Google_Playstore
Akindynos
2022-12-09
options(repos = list(CRAN="http://cran.rstudio.com/"))##Insert dataframe and the right packages
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 0.3.5
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(ggplot2)
library(lubridate)## Loading required package: timechange
##
## Attaching package: 'lubridate'
##
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(modelr)
install.packages("naniar")## Installing package into 'C:/Users/Costa/AppData/Local/R/win-library/4.2'
## (as 'lib' is unspecified)## package 'naniar' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\Costa\AppData\Local\Temp\RtmpEBZn2u\downloaded_packageslibrary(naniar)
library(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testlibrary(plyr)## ------------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## ------------------------------------------------------------------------------
##
## Attaching package: 'plyr'
##
## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following object is masked from 'package:purrr':
##
## compactlibrary(dplyr)
googleplaystore <- read_csv("D:/ANALYTICS/playstore/googleplaystore.csv")## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)## Rows: 10841 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): App, Category, Size, Installs, Type, Price, Content Rating, Genres...
## dbl (2): Rating, Reviews
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.View(googleplaystore)##nest step Computes the number of duplicate rows in the dataset using the duplicated() and sum() functions. The duplicated() function returns a logical vector indicating which rows are duplicates, and the sum() function counts the number of TRUE values in the vector.
Shows the first six rows of the dataset using the head() function. The head() function returns the first n rows of a dataset, where n is a specified number. By default, n is set to 6.
Shows the structure of the dataset using the str() function. The str() function shows the structure of an R object, including the class, length, and first few elements of each component.
Visualizes the missing values in the dataset using the vis_miss() function from the naniar package. The vis_miss() function creates a visualization that shows the distribution of missing values in the dataset, including the number and percentage of missing values for each column. Note that the naniar package must be installed and loaded in the current R session in order to use the vis_miss() function.
sum(duplicated(googleplaystore))## [1] 483head(googleplaystore)## # A tibble: 6 × 13
## App Categ…¹ Rating Reviews Size Insta…² Type Price Conte…³ Genres Last …⁴
## <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Photo… ART_AN… 4.1 159 19M 10,000+ Free 0 Everyo… Art &… Januar…
## 2 Color… ART_AN… 3.9 967 14M 500,00… Free 0 Everyo… Art &… Januar…
## 3 U Lau… ART_AN… 4.7 87510 8.7M 5,000,… Free 0 Everyo… Art &… August…
## 4 Sketc… ART_AN… 4.5 215644 25M 50,000… Free 0 Teen Art &… June 8…
## 5 Pixel… ART_AN… 4.3 967 2.8M 100,00… Free 0 Everyo… Art &… June 2…
## 6 Paper… ART_AN… 4.4 167 5.6M 50,000+ Free 0 Everyo… Art &… March …
## # … with 2 more variables: `Current Ver` <chr>, `Android Ver` <chr>, and
## # abbreviated variable names ¹Category, ²Installs, ³`Content Rating`,
## # ⁴`Last Updated`str(googleplaystore)## spc_tbl_ [10,841 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ App : chr [1:10841] "Photo Editor & Candy Camera & Grid & ScrapBook" "Coloring book moana" "U Launcher Lite – FREE Live Cool Themes, Hide Apps" "Sketch - Draw & Paint" ...
## $ Category : chr [1:10841] "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" ...
## $ Rating : num [1:10841] 4.1 3.9 4.7 4.5 4.3 4.4 3.8 4.1 4.4 4.7 ...
## $ Reviews : num [1:10841] 159 967 87510 215644 967 ...
## $ Size : chr [1:10841] "19M" "14M" "8.7M" "25M" ...
## $ Installs : chr [1:10841] "10,000+" "500,000+" "5,000,000+" "50,000,000+" ...
## $ Type : chr [1:10841] "Free" "Free" "Free" "Free" ...
## $ Price : chr [1:10841] "0" "0" "0" "0" ...
## $ Content Rating: chr [1:10841] "Everyone" "Everyone" "Everyone" "Teen" ...
## $ Genres : chr [1:10841] "Art & Design" "Art & Design;Pretend Play" "Art & Design" "Art & Design" ...
## $ Last Updated : chr [1:10841] "January 7, 2018" "January 15, 2018" "August 1, 2018" "June 8, 2018" ...
## $ Current Ver : chr [1:10841] "1.0.0" "2.0.0" "1.2.4" "Varies with device" ...
## $ Android Ver : chr [1:10841] "4.0.3 and up" "4.0.3 and up" "4.0.3 and up" "4.2 and up" ...
## - attr(*, "spec")=
## .. cols(
## .. App = col_character(),
## .. Category = col_character(),
## .. Rating = col_double(),
## .. Reviews = col_double(),
## .. Size = col_character(),
## .. Installs = col_character(),
## .. Type = col_character(),
## .. Price = col_character(),
## .. `Content Rating` = col_character(),
## .. Genres = col_character(),
## .. `Last Updated` = col_character(),
## .. `Current Ver` = col_character(),
## .. `Android Ver` = col_character()
## .. )
## - attr(*, "problems")=<externalptr>vis_miss(googleplaystore)## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## ℹ Please use `gather()` instead.
## ℹ The deprecated feature was likely used in the visdat package.
## Please report the issue at <]8;;https://github.com/ropensci/visdat/issueshttps://github.com/ropensci/visdat/issues]8;;>.
#dropping duplicate records
app_data <- unique(googleplaystore)
glimpse(app_data)## Rows: 10,358
## Columns: 13
## $ App <chr> "Photo Editor & Candy Camera & Grid & ScrapBook", "Co…
## $ Category <chr> "ART_AND_DESIGN", "ART_AND_DESIGN", "ART_AND_DESIGN",…
## $ Rating <dbl> 4.1, 3.9, 4.7, 4.5, 4.3, 4.4, 3.8, 4.1, 4.4, 4.7, 4.4…
## $ Reviews <dbl> 159, 967, 87510, 215644, 967, 167, 178, 36815, 13791,…
## $ Size <chr> "19M", "14M", "8.7M", "25M", "2.8M", "5.6M", "19M", "…
## $ Installs <chr> "10,000+", "500,000+", "5,000,000+", "50,000,000+", "…
## $ Type <chr> "Free", "Free", "Free", "Free", "Free", "Free", "Free…
## $ Price <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"…
## $ `Content Rating` <chr> "Everyone", "Everyone", "Everyone", "Teen", "Everyone…
## $ Genres <chr> "Art & Design", "Art & Design;Pretend Play", "Art & D…
## $ `Last Updated` <chr> "January 7, 2018", "January 15, 2018", "August 1, 201…
## $ `Current Ver` <chr> "1.0.0", "2.0.0", "1.2.4", "Varies with device", "1.1…
## $ `Android Ver` <chr> "4.0.3 and up", "4.0.3 and up", "4.0.3 and up", "4.2 …app_data %>%
filter(is.na(Rating) | is.nan(Rating)) %>%
select(Rating)## # A tibble: 1,465 × 1
## Rating
## <dbl>
## 1 NaN
## 2 NaN
## 3 NaN
## 4 NaN
## 5 NaN
## 6 NaN
## 7 NaN
## 8 NaN
## 9 NaN
## 10 NaN
## # … with 1,455 more rowsReplace non numeric values in “Rating” with 0 value
app_data["Rating"][is.na(app_data["Rating"])] <- 0
print(app_data)## # A tibble: 10,358 × 13
## App Categ…¹ Rating Reviews Size Insta…² Type Price Conte…³ Genres Last …⁴
## <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Phot… ART_AN… 4.1 159 19M 10,000+ Free 0 Everyo… Art &… Januar…
## 2 Colo… ART_AN… 3.9 967 14M 500,00… Free 0 Everyo… Art &… Januar…
## 3 U La… ART_AN… 4.7 87510 8.7M 5,000,… Free 0 Everyo… Art &… August…
## 4 Sket… ART_AN… 4.5 215644 25M 50,000… Free 0 Teen Art &… June 8…
## 5 Pixe… ART_AN… 4.3 967 2.8M 100,00… Free 0 Everyo… Art &… June 2…
## 6 Pape… ART_AN… 4.4 167 5.6M 50,000+ Free 0 Everyo… Art &… March …
## 7 Smok… ART_AN… 3.8 178 19M 50,000+ Free 0 Everyo… Art &… April …
## 8 Infi… ART_AN… 4.1 36815 29M 1,000,… Free 0 Everyo… Art &… June 1…
## 9 Gard… ART_AN… 4.4 13791 33M 1,000,… Free 0 Everyo… Art &… Septem…
## 10 Kids… ART_AN… 4.7 121 3.1M 10,000+ Free 0 Everyo… Art &… July 3…
## # … with 10,348 more rows, 2 more variables: `Current Ver` <chr>,
## # `Android Ver` <chr>, and abbreviated variable names ¹Category, ²Installs,
## # ³`Content Rating`, ⁴`Last Updated`Now there are no NaN values in “Rating”
app_data %>%
filter(is.na(Rating) | is.nan(Rating)) %>%
select(Rating)## # A tibble: 0 × 1
## # … with 1 variable: Rating <dbl>str(app_data)## tibble [10,358 × 13] (S3: tbl_df/tbl/data.frame)
## $ App : chr [1:10358] "Photo Editor & Candy Camera & Grid & ScrapBook" "Coloring book moana" "U Launcher Lite – FREE Live Cool Themes, Hide Apps" "Sketch - Draw & Paint" ...
## $ Category : chr [1:10358] "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" ...
## $ Rating : num [1:10358] 4.1 3.9 4.7 4.5 4.3 4.4 3.8 4.1 4.4 4.7 ...
## $ Reviews : num [1:10358] 159 967 87510 215644 967 ...
## $ Size : chr [1:10358] "19M" "14M" "8.7M" "25M" ...
## $ Installs : chr [1:10358] "10,000+" "500,000+" "5,000,000+" "50,000,000+" ...
## $ Type : chr [1:10358] "Free" "Free" "Free" "Free" ...
## $ Price : chr [1:10358] "0" "0" "0" "0" ...
## $ Content Rating: chr [1:10358] "Everyone" "Everyone" "Everyone" "Teen" ...
## $ Genres : chr [1:10358] "Art & Design" "Art & Design;Pretend Play" "Art & Design" "Art & Design" ...
## $ Last Updated : chr [1:10358] "January 7, 2018" "January 15, 2018" "August 1, 2018" "June 8, 2018" ...
## $ Current Ver : chr [1:10358] "1.0.0" "2.0.0" "1.2.4" "Varies with device" ...
## $ Android Ver : chr [1:10358] "4.0.3 and up" "4.0.3 and up" "4.0.3 and up" "4.2 and up" ...##Replacing symbols ## Price - Remove “$” sign
app_data$Price <- str_replace_all(app_data$Price, "\\$","")##Installs - Remove “+” , “,” symbols
app_data$Installs <- str_replace_all(app_data$Installs, "\\+","")
app_data$Installs <- str_replace_all(app_data$Installs, "\\,", "")##Standardize types Some columns are in character type whereas they should be numeric or integer. Rating, Reviews , Price, Installs
app_data$Rating <- as.numeric(app_data$Rating)
app_data$Reviews <- as.numeric(app_data$Reviews)
app_data$Price <- as.numeric(app_data$Price)## Warning: NAs introduced by coercionapp_data$Installs <- as.integer(app_data$Installs)## Warning: NAs introduced by coercionRating is only up to 5
filter(app_data, Rating >= 0 & Rating <= 5)## # A tibble: 10,357 × 13
## App Categ…¹ Rating Reviews Size Insta…² Type Price Conte…³ Genres Last …⁴
## <chr> <chr> <dbl> <dbl> <chr> <int> <chr> <dbl> <chr> <chr> <chr>
## 1 Phot… ART_AN… 4.1 159 19M 1e4 Free 0 Everyo… Art &… Januar…
## 2 Colo… ART_AN… 3.9 967 14M 5e5 Free 0 Everyo… Art &… Januar…
## 3 U La… ART_AN… 4.7 87510 8.7M 5e6 Free 0 Everyo… Art &… August…
## 4 Sket… ART_AN… 4.5 215644 25M 5e7 Free 0 Teen Art &… June 8…
## 5 Pixe… ART_AN… 4.3 967 2.8M 1e5 Free 0 Everyo… Art &… June 2…
## 6 Pape… ART_AN… 4.4 167 5.6M 5e4 Free 0 Everyo… Art &… March …
## 7 Smok… ART_AN… 3.8 178 19M 5e4 Free 0 Everyo… Art &… April …
## 8 Infi… ART_AN… 4.1 36815 29M 1e6 Free 0 Everyo… Art &… June 1…
## 9 Gard… ART_AN… 4.4 13791 33M 1e6 Free 0 Everyo… Art &… Septem…
## 10 Kids… ART_AN… 4.7 121 3.1M 1e4 Free 0 Everyo… Art &… July 3…
## # … with 10,347 more rows, 2 more variables: `Current Ver` <chr>,
## # `Android Ver` <chr>, and abbreviated variable names ¹Category, ²Installs,
## # ³`Content Rating`, ⁴`Last Updated`unique(app_data$Category)## [1] "ART_AND_DESIGN" "AUTO_AND_VEHICLES" "BEAUTY"
## [4] "BOOKS_AND_REFERENCE" "BUSINESS" "COMICS"
## [7] "COMMUNICATION" "DATING" "EDUCATION"
## [10] "ENTERTAINMENT" "EVENTS" "FINANCE"
## [13] "FOOD_AND_DRINK" "HEALTH_AND_FITNESS" "HOUSE_AND_HOME"
## [16] "LIBRARIES_AND_DEMO" "LIFESTYLE" "GAME"
## [19] "FAMILY" "MEDICAL" "SOCIAL"
## [22] "SHOPPING" "PHOTOGRAPHY" "SPORTS"
## [25] "TRAVEL_AND_LOCAL" "TOOLS" "PERSONALIZATION"
## [28] "PRODUCTIVITY" "PARENTING" "WEATHER"
## [31] "VIDEO_PLAYERS" "NEWS_AND_MAGAZINES" "MAPS_AND_NAVIGATION"
## [34] "1.9"We have 33 Categories and 9660 entries
##Remove duplicates by App
newapp_data<-distinct(app_data, App, .keep_all = TRUE)
##then remove one category that was wrong (1.9)
newapp_data<-subset(newapp_data, Category != "1.9")
unique(newapp_data$Category)## [1] "ART_AND_DESIGN" "AUTO_AND_VEHICLES" "BEAUTY"
## [4] "BOOKS_AND_REFERENCE" "BUSINESS" "COMICS"
## [7] "COMMUNICATION" "DATING" "EDUCATION"
## [10] "ENTERTAINMENT" "EVENTS" "FINANCE"
## [13] "FOOD_AND_DRINK" "HEALTH_AND_FITNESS" "HOUSE_AND_HOME"
## [16] "LIBRARIES_AND_DEMO" "LIFESTYLE" "GAME"
## [19] "FAMILY" "MEDICAL" "SOCIAL"
## [22] "SHOPPING" "PHOTOGRAPHY" "SPORTS"
## [25] "TRAVEL_AND_LOCAL" "TOOLS" "PERSONALIZATION"
## [28] "PRODUCTIVITY" "PARENTING" "WEATHER"
## [31] "VIDEO_PLAYERS" "NEWS_AND_MAGAZINES" "MAPS_AND_NAVIGATION"str(newapp_data)## tibble [9,659 × 13] (S3: tbl_df/tbl/data.frame)
## $ App : chr [1:9659] "Photo Editor & Candy Camera & Grid & ScrapBook" "Coloring book moana" "U Launcher Lite – FREE Live Cool Themes, Hide Apps" "Sketch - Draw & Paint" ...
## $ Category : chr [1:9659] "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" ...
## $ Rating : num [1:9659] 4.1 3.9 4.7 4.5 4.3 4.4 3.8 4.1 4.4 4.7 ...
## $ Reviews : num [1:9659] 159 967 87510 215644 967 ...
## $ Size : chr [1:9659] "19M" "14M" "8.7M" "25M" ...
## $ Installs : int [1:9659] 10000 500000 5000000 50000000 100000 50000 50000 1000000 1000000 10000 ...
## $ Type : chr [1:9659] "Free" "Free" "Free" "Free" ...
## $ Price : num [1:9659] 0 0 0 0 0 0 0 0 0 0 ...
## $ Content Rating: chr [1:9659] "Everyone" "Everyone" "Everyone" "Teen" ...
## $ Genres : chr [1:9659] "Art & Design" "Art & Design;Pretend Play" "Art & Design" "Art & Design" ...
## $ Last Updated : chr [1:9659] "January 7, 2018" "January 15, 2018" "August 1, 2018" "June 8, 2018" ...
## $ Current Ver : chr [1:9659] "1.0.0" "2.0.0" "1.2.4" "Varies with device" ...
## $ Android Ver : chr [1:9659] "4.0.3 and up" "4.0.3 and up" "4.0.3 and up" "4.2 and up" ...options(scipen = 999)
ggplot(newapp_data, aes(x = Category, y = Installs)) +
geom_bar(stat = "identity", width = 0.7, fill = "red") +
coord_flip() +
labs(title = "Total App Installation for Each Category") +
theme(axis.text.x = element_text(angle = 90))
##The most populars: 1) by Reviews
ggplot(newapp_data, aes(x = Category, y = Reviews)) +
geom_bar(stat = "identity", width = 0.7, fill = "indianred") +
coord_flip() +
labs(title = "Total App Reviews for Each Category") +
theme(axis.text.x = element_text(angle = 90))
Games are reviewed more than any other category, with a very big
difference from the second one, Communication apps.
2. By the number of installations
ggplot(newapp_data, aes(x = Category, y = Installs)) +
geom_bar(stat = "identity", width = 0.9, fill = "indianred") +
coord_flip() +
labs(title = "Total App Installations for Each Category") +
theme(axis.text.x = element_text(angle = 90))
Again Games and Communications with the most installations.
Factors of the most popular Category
Installs, reviews and ratings! Their combination is a very good value to show how popular one category is.
newapp_data1 <- newapp_data %>% select(Rating, Installs, Reviews)
summary(newapp_data1)## Rating Installs Reviews
## Min. :0.000 Min. : 0 Min. : 0
## 1st Qu.:3.600 1st Qu.: 1000 1st Qu.: 25
## Median :4.200 Median : 100000 Median : 967
## Mean :3.541 Mean : 7777507 Mean : 216593
## 3rd Qu.:4.500 3rd Qu.: 1000000 3rd Qu.: 29401
## Max. :5.000 Max. :1000000000 Max. :78158306What is the average number of the Ratings?
ggplot(newapp_data1, aes(y=Rating)) +
geom_boxplot() +
ggtitle("App Rating") +
ylab("Rating")
Most of the ratings are between 3,5 and 4, with max 5.
ggplot(data = newapp_data1, aes(x = Reviews, y = Rating, color = Installs)) +
geom_jitter() +
labs(title = "Relationship between reviews, rating, and installations",
x = "Number of Reviews",
y = "Rating") 
The rating remains stable regarding the number of installations. But as
reviews are getting more, the number of installations is increased.
##Correlation And bellow we can see the correlation between those 2 factors:
install.packages("GGally")## Installing package into 'C:/Users/Costa/AppData/Local/R/win-library/4.2'
## (as 'lib' is unspecified)## package 'GGally' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\Costa\AppData\Local\Temp\RtmpEBZn2u\downloaded_packageslibrary (GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2newapp_data1cor <- newapp_data1[,c(1,2,3)]
# Visualization
ggcorr(newapp_data1cor[,1:3],geom = "circle", nbreaks = 5)
##Rating distribution
ggplot(newapp_data1, aes(x=Rating)) + geom_histogram(binwidth=.5)
mean(newapp_data1$Rating)## [1] 3.541143median(newapp_data1$Rating)## [1] 4.2Most rating is between 3.5-5
Most downloadable categories in Games
newapp_data %>%
select(Genres, Rating, Size, Reviews, Installs) %>%
distinct() %>%
group_by(Genres) %>%
summarize(mean_rating = mean(Rating),
mean_size = mean(Size),
mean_reviews = mean(Reviews),
mean_installs = mean(Installs)) %>%
arrange(-mean_installs) %>%
head()## Warning in mean.default(Size): argument is not numeric or logical: returning NA## mean_rating mean_size mean_reviews mean_installs
## 1 3.563748 NA 218056.2 7830080In “games” we have “Adventures” as the most downloadable game category, followed by “Arcade” in second place “Casual” third place..
##The most installed games
newapp_data %>%
select(App,Category, Rating,Installs)%>%
filter(Category=="GAME")%>%
arrange(-Installs)%>%
head()## # A tibble: 6 × 4
## App Category Rating Installs
## <chr> <chr> <dbl> <int>
## 1 Subway Surfers GAME 4.5 1000000000
## 2 Candy Crush Saga GAME 4.4 500000000
## 3 Temple Run 2 GAME 4.3 500000000
## 4 Pou GAME 4.3 500000000
## 5 My Talking Tom GAME 4.5 500000000
## 6 ROBLOX GAME 4.5 100000000