Google Analytics Package in R
Any analyst in the world knows what R and Python are. There is a new Package released in R.
Released by Mark Edmondson, It is called googleAnalyticsR. It uses the latest Version of the GA analytics API. Thus, any digital analyst, even those new to R can start using the power of R to get to the "what" of the business question. The code given below will pull Basic data for the past quarter and enable an analysts can the trend.
Each line above has multiple functions so that any user can just insert the option they want. You can use the above to pull data across various accounts, dimension, metrics and customize the graph. Like I said earlier, R does not need introduction. Compared to excel, R does calculations much faster. Being open source, there are more open source packages that we need. They enable powerful operations that save time on data arrangement and you do need a budget approval from managers to use it.
Released by Mark Edmondson, It is called googleAnalyticsR. It uses the latest Version of the GA analytics API. Thus, any digital analyst, even those new to R can start using the power of R to get to the "what" of the business question. The code given below will pull Basic data for the past quarter and enable an analysts can the trend.
# One time install of the packages required for this task
install.packages("ggplot2 ")
install.packages("plotly ")
install.packages("htmlwidgets ")
install.packages("googleAnalyticsR ")
#Add the basic Packages required for this task
library(ggplot2)
library(plotly)
library(htmlwidgets)
library(googleAnalyticsR)
# Set the view ID that we'll be using. You can get the view ID for a specific view from analytics edge or by logging into the Google Analytics Query Explorer at https://ga-dev-tools.appspot.com/query-explorer/. It's the "ids" value.
view_id <- XXXXXXXX
# Authorize Google Analytics. Use (new_user = TRUE) if you check multiple views
#ga_auth(new_user = TRUE)
ga_auth()
# Get the data from Google Analytics without Sampling
gadata <- google_analytics_4(view_id,
date_range = c("2016-08-01", "2017-03-31"),
metrics = "sessions", dimensions = c("date", "channelGrouping", "deviceCategory"),anti_sample = TRUE)
# Create your graphs
my_graph<- ggplot(gadata,aes(x=date,y=sessions))+stat_summary(fun.y=sum,geom = "bar")+facet_wrap(~channelGrouping + deviceCategory, scales = "free")
# Make Graphs interactive for exploration
ggplotly(my_graph)
Each line above has multiple functions so that any user can just insert the option they want. You can use the above to pull data across various accounts, dimension, metrics and customize the graph. Like I said earlier, R does not need introduction. Compared to excel, R does calculations much faster. Being open source, there are more open source packages that we need. They enable powerful operations that save time on data arrangement and you do need a budget approval from managers to use it.
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