Table Of ContentData Visualization and Exploration
with R
A practical guide to using R, RStudio, and Tidyverse for data
visualization, exploration, and data science applications.
Eric Pimpler
Introduction to Data Visualization
and Exploration with R
A practical guide to using R, RStudio, and tidyverse for data
visualization, exploration, and data science applications.
Eric Pimpler
Geospatial Training Services 215 W Bandera #114-104
Boerne, TX 78006
PH: 210-260-4992
Email:
About the Author
Eric Pimpler
Eric Pimpler is the founder and
owner of Geospatial Training Services (geospatialtraining.com) and have over
25 years of experience implementing and teaching GIS solutions using Esri
software. Currently he focuses on data science applications with R along with
ArcGIS Pro and Desktop scripting with Python and the development of custom
ArcGIS Enterprise (Server) and ArcGIS Online web and mobile applications
with JavaScript.
Eric is the also the author of several other books including Introduction to
Programming ArcGIS Pro with Python
(https://www.amazon.com/dp/1979451079/re
(https://www.amazon.com/dp/1979451079/re
1&keywords=Programming+ArcGIS+Pro+with +Python), Programming
ArcGIS with Python Cookbook (https://www.packtpub.com/ application-
development/programmingarcgis-python-cookbook-second-edition), Spatial
Analytics with ArcGIS (https://www. packtpub.com/application-development/
spatial-analytics-arcgis), Building Web and Mobile ArcGIS Server Applications
with JavaScript (https://www.packtpub.com/ application-development/building-
weband-mobile-arcgis-server-applicationsjavascript), and ArcGIS Blueprints
(https:// www.packtpub.com/application
development/arcgis-blueprints).
If you need consulting assistance with your data science or GIS projets please
contact Eric at
Downloading and Installing Exercise Data for this
Book
This is intended as a hands-on exercise book and is designed to give you as
much handson coding experience with R as possible. Many of the exercises in
this book require that you load data from a file-based data source such as a CSV
file. These files will need to be installed on your computer before continuing
with the exercises in this chapter as well as the rest of the book. Please follow
the instructions below to download and install the exercise data
1. In a web browser go to one of the links below to download the exercise data:
https://www.dropbox.com/s/5p7j7nl8hgijsnx/IntroR.zip?dl=0.
https://s3.amazonaws.com/VirtualGISClassroom/IntroR/IntroR.zip
2. This will download a file called IntroR.zip.
3. The exercise data can be unzipped to any location on your computer. After
unzipping the IntroR.zip file you will have a folder structure that includes IntroR
as the top-most folder with sub-folders called Data and Solutions. The Data
folder contains the data that will be used in the exercises in the book, while the
Solutions folder contains solution files for the R script that you will write.
RStudio can be used on Windows, Mac, or Linux so rather than specifying a
specific folder to place the data I will leave the installation location up to you.
Just remember where you unzip the data because you’ll need to reference the
location when you set the working directory.
4. For reference purposes I have installed the data to the desktop of my Mac
computer under IntroR\Data. You will see this location referenced at various
locations throughout the book. However, keep in mind that you can install the
data anywhere.
Table of Contents
CHAPTER 1: Introduction to R and RStudio
....................................................... 9
Introduction to RStudio
...........................................................................................................10
Exercise 1: Creating variables and assigning data
.............................................................27
Exercise 2: Using vectors and factors
....................................................................................32
Exercise 3: Using lists
.................................................................................................................36
Exercise 4: Using data classes
................................................................................................39
Exercise 5: Looping statements
..............................................................................................46
Exercise 6: Decision support statements – if | else
..............................................................48
Exercise 7: Using functions
......................................................................................................51
Exercise 8: Introduction to tidyverse
......................................................................................53
CHAPTER 2: The Basics of Data Exploration and Visualization with R
.......... 57
Exercise 1: Installing and loading tidyverse
..........................................................................58
Exercise 2: Loading and examining a
dataset.....................................................................60
Exercise 3: Filtering a dataset
.................................................................................................64
Exercise 4: Grouping and summarizing a dataset
...............................................................65
Exercise 5: Plotting a dataset
.................................................................................................66
Exercise 6: Graphing burglaries by month and year
...........................................................67
CHAPTER 3: Loading Data into R
...................................................................... 73
Exercise 1: Loading a csv file with read.table()
....................................................................73
Exercise 2: Loading a csv file with read.csv()
.......................................................................76
Exercise 3: Loading a tab delimited file with read.table()
..................................................77
Exercise 4: Using readr to load data
.....................................................................................77
CHAPTER 4: Transforming Data
........................................................................ 83
Exercise 1: Filtering records to create a subset
....................................................................84
Exercise 2: Narrowing the list of columns with select()
........................................................87
Exercise 3: Arranging Rows
.....................................................................................................90
Exercise 4: Adding Rows with mutate()
.................................................................................92
Exercise 5: Summarizing and Grouping
.................................................................................94
Exercise 6: Piping
......................................................................................................................97
Exercise 7: Challenge
..............................................................................................................99
CHAPTER 5: Creating Tidy Data .....................................................................
101
Exercise 1: Gathering
............................................................................................................102
Exercise 2: Spreading
............................................................................................................107
Exercise 3: Separating
...........................................................................................................110
Exercise 4: Uniting
..................................................................................................................113
CHAPTER 6: Basic Data Exploration Techniques in R ...................................
115
Exercise 1: Measuring Categorical Variation with a Bar Chart
........................................116
Exercise 2: Measuring Continuous Variation with a Histogram
.........................................118
Exercise 3: Measuring Covariation with Box Plots
..............................................................120
Exercise 4: Measuring Covariation with Symbol Size
.........................................................122
Exercise 5: 2D bin and hex charts
........................................................................................124
Exercise 6: Generating Summary Statistics
.........................................................................126
CHAPTER 7: Basic Data Visualization Techniques ........................................
129
Step 1: Creating a scatterplot
..............................................................................................130
Step 2: Adding a regression line to the scatterplot
...........................................................133
Step 3: Plotting categories
....................................................................................................136
Step 4: Labeling the graph
...................................................................................................137
Step 5: Legend layouts
..........................................................................................................144
Step 6: Creating a facet
.......................................................................................................146
Step 7:
Theming......................................................................................................................147
Step 8: Creating bar charts
