In this post, I will analyse the Tidytuesday Dataset about a video game from the Steam store using R and Rstudio.
Load the packages
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library(tidyverse)
library(lubridate)
library(magrittr)
library(ggthemr)
library(RColorBrewer)
library(gganimate)
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Load the data
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video_game <-
read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-07-30/video_games.csv")
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Lets take a look at our dataframe.
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# A tibble: 6 x 10
number game release_date price owners developer publisher
<dbl> <chr> <chr> <dbl> <chr> <chr> <chr>
1 1 Half… Nov 16, 2004 9.99 10,00… Valve Valve
2 3 Coun… Nov 1, 2004 9.99 10,00… Valve Valve
3 21 Coun… Mar 1, 2004 9.99 10,00… Valve Valve
4 47 Half… Nov 1, 2004 4.99 5,000… Valve Valve
5 36 Half… Jun 1, 2004 9.99 2,000… Valve Valve
6 52 CS2D Dec 24, 2004 NA 1,000… Unreal S… Unreal S…
# … with 3 more variables: average_playtime <dbl>,
# median_playtime <dbl>, metascore <dbl>
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number game release_date
Min. : 1 Length:26688 Length:26688
1st Qu.: 821 Class :character Class :character
Median :2356 Mode :character Mode :character
Mean :2904
3rd Qu.:4523
Max. :8846
price owners developer
Min. : 0.490 Length:26688 Length:26688
1st Qu.: 2.990 Class :character Class :character
Median : 5.990 Mode :character Mode :character
Mean : 8.947
3rd Qu.: 9.990
Max. :595.990
NA's :3095
publisher average_playtime median_playtime
Length:26688 Min. : 0.000 Min. : 0.00
Class :character 1st Qu.: 0.000 1st Qu.: 0.00
Mode :character Median : 0.000 Median : 0.00
Mean : 9.057 Mean : 5.16
3rd Qu.: 0.000 3rd Qu.: 0.00
Max. :5670.000 Max. :3293.00
NA's :9 NA's :12
metascore
Min. :20.00
1st Qu.:66.00
Median :73.00
Mean :71.89
3rd Qu.:80.00
Max. :98.00
NA's :23838
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video_game dataframe has 26688 rows and 10 columns.
Missing value
Lets check for the missing value in the dataframe.
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colSums(is.na(video_game))
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number game release_date price
0 3 0 3095
owners developer publisher average_playtime
0 151 95 9
median_playtime metascore
12 23838
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Above output shows that We have missing value in this data frame. We will use the median value to replace the missing value of numerical columns.
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video_game <- video_game %>% mutate(metascore = replace(
metascore,
is.na(metascore),
median(metascore, na.rm = TRUE)
))
video_game <- video_game %>% mutate(price = replace(
price,
is.na(price),
median(price, na.rm = TRUE)
))
video_game <- video_game %>% mutate(average_playtime = replace(
average_playtime,
is.na(average_playtime),
median(average_playtime, na.rm = TRUE)
))
video_game <- video_game %>% mutate(median_playtime = replace(
median_playtime,
is.na(median_playtime),
median(median_playtime, na.rm = TRUE)
))
video_game <- video_game %>% mutate(
year = year(mdy(release_date)),
month = month(mdy(release_date), label = TRUE),
weekday = wday(mdy(release_date), label = TRUE)
)
video_game <- na.omit(video_game)
colSums(is.na(video_game))
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number game release_date
0 0 0
price owners developer
0 0 0
publisher average_playtime median_playtime
0 0 0
metascore year month
0 0 0
weekday release_date_pretty age
0 0 0
max_owners min_owners
0 0
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We replaced the numerical values with the median of the respective values and dropped the missing rows in publisher and developer column.
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video_game %<>%
mutate(
max_owners = str_trim(word(owners, 2, sep = "\\..")),
max_owners = as.numeric(str_replace_all(max_owners, ",", "")),
min_owners = str_trim(word(owners, 1, sep = "\\..")),
min_owners = as.numeric(str_replace_all(min_owners, ",", ""))
)
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The owner column has the range of owner of games and the code create max and min owner of the games.
Let’s see the final data frame:
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# A tibble: 6 x 17
number game release_date price owners developer publisher
<dbl> <chr> <chr> <dbl> <chr> <chr> <chr>
1 1 Half… Nov 16, 2004 9.99 10,00… Valve Valve
2 3 Coun… Nov 1, 2004 9.99 10,00… Valve Valve
3 21 Coun… Mar 1, 2004 9.99 10,00… Valve Valve
4 47 Half… Nov 1, 2004 4.99 5,000… Valve Valve
5 36 Half… Jun 1, 2004 9.99 2,000… Valve Valve
6 52 CS2D Dec 24, 2004 5.99 1,000… Unreal S… Unreal S…
# … with 10 more variables: average_playtime <dbl>,
# median_playtime <dbl>, metascore <dbl>, year <dbl>, month <ord>,
# weekday <ord>, release_date_pretty <date>, age <dbl>,
# max_owners <dbl>, min_owners <dbl>
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Visualization of Data
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discrete_pal <- c(
"#fa4234", "#f5b951", "#e8f538", "#52b0f7",
"#b84ef5", "#4ed1f5", "#45b58e", "#1a1612",
"#474ccc", "#47cc92", "#a0cc47", "#c96f32"
)
ggthemr("flat")
ggplot(data = video_game %>% group_by(developer) %>%
tally(sort = TRUE) %>% head(10)) +
geom_col(aes(x = reorder(developer, n), y = n, fill = n)) +
labs(
title = "Developer with most Games", x = NULL, y = "Game Count",
fill = "Game Count", caption = "by: @diwastha"
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 40, hjust = 1))
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ggplot(data = video_game %>% group_by(publisher) %>%
tally(sort = TRUE) %>% head(10)) +
geom_col(aes(x = reorder(publisher, n), y = n, fill = n)) +
labs(
title = "Publisher with most Games", y = "Game Count", x = NULL,
fill = "Game Count", caption = "by: @diwastha"
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 40, hjust = 1))
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Big Fish Games is the biggest publisher by the game count as it publishes lots of free to play and casual games. Sega which is the second-largest publisher has less than half of the game published by the Big Fish Games.
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video_game %>%
select(-number) %>%
mutate(max_owners = as.factor(max_owners / 1000000)) %>%
group_by(publisher) %>%
mutate(n = n()) %>%
ungroup() %>%
filter(n >= 80) %>%
ggplot(aes(publisher, max_owners, color = publisher)) +
geom_jitter(show.legend = FALSE, size = 2, alpha = 0.5) +
labs(
title = "Distribution of ownership of top publishers",
y = "Estimated Game Ownership per Million", x = NULL
) +
theme_minimal() + scale_color_manual(values = discrete_pal) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
stat_summary(
fun.y = mean, geom = "line", shape = 20,
size = 5, color = "#555555"
)
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The Big Fish Games has released 265 games but the player base of their games is very small. Other publishers in this group have a much larger player base like Ubisoft, Square Enix, SEGA has much larger player base for their games.
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ggplot(data = video_game %>% arrange(desc(average_playtime)) %>% head(20)) +
geom_col(aes(x = reorder(game, average_playtime), y = average_playtime, fill = metascore)) +
labs(
title = "Games with most metascore", x = NULL, y = "Average Playtime",
caption = "by: @diwastha"
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
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ggplot(data = video_game %>% group_by(max_owners) %>%
arrange(desc(max_owners)) %>% head(10)) +
geom_col(aes(x = reorder(game, max_owners), y = max_owners)) +
labs(
title = "Most Sold Video Games", y = "Number of copies sold", x = NULL,
caption = "by: @diwastha"
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
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Dota2, Team Fortress 2 is free to play multiplayer games released on 2013 and 2007 respectively. The Player Unknown BattleGround (PUBG) is also a multiplayer battle royale shooter which is a huge hit. Counter-Strike is currently available free in steam which may have increased the owner count. All Top five games are multiplayer games with a strong player base and metascore.
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ggplot(data = video_game %>% group_by(max_owners) %>%
arrange(desc(max_owners)) %>% head(10)) +
geom_col(aes(x = reorder(game, price), y = price, fill = price)) +
labs(
title = "Price of most sold games", y = "Price in $", x = NULL,
caption = "by: @diwastha"
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
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PUBG is currently one of the most popular games in the steam store and huge player base. Other games in this group have a very low price or currently free in the Steam store.
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ggthemr("flat")
ggplot(
data = video_game %>% group_by(year) %>%
summarise(avg_price = mean(price)),
aes(x = year, y = avg_price)
) +
geom_line() + geom_point(color = "red") +
labs(
title = "Average Price of the Game over the year",
x = NULL, y = "price in $", caption = "by: @diwastha"
) +
theme_minimal()
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The average game price was rising up to the year 2013 then it started to decrease. It may be due to the increment in the release of the game with a very low price or freemium model.
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ggplot(data = video_game) + geom_bar(aes(x = year)) +
labs(
title = "Number of active games since release year",
y = "Number of Game", x = NULL, caption = "by: @diwastha"
) +
theme_minimal()
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There is growth in the release of the games from the year 2015 onward.
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ggplot(data = video_game) + geom_bar(aes(x = month)) +
transition_time(year) +
labs(
title = "Games released in each month",
subtitle = "Year: {frame_time}",
y = "Number of Game", x = NULL, caption = "by: @diwastha"
) +
theme_minimal()
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The above animation shows the number of games released on each month of the year.
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ggplot(data = video_game %>% mutate(release_date = mdy(release_date)) %>%
group_by(release_date) %>%
summarise(count_games = n()) %>%
mutate(
day_of_week = weekdays(release_date),
weekend = ifelse(day_of_week %in% c("Saturday", "Sunday"),
"yes",
"no"
)
)) +
geom_jitter(aes(x = release_date, y = count_games, color = weekend), alpha = 0.4) +
labs(title = "Games released on weekend", y = "Number of Games", x = NULL) +
theme_minimal()
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There is a rise in the release of games since 2015 and much of the games are released on workdays than the weekend days.
Wrapping up
Tidytuesday repo has a great dataset for analysis and visualization. You can also extract other data from the repo and work on it. I have taken some references from the work of other people while working on this data and they are:
