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Home > Data > Data Visualization > R Data Visualization Recipes
R Data Visualization Recipes
R Data Visualization Recipes

R Data Visualization Recipes: A cookbook with 65+ data visualization recipes for smarter decision-making

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Arrow left icon
Profile Icon Bianchi Lanzetta
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$32.99
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Paperback Nov 2017 366 pages 1st Edition
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R Data Visualization Recipes

Plotting Two Continuous Variables

In this chapter, we will cover the following recipes:

  • 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

Introduction

Investigating the relationship between two variables may be much easier than investigating it for several variables simultaneously. There is one good reason for that: we can visualize bivariate relationships way better. Problems with numerous amount of variables are often split into several problems with only two variables.

There are several visualizations that supports the two variables context. The most popular of them may be the scatterplots. People are familiar with them, on the other hand there is a problem that haunts many scatterplots: over-plottingting. This chapter begins with recipes to draw very simple scatterplots, going all the way to explore solutions available when it comes to deal with over-plottingting, and demonstrate how enhance scattersplots by setting up marginal graphics.

Plotting a basic scatterplot

Scatterplots play a major role in the representation of two continuous variables. Making simple scatterplots is a very easy task to handle using ggplot2, ggvis, or plotly. This recipe uses a data frame called iris to draw plots, it comes with base R (datasets package).

Before using data coming from a package, you may want to try entering ?<package name>::<data frame name> into your console. For this recipe, that would go as: ?datasets::iris. This is may lead you towards data documentation, this way you get to know each variable coming from the data frame.

From the various features presented by this data set, this recipe uses Petal.Width and Petal.Length. They respectively account for iris' petal widths and lengths measured in centimeters. Besides drawing the plots, this recipe also teaches how to add a title to them...

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

Version 0.4.3 of ggvis does not have a function to add titles to plots, but still there is a known way to hack the add_axis() function to work as a title function. If a user expects to explore this device many times, it's advised to wrap it into a function. Besides making the code more readable, it's a quicker way to address the problem.

Getting ready

This recipe does not only teach how to craft the hack function but also to experiment it on the previous plot, so make sure to have sca3 from the earlier recipe loaded into your environment. Alternatively, you can use another ggvis object of your own.

...

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...

  1. Change the shape and colour arguments to get a better result:
> library...

Plotting a shape reference palette for ggplot2

Shapes are picked following a default scale when you input a variable to work as shape using ggplot2. You can always choose to tweak this scale to one of your preference. To do so you need to know which shapes are available and how you can call for them. This recipe simply draws the following shape palette:

Figure 2.4 - ggplot2 shape palette.

It shows available points, plus the number used to call for them. Now let's explore the code that built it.

How to do it...

Draw a suitable data frame using rep() and seq() functions, them let's plot those using geom_point():

> palette <- data.frame(x = rep(seq(1,5,1),5))
> palette$y <- c(rep(5,5),rep(4,5)...

Introduction

Investigating the relationship between two variables may be much easier than investigating it for several variables simultaneously. There is one good reason for that: we can visualize bivariate relationships way better. Problems with numerous amount of variables are often split into several problems with only two variables.

There are several visualizations that supports the two variables context. The most popular of them may be the scatterplots. People are familiar with them, on the other hand there is a problem that haunts many scatterplots: over-plottingting. This chapter begins with recipes to draw very simple scatterplots, going all the way to explore solutions available when it comes to deal with over-plottingting, and demonstrate how enhance scattersplots by setting up marginal graphics.

Plotting a basic scatterplot

Scatterplots play a major role in the representation of two continuous variables. Making simple scatterplots is a very easy task to handle using ggplot2, ggvis, or plotly. This recipe uses a data frame called iris to draw plots, it comes with base R (datasets package).

Note

Before using data coming from a package, you may want to try entering ?<package name>::<data frame name> into your console. For this recipe, that would go as: ?datasets::iris. This is may lead you towards data documentation, this way you get to know each variable coming from the data frame.

From the various features presented by this data set, this recipe uses Petal.Width and Petal.Length. They respectively account for iris' petal widths and lengths measured in centimeters. Besides drawing the plots, this recipe also teaches how to add a title to them. So, move on to the coding!

How to do it...

  1. Initialize a ggplot and then give it the point geometry:
> library(ggplot2)
> sca1 ...

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

Version 0.4.3 of ggvisdoes not have a function to add titles to plots, but still there is a known way to hack the add_axis() function to work as a title function. If a user expects to explore this device many times, it's advised to wrap it into a function. Besides making the code more readable, it's a quicker way to address the problem.

Getting ready

This recipe does not only teach how to craft the hack function but also to experiment it on the previous plot, so make sure to have sca3 from the earlier recipe loaded into your environment. Alternatively, you can use another ggvis object of your own.

How to do it...

  1. Wrap the add_axis() function with several arguments declared to work as a title function:
> library(ggvis)
> ggvis_title <- function(vis, plot_title, title_size = 18, shift = 0, ...){ 
    add_axis(vis, 'x', ticks = 0, orient = 'top', 
             properties = axis_props( axis = list(strokeWidth = 0),
       ...

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...

  1. 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.

  1. plotly can also handle such...

Plotting a shape reference palette for ggplot2

Shapes are picked following a default scale when you input a variable to work as shape using ggplot2. You can always choose to tweak this scale to one of your preference. To do so you need to know which shapes are available and how you can call for them. This recipe simply draws the following shape palette:

Figure 2.4 - ggplot2 shape palette.

It shows available points, plus the number used to call for them. Now let's explore the code that built it.

How to do it...

Draw a suitable data frame using rep() and seq() functions, them let's plot those using geom_point():

> palette <- data.frame(x = rep(seq(1,5,1),5))
> palette$y <- c(rep(5,5),rep(4,5),rep(3,5),rep(2,5),rep(1,5))
> library(ggplot2)
> ggplot(data = palette,aes(x,y)) +
    geom_point(shape = seq(1,25,1), size = 10, fill ='white') +
    scale_size(range = c(2, 10)) +
    geom_text(nudge_y = .3, label = seq(1,25,1))

Function geom_text() is plotting the reference numbers related...

Dealing with over-plotting, reducing points

There are mainly three techniques used to deal with over-plot. They are: (i) adopting smaller points,(ii) jittering data, and (iii) alpha blending. These are useful tools, not only to deal with over-plot but also to check if there is over-plotting.

However, these are not the only options; for example, alternative geometries can also be implemented. No matter how troublesome over-plotting may be there are good solutions available.There is not a single solution that is better for all the situations, so you must know a bunch of them. 

This recipe advises how to apply a technique based on point size reduction using ggplot2, ggvis and plotly. In order to do so, we are trusting the ggplot2::diamonds data frame. Keep in mind that reducing points works better for cases where points are very close to each other but do not actually occupy the same coordinates.

How to do it...

  1. Set shape to '.' in order to reduce points using ggplot2:
> library(ggplot2)
&gt...

Dealing with over-plotting, jittering points

Size reduction is never an option when there are too many points sharing the exact same coordinates; it simply is not the right tool for the job. A clear option therefore is to jitter the data, that is, add a little noise to the data so that the points move around a little bit and the over-plotting kind of wears off.

Two points must be highlighted here. Jittering may be a good way to adjust the plot but not to adjust the data, so do not use jittered data for modeling and always be honest when transformations of that nature take place. Second point is that as long it may work pretty well when many points share coordinates. Although, if too many points are only close enough but do no share same coordinates there is a chance that jittering will work very badly.

Now let's go back to the iris data set and demonstrate how this technique can be applied using ggplot2, ggvis and plotly.

How to do it...

  1. With ggplot2, set potion = 'jitter' in order to obtain...

Dealing with over-plotting, alpha blending

Another popular technique is known as alpha blending. It consists on making points translucent, this way the audience gets to know if points are stacked or not.  Between all the techniques demonstrated so far, alpha blending must be the most popular one. This recipe teaches how to apply alpha blending using ggplot2, plotly, and ggvis.

How to do it...

  1. Set the alpha parameter to apply alpha blending to ggplot:
> library(ggplot2)
> sca1 <- ggplot( iris, aes( x = Petal.Length, y = Petal.Width))
> sca1 + geom_point( alpha = .5 , 
                     aes(shape = Species, colour = Species))

The following figure 2.7 shows alpha blending working:

Figure 2.7 - alpha blending with ggplot2.

  1. Setting alpha parameter with plotly will also apply alpha blending:
> library(plotly)
> sca9 <- plot_ly( iris, x = ~Petal.Length, y = ~Petal.Width, 
>                  type = 'scatter', mode = 'markers', alpha = .5, symbol = ~Species)
> sca9
  1. ggvis applies...

Rug the margins using geom_rug()

Up till now, the chapter has focused on how to draw scatterplots and solutions related to over-plotting. Upcoming recipes, including this one, shall focus on enhancing scatterplots. If there is a bivariate relation to be displayed there is also two univariate distributions to show. How can they be used to improve the plots? 

Answer lies in filling the margins with supplemental plots carrying representations of underlying univariate distributions. Still relying on the iris data set framework, this recipe introduces a simple solution, almost restricted to ggplot2. Let's rug plots in the margins with geom_rug().

How to do it...

  1. Draw a scatterplot using ggplot2 and sum the geom_rug() layer:
> set.seed(50) ; library(ggplot2)
> rug <- ggplot(iris,
                aes(x = Petal.Length, 
                    y = Petal.Width, 
                    colour = Species))
> rug <- rug +
    geom_jitter(aes( shape = Species), alpha = .4) +
    geom_rug(position...

Adding marginal histograms using ggExtra

Another way to go is to draw histograms or even density distributions in the margins. Drawing tailor made plots in the margins would require more code. On the other hand, if there is no need for greater customization ggExtra package can be used to spare many code lines. This recipe is demonstrating how to use ggExtra to easily draw histograms in the margins of a scatterplot. 

Getting ready

In order to properly execute this recipe, the ggExtra package must be locked and loaded. Run the following code to make that happen:

> if( !require(ggExtra)){ install.packages('ggExtra')}

Once ggExtra is installed we can go on.

How to do it...

  1. Draw a ggplot2 scatterplot like this:
library(ggplot2)
base_p <- ggplot(iris, aes(x = Petal.Length, y = Petal.Width, colour = Species))
scatter <- base_p + geom_point( alpha = .5, aes(shape = Species)) + 
  geom_rug(alpha = .5, sides = 'tr', show.legend = F) +
  theme(legend.position = 'bottom')
  1. Load ggExtra and input the...
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Key benefits

  • 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

Description

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.

Who is this book 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
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Publication date : Nov 22, 2017
Length: 366 pages
Edition : 1st
Language : English
ISBN-13 : 9781788398312
Category :
Data
Languages :
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Concepts :
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Tools :
ggplot

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Length: 366 pages
Edition : 1st
Language : English
ISBN-13 : 9781788398312
Category :
Data
Languages :
R
Concepts :
Data Visualization
Tools :
ggplot

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Table of Contents

12 Chapters
Installation and Introduction Chevron down icon Chevron up icon
Installation and Introduction
Introduction
Installing and loading graphics packages
Using ggplot2, plotly, and ggvis
Making plots using primitives
Plotting Two Continuous Variables Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
Making Interactive Plots
Introduction
Using ggiraph to create interactive plots
Using gganimate to craft animated ggplots
Crafting animated plots with tweenr
Building Shiny Dashboards Chevron down icon Chevron up icon
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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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
(1 Ratings)
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Amazon Customer Jan 16, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
A clear and compelling read, and a great way to get a handle on the major methods of data visualization in R.
Amazon Verified review Amazon

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About the author

Profile icon Bianchi Lanzetta
Bianchi Lanzetta
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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.
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Shipping Details

USA:

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Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

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Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

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Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela