googleAnalyticsReports

</br>

The goal of googleAnalyticsReports is to clean data from GA API and generate some standard plots for you.

This package expects that the user already has data from GA, and especially, that this data has a column: sourceMedium to work on. This means that in order tu clean the data, sourceMedium must be present as a dimension in the call to the API.

How to install googleAnalyticsReports?

</br>

For the moment the package is only available in Github, you can install it with:

devtools::install_github("OmarGonD/googleAnalyticsReports")

For testing, the package comes with real data from a personal web page of mine for all 2017. To use it load it with:

library(googleAnalyticsReports)
data(gadata)

After it's loaded you can start using the package like this:

a <- ga_clean_data(gadata)

ga_sessions_per_month(a)

To use your own data I suggest that you use the googleAnalyticsR from Mark Edmonson to make a call like the following to the GA API.

Install it with:

devtools::install_github("MarkEdmondson1234/googleAnalyticsR")

Make an API call like this:

library(googleAnalyticsR)

ga_auth()

my_accounts <- ga_account_list()
View(my_accounts)

my_id <- 123456789 #use your view id here 

start_date <- "2018-01-01"
final_date <- "2018-01-31"

my_data <- google_analytics_4(my_id, 
                              date_range = c(start_date, final_date),
                              metrics = "sessions",
                              dimensions = c("date", "sourceMedium"))

googleAnalyticsReports main functions are:

</br>

1. ga_clean_data() #cleans the sourceMedium column

2. ga_sessions_* functions to generate plots using ggplot2 as plotting library, for example: ga_sessions_per_month and ga_sessions_per_month_s (this not only returns the total sessions by month but also lets you identify the right sources for them).

Cleaning Google Analytics Data

</br>

This is a basic example which shows you how to clean the sourceMedium column using: ga_clean_data().

ga_clean_data() acepts the following arguments:

• data: data to be cleaned.
• language: desired language for your sources. For now the only options are en and es. Defaults to en.
• remove_spam: defaults to TRUE and removes all data considerd to be spam. If you would like to check that the function labels correctly these source, you can set it to FALSE and do a manual checking.

## use data sample for demostration

data(gadata) #load sample data contained in package

cleaned_data <- ga_clean_data(gadata)

head(cleaned_data , n=6) 

# A tibble: 6 x 5
  date       source                 medium   sessions sources
  <date>     <chr>                  <chr>       <dbl> <chr>       
1 2017-07-01 google                 organic      1.00 organic     
2 2017-07-03 google                 organic      6.00 organic     
3 2017-07-04 google                 organic      1.00 organic     
4 2017-07-05 (direct)               (none)       2.00 direct      
5 2017-07-05 earn-from-articles.com referral     3.00 spam        
6 2017-07-05 google                 organic      6.00 organic

#Sessions per month

</br>

After cleaning the data with you can call ga_sessions_per_month() to generate a graph showing the total sessions by month.

Thi function accepts these arguments:

• title: title of the plot
• x_title: title for the x axis.
• y_title: title for the y axis.
• label_size: size of the label for each column.
• bars_fill: should the RGB color desired for the bars.

#Sessions by source per month

</br>

ga_sessions_per_month_s() plots all sessions by month and by source. The fill of the bars is the variable sources from the data frame you passed as argument. This data frame should have been cleaned with ga_clean_data().

Note the final "s" in the function.

Thi function accepts these arguments:

• title: title of the plot
• x_title: title for the x axis.
• y_title: title for the y axis.
• label_size: size of the label for each column.

#Heatmap of sessions by day of week and hour

</br>

You can get a heatmap by running:

</br>

library(googleAnalyticsR)
library(googleAnalyticsReports)


ga_auth()

my_accounts <- ga_account_list()
View(my_accounts)

my_id <- 88090999 #use your view id here 

start_date <- "2017-01-01"
final_date <- "2018-01-31"

df <- google_analytics_4(my_id, 
                         date_range = c(start_date, final_date),
                         metrics = "sessions",
                         dimensions = c("date", "hour", "sourceMedium"),
                         max=100000)


ga_heatmap_sessions(df, title = "Sesiones por día de la semana y hora",
                    legend_title = "Sesiones")
                    

</br> </br>

Thi function accepts these arguments:

• title: title of the plot
• x_title: title for the x axis. Defaults to blank.
• y_title: title for the y axis.
• legend_title: title for the legend.