R Data Visualization Recipes

R Data Visualization Recipes

By : Vitor Bianchi Lanzetta

Buy this Book

R Data Visualization Recipes

By: Vitor Bianchi Lanzetta

Buy this Book

Overview of this book

R is an open source language for data analysis and graphics that allows users to load various packages for effective and better data interpretation. Its popularity has soared in recent years because of its powerful capabilities when it comes to turning different kinds of data into intuitive visualization solutions. This book is an update to our earlier R data visualization cookbook with 100 percent fresh content and covering all the cutting edge R data visualization tools. This book is packed with practical recipes, designed to provide you with all the guidance needed to get to grips with data visualization using R. It starts off with the basics of ggplot2, ggvis, and plotly visualization packages, along with an introduction to creating maps and customizing them, before progressively taking you through various ggplot2 extensions, such as ggforce, ggrepel, and gganimate. Using real-world datasets, you will analyze and visualize your data as histograms, bar graphs, and scatterplots, and customize your plots with various themes and coloring options. The book also covers advanced visualization aspects such as creating interactive dashboards using Shiny By the end of the book, you will be equipped with key techniques to create impressive data visualizations with professional efficiency and precision.

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

Free Chapter

Installation and Introduction

Introduction

Installing and loading graphics packages

Using ggplot2, plotly, and ggvis

Making plots using primitives

Plotting Two Continuous Variables

Introduction

Plotting a basic scatterplot

Hacking ggvis add_axis() function to operate as a title function

Plotting a scatterplot with shapes and colors

Plotting a shape reference palette for ggplot2

Dealing with over-plotting, reducing points

Dealing with over-plotting, jittering points

Dealing with over-plotting, alpha blending

Rug the margins using geom_rug()

Adding marginal histograms using ggExtra

Drawing marginal histogram using gridExtra

Crafting marginal plots with plotly

Adding regression lines

Adding quantile regression lines

Drawing publish-quality scatterplots

Plotting a Discrete Predictor and a Continuous Response

Introduction

Installing car package and getting familiar to data

Drawing simple box plots

Adding notches and jitters to box plots

Drawing bivariate dot plots using ggplot2

Using more suitable colors for geom_dotplot

Combining box with dot plots

Using point geometry to work as dots using ggvis, plotly and ggplot2

Crafting simple violin plots

Using stat_summary to customize violin plots

Manually sorting and coloring violins

Using joy package to replace violins

Creating publication quality violin plots

Plotting One Variable

Introduction

Creating a simple histogram using geom_histogram()

Creating an histogram with custom colors and bins width

Crafting and coloring area plots using geom_area() and more

Drawing density plots using geom_density()

Drawing univariate colored dot plots with geom_dotplot()

Crafting univariate bar charts

Using rtweet and ggplot2 to plot twitter words frequencies

Drawing publish quality density plot

Making Other Bivariate Plots

Introduction

Creating simple stacked bar graphs

Crafting proportional stacked bar

Plotting side-by-side bar graph

Plotting a bar graphic with aggregated data using geom_col()

Adding variability estimates to plots with geom_errrorbar()

Making line plots

Making static and interactive hexagon plots

Adjusting your hexagon plot

Developing a publish quality proportional stacked bar graph

Creating Maps

Introduction

Making simple maps - 1854 London Streets

Creating an interactive cholera map using plotly

Crafting choropleth maps using ggplot2

Zooming in on the map

Creating different maps based on different map projection types

Handling shapefiles to map Afghanistan health facilities

Crafting an interactive globe using plotly

Creating high quality maps

Faceting

Introduction

Creating a faceted bar graph

Crafting faceted histograms

Creating a facet box plot

Crafting a faceted line plot

Making faceted scatterplots

Creating faceted maps

Drawing facets using plotly

Plotting a high quality faceted bar graph

Designing Three-Dimensional Plots

Introduction

Drawing a simple contour plot using ggplot2

Picking a custom number of contour lines

Using the directlabels package to label the contours

Crafting a simple tile plot with ggplot2

Creating simple raster plots with ggplot2

Designing a three-dimensional plot with plotly

Crafting a publication quality contour plot

Using Theming Packages

Introduction

Drawing a bubble plot

Popular themes with ggthemes

Applying sci themes with ggsci

Importing new fonts with the extrafont package

Using ggtech to mimic tech companies themes

Wrapping a custom theme function

Applying awesome themes and checking misspells with hrbrthemes

Designing More Specialized Plots

Introduction

Drawing wonderful facets zoom with the ggforce package

Drawing sina plots with ggforce

Using ggrepel to plot non-overlaying texts

Visualizing relational data structures with ggraph

Draw alternative lollipop and density plots with ggalt

Making Interactive Plots

Introduction

Using ggiraph to create interactive plots

Using gganimate to craft animated ggplots

Crafting animated plots with tweenr

Building Shiny Dashboards

Introduction

Installing and loading a shiny package

Creating basic shiny interactive plots

Developing intermediate shiny interactive plots

Building a shiny dashboard

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Plotting a scatterplot with shapes and colors

There are several aesthetics coming out from geom_points() that can be changed. Typing ?geom_point into the R console will take you to the function documentation, which comes with a complete list of aesthetics understood by the function. The mandatory ones come in bold.

Names given are nothing but self-explanatory. Besides the mandatory x and y values, optional values range from alpha to stroke. For this particular recipe, we're settling for changes in the shape and colours arguments. Recipe also aims for similar results using both ggvis and plotly.

How to do it...

Change the shape and colour arguments to get a better result:

> library(ggplot2)
> sca1 <- ggplot(data = iris, aes(x = Petal.Length, y = Petal.Width))
> sca1 + geom_point(aes(shape = Species, colour = Species))

Now each iris species is designated by a unique combination of shapes and colors:

Figure 2.3 - Adding shapes and colors to a scatter plot.

plotly can also handle such...

R Data Visualization Recipes

By : Vitor Bianchi Lanzetta

R Data Visualization Recipes

By: Vitor Bianchi Lanzetta

Overview of this book

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