Book Image

Advanced Analytics with R and Tableau

By : Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler
Book Image

Advanced Analytics with R and Tableau

By: Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler

Overview of this book

Tableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation. Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics. By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.
Table of Contents (16 chapters)
Advanced Analytics with R and Tableau
About the Authors
About the Reviewers
Customer Feedback

Modeling in R

In this example, we will use the rpart package, which is used to build a decision tree. The tree with the minimum prediction error is selected. After that, the tree is applied to make predictions for unlabeled data with the predict function.

One way to call rpart is to give it a list of variables and see what happens. Although we have discussed missing values, rpart has built-in code for dealing with missing values. So let's dive in, and look at the code.

Firstly, we need to call the libraries that we need:


Next, let's load in the data, which will be in the AdultUCI variable:

## 75% of the sample size
sample_size <- floor(0.80 * nrow(AdultUCI))

## set the seed to make your partition reproductible

## Set a variable to have the sample size
training.indicator <- sample(seq_len(nrow(AdultUCI)), size = sample_size)

# Set up the training and test sets...