Android Smartphone Analysis in R [Code + Video]
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In this post, We’ll learn how to take analyse your Android Smartphone usage data.
Steps:
Download your
MyActivityData from Google Takeout – https://takeout.google.com/ (after Selectingjsonformat – instead of the defaulthtmlformat)When the download is available, save the
zipfile and unzip them to findMyActivity.jsoninside last-level of the folderCreate a new R project (using your RStudio) with the
MyActivity.jsonfile in the project folderFollow the below codes along with this Video
Youtube – https://www.youtube.com/watch?v=fv0idLNWfqg
Loading libraries
library(jsonlite) library(tidyverse) library(patchwork)
Reading Input json using jsonlite package
android <- jsonlite::fromJSON("MyActivity.json")
Glimpse of the dataframe
glimpse(android) ## Observations: 37,773 ## Variables: 5 ## $ header <chr> "Sense Home Launcher-News,Theme", "Google Chrome: Fast & S... ## $ title <chr> "Used Sense Home Launcher-News,Theme", "Used Google Chrome... ## $ titleUrl <chr> "https://play.google.com/store/apps/details?id=com.htc.lau... ## $ time <chr> "2020-05-06T15:50:53.817Z", "2020-05-06T15:47:53.613Z", "2... ## $ products <list> ["Android", "Android", "Android", "Android", "Android", "...
Data Preprocessing - Date Time
android$time <- parse_datetime(android$time,locale = locale(tz = "Asia/Calcutta"))
summary(android$time)
## Min. 1st Qu. Median
## "2017-01-06 16:08:01" "2019-07-21 18:30:18" "2019-10-14 19:53:11"
## Mean 3rd Qu. Max.
## "2019-10-04 14:01:15" "2020-01-17 17:10:47" "2020-05-06 21:20:53"
android %>%
mutate(date = lubridate::date(time),
year = lubridate::year(time)) -> android
android_latest <- android %>%
filter(year %in% c(2019,2020))
Number of Unique Apps
android_latest %>%
count(header, sort = TRUE) %>%
head(20) %>%
mutate(header = fct_reorder(header, n)) %>%
ggplot() + geom_col(aes(y = header, x = n)) +
theme_minimal() +
labs(title = "Most used Apps - Overall",
subtitle = "Android Smartphone usage",
caption = "Data:Google Takeout")
Apps Comparison
android_latest %>%
filter(year %in% '2019') %>%
group_by(year, header) %>%
summarise(n = n()) %>%
arrange(desc(n)) %>%
head(10) %>% #View()
mutate(header = fct_reorder(header, n)) %>%
ggplot() + geom_col(aes(y = header, x = n)) +
# facet_wrap(~year, scales = "free") +
theme_minimal() +
labs(title = "Most used Apps - 2019",
subtitle = "Android Smartphone usage",
caption = "Data:Google Takeout") -> p2019
android_latest %>%
filter(year %in% '2020') %>%
group_by(year, header) %>%
summarise(n = n()) %>%
arrange(desc(n)) %>%
head(10) %>% #View()
mutate(header = fct_reorder(header, n)) %>%
ggplot() + geom_col(aes(y = header, x = n)) +
# facet_wrap(~year, scales = "free") +
theme_minimal() +
labs(title = "Most used Apps - 2020",
subtitle = "Android Smartphone usage",
caption = "Data:Google Takeout") -> p2020
p2019 / p2020
Usage Timeline
android_latest %>% count(date) %>% ggplot() + geom_line(aes(date,n))
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