eng, eng, rus, eng, rus, eng, rus, eng, eng, eng, rus, eng, eng, rus, eng, rus, eng, rus, eng, eng, eng, eng, rus, rus, eng, rus, rus, eng, eng, 250835, 70701, 50554, 51712, 42555, 24145, 12936, 18117, 14392, 27260, 7452, 9266, 6588, 11967, 16473, 37397, 14989, 6971, 7682, 4962, 6670, 6814, 7852, 12048, 3161, 7218, 10101, 3419, 3188, 1485, 9134, 9156, 2511, 2527, 6411, 7983, 9867, 2997, 568, 546, 5468, 1225, 1280, 4515, 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Playable without Timed Input, Сцены жестокости, Camera Comfort, Color Alternatives, Subtitle Options, Adjustable Text Size, singleplayer, dlia-odnogo-igroka, ekshen, steam-achievements, multiplayer, dlia-neskolkikh-igrokov, glubokii-siuzhet, co-op, story-rich, cooperative, shuter, ot-pervogo-litsa, first-person, partial-controller-support, fps, shuter-ot-pervogo-litsa, online-co-op, gore, nasilie, violent, pvp, cross-platform-multiplayer, zombies, igrok-protiv-igroka, zombi-2, war, in-app-purchases, voina, captions-available, dlia-vzroslykh, mature, cinematic, futuristic, voennye-deistviia, budushchee, military, online-pvp, kinematografichnaia, pve, psikhologicheskaia, psychological, kastomizatsiia-oruzhiia, gun-customization, igrok-protiv-ii, hdr-available, stereo-sound, custom-volume-controls, surround-sound-2, playable-without-timed-input, stseny-zhestokosti, camera-comfort, color-alternatives, subtitle-options, adjustable-text-size, eng, rus, rus, eng, eng, rus, rus, eng, eng, eng, 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4 31, 42396, 42417, 42392, 40847, 7, 40836, 40849, 13, 42425, 42400, 7808, 42401, 42, 18, 411, 42428, 42429, 8, 42423, 32, 16, 30, 42427, 9, 40850, 42452, 1, 42433, 42416, 42436, 115, 80, 63, 42453, 70, 42430, 167, 11, 42450, 42451, 40856, 406, 808, 974, Singleplayer, Для одного игрока, Экшен, Приключение, Steam Achievements, Multiplayer, Full controller support, Steam Cloud, Atmospheric, Для нескольких игроков, Атмосфера, steam-trading-cards, Отличный саундтрек, Great Soundtrack, Co-op, cooperative, Шутер, От первого лица, First-Person, Научная фантастика, Sci-fi, Horror, FPS, Шутер от первого лица, Online Co-Op, Steam Leaderboards, Выживание, Survival, Совместная игра по сети, Контроллер, Тактика, Controller, Tactical, Zombies, ММО, War, Война, Futuristic, Team-Based, Командная, Будущее, Valve Anti-Cheat enabled, Story, character, death, singleplayer, dlia-odnogo-igroka, ekshen, prikliuchenie, steam-achievements, multiplayer, full-controller-support, steam-cloud, atmospheric, dlia-neskolkikh-igrokov, atmosfera, steam-trading-cards, otlichnyi-saundtrek, great-soundtrack, co-op, cooperative, shuter, ot-pervogo-litsa, first-person, nauchnaia-fantastika, sci-fi, horror, fps, shuter-ot-pervogo-litsa, online-co-op, steam-leaderboards, vyzhivanie, survival, sovmestnaia-igra-po-seti, kontroller, taktika, controller, tactical, zombies, mmo-2, war, voina, futuristic, team-based, komandnaia, budushchee, valve-anti-cheat-enabled, story, character, death, eng, rus, rus, rus, eng, eng, eng, eng, eng, rus, rus, eng, rus, eng, eng, eng, rus, rus, eng, rus, eng, eng, eng, rus, eng, eng, rus, eng, rus, rus, rus, eng, eng, eng, rus, eng, rus, eng, eng, rus, rus, eng, eng, eng, eng, 250935, 70781, 50602, 49201, 51769, 42579, 24170, 25770, 39736, 12951, 6083, 7568, 4675, 3450, 14408, 6595, 11974, 16492, 37416, 10967, 22074, 49188, 14994, 6975, 7693, 8472, 8291, 10846, 1226, 9492, 5249, 15050, 6625, 11215, 2873, 10103, 3190, 6927, 2076, 1776, 5062, 105, 11557, 8889, 3810, https://media.rawg.io/media/games/960/960b601d9541cec776c5fa42a00bf6c4.jpg, https://media.rawg.io/media/games/49c/49c3dfa4ce2f6f140cc4825868e858cb.jpg, https://media.rawg.io/media/games/587/587588c64afbff80e6f444eb2e46f9da.jpg, https://media.rawg.io/media/games/26d/26d4437715bee60138dab4a7c8c59c92.jpg, https://media.rawg.io/media/games/713/713269608dc8f2f40f5a670a14b2de94.jpg, https://media.rawg.io/media/games/157/15742f2f67eacff546738e1ab5c19d20.jpg, https://media.rawg.io/media/games/4cf/4cfc6b7f1850590a4634b08bfab308ab.jpg, https://media.rawg.io/media/games/120/1201a40e4364557b124392ee50317b99.jpg, https://media.rawg.io/media/games/7cf/7cfc9220b401b7a300e409e539c9afd5.jpg, https://media.rawg.io/media/games/736/73619bd336c894d6941d926bfd563946.jpg, https://media.rawg.io/media/games/d1a/d1a2e99ade53494c6330a0ed945fe823.jpg, https://media.rawg.io/media/games/48c/48cb04ca483be865e3a83119c94e6097.jpg, https://media.rawg.io/media/games/b8c/b8c243eaa0fbac8115e0cdccac3f91dc.jpg, 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5 31, 42396, 42417, 40847, 7, 40836, 13, 42425, 42400, 42401, 42, 42394, 18, 118, 42442, 36, 411, 42428, 42429, 8, 42423, 32, 30, 42427, 42413, 42491, 26, 40850, 40852, 63, 42544, 167, 42643, 42451, 188, 40856, 197, 42630, 981, 178, 269, 42448, Singleplayer, Для одного игрока, Экшен, Steam Achievements, Multiplayer, Full controller support, Atmospheric, Для нескольких игроков, Атмосфера, Отличный саундтрек, Great Soundtrack, Глубокий сюжет, Co-op, Story Rich, Открытый мир, Open World, cooperative, Шутер, От первого лица, First-Person, Научная фантастика, Sci-fi, FPS, Шутер от первого лица, Симулятор, Мясо, Gore, Steam Leaderboards, Steam Workshop, Zombies, Зомби, Futuristic, Паркур, Будущее, Parkour, Valve Anti-Cheat enabled, Robots, Роботы, battle, Illuminati, Quick-Time Events, Иллюминаты, singleplayer, dlia-odnogo-igroka, ekshen, steam-achievements, multiplayer, full-controller-support, atmospheric, dlia-neskolkikh-igrokov, atmosfera, otlichnyi-saundtrek, great-soundtrack, glubokii-siuzhet, co-op, story-rich, otkrytyi-mir, open-world, cooperative, shuter, ot-pervogo-litsa, first-person, nauchnaia-fantastika, sci-fi, fps, shuter-ot-pervogo-litsa, simuliator, miaso, gore, steam-leaderboards, steam-workshop, zombies, zombi-2, futuristic, parkur-2, budushchee, parkour, valve-anti-cheat-enabled, robots, roboty, battle, illuminati, quick-time-events, illiuminaty, eng, rus, rus, eng, eng, eng, eng, rus, rus, rus, eng, rus, eng, eng, rus, eng, eng, rus, rus, eng, rus, eng, eng, rus, rus, rus, eng, eng, eng, eng, rus, eng, rus, rus, eng, eng, eng, rus, eng, eng, eng, rus, 251028, 70855, 50629, 51821, 42592, 24189, 39760, 12960, 6083, 4675, 3450, 18148, 14415, 27291, 7466, 9280, 6600, 11981, 16517, 37441, 10975, 22082, 15001, 6982, 26191, 4962, 6681, 8478, 1772, 11216, 3567, 6931, 1788, 5066, 4110, 105, 8879, 2470, 10707, 378, 935, 322, https://media.rawg.io/media/games/d58/d588947d4286e7b5e0e12e1bea7d9844.jpg, 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https://media.rawg.io/media/games/736/736c0eaec96d848d7824b33298a182f2.jpg, https://media.rawg.io/media/games/123/123e035701a975f5d96c233f4048eed2.jpg
6 31, 42396, 42417, 40847, 7, 40836, 42425, 18, 411, 42428, 42429, 8, 30, 42427, 9, 42491, 26, 42402, 34, 42436, 80, 157, 40832, 63, 42434, 42544, 70, 40837, 42431, 81, 45878, 413, 171, 981, 70351, 4565, 3109, 42668, 251, 42652, 274, 87088, Singleplayer, Для одного игрока, Экшен, Steam Achievements, Multiplayer, Full controller support, Для нескольких игроков, Co-op, cooperative, Шутер, От первого лица, First-Person, FPS, Шутер от первого лица, Online Co-Op, Мясо, Gore, Насилие, Violent, Тактика, Tactical, PvP, Cross-Platform Multiplayer, Zombies, Игрок против игрока, Зомби, War, In-App Purchases, Военные действия, Military, Online PvP, online, PvE, battle, Сетевой кооператив, offline, weapons, Холодная война, Cold War, Кастомизация оружия, Gun Customization, Игрок против ИИ, singleplayer, dlia-odnogo-igroka, ekshen, steam-achievements, multiplayer, full-controller-support, dlia-neskolkikh-igrokov, co-op, cooperative, shuter, ot-pervogo-litsa, first-person, fps, shuter-ot-pervogo-litsa, online-co-op, miaso, gore, nasilie, violent, taktika, tactical, pvp, cross-platform-multiplayer, zombies, igrok-protiv-igroka, zombi-2, war, in-app-purchases, voennye-deistviia, military, online-pvp, online, pve, battle, setevoi-kooperativ, offline, weapons, kholodnaia-voina, cold-war, kastomizatsiia-oruzhiia, gun-customization, igrok-protiv-ii, eng, rus, rus, eng, eng, eng, rus, eng, eng, rus, rus, eng, eng, rus, eng, rus, eng, rus, eng, rus, eng, eng, eng, eng, rus, rus, eng, eng, rus, eng, eng, eng, eng, eng, rus, eng, eng, rus, eng, rus, eng, rus, 251028, 70855, 50629, 51821, 42592, 24189, 12960, 14415, 6600, 11981, 16517, 37441, 15001, 6982, 7700, 4962, 6681, 6823, 7861, 5254, 6630, 12078, 3167, 11216, 7236, 3567, 10104, 3427, 2513, 2529, 6430, 6552, 8009, 10707, 3007, 1073, 2314, 341, 313, 858, 842, 5494, https://media.rawg.io/media/games/d58/d588947d4286e7b5e0e12e1bea7d9844.jpg, https://media.rawg.io/media/games/021/021c4e21a1824d2526f925eff6324653.jpg, 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esrb_rating.id esrb_rating.name esrb_rating.slug esrb_rating.name_en
1 4 Mature mature Mature
2 NA <NA> <NA> <NA>
3 5 Adults Only adults-only Adults Only
4 4 Mature mature Mature
5 4 Mature mature Mature
6 NA <NA> <NA> <NA>
esrb_rating.name_ru user_game reviews_count saturated_color dominant_color
1 С 17 лет NA 1891 0f0f0f 0f0f0f
2 <NA> NA 114 0f0f0f 0f0f0f
3 Только для взрослых NA 19 0f0f0f 0f0f0f
4 С 17 лет NA 1447 0f0f0f 0f0f0f
5 С 17 лет NA 1517 0f0f0f 0f0f0f
6 <NA> NA 392 0f0f0f 0f0f0f
short_screenshots
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parent_platforms
1 1, 2, 3, 4, 5, 7, PC, PlayStation, Xbox, iOS, Apple Macintosh, Nintendo, pc, playstation, xbox, ios, mac, nintendo
2 1, 2, 3, PC, PlayStation, Xbox, pc, playstation, xbox
3 1, 2, 3, PC, PlayStation, Xbox, pc, playstation, xbox
4 1, 2, 3, 7, PC, PlayStation, Xbox, Nintendo, pc, playstation, xbox, nintendo
5 1, 2, 3, PC, PlayStation, Xbox, pc, playstation, xbox
6 1, 2, 3, PC, PlayStation, Xbox, pc, playstation, xbox
genres community_rating
1 2, 4, Shooter, Action, shooter, action NA
2 2, 4, Shooter, Action, shooter, action NA
3 2, 4, Shooter, Action, shooter, action NA
4 2, 4, Shooter, Action, shooter, action NA
5 2, 4, Shooter, Action, shooter, action NA
6 2, 4, Shooter, Action, shooter, action NAI wanted to look at how this game shows up in our combined data.
combined_data <- combined_wii
cod_sales <- combined_data %>%
filter(str_detect(Name, "Call of Duty"))
head(cod_sales)# A tibble: 6 × 16
Rank Name Platform Year Genre Publisher NA_Sales EU_Sales JP_Sales
<dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 30 Call of Duty:… X360 2011 Shoo… Activisi… 9.03 4.28 0.13
2 32 Call of Duty:… X360 2010 Shoo… Activisi… 9.67 3.73 0.11
3 34 Call of Duty:… PS4 2015 Shoo… Activisi… 5.77 5.81 0.35
4 35 Call of Duty:… PS3 2012 Shoo… Activisi… 4.99 5.88 0.65
5 36 Call of Duty:… X360 2012 Shoo… Activisi… 8.25 4.3 0.07
6 37 Call of Duty:… X360 2009 Shoo… Activisi… 8.52 3.63 0.08
# ℹ 7 more variables: Other_Sales <dbl>, Global_Sales <dbl>, rating <dbl>,
# rating_top <int>, metacritic <int>, score <chr>, ratings_count <int>We can combine these like we did before with the Wii sports games.
This time the columns are already created so we have to also coalesce the data frames.
I will update the function I made to do this.
If we were doing it one by one we could combine it all in one step but I wanted a way that made the data frame step by step.
# Update the function to handle the columns already being present.
combine_data <- function(df, df_reviews){
# Select columns from reviews that we want to add.
head(df_reviews)
df_reviews <- df_reviews %>% select( # Select different rating to use, `rating` seems to be the main one.
Name = name,
rating,
rating_top,
metacritic,
score,
ratings_count
)
combined <- left_join(df, df_reviews, by = "Name") %>%
mutate(
rating = coalesce(rating.x, rating.y), # Because the dataframe already has these columns we can coalesce. This will take the non na value.
rating_top = coalesce(rating_top.x, rating_top.y),
metacritic = coalesce(metacritic.x, metacritic.y),
score = coalesce(score.x, score.y),
ratings_count = coalesce(ratings_count.x, ratings_count.y)
) %>%
select(-ends_with(".x"), -ends_with(".y")) # Remove the .x and .y columns. These are the duplicates from the original left_join
return(combined)
}
combined_data <- combine_data(combined_data, cod_response_df)
(combined_data %>% filter(!is.na(rating) & (str_detect(Name, "Call of Duty")))) # Show the cod games that were filled# A tibble: 9 × 16
Rank Name Platform Year Genre Publisher NA_Sales EU_Sales JP_Sales
<dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 32 Call of Duty:… X360 2010 Shoo… Activisi… 9.67 3.73 0.11
2 35 Call of Duty:… PS3 2012 Shoo… Activisi… 4.99 5.88 0.65
3 36 Call of Duty:… X360 2012 Shoo… Activisi… 8.25 4.3 0.07
4 41 Call of Duty:… PS3 2010 Shoo… Activisi… 5.98 4.44 0.48
5 1128 Call of Duty:… PC N/A Shoo… Activisi… 0.58 0.81 0
6 1219 Call of Duty:… PC 2012 Shoo… Activisi… 0.63 0.69 0
7 1381 Call of Duty:… Wii 2010 Shoo… Activisi… 0.82 0.47 0
8 3213 Call of Duty:… DS 2010 Shoo… Activisi… 0.54 0.05 0
9 4556 Call of Duty:… WiiU 2012 Shoo… Activisi… 0.21 0.18 0
# ℹ 7 more variables: Other_Sales <dbl>, Global_Sales <dbl>, rating <dbl>,
# rating_top <int>, metacritic <int>, score <chr>, ratings_count <int>While looking at the data I notice that not all of the Call of Duty games from the reviews have matches to our sales data.
It is entirely possible that these data sets do not have complete overlap. I will leave these values as NA. If we have enough data I will leave it like this.
Now I want to iterate through the data frame based on null ratings.
I used the latest free version of chat gpt to help generate this code.
This code keeps track of the already tried rows
attempted_ids <- character() # Keep track of attempted namesNow perform the api calls, cache = true keeps the results in memory for the knit.
remaining_ids <- combined_data %>% # Get the names that still need to be tried
filter(is.na(rating)) %>% # Rows that have no rating
distinct(Name) # No need to run the same game twice(its separated by Platform)
while(nrow(remaining_ids) > 0) { # while we have remaining ids
id <- remaining_ids$Name[1] # Get the name as id
# mark as attempted BEFORE api call
attempted_ids <- c(attempted_ids, id) # Update attempted_ids with the extra name
api_result <- search_api(id) # Search for the name
# print(id) Testing, seeing which is the last item that causes an error
# update data frame
if (!is.null(api_result)) {
if(length(api_result) != 0) # Check empty/na api_result
combined_data <- combine_data(combined_data, api_result)
}
# only unresolved AND unattempted ids
remaining_ids <- combined_data %>%
filter(is.na(rating)) %>%
distinct(Name) %>%
filter(!Name %in% attempted_ids) #Names that are not attempted
}We can check which ratings are still null
combined_null <- combined_data %>% filter(is.na(rating)) # Check rows with no rating
nrow(combined_null)[1] 6679head(combined_null)# A tibble: 6 × 16
Rank Name Platform Year Genre Publisher NA_Sales EU_Sales JP_Sales
<dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 5 Pokemon Red/P… GB 1996 Role… Nintendo 11.3 8.89 10.2
2 6 Tetris GB 1989 Puzz… Nintendo 23.2 2.26 4.22
3 10 Duck Hunt NES 1984 Shoo… Nintendo 26.9 0.63 0.28
4 11 Nintendogs DS 2005 Simu… Nintendo 9.07 11 1.93
5 13 Pokemon Gold/… GB 1999 Role… Nintendo 9 6.18 7.2
6 20 Brain Age: Tr… DS 2005 Misc Nintendo 4.75 9.26 4.16
# ℹ 7 more variables: Other_Sales <dbl>, Global_Sales <dbl>, rating <dbl>,
# rating_top <int>, metacritic <int>, score <chr>, ratings_count <int>We can also check the ratings that got values
combined_filled <- combined_data %>% filter(!is.na(rating))
nrow(combined_filled)[1] 9919head(combined_filled)# A tibble: 6 × 16
Rank Name Platform Year Genre Publisher NA_Sales EU_Sales JP_Sales
<dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 1 Wii Sports Wii 2006 Spor… Nintendo 41.5 29.0 3.77
2 2 Super Mario B… NES 1985 Plat… Nintendo 29.1 3.58 6.81
3 3 Mario Kart Wii Wii 2008 Raci… Nintendo 15.8 12.9 3.79
4 4 Wii Sports Re… Wii 2009 Spor… Nintendo 15.8 11.0 3.28
5 7 New Super Mar… DS 2006 Plat… Nintendo 11.4 9.23 6.5
6 8 Wii Play Wii 2006 Misc Nintendo 14.0 9.2 2.93
# ℹ 7 more variables: Other_Sales <dbl>, Global_Sales <dbl>, rating <dbl>,
# rating_top <int>, metacritic <int>, score <chr>, ratings_count <int>Now that I have the data,, and I still want the data to be accessible and reproducible I will save the data to a csv.
write.csv(combined_data, "combined_data.csv", row.names = TRUE)Cleaning Data
We can see that much of our data is filled in. There are a lot of rows that are still NA. This could be for several reasons like mismatching names or just the lack of a review for that specific game. I tried a little fixing initially but it seemed inconsequential to fix specific game names and such. To fix this problem I will just drop these values.
I will focus on the rating column. According to the api this is the column that can not be NA in the response and is the main rating.
cleaned_df <- combined_data %>%
filter(!is.na(rating))Now I will pivot longer based on the sales rows
cleaned_df <- cleaned_df %>%
pivot_longer(
cols = ends_with("_Sales"), # The columns that end in _Sales
names_to = "Region",
names_pattern = "(.*)_Sales", # Get the grouping for the Region.
values_to = "Sales"
)
head(cleaned_df)# A tibble: 6 × 13
Rank Name Platform Year Genre Publisher rating rating_top metacritic score
<dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <int> <int> <chr>
1 1 Wii S… Wii 2006 Spor… Nintendo 4.21 4 76 72.3…
2 1 Wii S… Wii 2006 Spor… Nintendo 4.21 4 76 72.3…
3 1 Wii S… Wii 2006 Spor… Nintendo 4.21 4 76 72.3…
4 1 Wii S… Wii 2006 Spor… Nintendo 4.21 4 76 72.3…
5 1 Wii S… Wii 2006 Spor… Nintendo 4.21 4 76 72.3…
6 2 Super… NES 1985 Plat… Nintendo 4.32 4 NA 96.5…
# ℹ 3 more variables: ratings_count <int>, Region <chr>, Sales <dbl>We can also select the columns that we want to work with. I will take just the rating and metacritic for the rating values. Metacritic is sometimes NA
cleaned_df <- cleaned_df %>%
select(Rank, Name, Platform, Year, Genre, Publisher, Region, Sales, Rating = rating, Metacritic = metacritic )
head(cleaned_df) # A tibble: 6 × 10
Rank Name Platform Year Genre Publisher Region Sales Rating Metacritic
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <int>
1 1 Wii Sports Wii 2006 Spor… Nintendo NA 41.5 4.21 76
2 1 Wii Sports Wii 2006 Spor… Nintendo EU 29.0 4.21 76
3 1 Wii Sports Wii 2006 Spor… Nintendo JP 3.77 4.21 76
4 1 Wii Sports Wii 2006 Spor… Nintendo Other 8.46 4.21 76
5 1 Wii Sports Wii 2006 Spor… Nintendo Global 82.7 4.21 76
6 2 Super Mar… NES 1985 Plat… Nintendo NA 29.1 4.32 NAAnalyzing Data
Now that the data is in a tidy format with information we want we can now start to analyze it.
Our initial question was trying to find a correlation between Sales amount and ratings.
I will filter for the global region and graph sales vs rating.
I also tried adjusting the alpha value due to the high overlap of points.
global_df <- cleaned_df %>%
filter(Region == "Global")
ggplot(global_df, aes(x = Sales, y = Rating)) +
geom_point(alpha = 0.1) +
labs(
title = "Global Sales of Video Games vs Rawg.io Ratings",
x = "Global Sales(Million of Units)",
y = "Rawgio Rating"
)
We also notice that there are a lot of games that got a review of 0. I am not sure whether or not these values are intentional or not. It may depend on the game.
We can see that all games that have more than 10 million sales do not have a rating lower than 3. Games that are rating less than 3 are all games that have less than 10 million global sales. I will remove the 0 rated games.
cleaned_zero_removed <- cleaned_df %>%
filter(Rating > 0)Now the global graph again
global_df <- cleaned_zero_removed %>%
filter(Region == "Global")
ggplot(global_df, aes(x = Sales, y = Rating)) +
geom_point(alpha = 0.1) +
labs(
title = "Global Sales of Video Games vs Rawg.io Ratings",
x = "Global Sales(Million of Units)",
y = "Rawg.io Rating"
)
We can see in the graph above that most games have less than 10 million sales and games tend to be rated higher
I am also curious to see specific countries. I will look at North Americas sales vs reviews. I am expecting it to be very similar to the global sales.
It is very similar.
NA_df <- cleaned_zero_removed %>%
filter(Region == "NA")
ggplot(global_df, aes(x = Sales, y = Rating)) +
geom_point(alpha = 0.1) +
labs(
title = "North America Sales of Video Games vs Rawg.io Ratings",
x = "NA Sales(Million of Units)",
y = "Rawg.io Rating"
)
While there seems to be a positive correlation between sales and reviews we can use statistical methods in R to actually find the correlation. I will go back to using the global dataset.
cor(global_df$Sales, global_df$Rating)[1] 0.164879We get a correlation of .16 when using the default correlation in R (pearson) This is a weak but positive correlation.
Higher game sales can slightly be associated with a higher Rawg.io rating
This may not be the best method given we have some outliers.
Below is a graph with a linear model attached because so much of the data is clustered at the start this is not very helpful because of this.
ggplot(global_df, aes(x = Sales, y = Rating)) +
geom_point(alpha = 0.1) +
geom_smooth(method = "lm") +
labs(
title = "Global Sales of Video Games vs Rawg.io Ratings",
x = "Global Sales(Million of Units)",
y = "Rawg.io Rating"
)`geom_smooth()` using formula = 'y ~ x'
Because we have some outliers we can use Spearman to also create a correlation.
Spearman looks at the rank of the data rather than the raw values. This makes better for when we have large outliers and such. When doing this we still get a slightly positive correlation. This is something we did not cover but does a good job for this set of data.
cor(global_df$Sales, global_df$Rating, method = "spearman", use = "complete.obs")[1] 0.2027387Spearman correlation does not give us a representation of the linearity of the data but whether or not the data has an increasing/decreasing trend. This correlation would indicate that there is a slight trend that as the Sales of a game increases, that the rating (from rawg.io) also increases. This makes sense as popular games tend to be rated higher. I am little surprised that the correlation was not higher though.
Conclusion
In conclusion we were able to extract data from a csv and from a public API to determine a relationship between game sales and game reviews from the api Rawg.io.
We had issues with our limited query amounts and the time it took but we were able to solve those issues by dynamically filling multiple rows in our dataframe with each api call.
After collecting the data we turned it into a tidy data set with pivot longer and dropping rows that did not get filled in properly.
We then graphed the data based on the global region(all regions) and saw trends that would indicate a positive correlation between sales and reviews. We also used a new correlation, Spearman Correlation to see if our data had an increasing trend. Due to the outliers in our data it made sense to use this type of correlation over pearson or kendall. With this we saw a positive correlation and we can conclude that there is a slighly positive trend in that higher video game sales tend to have higher reviews on Rawg.io.