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Applied Data Visualization with R and ggplot2
Applied Data Visualization with R and ggplot2

Applied Data Visualization with R and ggplot2: Create useful, elaborate, and visually appealing plots

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Profile Icon Dr. Tania Moulik
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Paperback Sep 2018 140 pages 1st Edition
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Arrow left icon
Profile Icon Dr. Tania Moulik
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$32.99
Paperback Sep 2018 140 pages 1st Edition
eBook
$17.99 $25.99
Paperback
$32.99
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Free Trial
Renews at $19.99p/m
eBook
$17.99 $25.99
Paperback
$32.99
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Renews at $19.99p/m

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Applied Data Visualization with R and ggplot2

Grammar of Graphics and Visual Components

In this chapter, we will explore the concept of the Grammar of Graphics in detail and use it to customize graphs to create better visualizations.

We need to customize graphs because default graphs may have fonts that are not visible in a presentation or document, or have scales that do not convey much information about the data. Sometimes, a company may require a uniform style for all their graphs to distinguish themselves, in which case, you would need to define and use the same style for all graphs. We may also need to split data into different subsets in order to understand it in greater detail. This chapter will explore these aspects in detail and explain how to change the default structure of a graph.

By the end of this chapter, you will be able to:

  • Apply the Grammar of Graphics techniques to layers, scales, and coordinates
  • Utilize...

More on the Grammar of Graphics

The Grammar of Graphics is the language used to describe the various components of a graphic that represent data in a visualization. In this topic, you will learn more about the Grammar of Graphics and will use it to make plots. You will e-encounter some of the Grammar of Graphics terms used in the previous chapter.

We will now break down the Grammar of Graphics language, in order to understand the terms in greater detail. 

Layers

In ggplot2, every plot is built up as a layer. Layers are made up of geometric objects (geoms), their statistical transformations (stats), and their thematic aspects. Hence, each plot can be thought of as a separate variable, in and of itself. Aesthetic...

Facets

In data visualization, we sometimes have the need to compare different groups, looking at data alongside each other. One method for doing this is creating a subplot for each group. These kinds of plots are known as Trellis displays. In ggplot2, they're called facets. Facets divide the data by some discrete or categorical variable and display the same type of graph for each data subset.

Let's look at electricity consumption versus GDP for different countries, which we calculated in the previous activity.

We don't know which country has the highest GDP or electricity consumption. Let's split the data now.

Using Facets to Split Data

In this section, we'll plot subsets of data as separate subplots...

Changing Styles and Colors

Aside from faceting, we can also produce a color differentiated plot. It can be advantageous to use a color differentiated plot when the shapes are very similar and there is some overlap. To see small differences, it is useful to use colors. For example, we can plot the Electricity consumption versus GDP by using different colors or shapes for the countries.

Using Different Colors to Group Points by a Variable

In this section, we'll produce a color differentiated scatter plot with respect to a third variable. Let's begin by implementing the following steps:

  1. Choose a subset of dataset 1 (gapminder) and select a few countries. Use the following subset command:
dfs <- subset(df,Country...

Geoms and Statistical Summaries

Sometimes, you will need to calculate statistical summaries, such as the mean, median, or a quartile of a variable, and view changes with respect to another variable. This can be done by using grouping commands.

Let's plot Genre versus AudienceScore for the HollywoodMovies dataset. Change the angle of the axis labeling text, in order to make it less cluttered, using the following command:

ggplot(HollywoodMovies,aes(Genre,AudienceScore))+geom_point()+theme(axis.text.x=element_text(angle=40))

You'll get the following output:

Using Grouping to Create a Summarized Plot

In this section, we'll use grouping to summarize multiple y values for a given x value. Let...

Summary

In this chapter, you learned about the Grammar of Graphics in detail, changing the various theme and color aspects of graphs to create better visuals and reveal further details about data. You also learned how to provide more useful information in scatter plots, using grouping and summarizing to calculate quantities such as the mean, median, quartile, and so on.

In the next chapter, we will work on more advanced plotting techniques, which are not needed quite as often but may be required in some cases.

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Key benefits

  • Discover structure of ggplot2, grammar of graphics, and geometric objects
  • Study how to design and implement visualization from scratch
  • Explore the advantages of using advanced plots

Description

Applied Data Visualization with R and ggplot2 introduces you to the world of data visualization by taking you through the basic features of ggplot2. To start with, you’ll learn how to set up the R environment, followed by getting insights into the grammar of graphics and geometric objects before you explore the plotting techniques. You’ll discover what layers, scales, coordinates, and themes are, and study how you can use them to transform your data into aesthetical graphs. Once you’ve grasped the basics, you’ll move on to studying simple plots such as histograms and advanced plots such as superimposing and density plots. You’ll also get to grips with plotting trends, correlations, and statistical summaries. By the end of this book, you’ll have created data visualizations that will impress your clients.

Who is this book for?

Applied Data Visualization with R and ggplot2 is for you if you are a professional working with data and R. This book is also for students who want to enhance their data analysis skills by adding informative and professional visualizations. It is assumed that you know basics of the R language and its commands and objects.

What you will learn

  • Set up the R environment, RStudio, and understand structure of ggplot2
  • Distinguish variables and use best practices to visualize them
  • Change visualization defaults to reveal more information about data
  • Implement the grammar of graphics in ggplot2 such as scales and faceting
  • Build complex and aesthetic visualizations with ggplot2 analysis methods
  • Logically and systematically explore complex relationships
  • Compare variables in a single visual, with advanced plotting methods
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Publication date : Sep 28, 2018
Length: 140 pages
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Language : English
ISBN-13 : 9781789612158
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Table of Contents

5 Chapters
Basic Plotting in ggplot2 Chevron down icon Chevron up icon
Grammar of Graphics and Visual Components Chevron down icon Chevron up icon
Advanced Geoms and Statistics Chevron down icon Chevron up icon
Solutions Chevron down icon Chevron up icon
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