Website Traffic Analysis Report

library(dplyr)
library(bigrquery) library(ggplot2) library(magrittr)

Introduction

This report analyzes the website traffic and user behavior of my personal blog using data collected from Google Analytics 4 (GA4) and exported to Google BigQuery.
The purpose is to understand how users interact with my website, what type of content performs best, and how I can optimize the website for better engagement and conversions.

project <- “ic-term-project-website” dataset <- “analytics_505865185” table <- “events_*”

sql <- ” SELECT event_date, event_name, COUNT(*) AS events FROM ic-term-project-website.analytics_505865185.events_* GROUP BY event_date, event_name ORDER BY event_date ”

ga_data <- bq_project_query(project, sql) %>% bq_table_download()

Data Source

The data used in this report comes from: - Google Analytics 4 (GA4) - Automatic daily export to BigQuery - Dataset: analytics_505865185 - Table: events_*

Below is the code for loading and processing the data:

library(bigrquery)
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(ggplot2)

project <- "ic-term-project-website"
dataset <- "analytics_505865185"

sql <- "
SELECT event_date, event_name, COUNT(*) AS events
FROM `ic-term-project-website.analytics_505865185.events_*`
GROUP BY event_date, event_name
ORDER BY event_date
"

ga_data <- bq_project_query(project, sql) %>% 
  bq_table_download()

! Using an auto-discovered, cached token.

  To suppress this message, modify your code or options to clearly consent to
  the use of a cached token.

  See gargle's "Non-interactive auth" vignette for more details:

  <https://gargle.r-lib.org/articles/non-interactive-auth.html>

ℹ The bigrquery package is using a cached token for 'wenting4685@gmail.com'.

## Visualization 1: Daily Events Trend

daily_events <- ga_data %>%
    group_by(event_date) %>%
    summarise(total_events = sum(events))

plot(daily_events$event_date,
     daily_events$total_events,
     type="l",
     col="blue",
     xlab="Date",
     ylab="Total Events",
     main="Daily Total Events Trend")

## Visualization 2: Top Events (Most Frequent Event Names)

top_events <- ga_data %>%
  group_by(event_name) %>%
  summarise(total = sum(events)) %>%
  arrange(desc(total)) %>%
  head(10)

barplot(
  top_events$total,
  names.arg = top_events$event_name,
  col = "skyblue",
  las = 2,
  main = "Top 10 Most Frequent Events",
  ylab = "Event Count"
)

## Visualization 3: Traffic by Device Category

device_traffic <- ga_data %>%
summarise(total = sum(events))

pie(
  device_traffic$total,
  labels = paste0(device_traffic$device_category, " (", device_traffic$total, ")"),
  col = rainbow(length(device_traffic$device_category)),
  main = "Traffic Distribution by Device Category"
)

Warning: Unknown or uninitialised column: `device_category`.
Unknown or uninitialised column: `device_category`.