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R Data Visualization Cookbook

You're reading from   R Data Visualization Cookbook Over 80 recipes to analyze data and create stunning visualizations with R

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781783989508
Length 236 pages
Edition 1st Edition
Languages
Tools
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Toc

Table of Contents (12) Chapters Close

Preface 1. A Simple Guide to R FREE CHAPTER 2. Basic and Interactive Plots 3. Heat Maps and Dendrograms 4. Maps 5. The Pie Chart and Its Alternatives 6. Adding the Third Dimension 7. Data in Higher Dimensions 8. Visualizing Continuous Data 9. Visualizing Text and XKCD-style Plots 10. Creating Applications in R Index

A simple bar plot

A bar plot can often be confused with histograms (studied later in this chapter). Histograms are used to study the distribution of data whereas bar plots are used to study categorical data. Both the plots may look similar to the naked eye but the main difference is that the width of a bar plot is not of significance, whereas in histograms the width of the bars signifies the frequency of data.

In this recipe, I have made use of the infant mortality rate in India. The data is made available by the Government of India. The main objective is to study the basics of a bar plot in R as shown in the following screenshot:

A simple bar plot

How to do it…

We start the recipe by importing our data in R using the read.csv() function. R will search for the data under the current directory, and hence we use the setwd() function to set our working directory:

setwd("D:/book/scatter_Area/chapter2")
data = read.csv("infant.csv", header = TRUE)

Once we import the data, we would like to...

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