Categorisation is another useful application for web data you can perform in R.

In this case, we are looking to how we can classify web data to create different segments created by the data (as opposed to what you may pick), or if you give categories to R if they can help you define those categories via other metrics.

Decision trees

Decision trees are typically read top to bottom, and represent where the model thinks you can split your data. As you move down

library(knitr) # used for kable that makes nice RMarkdown tables
web_data <- read.csv("./data/gadata_example_2.csv", stringsAsFactors = FALSE)
kable(head(web_data[-1]), row.names = FALSE)
date channelGrouping deviceCategory sessions pageviews entrances bounces
2016-01-01 (Other) desktop 19 23 19 15
2016-01-01 (Other) mobile 112 162 112 82
2016-01-01 (Other) tablet 24 41 24 19
2016-01-01 Direct desktop 133 423 133 61
2016-01-01 Direct mobile 345 878 344 172
2016-01-01 Direct tablet 126 237 126 77 
library(dplyr) # data transformation
cats <- web_data %>% select(deviceCategory, channelGrouping, sessions)

kable(head(cats), row.names = FALSE)
deviceCategory channelGrouping sessions
desktop (Other) 19
mobile (Other) 112
tablet (Other) 24
desktop Direct 133
mobile Direct 345
tablet Direct 126 
library(rpart) # creates decision trees

tree <- rpart(deviceCategory ~ ., cats)
plot(tree)
text(tree)

The labels are a bit obscure as they abbreviate the channel names.

The package rpart.plot makes the plots much nicer and easier to interpret:

library(rpart.plot)  # pretty trees
rpart.plot(tree, type=1)

Each node shows:

From this you can see from this data at a glance that for sessions under 300, Tablet traffic was not search related at all, favouring Email, Referral, Social and Video.

Machine learning

Categorisation is a use case for machine learning, as it utilises self-validation checks to check your categorisatons. Two methods are talked about in more length in the blog post, “Introduction to Machine Learning with Web Analytics: Random Forests and K-Means”.