Chapter 1, Getting Ready for Tableau and R, shows how to connect Tableau Desktop with R through calculated fields and take advantage of R functions, libraries, packages, and even saved models. We'll also cover Tableau Server configuration with R through an instance of Rserve (through the tabadmin utility), allowing anyone to view a dashboard containing R functionality. Combining R with Tableau gives you the ability to bring deep statistical analysis into a drag-and-drop visual analytics environment.

Chapter 2, The Power of R, integrates both the platforms in the previous chapter; we'll walk through different ways in which readers can use R to combine and compare data for analysis. We will cover, with examples, the core essentials of R programming such as variables, data structures in R, control mechanisms in R, and how to execute these commands in R before proceeding to later chapters that heavily rely on these concepts to script complex analytical operations.

Chapter 3, A Methodology for Advanced Analytics using Tableau and R, creates a roadmap for our analytics investigation. You'll learn how to assess the performance of both supervised and unsupervised learning algorithms, and the importance of testing. Using R and Tableau, we will explore why and how you should split your data into a training set and a test set. In order to understand how to display the data accurately as well as beautifully in Tableau, the concepts of bias and variance are explained.

Chapter 4, Prediction with R and Tableau Using Regression, considers regression from an analytics point of view. In this chapter, we look at the predictive capabilities and performance of regression algorithms. At the end of this chapter, you'll have experience in simple linear regression, multi-linear regression, and k-nearest neighbors regression using a business-oriented understanding of the actual use cases of regression techniques.

Chapter 5, Classifying Data with Tableau, shows ways to perform classification using R and visualize the results in Tableau. Classification is one of the most important tasks in analytics today. By the end of this chapter, you'll build a decision tree and classify unseen observations with k-nearest neighbors, with a focus on a business-oriented understanding of the business question using classification algorithms.

Chapter 6, Advanced Analytics Using Clustering, gives a business-oriented understanding of the business questions using clustering algorithms and applying visualization techniques that best suit the scenario.

Chapter 7, Advanced Analytics with Unsupervised Learning, teaches k-means clustering and hierarchical clustering. It has a business-oriented understanding of the business question using unsupervised learning algorithms.

Chapter 8, Interpreting Your Results f or Your Audience. How do you interpret the results and the numbers when you have them? What does a p-value mean? Analytical investigations will result in a variety of relationships in data, but the audience may have problems understanding the results. Statistical tests state a null and an alternative hypothesis, and then calculate a test statistic and report an associated p-value. In this chapter, we will look at ways in which we can answer "what if?" questions and applicable customer scenarios using cohort analysis, with a focus on how we can display the results so that the audience can make a conclusion from the tests.

You'll need the following software:

This book will appeal to Tableau users who want to go beyond the Tableau interface and deploy the full potential of Tableau, by using R to perform advanced analytics with Tableau.

A basic familiarity with R is useful but not compulsory, as the book starts off with concrete examples of R and will move on quickly to more advanced spheres of analytics using online data sources to support hands-on learning. Those R developers who want to integrate R with Tableau will also benefit from this book.

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We can include other contexts through the use of the include directive."

A block of code is set as follows:

df = data.frame(
Year=c(2013, 2013, 2013), 
Country=c("Arab World","Carribean States", "Central Europe"),
LifeExpectancy=c(71, 72, 76))

Any command-line input or output is written as follows:

IrisBySpecies <- split(iris,iris$Species)

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this:"You can now just click on Stream to access the live stream from the camera."

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