Capstone Project: Video Games Sales
Jia Jing Liew
2022-06-27
Introduction
For the capstone project, I have selected the video game sales analysis
to work on. For the case study, I will perform the junior data analyst
for the marketing team at little game company where in Chicago. Now, my
company want creating a new game, I need know which genre of game, who
is the greatest consumers group. I will use RStudio for analysing.
Link
Ask
1.How many game sales on global from 2013 to 2020?
2.Which area are the most sales volume?
3.Which kind of game is the most popular?Why it is the most popular genre?
4.In the global, is that having the lowest marketing place?
5.Which platform is the most popular?Prepare
Loading some packages.
###Setting langguage and installing packages
Sys.setenv(LANG="en")
setwd("~/Project 03")
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.1.3## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.6 v purrr 0.3.4
## v tibble 3.1.7 v dplyr 1.0.9
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1## Warning: package 'ggplot2' was built under R version 4.1.3## Warning: package 'tibble' was built under R version 4.1.3## Warning: package 'tidyr' was built under R version 4.1.3## Warning: package 'readr' was built under R version 4.1.3## Warning: package 'purrr' was built under R version 4.1.3## Warning: package 'dplyr' was built under R version 4.1.3## Warning: package 'stringr' was built under R version 4.1.3## Warning: package 'forcats' was built under R version 4.1.3## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(skimr)## Warning: package 'skimr' was built under R version 4.1.3library(janitor)## Warning: package 'janitor' was built under R version 4.1.3##
## Attaching package: 'janitor'## The following objects are masked from 'package:stats':
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## chisq.test, fisher.testlibrary(scales)## Warning: package 'scales' was built under R version 4.1.3##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
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## discard## The following object is masked from 'package:readr':
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## col_factorlibrary(dplyr)
library(DataExplorer)## Warning: package 'DataExplorer' was built under R version 4.1.3library(ggplot2)Process
Above we have already prepared the packages. Now, we are starting cleaning data. The data may be have some dirty data will affect analysis, so i using clean_names function. And, using plot_intro, plot_missing, profile_missing to ensure the structure of data. I use RStudio to clean and process data.
###Loading the data
salesps4<-read.csv("~/Project 03/PS4_GamesSales.csv")
salesxbox<-read.csv("~/Project 03/XboxOne_GameSales.csv")
videogamessales<-read.csv("~/Project 03/Video_Games_Sales_as_at_22_Dec_2016.csv")
###Checking the data structure
glimpse(salesps4)## Rows: 1,034
## Columns: 9
## $ Game <chr> "Grand Theft Auto V", "Call of Duty: Black Ops 3", "Red ~
## $ Year <chr> "2014", "2015", "2018", "2017", "2017", "2016", "2016", ~
## $ Genre <chr> "Action", "Shooter", "Action-Adventure", "Shooter", "Spo~
## $ Publisher <chr> "Rockstar Games", "Activision", "Rockstar Games", "Activ~
## $ North.America <dbl> 6.06, 6.18, 5.26, 4.67, 1.27, 1.26, 4.49, 3.64, 3.11, 2.~
## $ Europe <dbl> 9.71, 6.05, 6.21, 6.21, 8.64, 7.95, 3.93, 3.39, 3.83, 3.~
## $ Japan <dbl> 0.60, 0.41, 0.21, 0.40, 0.15, 0.12, 0.21, 0.32, 0.19, 0.~
## $ Rest.of.World <dbl> 3.02, 2.44, 2.26, 2.12, 1.73, 1.61, 1.70, 1.41, 1.36, 1.~
## $ Global <dbl> 19.39, 15.09, 13.94, 13.40, 11.80, 10.94, 10.33, 8.76, 8~glimpse(salesxbox)## Rows: 613
## Columns: 10
## $ Pos <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1~
## $ Game <chr> "Grand Theft Auto V", "Call of Duty: Black Ops 3", "Call~
## $ Year <chr> "2014", "2015", "2017", "2018", "2014", "2014", "2016", ~
## $ Genre <chr> "Action", "Shooter", "Shooter", "Action-Adventure", "Mis~
## $ Publisher <chr> "Rockstar Games", "Activision", "Activision", "Rockstar ~
## $ North.America <dbl> 4.70, 4.63, 3.75, 3.76, 3.23, 3.25, 3.37, 2.94, 2.94, 2.~
## $ Europe <dbl> 3.25, 2.04, 1.91, 1.47, 1.71, 1.49, 1.26, 1.62, 1.49, 1.~
## $ Japan <dbl> 0.01, 0.02, 0.00, 0.00, 0.00, 0.01, 0.02, 0.02, 0.03, 0.~
## $ Rest.of.World <dbl> 0.76, 0.68, 0.57, 0.54, 0.49, 0.48, 0.48, 0.45, 0.45, 0.~
## $ Global <dbl> 8.72, 7.37, 6.23, 5.77, 5.43, 5.22, 5.13, 5.03, 4.92, 4.~glimpse(videogamessales)## Rows: 16,719
## Columns: 16
## $ Name <chr> "Wii Sports", "Super Mario Bros.", "Mario Kart Wii", "~
## $ Platform <chr> "Wii", "NES", "Wii", "Wii", "GB", "GB", "DS", "Wii", "~
## $ Year_of_Release <chr> "2006", "1985", "2008", "2009", "1996", "1989", "2006"~
## $ Genre <chr> "Sports", "Platform", "Racing", "Sports", "Role-Playin~
## $ Publisher <chr> "Nintendo", "Nintendo", "Nintendo", "Nintendo", "Ninte~
## $ NA_Sales <dbl> 41.36, 29.08, 15.68, 15.61, 11.27, 23.20, 11.28, 13.96~
## $ EU_Sales <dbl> 28.96, 3.58, 12.76, 10.93, 8.89, 2.26, 9.14, 9.18, 6.9~
## $ JP_Sales <dbl> 3.77, 6.81, 3.79, 3.28, 10.22, 4.22, 6.50, 2.93, 4.70,~
## $ Other_Sales <dbl> 8.45, 0.77, 3.29, 2.95, 1.00, 0.58, 2.88, 2.84, 2.24, ~
## $ Global_Sales <dbl> 82.53, 40.24, 35.52, 32.77, 31.37, 30.26, 29.80, 28.92~
## $ Critic_Score <int> 76, NA, 82, 80, NA, NA, 89, 58, 87, NA, NA, 91, NA, 80~
## $ Critic_Count <int> 51, NA, 73, 73, NA, NA, 65, 41, 80, NA, NA, 64, NA, 63~
## $ User_Score <dbl> 8.0, NA, 8.3, 8.0, NA, NA, 8.5, 6.6, 8.4, NA, NA, 8.6,~
## $ User_Count <int> 322, NA, 709, 192, NA, NA, 431, 129, 594, NA, NA, 464,~
## $ Developer <chr> "Nintendo", "", "Nintendo", "Nintendo", "", "", "Ninte~
## $ Rating <chr> "E", "", "E", "E", "", "", "E", "E", "E", "", "", "E",~salesxbox<-salesxbox[,-1]
glimpse(salesxbox)## Rows: 613
## Columns: 9
## $ Game <chr> "Grand Theft Auto V", "Call of Duty: Black Ops 3", "Call~
## $ Year <chr> "2014", "2015", "2017", "2018", "2014", "2014", "2016", ~
## $ Genre <chr> "Action", "Shooter", "Shooter", "Action-Adventure", "Mis~
## $ Publisher <chr> "Rockstar Games", "Activision", "Activision", "Rockstar ~
## $ North.America <dbl> 4.70, 4.63, 3.75, 3.76, 3.23, 3.25, 3.37, 2.94, 2.94, 2.~
## $ Europe <dbl> 3.25, 2.04, 1.91, 1.47, 1.71, 1.49, 1.26, 1.62, 1.49, 1.~
## $ Japan <dbl> 0.01, 0.02, 0.00, 0.00, 0.00, 0.01, 0.02, 0.02, 0.03, 0.~
## $ Rest.of.World <dbl> 0.76, 0.68, 0.57, 0.54, 0.49, 0.48, 0.48, 0.45, 0.45, 0.~
## $ Global <dbl> 8.72, 7.37, 6.23, 5.77, 5.43, 5.22, 5.13, 5.03, 4.92, 4.~###Checking the data in visualization
salesps4 %>% plot_intro()
salesps4 %>% plot_missing()
salesps4 %>% profile_missing()## feature num_missing pct_missing
## 1 Game 0 0.0000000
## 2 Year 0 0.0000000
## 3 Genre 0 0.0000000
## 4 Publisher 0 0.0000000
## 5 North.America 0 0.0000000
## 6 Europe 0 0.0000000
## 7 Japan 0 0.0000000
## 8 Rest.of.World 0 0.0000000
## 9 Global 11 0.0106383salesxbox %>% plot_intro()
salesxbox %>% plot_missing()
salesxbox %>% profile_missing()## feature num_missing pct_missing
## 1 Game 0 0.00000000
## 2 Year 0 0.00000000
## 3 Genre 0 0.00000000
## 4 Publisher 0 0.00000000
## 5 North.America 0 0.00000000
## 6 Europe 0 0.00000000
## 7 Japan 0 0.00000000
## 8 Rest.of.World 0 0.00000000
## 9 Global 8 0.01305057videogamessales %>% plot_intro()
videogamessales %>% plot_missing()
videogamessales %>% profile_missing()## feature num_missing pct_missing
## 1 Name 0 0.0000000000
## 2 Platform 0 0.0000000000
## 3 Year_of_Release 0 0.0000000000
## 4 Genre 0 0.0000000000
## 5 Publisher 0 0.0000000000
## 6 NA_Sales 0 0.0000000000
## 7 EU_Sales 0 0.0000000000
## 8 JP_Sales 0 0.0000000000
## 9 Other_Sales 0 0.0000000000
## 10 Global_Sales 2 0.0001196244
## 11 Critic_Score 8582 0.5133082122
## 12 Critic_Count 8582 0.5133082122
## 13 User_Score 9129 0.5460254800
## 14 User_Count 9129 0.5460254800
## 15 Developer 0 0.0000000000
## 16 Rating 0 0.0000000000###Cleaning the data
salesps4<-clean_names(salesps4)
salesxbox<-clean_names(salesxbox)
videogamessales<-clean_names(videogamessales)
###Ensure NA in your data
colSums(is.na(salesps4))## game year genre publisher north_america
## 0 0 0 0 0
## europe japan rest_of_world global
## 0 0 0 11colSums(is.na(salesxbox))## game year genre publisher north_america
## 0 0 0 0 0
## europe japan rest_of_world global
## 0 0 0 8colSums(is.na(videogamessales))## name platform year_of_release genre publisher
## 0 0 0 0 0
## na_sales eu_sales jp_sales other_sales global_sales
## 0 0 0 0 2
## critic_score critic_count user_score user_count developer
## 8582 8582 9129 9129 0
## rating
## 0###Filtering NA in data
salesps4<-filter(salesps4,!is.na(global))
salesxbox<-filter(salesxbox,!is.na(global))
videogamessales<-filter(videogamessales,!is.na(global_sales) & !is.na(critic_score) & !is.na(critic_count) & !is.na(user_score) & !is.na(user_count))
###Checking again data structure
salesps4 %>% plot_intro()
salesps4 %>% plot_missing()
salesps4 %>% profile_missing()## feature num_missing pct_missing
## 1 game 0 0
## 2 year 0 0
## 3 genre 0 0
## 4 publisher 0 0
## 5 north_america 0 0
## 6 europe 0 0
## 7 japan 0 0
## 8 rest_of_world 0 0
## 9 global 0 0salesxbox %>% plot_intro()
salesxbox %>% plot_missing()
salesxbox %>% profile_missing()## feature num_missing pct_missing
## 1 game 0 0
## 2 year 0 0
## 3 genre 0 0
## 4 publisher 0 0
## 5 north_america 0 0
## 6 europe 0 0
## 7 japan 0 0
## 8 rest_of_world 0 0
## 9 global 0 0videogamessales %>% plot_intro()
videogamessales %>% plot_missing()
videogamessales %>% profile_missing()## feature num_missing pct_missing
## 1 name 0 0
## 2 platform 0 0
## 3 year_of_release 0 0
## 4 genre 0 0
## 5 publisher 0 0
## 6 na_sales 0 0
## 7 eu_sales 0 0
## 8 jp_sales 0 0
## 9 other_sales 0 0
## 10 global_sales 0 0
## 11 critic_score 0 0
## 12 critic_count 0 0
## 13 user_score 0 0
## 14 user_count 0 0
## 15 developer 0 0
## 16 rating 0 0###Check NA again
colSums(is.na(salesps4))## game year genre publisher north_america
## 0 0 0 0 0
## europe japan rest_of_world global
## 0 0 0 0colSums(is.na(salesxbox))## game year genre publisher north_america
## 0 0 0 0 0
## europe japan rest_of_world global
## 0 0 0 0colSums(is.na(videogamessales))## name platform year_of_release genre publisher
## 0 0 0 0 0
## na_sales eu_sales jp_sales other_sales global_sales
## 0 0 0 0 0
## critic_score critic_count user_score user_count developer
## 0 0 0 0 0
## rating
## 0###Processing data in period from 2013 to 2020
salesps4<-subset(salesps4,year<="2020")
salesxbox<-subset(salesxbox,year<="2020")
videogamessales<-subset(videogamessales, year_of_release<="2020" & year_of_release>="2013")Analysis
From the above data has been cleaning and processing, now, I plot the barplot to show the games sales which on platform PS4 and XBox respectively, from 2013 to 2020. The graphs are showing below.
ggplot(salesps4,aes(x=year,y=north_america))+geom_bar(stat='identity',color='lightblue')+labs(x='Year',y='North America Sales',title="North America Sales Volume from 2013 to 2020",tag="PS4")+geom_text(aes(x=1,y=50),label=sum(salesps4$north_america))
ggplot(salesps4,aes(x=year,y=europe))+geom_bar(stat='identity',color='lightblue')+labs(x='Year',y='Europe Sales',title="Europe Sales Volume from 2013 to 2020",tag="PS4")+geom_text(aes(x=1,y=50),label=sum(salesps4$europe))
ggplot(salesps4,aes(x=year,y=japan))+geom_bar(stat='identity',color='lightblue')+labs(x='Year',y='Japan Sales',title="Japan Sales Volume from 2013 to 2020",tag="PS4")+geom_text(aes(x=1,y=20),label=sum(salesps4$japan))
ggplot(salesps4,aes(x=year,y=rest_of_world))+geom_bar(stat='identity',color='lightblue')+labs(x='Year',y='Rest of World Sales',title="Rest of World Sales Volume from 2013 to 2020",tag="PS4")+geom_text(aes(x=1,y=30),label=sum(salesps4$rest_of_world))
ggplot(salesps4,aes(x=year,y=global))+geom_bar(stat='identity',color='lightblue')+labs(x='Year',y='Global Sales',title="Global Sales Volume from 2013 to 2020",tag="PS4")+geom_text(aes(x=1,y=150),label=sum(salesps4$global))
ggplot(salesxbox,aes(x=year,y=north_america))+geom_bar(stat='identity',color='lightgreen')+labs(x='Year',y='North America Sales',title="North America Sales Volume from 2013 to 2020",tag="XBox")+geom_text(aes(x=1,y=50),label=sum(salesxbox$north_america))
ggplot(salesxbox,aes(x=year,y=europe))+geom_bar(stat='identity',color='lightgreen')+labs(x='Year',y='Europe Sales',title="Europe Sales Volume from 2013 to 2020",tag="XBox")+geom_text(aes(x=1,y=30),label=sum(salesxbox$europe))
ggplot(salesxbox,aes(x=year,y=japan))+geom_bar(stat='identity',color='lightgreen')+labs(x='Year',y='Japan Sales',title="Japan Sales Volume from 2013 to 2020",tag="XBox")+geom_text(aes(x=1,y=1.5),label=sum(salesxbox$japan))
ggplot(salesxbox,aes(x=year,y=rest_of_world))+geom_bar(stat='identity',color='lightgreen')+labs(x='Year',y='Rest of World Sales',title="Rest of World Sales Volume from 2013 to 2020",tag="XBox")+geom_text(aes(x=1,y=8),label=sum(salesxbox$rest_of_world))
ggplot(salesxbox,aes(x=year,y=global))+geom_bar(stat='identity',color='lightgreen')+labs(x='Year',y='Global Sales',title="Global Sales Volume from 2013 to 2020",tag="XBox")+geom_text(aes(x=1,y=70),label=sum(salesxbox$global))
Both PS4 and XBox, in the Japan marketing is the lowest.
In here, I show the genre game which presented the most popular genre.
salesps4genre<-salesps4 %>% group_by(genre) %>% summarise(Count = n(),Perc=round(n()/nrow(.)*100,2)) %>% arrange(desc(Count))
salesps4genre## # A tibble: 17 x 3
## genre Count Perc
## <chr> <int> <dbl>
## 1 Action 204 25
## 2 Role-Playing 105 12.9
## 3 Shooter 74 9.07
## 4 Adventure 70 8.58
## 5 Sports 69 8.46
## 6 Misc 53 6.5
## 7 Racing 47 5.76
## 8 Action-Adventure 38 4.66
## 9 Platform 33 4.04
## 10 Fighting 32 3.92
## 11 Strategy 25 3.06
## 12 Simulation 21 2.57
## 13 Music 18 2.21
## 14 Puzzle 9 1.1
## 15 MMO 8 0.98
## 16 Visual Novel 8 0.98
## 17 Party 2 0.25salesxboxgenre<-salesxbox %>% group_by(genre) %>% summarise(Count = n(),Perc=round(n()/nrow(.)*100,2)) %>% arrange(desc(Count))
salesxboxgenre## # A tibble: 16 x 3
## genre Count Perc
## <chr> <int> <dbl>
## 1 Action 116 23.2
## 2 Shooter 65 13
## 3 Sports 58 11.6
## 4 Racing 45 9
## 5 Adventure 36 7.2
## 6 Role-Playing 35 7
## 7 Misc 29 5.8
## 8 Action-Adventure 28 5.6
## 9 Simulation 19 3.8
## 10 Platform 18 3.6
## 11 Fighting 16 3.2
## 12 Strategy 14 2.8
## 13 Music 13 2.6
## 14 Puzzle 4 0.8
## 15 MMO 2 0.4
## 16 Visual Novel 2 0.4Above tables are showing the game sales and the genre of game, ‘Action’ is the most popular genre.
ggplot(salesps4, aes(x =genre,y = north_america))+geom_bar(stat="identity", color='blue')+labs(x = "Genre", y = "North.America Sales",title="North.America Sales Volume Of Games Genre",tag="PS4")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=75),label=sum(salesps4$north_america))
ggplot(salesps4, aes(x =genre,y =europe ))+geom_bar(stat="identity", color='blue')+labs(x = "Genre", y = "Europe Sales",title="Europe Sales Volume Of Games Genre",tag="PS4")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=75),label=sum(salesps4$europe))
ggplot(salesps4, aes(x =genre,y =japan))+geom_bar(stat="identity", color='blue')+labs(x = "Genre", y = "Japan Sales",title="Japan Sales Volume Of Games Genre",tag="PS4")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=13),label=sum(salesps4$japan))
ggplot(salesps4, aes(x =genre,y =rest_of_world))+geom_bar(stat="identity", color='blue')+labs(x = "Genre", y = "Rest.of.World Sales",title="Rest.of.World Sales Volume Of Games Genre",tag="PS4")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=30),label=sum(salesps4$rest_of_world))
ggplot(salesps4, aes(x =genre,y =global))+geom_bar(stat="identity", color='blue')+labs(x = "Genre", y = "Global Sales",title="Global Sales Volume Of Games Genre",tag="PS4")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=160),label=sum(salesps4$global))
ggplot(salesxbox, aes(x =genre,y = north_america))+geom_bar(stat="identity", color='green')+labs(x = "Genre", y = "North.America Sales",title="North.America Sales Volume Of Games Genre",tag="XBox")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=60),label=sum(salesxbox$north_america))
ggplot(salesxbox, aes(x =genre,y =europe ))+geom_bar(stat="identity", color='green')+labs(x = "Genre", y = "Europe Sales",title="Europe Sales Volume Of Games Genre",tag="XBox")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=30),label=sum(salesxbox$europe))
ggplot(salesxbox, aes(x =genre,y =japan))+geom_bar(stat="identity", color='green')+labs(x = "Genre", y = "Japan Sales",title="Japan Sales Volume Of Games Genre",tag="XBox")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=5),label=sum(salesxbox$japan))
ggplot(salesxbox, aes(x =genre,y =rest_of_world))+geom_bar(stat="identity", color='green')+labs(x = "Genre", y = "Rest.of.World Sales",title="Rest.of.World Sales Volume Of Games Genre",tag="XBox")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=10),label=sum(salesxbox$rest_of_world))
ggplot(salesxbox, aes(x =genre,y =global))+geom_bar(stat="identity", color='green')+labs(x = "Genre", y = "Global Sales",title="Global Sales Volume Of Games Genre",tag="XBox")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=100),label=sum(salesxbox$global))
salesps4 %>% group_by(publisher) %>% summarise(Count = n(),Perc=round(n()/nrow(.)*100,2)) %>% arrange(desc(Count))## # A tibble: 147 x 3
## publisher Count Perc
## <chr> <int> <dbl>
## 1 Namco Bandai Games 56 6.86
## 2 Sony Interactive Entertainment 47 5.76
## 3 Ubisoft 44 5.39
## 4 Square Enix 40 4.9
## 5 Tecmo Koei 37 4.53
## 6 Activision 30 3.68
## 7 Capcom 30 3.68
## 8 Warner Bros. Interactive Entertainment 27 3.31
## 9 Sony Computer Entertainment 25 3.06
## 10 Electronic Arts 21 2.57
## # ... with 137 more rowssalesxbox %>% group_by(publisher) %>% summarise(Count = n(),Perc=round(n()/nrow(.)*100,2)) %>% arrange(desc(Count))## # A tibble: 96 x 3
## publisher Count Perc
## <chr> <int> <dbl>
## 1 Ubisoft 44 8.8
## 2 Microsoft Studios 31 6.2
## 3 Activision 29 5.8
## 4 Warner Bros. Interactive Entertainment 26 5.2
## 5 Electronic Arts 22 4.4
## 6 Capcom 19 3.8
## 7 EA Sports 19 3.8
## 8 Namco Bandai Games 19 3.8
## 9 505 Games 16 3.2
## 10 THQ Nordic 16 3.2
## # ... with 86 more rowsIn the two type platforms, on PS4 the game of action is having the most sales, on XBox the game of ‘Shooter’ is the best sales.
ggplot(videogamessales, aes(x =genre,y =na_sales))+geom_bar(stat="identity", color='blue')+labs(x = "Genre", y = "North.America Sales",title="North.America Sales Volume Of Games Genre")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=100),label=sum(videogamessales$na_sales))
ggplot(videogamessales, aes(x =genre,y =eu_sales ))+geom_bar(stat="identity", color='blue')+labs(x = "Genre", y = "Europe Sales",title="Europe Sales Volume Of Games Genre")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=100),label=sum(videogamessales$eu_sales))
ggplot(videogamessales, aes(x =genre,y =jp_sales))+geom_bar(stat="identity", color='blue')+labs(x = "Genre", y = "Japan Sales",title="Japan Sales Volume Of Games Genre")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=25),label=sum(videogamessales$jp_sales))
ggplot(videogamessales, aes(x =genre,y =other_sales ))+geom_bar(stat="identity", color='blue')+labs(x = "Genre", y = "Rest.of.World Sales",title="Rest.of.World Sales Volume Of Games Genre")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=30),label=sum(videogamessales$other_sales ))
ggplot(videogamessales, aes(x =genre,y =global_sales))+geom_bar(stat="identity", color='blue')+labs(x = "Genre", y = "Global Sales",title="Global Sales Volume Of Games Genre")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=250),label=sum(videogamessales$global_sales))
ggplot(videogamessales, aes(x =platform,y =na_sales))+geom_bar(stat="identity", color='yellow')+labs(x = "Platform", y = "North.America Sales",title="North.America Sales Volume Of Games Genre")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=80),label=sum(videogamessales$na_sales))
ggplot(videogamessales, aes(x =platform,y =eu_sales ))+geom_bar(stat="identity", color='yellow')+labs(x = "Platform", y = "Europe Sales",title="Europe Sales Volume Of Games Genre")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=110),label=sum(videogamessales$eu_sales))
ggplot(videogamessales, aes(x =platform,y =jp_sales))+geom_bar(stat="identity", color='yellow')+labs(x = "Platform", y = "Japan Sales",title="Japan Sales Volume Of Games Genre")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=25),label=sum(videogamessales$jp_sales))
ggplot(videogamessales, aes(x =platform,y =other_sales ))+geom_bar(stat="identity", color='yellow')+labs(x = "Platform", y = "Rest.of.World Sales",title="Rest.of.World Sales Volume Of Games Genre")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=50),label=sum(videogamessales$other_sales ))
ggplot(videogamessales, aes(x =platform,y =global_sales))+geom_bar(stat="identity", color='yellow')+labs(x = "Platform", y = "Global Sales",title="Global Sales Volume Of Games Genre")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 12),axis.text.y = element_text(vjust = 1, hjust = 1, size = 12))+geom_text(aes(x=1,y=250),label=sum(videogamessales$global_sales))
videogamessales %>% group_by(platform) %>% summarise(Count = n(),Perc=round(n()/nrow(.)*100,2)) %>% arrange(desc(Count))## # A tibble: 9 x 3
## platform Count Perc
## <chr> <int> <dbl>
## 1 PS4 249 25.5
## 2 XOne 165 16.9
## 3 PC 148 15.2
## 4 PS3 120 12.3
## 5 X360 81 8.3
## 6 PSV 76 7.79
## 7 WiiU 69 7.07
## 8 3DS 67 6.86
## 9 PSP 1 0.1videogamessales %>% group_by(genre) %>% summarise(Count = n(),Perc=round(n()/nrow(.)*100,2)) %>% arrange(desc(Count))## # A tibble: 12 x 3
## genre Count Perc
## <chr> <int> <dbl>
## 1 Action 309 31.7
## 2 Shooter 132 13.5
## 3 Role-Playing 127 13.0
## 4 Sports 110 11.3
## 5 Racing 59 6.05
## 6 Platform 50 5.12
## 7 Adventure 46 4.71
## 8 Fighting 42 4.3
## 9 Misc 42 4.3
## 10 Simulation 26 2.66
## 11 Strategy 26 2.66
## 12 Puzzle 7 0.72