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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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 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.
Style and approach
This book is packed with practical recipes, designed to provide you with all the guidance needed to get to grips with data visualization with R. You will learn to leverage the power of R and ggplot2 to create highly customizable data visualizations of varying complexities. The readers will then learn how to create, design, and build interactive dashboards using Shiny.
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R Data Visualization Recipes
Copyright © 2017 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
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First published: November 2017
Production reference: 1201117
ISBN 978-1-78839-831-2
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Credits
Author
Vitor Bianchi Lanzetta
Copy Editor
Tasneem Fatehi
Reviewer
Radovan Kavicky
Project Coordinator
Manthan Patel
Commissioning Editor
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Proofreader
Safis Editing
Acquisition Editor
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Indexer
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Graphics
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Production Coordinator
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About the Author
Vitor Bianchi Lanzetta is a young economist and data science enthusiast. As soon as his graduation began at University of São Paulo, one of Latin America's most renowned universities, he was introduced to R by his statistics professor. it was love at first sight.
Since then, he has never stopped using it. He joined the university team to compete in the CFA Global Challenge, where he and his team had a whole valuation model translated into R along with a Monte Carlo simulation and a bunch of visualizations done through the language.
While researching commodity prices prediction, Vitor used R from adjusting simple conditional mean-based models to artificial neural networks, along with all the visualizations.
About the Reviewer
Radovan Kavický is the principal data scientist and president at GapData Institute based in Bratislava, Slovakia, where he harnesses the power of data and wisdom of economics for public good.
He is a macroeconomist by education and consultant and analyst by professional background, with 8+ years of experience in consulting for clients from public and private sectors. He has strong mathematical and analytical skills and is able to deliver top-level research and analytical work.
From MATLAB, SAS & Stata switched to Python, R & Tableau.
Radovan is a member of the Slovak Economic Association (SEA) and evangelist of open data, open budget initiative and open government partnership. He is founder of PyData Bratislava, R <- Slovakia, and SK/CZ Tableau User Group (skczTUG).
He is a speaker at @TechSummit (Bratislava, 2017) and @PyData (Berlin, 2017), and member of the global Tableau #DataLeader network (2017).
You can follow him on Twitter at @radovankavicky, @GapDataInst, or @PyDataBA. His full profile and experience are available at https://www.linkedin.com/in/radovankavicky/ and https://github.com/radovankavicky.
GapData Institute: https://www.gapdata.org
<p>Practical Data Science,Data Visualization in Python, R & Tableau;Open Data and Public Administration in Slovakia
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Table of Contents
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
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
How to do it...
How it works...
There's more
See also
Making plots using primitives
How to do it...
How it works...
There's more...
Plotting Two Continuous Variables
Introduction
Plotting a basic scatterplot
How to do it...
How it works...
There's more...
Hacking ggvis add_axis() function to operate as a title function
Getting ready
How to do it...
How it works...
Plotting a scatterplot with shapes and colors
How to do it...
How it works...
Plotting a shape reference palette for ggplot2
How to do it...
How it works...
There's more...
Dealing with over-plotting, reducing points
How to do it...
How it works...
There's more...
Dealing with over-plotting, jittering points
How to do it...
How it works...
Dealing with over-plotting, alpha blending
How to do it...
How it works...
There's more...
Rug the margins using geom_rug()
How to do it...
How it works...
Adding marginal histograms using ggExtra
Getting ready
How to do it...
How it works...
Drawing marginal histogram using gridExtra
Getting ready
How to do it...
How it works...
Crafting marginal plots with plotly
How to do it...
How it works...
Adding regression lines
How to do it...
How it works...
Adding quantile regression lines
Getting ready
How to do it...
How it works...
Drawing publish-quality scatterplots
Getting ready
How to do it...
How it works...
See also
Plotting a Discrete Predictor and a Continuous Response
Introduction
Installing car package and getting familiar to data
How to do it...
How does it works...
There is more
See also
Drawing simple box plots
Getting ready
How to do it...
How it works...
Adding notches and jitters to box plots
Getting ready
How to do it...
How it works...
Drawing bivariate dot plots using ggplot2
Getting ready
How to do it...
How does it work...
There is more
Using more suitable colors for geom_dotplot
Getting ready
How to do it...
How it works...
Combining box with dot plots
Getting ready
How it works...
How it works...
See also
Using point geometry to work as dots using ggvis, plotly and ggplot2
Getting ready...
How it works...
How it works...
There is more
Crafting simple violin plots
How to do it...
How it works...
Using stat_summary to customize violin plots
Getting ready
How it works...
How it works...
There is more...
Manually sorting and coloring violins
Getting ready
How to do it...
How it works...
Using joy package to replace violins
Getting ready
How to do it...
How it works...
See also
Creating publication quality violin plots
Getting Ready
How to do it...
How it works...
See also
Plotting One Variable
Introduction
Creating a simple histogram using geom_histogram()
Getting ready
How to do it...
How does it work...
Creating an histogram with custom colors and bins width
Getting ready
How to do it...
How it works...
Crafting and coloring area plots using geom_area() and more
Getting ready
How to do it...
How it works...
Drawing density plots using geom_density()
How to do it...
How it works...
See also
Drawing univariate colored dot plots with geom_dotplot()
Getting ready
How to do it...
How it works...
See also
Crafting univariate bar charts
Getting ready
How to do it...
How it works...
Using rtweet and ggplot2 to plot twitter words frequencies
Getting ready...
How to do it...
How it works...
See also
Drawing publish quality density plot
How to do it...
How it works...
Making Other Bivariate Plots
Introduction
Creating simple stacked bar graphs
Getting ready
How to do it...
How it works...
Crafting proportional stacked bar
Getting ready
How to do it...
How it works...
See also
Plotting side-by-side bar graph
Getting ready
How to do it...
How it works...
See also
Plotting a bar graphic with aggregated data using geom_col()
Getting ready
How to do it...
How it works...
Adding variability estimates to plots with geom_errrorbar()
Getting ready
How to do it...
How it works...
There's more...
See also
Making line plots
Getting ready
How to do it...
How it works...
Making static and interactive hexagon plots
Getting ready
How to do it...
How it works...
There is more...
Adjusting your hexagon plot
Getting ready
How to do it...
How it works...
See also
Developing a publish quality proportional stacked bar graph
Getting ready
How to do it...
How it works...
Creating Maps
Introduction
Making simple maps - 1854 London Streets
Getting ready
How to do it...
How it works...
Creating an interactive cholera map using plotly
Getting ready
How to do it...
How it works...
Crafting choropleth maps using ggplot2
Getting ready
How to do it...
How it works...
Zooming in on the map
Getting ready
How to do it...
How it works...
See also
Creating different maps based on different map projection types
Getting ready
How to do it...
How it works...
See also
Handling shapefiles to map Afghanistan health facilities
Getting ready
How to do it...
How it works...
See also
Crafting an interactive globe using plotly
Getting ready
How to do it...
How it works...
Creating high quality maps
Getting ready
How to do it...
How it works...
See also
Faceting
Introduction
Creating a faceted bar graph
How to do it...
How it works...
Crafting faceted histograms
Getting ready
How to do it...
How it works...
Creating a facet box plot
How to do it...
How it works...
Crafting a faceted line plot
Getting ready
How to do it...
How it works...
There is more
Making faceted scatterplots
Getting ready
How to do it...
How it works...
Creating faceted maps
Getting ready
How to do it...
How it works...
See also
Drawing facets using plotly
How to do it...
How it works...
See also
Plotting a high quality faceted bar graph
How to do it...
How it works...
Designing Three-Dimensional Plots
Introduction
Drawing a simple contour plot using ggplot2
How to do it...
How it works...
Picking a custom number of contour lines
How to do it...
How it works...
Using the directlabels package to label the contours
Getting ready
How to do it...
How it works...
See also
Crafting a simple tile plot with ggplot2
How to do it...
How it works...
Creating simple raster plots with ggplot2
How to do it...
How it works...
There is more
Designing a three-dimensional plot with plotly
Getting ready
How to do it...
How it works...
Crafting a publication quality contour plot
How to do it...
How it works...
Using Theming Packages
Introduction
Drawing a bubble plot
Getting ready
How to do it...
How it works...
Popular themes with ggthemes
Getting Ready
How to do it...
How it works...
There's more...
See also
Applying sci themes with ggsci
Getting Ready
How to do it...
How it works...
See also
Importing new fonts with the extrafont package
Getting Ready
How to do it...
How it works...
There's more...
Using ggtech to mimic tech companies themes
Getting Ready
How to do it...
How it works...
There's more...
See also
Wrapping a custom theme function
Getting ready
How to do it...
How it works...
See also
Applying awesome themes and checking misspells with hrbrthemes
Getting Ready
How to do it...
How it works...
There's more...
See also
Designing More Specialized Plots
Introduction
Drawing wonderful facets zoom with the ggforce package
Getting Ready
How to do it...
How it works...
See also
Drawing sina plots with ggforce
Getting Ready
How to do it...
How it works...
Using ggrepel to plot non-overlaying texts
Getting Ready
How to do it...
How it works...
There's more...
See also
Visualizing relational data structures with ggraph
Getting Ready
How to do it...
How it works...
See also
Draw alternative lollipop and density plots with ggalt
Getting Ready
How to do it...
How it works...
See also
Making Interactive Plots
Introduction
Using ggiraph to create interactive plots
Getting ready
How to do it...
How it works...
See also
Using gganimate to craft animated ggplots
Getting ready
How to do it...
How it works...
See also
Crafting animated plots with tweenr
Getting ready
How to do it...
How it works...
See also
Building Shiny Dashboards
Introduction
Installing and loading a shiny package
How to do it...
How it works...
Creating basic shiny interactive plots
Getting ready
How to do it...
How it works...
See also
Developing intermediate shiny interactive plots
Getting ready
How to do it...
How it works...
There's more...
Building a shiny dashboard
Getting ready
How to do it...
How it works...
See also
Preface
Visualizations are amazing to investigate. You can explore data, reach insights, and transmit them directly to your audience. When several paragraphs may be needed to prove a point, good figures do it better in just a glance; they literally do. Fortunately, as computers keep evolving, crafting first-class graphs is not that difficult anymore.
R has proven itself an amazing tool for both data analysis and visualization. It all began by as an open source, well-designed, and multi-propose programming language that did not require much programming background to use. So, as R rose in popularity, a vivid and helpful community has also arisen.
The community itself has done much to keep improving the language. They have designed and shared tons of packages, which brings us to here and now. This book is full of examples on how to easily brew pretty graphics using very popular packages. It aims for teaching the nuts and bolts related to many different visuals, along with very useful tricks to adapt and enhance your figures.
Examples range from static scatterplots built using ggplot2 to interactive globes formed by plotly. Learn how to plot the most tweeted words by some user, draw true 3D interactive surfaces, and build shiny dashboards. Also you will learn how to apply a theme that will support your analysis.
What this book covers
Chapter 1, Installation and Introduction, teaches how to install the three most popular R visualization packages: ggplot2, ggvis, and plotly. This chapter introduces the ways a package can be installed: either from a CRAN repository or a GitHub one. You also get to know how to call functions from those packages while going through some examples demonstrating the basic framework of those packages.
Chapter 2, Plotting Two Continuous Variables, dives into scatterplots, the most popular way to plot two continuous variables. We learn how to change shapes, colors and sizes. The main problem imbued to scatterplots is called over plotting; this chapter explores related solutions. Also we see how to draw marginal plots and produce high-quality scatterplots.
Chapter 3, Plotting a Discrete Predictor and a Continuous Response, covers distributions over categories, a great way to explore your data. It can be handled using R to draw one box plots, bivariate dot plots, and violin plots. Learn how to draw and combine each of them. Meet nice additional features while learning the art of crafting to publish quality violin plots.
Chapter 4, Plotting One Variable, covers histograms, density plots, area plots, univariate dot plots, and univariate bar graphs. Learn how to brew these visuals under different R packages. Meet the useful modifications achieved by little code tweaks.
Chapter 5, Making Other Bivariate Plots. Trust this chapter to build bars of all kinds, line graphs, hexagon plots, and deploy variability estimates (error bars). Bars can be displayed as stacked, proportional, and/or side by side. Despite the wide variability of bars available, there are also some useful tricks to learn.
Chapter 6, Creating Maps, Demonstrating spatial correlation is mustily done through maps. Learn how points, lines, and polygons can be used to craft maps through R packages. Meet the John Snow cholera map, Tolkien's Middle Earth map, and an interactive globe of banking crisis. Also learn how to read shapefiles from temporary directories and how projection types can be coerced.
Chapter 7, Faceting, covers an important device that allows comprehension on more complex data relations. Recipes within this chapter teach how different kinds of visuals can easily reach out for faceting using ggplot2. Additionally, this chapter teaches how those can be coerced into interactive visuals by relying on the plotly package.
Chapter 8, Designing Three-Dimensional Plots. Although ggplot2 is not capable of drawing true 3D surfaces, it can draw their 2D representations. This chapter demonstrates the craft of contour, tile, and raster plots using ggplot2. It also demonstrates how to draw true and interactive 3D surfaces using plotly.
Chapter 9, Using Theming Packages, teaches how several themming packages can be used to coerce several themes to a ggplot. These range from themes inspired by The Simpsons and Star Trek to respectful academic journal themes. Also learn how to wrap a theme of your own into a function.
Chapter 10, Designing More Specialized Plots. Meet the complementary ggplot2 packages: ggforce, ggrepel, ggraph, and ggalt. These can be used to simple tasks like zooming in to more complex tasks like avoiding labels and texts inside the graph to overlay each other.
Chapter 11, Making Interactive Plots, explains how the ggiraph, gganimate, and tweenr packages can be used to create ggplot2 based animations and design interactive features. Animations are an useful way to denote how data variables have evolved with wonderful applications to all kinds of visuals.
Chapter 12, Building Shiny Dashboards. shiny allows the user to make interactive web applications direct from R. The package shinydashboard makes easier to create dash boards using shiny. This recipe demonstrates how to construct those while customizing styles, side bars, and more.
What you need for this book
First things first, download the latest version of R (https://cran.r-project.org/). Additionally, recipes were written using a IDE named RStudio (free version). I do prefer to write R codes under RStudio IDE; if you do not have the software yet, you can get it from https://www.rstudio.com/products/rstudio/download/.
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 will be useful but not necessary. Some programming knowledge of R is required for this book.
Conventions
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning. Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "This chapter covers basic aspects of three of them:ggplot2,plotly, and ggvis."
A block of code is set as follows:
> install.packages(c('devtools','plotly','ggvis'))> devtools::install_github('hadley/ggplot2')
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Open your RStudio, go to Tools|Install Packages..."
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Downloading the example code
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Errata
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Questions
If you have a problem with any aspect of this book, you can contact us at [email protected], and we will do our best to address the problem.
Installation and Introduction
Following recipes are covered in this chapter:
Installing and loading graphics packages
Using ggplot2, plotly, and ggvis
Making plots using primitives
Introduction
R is a free open language and environment for statistical computing and graphics. It particularly gained wide popularity among scientists from different fields, journalists, and private companies. There are various reasons for that, openness and gratuity may be couple of them. Also, R requires minimal programming background and has a vibrant online community.
From community, a bunch of useful graphical packages had come. This chapter covers basic aspects of three of them: ggplot2, plotly, and ggvis. The first one (ggplot2) has been there for a long time, is very mature, and is very useful to build non-interactive graphics.
Both plotly and ggvis are much younger packages, which can build interactive plots. Both are shiny compatible and can well address the matter of web applications. Beginning with installation and loading, this chapter goes all the way through explaining the basic framework of all those three packages, while demonstrating how to use ggplot2 primitives.
Installing and loading graphics packages
Before starting, there are some habits you may want to cultivate in order to keep improving your R skills. First of all, whenever you program there may be some challenges to face. Usually those are tackled either by out-thinking the problem or by doing some research. You might want to remember what the problem was about and the solution, be that for times you face it again later or even for studying hours, keep a record of problems and solutions.
R Markdown documents are pretty useful if want to keep a track of your own development and optionally publish for others to see. Publishing the learning process is a good way to self-promote. Also, keep in mind that R is a programming language and often those can correctly pull a problem out in more than one way, be open-minded to seek different solutions.
First things first, in order to make good use of a package, you need to install the package and know how to call a package's function.
How to do it...
Run the following code to install the graphics packages properly:
> install.packages(c('devtools','plotly','ggvis'))> devtools::install_github('hadley/ggplot2')
How it works...
Most of the book covers three graphic packages—ggplot2, plotly, and ggvis. In order to install a new package, you can type the function install.packages() into the console. That function works for packages available at CRAN-like repositories and local files. In order to install packages from local files, you need to name more than just the first argument. Entering ?install.packages into RStudio console shall lead you to the function documentation at the Help tab.
Instants after running the recipe, all the packages (devtools included) covered in this chapter might already be properly installed. Check the Packages tab in your RStudio application (speed up the search by typing into the search engine); if everything went fine, these four may be shown under UserLibrary. Following image shows how it might look like:
If it fails, you may want to check the spelling and the internet connection. This function also gives some outputs that stand for warnings, progress reports, and results. Look for a message similar to package '<Package Name>' successfully unpacked and MD5 sums checked to make sure that all went fine. Checking the output is a good practice in order to know if the plan worked. It also give good clues about troubleshooting.
You may want to call a non-existing package (be creative here) and a package already installed and see what happens. Sometimes incompatibilities avoid proper download and installation. For example, missing Java or the proper architecture of Java may prevent you from installing the rJava package.
Realize that a package's name must be in the string format in order to work (remember to use ' '). It's also important to check the spelling. The function (calling and arguments) is case sensitive; if you miss even one letter or case, you will not find the desired package. Also note that the arguments where drew into a c() function. That is a vector (try ?c in the console).
As first argument of the install.packages() function, a vector of strings was given. That said, multiple packages can be downloaded and installed simultaneously. The same function might not install only the packages asked, but all the packages each of them rely on.
Now with a bunch of new functions at hand, the next step is to call a function from a specific package. There are several ways of doing that. One possible way to do it is typing <package name>::<package function>, latest code block done that when called install_github(), a function from coming from devtools package, so it was called this way: devtools::install_github().
There are pros and cons about calling a function this way. As for pros, you mostly avoid any name conflict that could possible happen between packages. Other than that, you also avoid loading the whole package when you only need to call a single function. Thus, calling a function this way may be useful in two occasions:
Name conflict is expected
Only few functions from that package may be requested and only a few times
Otherwise, if a package is required many times, typing <package name>:: before every function is anti-productive. It's possible to load and attach the whole package at once. Via RStudio interface, right below the window that shows environment objects, there is a window with a package tab. Below the package tab it's possible to check the box in order to load a package and uncheck to detach them.
Try to detach ggplot2 by unchecking the box; keep an eye on that box. You can load packages using functions. The require() and library() functions can be assigned to this task. Both don't need ' ' in order to function well like install.packages() does, but if you call the package name as a string it stills works. Note that both functions can only load one package a time.
Although require() and library() work in a very similar way, they do not work exactly the same. If require() fails it throws a warning, library() on the other hand will trow an error. There is more, require() returns a logical value that stands for TRUE when the load succeeds and FALSE when it fails; library() returns no value.
For common loading procedures that is not a difference that should made into account, but if you want to create a function or loop that depends on loading a package and checking if it succeed, you may find easier to make it using require(). Using the logical operator & (and), it's possible to load all three packages at once and store the result in a single variable. Calling this variable will state TRUE if there is success for all and FALSE if a single one fails. This is done as follows:
> lcheck <- require(ggplot2) & require(plotly) & require(ggvis)> lcheck
For now you might be able to install R packages - from CRAN, Git repositories or local files - load and call a functions from an specific package. Now that you are familiar with R package's installation and loading procedures, the next section gives an introduction to the ggplot2 package framework.
There's more
Installation is also possible via RStudio features, which may seen more user friendly for newcomers. Open your RStudio, go to Tools > Install Packages..., type the packages' names (separate them with space or comma), and hit install. It fills the install.package() function and shows it in your console.
This is most indicated when you are not absolutely sure about the package name, but have a good clue. There is automatic suggestion thing that shall help you out to figure exactly what the package name is. You can also install packages from local files by using this feature. Look for an option called Install from and switch it to Package Archive File instead of Repository.
RStudios also gives you a Check For Packages Updates... option right below Install Packages... Hit it once in a while to make sure your packages are properly updated. Along with the packages to be updated it also shows what is new about them.
See also...
ggplot2
tidyverse reference manual:
http://ggplot2.tidyverse.org/reference/
ggvis
CRAN-R documentaion:
https://cran.r-project.org/web/packages/ggvis/ggvis.pdf
plotly
figure reference:
https://plot.ly/r/reference/
Using ggplot2, plotly, and ggvis
ggplot2, ggvis, and plotly have proven to be very useful graphical packages in the R universe. Each of them gained a respectful sum of popularity among R users, being recalled for the several graphical tasks each of them can handle in very elegant manners.
The purpose of this section is to give a brief introduction on the general framework of ggplot2 via some basic examples, and relate how to tackle similar quests using ggvis and plotly. Along the way, some pros and cons from each package will be highlighted.
Keep in mind that all the three packages are eligible for a large convoy of tasks. There are some jobs that a specific package is more suitable for and even some tasks that can be considered almost impracticable for others. This point will become clearer as the book goes on.
Getting ready
The only requirement this section holds is to have the ggplot2, ggvis, and plotly packages properly installed. Go back to Installing and loading graphics packages recipe if that is not the case. Once the installation is checked, it's time to know ggplot2 framework.
