You will need RGoogleAnalytics package to extract Google Analytics data in R. The package was developed by Michael Pearmain, and it provides functions for accessing and retrieving data from the Google Analytics API. This article is based on the package’s supporting documentation. To download the documentation use this link: RGoogleAnalytics documentation.
First, install the package RgoogleAnalytics. It requires the packages “lubridate” and “httr” to be installed as well.
If you have problems with downloading the packages, check your R version. RGoogleAnalytics requires R version 3.0.2 or newer.
Then, you will use the Auth function to authorize the RGoogleAnalytics package to your Google Analytics Account using Oauth2.0.
The function Auth expects a Client ID and Client Secret. To get these, you will have to register an application with the Google Analytics API:
1. Go to the Google Developers Console
2. Create a New Project and enable the Google Analytics API
3. On the Credentials screen, create a new Client ID for Application Type “Installed Application”
4. Copy the Client ID and Client Secret to your R Script
Enable Google Analytics API
Create a new Client ID
Now you can authorize the RGoogleAnalytics package to your Google Analytics Account.
The dataset has to be downloaded in your working directory. Getwd() funcion returns an absolute filepath representing the current working directory of the R process. To change your working directory in R you need to use setwd(dir) function or go to the File menu in the R Cosole and choose “Change dir”.
fileUrl < - "your link here"
download.file(fileUrl,destfile = "./salaries/computer.xls")
Next step is to install the xlsx R package if you have not done so previously. To install xlsx, use install.packages(“xlsx”), to ensure if you have it or no, enter find.package(“xlsx”) in the console. After xlsx is done installing, load it using library(xlsx).
All three options above are the same, we are choosing a certain column. For example, if we have a data frame survey which consists of 1,000 observations, and each observation is described by 3 variables: gender, age, and marital status, by using survey$age we can subset the column named “age” for all 1,000 observations.