Web Analytics with Hands-on Projects in R
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Web Analytics with Hands-on Projects in R

Perform efficient analytics in R to optimize your website and enhance your business
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0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
2 students enrolled
Created by Packt Publishing
Last updated 9/2017
English
English [Auto-generated]
Current price: $10 Original price: $125 Discount: 92% off
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Includes:
  • 3 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Work with Key Performance Indicators (KPIs)
  • Insights that we can gain from web analytics
  • Set up the googleAnalyticsR package
  • Retrieving data using googleAnalyticsR
  • Understanding Sessions vs Users
  • Extracting data for Sessions and Users
  • Best practices for web data analysis
  • Common pitfalls for web data analysis
  • Create an effective measurement model for analytics
View Curriculum
Requirements
  • A basic understanding of web analytics concepts will be helpful.
Description

R is a popular choice of tool for analysts, offering a large variety of libraries pertaining to each and every task in data analysis. With practical projects based on various real-world domains and use-cases where web analytics can be used, this video will be your companion for implementing the various web analytics techniques using the free, open source libraries provided by R.

This video will start by understanding the basics of web analytics, and why it is used in businesses worldwide. This will be followed by an introduction to R and its libraries, and understanding why R is a great choice for performing real-time analysis of your data. Analyse your website’s traffic and understand customers’ behaviour in real time, and use this information to optimize the performance of your website. You will also learn how to use the analytical capabilities of R to fully explore your Google Analytics Data and generate meaningful visualizations of your data using libraries like ggplot2 and generate reports based on various metrics and combinations. By the end of this tutorial, you will have a solid understanding of implementing the various web analytics techniques in R, and maximize your website’s performance.

About the author

William Shin received his PhD from Columbia University in 2017 in biological sciences with specialization in computational biology. He has been an avid user of R since 2009 and has been using statistics to gain insights into large datasets since 2007. He has been a consistent contributor in digital analytics, a frequent speaker at conferences, and a wide range of regional events around the world.

Who is the target audience?
  • Business analysts, web analysts, or anyone who wants to optimize their website performance and gain profits from using the open source R libraries will find this tutorial to be useful.
Compare to Other R Courses
Curriculum For This Course
25 Lectures
03:12:39
+
Introduction to Web Analytics and R
6 Lectures 35:19

This video provides an overview of the entire course.

Preview 06:24

What are insights I would be able to gain with Web Analytics that I would not be able to gain from traditional methods?

  • In the past, marketing decisions were based on assumptions
  • Web analytics track the complete customer experience
  • The data gained from Web Analytics (KPIs) can give you deep insights about your website  
Why Use Web Analytics?
05:32

Why is Google Analytics the most effective way to add web analytics functionality to our site?

  • Google Analytics is a popular tool
  • Google’s popularity means there are more resources for you to learn and use
  • Google’s free-to-use tier is sufficient for many businesses 
Why Use Google Analytics?
03:38

What is R and why should I use it along with Google Analytics?

  • R is free and powerful language and is easy to learn
  • R is already being used by analysts and data scientists around the world
  • R can be easily combined with Google Analytics data 
Why R Is Perfect for Web Analytics?
04:15

How do I set-up R and RStudio on my system?

  • Installation of R
  • Installation of RStudio
  • Demonstration to check whether R and RStudio have been installed correctly 
Installing R and RStudio
07:43

How do I sign-up for Google Analytics and add it to my website?

  • Sign-up for Google Analytics Account
  • Obtain Tracking-ID from Google Analytics
  • Add Tracking-ID script to the header injection file  
Installing and Setting Up Google Analytics Account
07:47
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Installing Packages and Dashboards
4 Lectures 28:43

How can R be used to access data from my Google Analytics account?

  • GoogleAnalyticsR is designed to integrate Google Analytics API and R
  • Demo of installation of GoogleAnalyticsR package in RStudio
  • Demo for performing basic authentication and query in R 
Preview 08:59

What is the simplest and most effective way to generate beautiful plots in R?

  • Ggplot2 is a popular plotting package for R
  • Demo of installation of ggplot2 package in RStudio
  • Demo for performing basic ploting using qplot function 
ggplot2 Package
06:21

Where can I learn more about GoogleAnalyticsR and ggplot2, as well as the Google API?

  • Harvard Tutorial provides great resources for ggplot2
  • Michael Brys has a fantastic book on the googleAnalyticsR package, available for free
  • Metrics and Dimensions for Google API can be explored on the Google Developers page  
Useful Links for googleAnalyticsR and ggplot2
03:09

What are the benefits of dashboards, and how can I generate them easily in R?

  • Dashboards combine the results of multiple analyses and allow for easy interpretation
  • Flexdashboard is a quick and easy way to generate dashboards in R
  • Demonstration of a dashboard generated using flexdashboard in RStudio 
Why Use Dashboards?
10:14
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Understanding Visitors
4 Lectures 47:29

How does Google track visitors and visits to our website?

  • Google defines “users” as specific visitors to a site
  • Google defines “sessions” as visits to a site
  • Single users can have multiple visits  
Preview 03:37

How does Google track new and returning visitors to our site, and what are some caveats to understanding the metric?

  • Google assigns a unique number to a visitor, which is then used to identify returning visitors
  • Returning Visitors who have deleted cookies, or using multiple devices may be counted as New Visitors
  • Demonstration plotting New versus Returning Visitors 
Who Is Coming Back to Our Site?
14:47

How can we understand and track Visitor Loyalty?

  • No single simple metric to measure Loyalty
  • A number of metrics can be combined to measure Loyalty
  • Demonstration plotting Return Visitor Rate (RVR) 
How Loyal Are Our Visitors?
15:37

How can our dashboard help us understand visitors, their return rates, and loyalty?

  • Demonstration of building business dashboard in R
  • Interpretation of business dashboard
  • Recommended actions to improve results 
Building Our Business Dashboard
13:28
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Which Pages Are Most Effective?
4 Lectures 37:38

What is the difference between Exit and Bounce Rates, and why do they matter?

  • Exit Rates measure the percentage of visitors who leave from a specific page, while Bounce Rates measure single-visits
  • A high Bounce Rate is usually a sign that something is wrong
  • A high Exit Rate may be part of the natural flow of the site, but should still be examined 
Preview 05:57

How can we use R to extract and plot pages along with their Exit and Bounce Rates?

  • Demonstration showing how R can be used to extract Page Paths along with Exit and Bounce Rates of each page
  • Demonstration of how the extracted data can be cleaned up for plotting
  • Plotting of Exit and Bounce Rates for each page 
Which Pages Have the Highest Exit and Bounce Rates?
12:34

How can our dashboard help us identify pages with high exit and bounce rates?

  • Demonstration of building business dashboard in R
  • Interpretation of business dashboard
  • Recommended actions to improve results 
Building Business Dashboard
12:15

What are ways that Bounce Rates can be decreased?

  • Analysis of where the problem is coming from
  • Optimize thought sequences and visitor experience
  • Recommended reading for further research 
How Can High Bounce Rates Be Improved?
06:52
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Looking Back: How Have Visitors Been Visiting My Website?
4 Lectures 29:15

What is the importance of multiple-time frame analysis and what insights can you gain from looking at multiple time-frames?

  • Looking only at a single time-frame will never give you the whole picture
  • Looking only at a single time-frame may cause you to identify the wrong problem
  • Multiple time-frame analysis will help you find the right comparison or metric to use 
Preview 05:47

What is context, and why is it essential for interpreting metrics correctly? What will you miss if you ignore context?

  • Context is what gives meaning to your metrics
  • Context is what allows you to turn a metric into an action
  • Combine context with goals to continue to use metrics to drive improvements 
Why Is Context Important?
03:32

How has my website been performing in the long-term?

  • Demonstration of building a Sessions per day plot for a 2-year period.
  • Demonstration of a RVR plot for a 2-year period.
  • Demonstration of a Bounce Rate and Exit Rate plot for a 2-year period.  
Website Performance for the Past Two Years
09:07

How can our dashboard help us understand the long-term performance of our site?

  • Demonstration of building business dashboard in R, including storyboard feature
  • Interpretation of business dashboard
  • Recommended further reading for further research 
Building Business Dashboard
10:49
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What Are Pitfalls and FAQs for First-Time Analysts?
3 Lectures 14:15

What can numbers tell you? What can they not tell you?

  • A single metric will never tell you the entire story
  • Combine the insights gained from multiple metrics to get a better picture of the whole
  • Understand that averages can mislead you and should be examined in additional detail 
Preview 04:53

Why is multiple time-frame analysis so important?

  • Looking only at a single time-frame (that is, only monthly, or weekly reports) will never give you the whole picture
  • Single time-frame analysis may cause you do focus on the wrong problem
  • Combine analysis from different time frames to fully understand site performance  
Multiple Time-Frame Analysis
04:57

Are there situations where “too much” analysis can occur? What are the pitfalls of too much analysis?

  • More data, more metrics, more analysis, doesn’t always result in better insights
  • Differentiate between “small decisions” and “large decisions” and focus on the results and metrics that matter the most
  • Simplify, Simplify, and Simplify 
Pitfalls of Too Much Analysis
04:25
About the Instructor
Packt Publishing
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