R data visualization recipes : a cook book with 65+ data visualization recipes for smarter decision-making / Vitor Bianchi Lanzetta.

Format:

Book

Author/Creator:

Lanzetta, Vitor Bianchi, author.

Language:

English

Subjects (All):

Information visualization.

R (Computer program language).

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt, 2017.

System Details:

text file

Biography/History:

Bianchi Lanzetta Vitor: Vitor Bianchi Lanzetta (@vitorlanzetta) has a master's degree in Applied Economics (University of So PauloUSP) and works as a data scientist in a tech start-up named RedFox Digital Solutions. He has also authored a book called R Data Visualization Recipes. The things he enjoys the most are statistics, economics, and sports of all kinds (electronics included). His blog, made in partnership with Ricardo Anjoleto Farias (@R_A_Farias), can be found at ArcadeData dot org, they kindly call it R-Cade Data.

Summary:

Translate your data into info-graphics using popular packages in R About This Book Use R's popular packages - such as ggplot2, ggvis, ggforce, and more - to create custom, interactive visualization solutions. Create, design, and build interactive dashboards using Shiny A highly practical guide to help you get to grips with the basics of data visualization techniques, and how you can implement them using R Who This Book Is For If you are looking to create custom data visualization solutions using the R programming language and are stuck somewhere in the process, this book will come to your rescue. Prior exposure to packages such as ggplot2 would be useful but not necessary. However, some R programming knowledge is required. What You Will Learn Get to know various data visualization libraries available in R to represent data Generate elegant codes to craft graphics using ggplot2, ggvis and plotly Add elements, text, animation, and colors to your plot to make sense of data Deepen your knowledge by adding bar-charts, scatterplots, and time series plots using ggplot2 Build interactive dashboards using Shiny. Color specific map regions based on the values of a variable in your data frame Create high-quality journal-publishable scatterplots Create and design various three-dimensional and multivariate plots In Detail 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 u...

Contents:

Cover

Copyright

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Installation and Introduction

Introduction

Installing and loading graphics packages

How to do it...

How it works...

There's more

See also...

Using ggplot2, plotly, and ggvis

Getting ready

See also

Making plots using primitives

There's more...

Chapter 2: Plotting Two Continuous Variables

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

How to do it.

How it works...

Chapter 3: Plotting a Discrete Predictor and a Continuous Response

Installing car package and getting familiar to data

How does it works...

There is more

Drawing simple box plots

Adding notches and jitters to box plots

Drawing bivariate dot plots using ggplot2

How does it work...

Using more suitable colors for geom_dotplot

Combining box with dot plots

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

Getting ready...

Crafting simple violin plots

Using stat_summary to customize violin plots

There is more...

Manually sorting and coloring violins

Using joy package to replace violins

Creating publication quality violin plots

Getting Ready

Chapter 4: Plotting One Variable

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()

How it works.

See also

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

Chapter 5: Making Other Bivariate Plots

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

Chapter 6: Creating Maps

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

Chapter 7: Faceting

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

Chapter 8: Designing Three-Dimensional Plots

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.

How to do it...

Chapter 9: Using Theming Packages

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

Chapter 10: Designing More Specialized Plots

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

Chapter 11: Making Interactive Plots

Using ggiraph to create interactive plots

Using gganimate to craft animated ggplots

Crafting animated plots with tweenr.

Getting ready.

Notes:

Includes index.

Description based on online resource; title from PDF title page (EBC, viewed December 19, 2017).

OCLC:

1017990322