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R Data Visualization Cookbook

R Data Visualization Cookbook

By : Gohil
4.2 (6)
Buy this Book
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R Data Visualization Cookbook

R Data Visualization Cookbook

4.2 (6)
By: Gohil
Buy this Book

Overview of this book

If you are a data journalist, academician, student or freelance designer who wants to learn about data visualization, this book is for you. Basic knowledge of R programming is expected.
Table of Contents (12 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Sections
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Conventions
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Reader feedback
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Customer support
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1
1. A Simple Guide to R
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1. A Simple Guide to R
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Installing packages and getting help in R
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Data types in R
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Special values in R
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Matrices in R
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Editing a matrix in R
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Data frames in R
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Editing a data frame in R
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Importing data in R
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Exporting data in R
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Writing a function in R
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Writing if else statements in R
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Basic loops in R
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Nested loops in R
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The apply, lapply, sapply, and tapply functions
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Using par to beautify a plot in R
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Saving plots
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2. Basic and Interactive Plots
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2. Basic and Interactive Plots
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Introduction
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Introducing a scatter plot
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Scatter plots with texts, labels, and lines
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Connecting points in a scatter plot
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Generating an interactive scatter plot
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A simple bar plot
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An interactive bar plot
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A simple line plot
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Line plot to tell an effective story
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Generating an interactive Gantt/timeline chart in R
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Merging histograms
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Making an interactive bubble plot
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Constructing a waterfall plot in R
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3. Heat Maps and Dendrograms
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3. Heat Maps and Dendrograms
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Introduction
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Constructing a simple dendrogram
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Creating dendrograms with colors and labels
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Creating a heat map
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Generating a heat map with customized colors
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Generating an integrated dendrogram and a heat map
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Creating a three-dimensional heat map and a stereo map
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Constructing a tree map in R
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4. Maps
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4. Maps
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Introduction
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Introducing regional maps
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Introducing choropleth maps
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A guide to contour maps
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Constructing maps with bubbles
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Integrating text with maps
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Introducing shapefiles
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Creating cartograms
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5. The Pie Chart and Its Alternatives
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5. The Pie Chart and Its Alternatives
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Introduction
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Generating a simple pie chart
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Constructing pie charts with labels
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Creating donut plots and interactive plots
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Generating a slope chart
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Constructing a fan plot
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6. Adding the Third Dimension
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6. Adding the Third Dimension
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Introduction
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Constructing a 3D scatter plot
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Generating a 3D scatter plot with text
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A simple 3D pie chart
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A simple 3D histogram
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Generating a 3D contour plot
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Integrating a 3D contour and a surface plot
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Animating a 3D surface plot
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7. Data in Higher Dimensions
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7. Data in Higher Dimensions
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Introduction
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Constructing a sunflower plot
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Creating a hexbin plot
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Generating interactive calendar maps
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Creating Chernoff faces in R
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Constructing a coxcomb plot in R
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Constructing network plots
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Constructing a radial plot
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Generating a very basic pyramid plot
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8. Visualizing Continuous Data
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8. Visualizing Continuous Data
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Introduction
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Generating a candlestick plot
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Generating interactive candlestick plots
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Generating a decomposed time series
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Plotting a regression line
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Constructing a box and whiskers plot
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Generating a violin plot
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Generating a quantile-quantile plot (QQ plot)
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Generating a density plot
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Generating a simple correlation plot
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9. Visualizing Text and XKCD-style Plots
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9. Visualizing Text and XKCD-style Plots
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Introduction
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Generating a word cloud
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Constructing a word cloud from a document
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Generating a comparison cloud
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Constructing a correlation plot and a phrase tree
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Generating plots with custom fonts
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Generating an XKCD-style plot
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10. Creating Applications in R
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10. Creating Applications in R
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Introduction
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Creating animated plots in R
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Creating a presentation in R
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A basic introduction to API and XML
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Constructing a bar plot using XML in R
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Creating a very simple shiny app in R
11
Index
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Index

Scatter plots with texts, labels, and lines

In the previous recipe, we studied how to construct a very basic scatter plot. In order for the plot to deliver a strong message, we need to add elements such as text, labels, and lines. The main objective of a visualization is to grab the attention of its audience and make the optimal use of the data available. The audience should be able to get most of its information from the visualization itself.

The following screenshot plots the child mortality rate in selected countries. The story we would like to share with the readers is the relationship between the child mortality rate and Gross Domestic Product (GDP) of a country. We can improve on our understanding of these relationships if the readers can compare extreme scenarios or compare a specific country with a benchmark (average child mortality rate).

Scatter plots with texts, labels, and lines

How to do it…

In the previous recipe, we used a dataset from the ISLR package. But what if we would like to import our own data in R? We...

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R Data Visualization Cookbook
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