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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
Profile icon Roberto Rösler
View More author details
Toc

Table of Contents (16) Chapters close

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

Data frames


The data frame is the main data structure in R. It's possible to envisage the data frame as a table of data, with rows and columns. Unlike the list structure, the data frame can contain different types of data. In R, we use the data.frame() command in order to create a data frame.

The data frame is extremely flexible for working with structured data, and it can ingest data from many different data types. Two main ways to ingest data into data frames involves the use of many data connectors, which connect to data sources such as databases, for example. There is also a command, read.table(), which takes in data.

Data Frame Structure

Here is an example, populated data frame. There are three columns, and two rows. The top of the data frame is the header. Each row holds a line of data row, starting with the row name, and then followed by the data itself. Each data member of a row is called a cell.

Example Data Frame Structure

In R, we can create data frames by accessing external data,...

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