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Advanced Analytics with R and Tableau

You're reading from  Advanced Analytics with R and Tableau

Product type Book
Published in Aug 2017
Publisher Packt
ISBN-13 9781786460110
Pages 178 pages
Edition 1st Edition
Languages
Authors (3):
Ruben Oliva Ramos Ruben Oliva Ramos
Profile icon Ruben Oliva Ramos
Jen Stirrup Jen Stirrup
Profile icon Jen Stirrup
Roberto Rösler Roberto Rösler
View More author details

Table of Contents (16) Chapters

Advanced Analytics with R and Tableau
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Advanced Analytics with R and Tableau 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

Introduction to dplyr


What is dplyr? Well, dplyr can be perceived as a grammar of data manipulation. It has been created for the R community by Hadley Wickham, Romain Francois, and RStudio.

What does dplyr give the Tableau user? We will use dplyr in order to cleanse, summarize, group, chain, filter, and visualize our data in Tableau.

Summarizing the data with dplyr

Firstly, let's import the packages that we need. These packages are listed in the following table, followed by the code itself.

Packages required for the hands-on exercise:

Package Name

Description

Reference

WDI

Search, extract, and format data from the World Bank's World Development Indicators

https://cran.r-project.org/web/packages/WDI/index.html

dplyr

dplyr is a grammar of data manipulation

 

As we walk through the script, the first thing we need to do is install the packages.

Once you have installed the packages, we need to call each library.

Once we have called the libraries, then we need to obtain the data from the...

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