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Advanced Analytics with R and Tableau

You're reading from   Advanced Analytics with R and Tableau Advanced analytics using data classification, unsupervised learning and data visualization

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781786460110
Length 178 pages
Edition 1st Edition
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Authors (3):
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Roberto Rösler Roberto Rösler
Author Profile Icon Roberto Rösler
Roberto Rösler
Ruben Oliva Ramos Ruben Oliva Ramos
Author Profile Icon Ruben Oliva Ramos
Ruben Oliva Ramos
Jen Stirrup Jen Stirrup
Author Profile Icon Jen Stirrup
Jen Stirrup
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Table of Contents (10) Chapters Close

Preface 1. Advanced Analytics with R and Tableau FREE CHAPTER 2. The Power of R 3. A Methodology for Advanced Analytics Using Tableau and R 4. Prediction with R and Tableau Using Regression 5. Classifying Data with Tableau 6. Advanced Analytics Using Clustering 7. Advanced Analytics with Unsupervised Learning 8. Interpreting Your Results for Your Audience Index

Core essentials of R programming

One of the reasons for R's success is its use of variables. Variables are used in all aspects of R programming. For example, variables can hold data, strings to access a database, whole models, queries, and test results. Variables are a key part of the modeling process, and their selection has a fundamental impact on the usefulness of the models. Therefore, variables are an important place to start since they are at the heart of R programming.

Variables

In the following section we will deal with the variables—how to create variables and working with variables.

Creating variables

It is very simple to create variables in R, and to save values in them. To create a variable, you simply need to give the variable a name, and assign a value to it.

In many other languages, such as SQL, it's necessary to specify the type of value that the variable will hold. So, for example, if the variable is designed to hold an integer or a string, then this is specified...

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