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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

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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