How To Calculate Month-On-Month Change In Website Traffic In R Studio

In order to plot this chart, what you’ll need:

ggplot

dplyr package + mutate function to create a calculated field [% change in monthly sessions]

In order to get the end result like this:

We first use the GoogleAnalyticsR package to pull the data from GA. Here, I have Sessions + yearMonth data for all of 2018.

We then call the library dplyr and use the mutate function to create a calculated field [month-on-month change] that shows the % values.

library(dplyr)
#new column added using a calculated field
gadata<- gadata %>% 
  mutate(mom_per_change = (sessions - lag(sessions))/lag(sessions))

As you can see in the chart, the y axis has the change in decimal format instead of percentages.

Adding the scale_y_continuous parameter to the ggplot now converts the y axis to percentage format.

#number format changed to show Y axis as %
ggplot(gadata, mapping=aes(x=yearMonth, 
      y=mom_per_change)) +
        geom_col() +
           scale_y_continuous(labels = scales::percent)

#Full code
library("googleAuthR")
library("googleAnalyticsR")
library("ggplot2")

ga_auth()
ga_account_list()


ga_id <- 1234567

gadata <- google_analytics(viewId = 1234567, 
    date_range = c("2018-01-01","2018-09-21"),
    metrics = c("sessions"),
    dimensions = c("yearMonth"),
    anti_sample = TRUE)

gadata

library(dplyr)

#new column added using a calculated field
gadata<- gadata %>% 
  mutate(mom_per_change = 
  (sessions - lag(sessions))/lag(sessions))

gadata

#Plot created to show % change
ggplot(gadata, mapping=aes(x=yearMonth, 
  y=mom_per_change)) +
  geom_col()

#number format changed to show Y axis as %
ggplot(gadata, mapping=aes(x=yearMonth, 
  y=mom_per_change)) +
  geom_col() +
  scale_y_continuous(labels = scales::percent)