How to use Google Analytics for Tracking using R.Rmd

---
title: "How to use Google Analytics for Tracking using R"
subtitle: "[thescienceofdating.rbind.io](https://thescienceofdating.rbind.io)"
output: html_notebook
---

```{r, include = FALSE}
knitr::opts_chunk$set( echo = FALSE, warnings = FALSE )
library(googleAnalyticsR)
library(googleAuthR)
library(ggplot2)
library(ggrepel)
library(modelr)
library(viridis)
library(scales)
```


# Introduction 

Google Analytics data can be mined directly in R using `googleanalyticsR` package, allowing us to perform statistical analysis that would better understand, improve and later optimize website performance. 


## Content

#### 1. Connect R and Google Analytics API. 

#### 2. Data overview. 

#### 3. Data visualization. 

#### 4. Data insights. 


## 1. Connect R and Google Analytics API. 

```{r connect_googleAnalyticsR, echo = TRUE}
# Connect R and Google Analytics
ga_auth()

# Generate a list of all the Google Analytics accounts you have access to
ga_accounts <- ga_account_list()

# ga_id contains the View ID for querying
ga_id <- pull(ga_accounts[2,1])

# Download all data and store it in a dataframe
# blog was launched on September 11, 2020
ga_df <- google_analytics(ga_id,
                          metrics = c("users", "sessions"),
                          dimensions = "date", 
                          date_range = c("2020-09-11", as.character(Sys.Date()-1))
                          )

# Obtain Google Analytics data by device category
byDevice_df <- google_analytics(ga_id, 
                                date_range = c("2020-09-11", as.character(Sys.Date()-1)),
                                metrics = c("sessions", "avgSessionDuration"),
                                dimensions = c("date", "deviceCategory")
                                )
```


## 2. Data overview.

Google's Basic visitor interaction model:

* **Visitor** — the client that visits the site, such as the browser or mobile phone operated by a person.

* **Session** — the period of time during which the visitor is active on the site.

* **Page** — activity on the user's behalf which sends a GIF request to the Analytics servers. This is typically characterized by a pageview, but it can include: a pageview or
an event (e.g. click on a movie button). 


Variables available in Google Analitics: 

* Users 

* Sesssions 

* Bounces (visitors who leave the website after visiting only one page)

* Devices used for visiting the website: desktop, mobile or tablet

* Users' location: continent and country

* Website traffic by channel (organic, social, direct)

* Campaign metrics

* Metrics by segments of users


## 3. Data visualization.

#### How did sessions evolve through time? 

Slow slope at the beginning, peaks of users when new posts are released. Intensive traffic appeared especially when the blog article was shared in **Facebook and Linkedin posts**. 

```{r dailyUsers, echo = TRUE, include = TRUE}
# Daily users with labels
ggplot(ga_df, aes(x = date, y = users, label = users)) + 
  geom_line() + 
  geom_point()+
  geom_label(aes(label = users), 
             size = 2, 
             fill = viridis(3)[2], 
             colour = "white", 
             fontface = "bold") + 
  scale_x_date(labels = date_format("%m-%Y")) +
  theme_light() + 
  ylab("Users") + 
  xlab("Date") +
  ggtitle("Daily users: September 2020 - February 2021") +
  theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 15)) 
```


#### What are the most frequenct devices used for visiting the blog?

```{r byDevice, echo = TRUE, include = TRUE, warnings = FALSE}
# Plot sessions with deviceCategory
byDevice_df %>%
  ggplot(aes(x = deviceCategory, y = sessions, fill = deviceCategory)) +   
  geom_bar(stat = "identity", position = position_dodge(-.9), show.legend = FALSE) + 
  theme_light() +
  scale_fill_manual(values = viridis::viridis(5), aesthetics = "fill") + 
  coord_flip() + 
  xlab("Sessions") + 
  ylab("Device Category") +
  theme(panel.grid = element_blank()) + 
  ggtitle("Sessions by device category") +
  theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 15)) 
```


#### tbd 


#### 4. Data insights. 

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