Data visualization with r rob kabacoff2018 09-032

Data Visualization with R
Rob Kabacoff
2018-09-03
2
Contents
Welcome 7
Preface 9
How to use this book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 9
Prequisites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 10
Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 10
1 Data Preparation 11
1.1 Importing data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 11
1.2 Cleaning data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 12

2 Introduction to ggplot2 19
2.1 A worked example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 19
2.2 Placing the data and mapping options . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 30
2.3 Graphs as objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 32
3 Univariate Graphs 35
3.1 Categorical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 35
3.2 Quantitative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 51
4 Bivariate Graphs 63
4.1 Categorical vs. Categorical . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 63
4.2 Quantitative vs. Quantitative . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 71
4.3 Categorical vs. Quantitative . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 79
5 Multivariate Graphs 103
5.1 Grouping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 103

6 Maps 115
6.1 Dot density maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 115
6.2 Choropleth maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 119
3
4 CONTENTS
7 Time-dependent graphs 127
7.1 Time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 127
7.2 Dummbbell charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 130
7.3 Slope graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 133
7.4 Area Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 135
8 Statistical Models 139
8.1 Correlation plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 139
8.2 Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 141

8.3 Logistic regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 145
8.4 Survival plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 147
8.5 Mosaic plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 150
9 Other Graphs 153
9.1 3-D Scatterplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 153
9.2 Biplots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 159
9.3 Bubble charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 161
9.4 Flow diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 163
9.5 Heatmaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 168
9.6 Radar charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 174
9.7 Scatterplot matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 176
9.8 Waterfall charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 178
9.9 Word clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . 180
10 Customizing Graphs 183
10.1 Axes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 183
10.2 Colors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 187
10.3 Points & Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 193
10.4 Legends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 195
10.5 Labels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 197
10.6 Annotations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 199
10.7 Themes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 206
11 Saving Graphs 219
11.1 Via menus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 219
11.2 Via code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 219
11.3 File formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 219

11.4 External editing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 221
CONTENTS 5
12 Interactive Graphs 223
12.1 leaflet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 223
12.2 plotly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 223
12.3 rbokeh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 226
12.4 rCharts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 226
12.5 highcharter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 226
13 Advice / Best Practices 231
13.1 Labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 231
13.2 Signal to noise ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 232
13.3 Color choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 234
13.4 y-Axis scaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . 234
13.5 Attribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 238
13.6 Going further . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 238
13.7 Final Note . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 239
A Datasets 241
A.1 Academic salaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 241
A.2 Starwars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 241
A.3 Mammal sleep . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 241
A.4 Marriage records . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 242
A.5 Fuel economy data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 242
A.6 Gapminder data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 242
A.7 Current Population Survey (1985) . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 242
A.8 Houston crime data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 242

A.9 US economic timeseries . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 243
A.10 Saratoga housing data . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 243
A.11 US population by age and year . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 243
A.12 NCCTG lung cancer data . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 243
A.13 Titanic data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 243
A.14 JFK Cuban Missle speech . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 244
A.15 UK Energy forecast data . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 244
A.16 US Mexican American Population . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 244
B About the Author 245
C About the QAC 247
6 CONTENTS
Welcome

R is an amazing platform for data analysis, capable of creating
almost any type of graph. This book helps
you create the most popular visualizations - from quick and
dirty plots to publication-ready graphs. The
text relies heavily on the ggplot2 package for graphics, but
other approaches are covered as well.
This work is licensed under a Creative Commons Attribution-
NonCommercial-NoDerivatives 4.0 Interna-
tional License.
My goal is make this book as helpful and user-friendly as
possible. Any feedback is both welcome and
appreciated.
7
8 CONTENTS
Preface
How to use this book
You don’t need to read this book from start to finish in order to
start building effective graphs. Feel free to
jump to the section that you need and then explore others that
you find interesting.
Graphs are organized by
• the number of variables to be plotted

• the type of variables to be plotted
• the purpose of the visualization
Chapter Description
Ch 1 provides a quick overview of how to get your data into R
and how to prepare it
for analysis.
Ch 2 provides an overview of the ggplot2 package.
Ch 3 describes graphs for visualizing the distribution of a single
categorical (e.g. race)
or quantitative (e.g. income) variable.
Ch 4 describes graphs that display the relationship between two
variables.
Ch 5 describes graphs that display the relationships among 3 or
more variables. It is
helpful to read chapters 3 and 4 before this chapter.
Ch 6 provides a brief introduction to displaying data
geographically.
Ch 7 describes graphs that display change over time.
Ch 8 describes graphs that can help you interpret the results of
statistical models.
Ch 9 covers graphs that do not fit neatly elsewhere (every book
needs a miscellaneous
chapter).
Ch 10 describes how to customize the look and feel of your
graphs. If you are going to
share your graphs with others, be sure to skim this chapter.
Ch 11 covers how to save your graphs. Different formats are
optimized for different

purposes.
Ch 12 provides an introduction to interactive graphics.
Ch 13 gives advice on creating effective graphs and where to go
to learn more. It’s
worth a look.
The Appendices describe each of the datasets used in this book,
and provides a short blurb about
the author and the Wesleyan Quantitative Analysis Center.
There is no one right graph for displaying data. Check out the
examples, and see which type best fits
your needs.
9
10 CONTENTS
Prequisites
It’s assumed that you have some experience with the R language
and that you have already installed R and
RStudio. If not, here are some resources for getting started:
• A (very) short introduction to R
• DataCamp - Introduction to R with Jonathon Cornelissen
• Quick-R
• Getting up to speed with R
Setup
In order to create the graphs in this guide, you’ll need to install
some optional R packages. To install all of

the necessary packages, run the following code in the RStudio
console window.
pkgs <- c("ggplot2", "dplyr", "tidyr",
"mosaicData", "carData",
"VIM", "scales", "treemapify",
"gapminder", "ggmap", "choroplethr",
"choroplethrMaps", "CGPfunctions",
"ggcorrplot", "visreg",
"gcookbook", "forcats",
"survival", "survminer",
"ggalluvial", "ggridges",
"GGally", "superheat",
"waterfalls", "factoextra",
"networkD3", "ggthemes",
"hrbrthemes", "ggpol",
"ggbeeswarm")
install.packages(pkgs)
Alternatively, you can install a given package the first time it is
needed.
For example, if you execute
library(gapminder)
and get the message
Error in library(gapminder) : there is no package called
‘gapminder’
you know that the package has never been installed. Simply
execute
install.packages("gapminder")

once and
library(gapminder)
will work from that point on.
https://cran.r-project.org/
https://www.rstudio.com/products/RStudio/#Desktop
https://cran.r-project.org/doc/contrib/Torfs+Brauer-Short-R-
Intro.pdf
https://www.datacamp.com/courses/free-introduction-to-r
http://www.statmethods.net
Chapter 1
Data Preparation
Before you can visualize your data, you have to get it into R.
This involves importing the data from an
external source and massaging it into a useful format.
1.1 Importing data
R can import data from almost any source, including text files,
excel spreadsheets, statistical packages, and
database management systems. We’ll illustrate these techniques
using the Salaries dataset, containing the 9
month academic salaries of college professors at a single
institution in 2008-2009.
1.1.1 Text files
The readr package provides functions for importing delimited
text files into R data frames.

library(readr)
# import data from a comma delimited file
Salaries <- read_csv("salaries.csv")
# import data from a tab delimited file
Salaries <- read_tsv("salaries.txt")
These function assume that the first line of data contains the
variable names, values are separated by commas
or tabs respectively, and that missing data are represented by
blanks. For example, the first few lines of the
comma delimited file looks like this.
"rank","discipline","yrs.since.phd","yrs.service","sex","salary"
"Prof","B",19,18,"Male",139750
"Prof","B",20,16,"Male",173200
"AsstProf","B",4,3,"Male",79750
"Prof","B",45,39,"Male",115000
"Prof","B",40,41,"Male",141500
"AssocProf","B",6,6,"Male",97000
Options allow you to alter these assumptions. See the
documentation for more details.
11
https://www.rdocumentation.org/packages/readr/versions/0.1.1/t
opics/read_delim
12 CHAPTER 1. DATA PREPARATION
1.1.2 Excel spreadsheets

The readxl package can import data from Excel workbooks.
Both xls and xlsx formats are supported.
library(readxl)
# import data from an Excel workbook
Salaries <- read_excel("salaries.xlsx", sheet=1)
Since workbooks can have more than one worksheet, you can
specify the one you want with the sheet option.
The default is sheet=1.
1.1.3 Statistical packages
The haven package provides functions for importing data from a
variety of statistical packages.
library(haven)
# import data from Stata
Salaries <- read_dta("salaries.dta")
# import data from SPSS
Salaries <- read_sav("salaries.sav")
# import data from SAS
Salaries <- read_sas("salaries.sas7bdat")
1.1.4 Databases
Importing data from a database requires additional steps and is
beyond the scope of this book. Depending on
the database containing the data, the following packages can
help: RODBC, RMySQL, ROracle, RPostgreSQL,
RSQLite, and RMongo. In the newest versions of RStudio, you
can use the Connections pane to quickly access

the data stored in database management systems.
1.2 Cleaning data
The processes of cleaning your data can be the most time-
consuming part of any data analysis. The most
important steps are considered below. While there are many
approaches, those using the dplyr and tidyr
packages are some of the quickest and easiest to learn.
Package Function Use
dplyr select select variables/columns
dplyr filter select observations/rows
dplyr mutate transform or recode variables
dplyr summarize summarize data
dplyr group_by identify subgroups for further processing
tidyr gather convert wide format dataset to long format
tidyr spread convert long format dataset to wide format
https://db.rstudio.com/rstudio/connections/
1.2. CLEANING DATA 13
Examples in this section will use the starwars dataset from the
dplyr package. The dataset provides
descriptions of 87 characters from the Starwars universe on 13
variables. (I actually prefer StarTrek, but we
work with what we have.)
1.2.1 Selecting variables
The select function allows you to limit your dataset to specified
variables (columns).
library(dplyr)

# keep the variables name, height, and gender
newdata <- select(starwars, name, height, gender)
# keep the variables name and all variables
# between mass and species inclusive
newdata <- select(starwars, name, mass:species)
# keep all variables except birth_year and gender
newdata <- select(starwars, -birth_year, -gender)
1.2.2 Selecting observations
The filter function allows you to limit your dataset to
observations (rows) meeting a specific criteria.
Multiple criteria can be combined with the & (AND) and | (OR)
symbols.
library(dplyr)
# select females
newdata <- filter(starwars,
gender == "female")
# select females that are from Alderaan
newdata <- select(starwars,
gender == "female" &
homeworld == "Alderaan")
# select individuals that are from
# Alderaan, Coruscant, or Endor
homeworld == "Alderaan" |

homeworld == "Coruscant" |
homeworld == "Endor")
# this can be written more succinctly as
homeworld %in% c("Alderaan", "Coruscant", "Endor"))
1.2.3 Creating/Recoding variables
The mutate function allows you to create new variables or
transform existing ones.
library(dplyr)
# convert height in centimeters to inches,
# and mass in kilograms to pounds
newdata <- mutate(starwars,
height = height * 0.394,
mass = mass * 2.205)
The ifelse function (part of base R) can be used for recoding
data. The format is ifelse(test, return
if TRUE, return if FALSE).
library(dplyr)
# if height is greater than 180
# then heightcat = "tall",
# otherwise heightcat = "short"

heightcat = ifelse(height > 180,
"tall",
"short")
# convert any eye color that is not
# black, blue or brown, to other
eye_color = ifelse(eye_color %in% c("black", "blue", "brown"),
eye_color,
"other")
# set heights greater than 200 or
# less than 75 to missing
height = ifelse(height < 75 | height > 200,
NA,
height)
1.2.4 Summarizing data
The summarize function can be used to reduce multiple values
down to a single value (such as a mean). It
is often used in conjunction with the by_group function, to
calculate statistics by group. In the code below,
the na.rm=TRUE option is used to drop missing values before
calculating the means.
library(dplyr)
# calculate mean height and mass
newdata <- summarize(starwars,

mean_ht = mean(height, na.rm=TRUE),
mean_mass = mean(mass, na.rm=TRUE))
newdata
## # A tibble: 1 x 2
## mean_ht mean_mass
## <dbl> <dbl>
## 1 174. 97.3
# calculate mean height and weight by gender
newdata <- group_by(starwars, gender)
newdata <- summarize(newdata,
mean_ht = mean(height, na.rm=TRUE),
mean_wt = mean(mass, na.rm=TRUE))
newdata
## # A tibble: 5 x 3
## gender mean_ht mean_wt
## <chr> <dbl> <dbl>
## 1 female 165. 54.0
## 2 hermaphrodite 175. 1358.
## 3 male 179. 81.0
## 4 none 200. 140.
## 5 <NA> 120. 46.3
1.2.5 Using pipes
Packages like dplyr and tidyr allow you to write your code in a

compact format using the pipe %>% operator.
Here is an example.
library(dplyr)
# calculate the mean height for women by species
newdata <- filter(starwars,
gender == "female")
newdata <- group_by(species)
newdata <- summarize(newdata,
mean_ht = mean(height, na.rm = TRUE))
# this can be written as
newdata <- starwars %>%
filter(gender == "female") %>%
group_by(species) %>%
summarize(mean_ht = mean(height, na.rm = TRUE))
The %>% operator passes the result on the left to the first
parameter of the function on the right.
1.2.6 Reshaping data
Some graphs require the data to be in wide format, while some
graphs require the data to be in long format.
You can convert a wide dataset to a long dataset using
library(tidyr)
long_data <- gather(wide_data,
key="variable",
value="value",
sex:income)

Table 1.2: Wide data
id name sex age income
01 Bill Male 22 55000
02 Bob Male 25 75000
03 Mary Female 18 90000
Table 1.3: Long data
id name variable value
01 Bill sex Male
02 Bob sex Male
03 Mary sex Female
01 Bill age 22
02 Bob age 25
03 Mary age 18
01 Bill income 55000
02 Bob income 75000
03 Mary income 90000
Conversely, you can convert a long dataset to a wide dataset
using
library(tidyr)
wide_data <- spread(long_data, variable, value)
1.2.7 Missing data
Real data are likely to contain missing values. There are three
basic approaches to dealing with missing
data: feature selection, listwise deletion, and imputation. Let’s

see how each applies to the msleep dataset
from the ggplot2 package. The msleep dataset describes the
sleep habits of mammals and contains missing
values on several variables.
1.2.7.1 Feature selection
In feature selection, you delete variables (columns) that contain
too many missing values.
data(msleep, package="ggplot2")
# what is the proportion of missing data for each variable?
pctmiss <- colSums(is.na(msleep))/nrow(msleep)
round(pctmiss, 2)
## name genus vore order conservation
## 0.00 0.00 0.08 0.00 0.35
## sleep_total sleep_rem sleep_cycle awake brainwt
## 0.00 0.27 0.61 0.00 0.33
## bodywt
## 0.00
Sixty-one percent of the sleep_cycle values are missing. You
may decide to drop it.
1.2.7.2 Listwise deletion
Listwise deletion involves deleting observations (rows) that
contain missing values on any of the variables of
interest.

# Create a dataset containing genus, vore, and conservation.
# Delete any rows containing missing data.
newdata <- select(msleep, genus, vore, conservation)
newdata <- na.omit(newdata)
1.2.7.3 Imputation
Imputation involves replacing missing values with “reasonable”
guesses about what the values would have
been if they had not been missing. There are several
approaches, as detailed in such packages as VIM, mice,
Amelia and missForest. Here we will use the kNN function from
the VIM package to replace missing values
with imputed values.
# Impute missing values using the 5 nearest neighbors
library(VIM)
newdata <- kNN(msleep, k=5)
Basically, for each case with a missing value, the k most similar
cases not having a missing value are selected.
If the missing value is numeric, the mean of those k cases is
used as the imputed value. If the missing value
is categorical, the most frequent value from the k cases is used.
The process iterates over cases and variables
until the results converge (become stable). This is a bit of an
oversimplification - see Imputation with R
Package VIM for the actual details.
Important caveate: Missing values can bias the results of studies
(sometimes severely). If you
have a significant amount of missing data, it is probably a good
idea to consult a statistician or
data scientist before deleting cases or imputing missing values.
https://www.jstatsoft.org/article/view/v074i07/v74i07.pdf

https://www.jstatsoft.org/article/view/v074i07/v74i07.pdf
Chapter 2
Introduction to ggplot2
This section provides an brief overview of how the ggplot2
package works. If you are simply seeking code to
make a specific type of graph, feel free to skip this section.
However, the material can help you understand
how the pieces fit together.
2.1 A worked example
The functions in the ggplot2 package build up a graph in layers.
We’ll build a a complex graph by starting
with a simple graph and adding additional elements, one at a
time.
The example uses data from the 1985 Current Population Survey
to explore the relationship between wages
(wage) and experience (expr).
# load data
data(CPS85 , package = "mosaicData")
In building a ggplot2 graph, only the first two functions
described below are required. The other functions
are optional and can appear in any order.
2.1.1 ggplot

The first function in building a graph is the ggplot function. It
specifies the
• data frame containing the data to be plotted
• the mapping of the variables to visual properties of the graph.
The mappings are placed within the
aes function (where aes stands for aesthetics).
# specify dataset and mapping
library(ggplot2)
ggplot(data = CPS85,
mapping = aes(x = exper, y = wage))
Why is the graph empty? We specified that the exper vari able
should be mapped to the x-axis and that the
wage should be mapped to the y-axis, but we haven’t yet
specified what we wanted placed on the graph.
19
https://ggplot2.tidyverse.org/
20 CHAPTER 2. INTRODUCTION TO GGPLOT2
0
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30

40
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exper
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Figure 2.1: Map variables
2.1. A WORKED EXAMPLE 21
2.1.2 geoms
Geoms are the geometric objects (points, lines, bars, etc.) that
can be placed on a graph. They are added
using functions that start with geom_. In this example, we’ll
add points using the geom_point function,
creating a scatterplot.
In ggplot2 graphs, functions are chained together using the +
sign to build a final plot.
# add points
ggplot(data = CPS85,
mapping = aes(x = exper, y = wage)) +
geom_point()
0

10
20
30
40
0 20 40
exper
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The graph indicates that there is an outlier. One individual has a
wage much higher than the rest. We’ll
delete this case before continuing.
# delete outlier
library(dplyr)
plotdata <- filter(CPS85, wage < 40)
# redraw scatterplot
ggplot(data = plotdata,
geom_point()
A number of parameters (options) can be specified in a geom_
function. Options for the geom_point function
include color, size, and alpha. These control the point color,
size, and transparency, respectively. Trans-

0
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exper
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Figure 2.2: Remove outlier
0
10
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0 20 40
exper
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Figure 2.3: Modify point color, transparency, and size
parency ranges from 0 (completely transparent) to 1 (completely
opaque). Adding a degree of transparency
can help visualize overlapping points.
# make points blue, larger, and semi-transparent
geom_point(color = "cornflowerblue",
alpha = .7,
size = 3)
Next, let’s add a line of best fit. We can do this with the
geom_smooth function. Options control the type of
line (linear, quadratic, nonparametric), the thickness of the line,
the line’s color, and the presence or absence
of a confidence interval. Here we request a linear regression
(method = lm) line (where lm stands for linear
model).
# add a line of best fit.
geom_point(color = "cornflowerblue",
alpha = .7,
size = 3) +

geom_smooth(method = "lm")
Wages appears to increase with experience.
0
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exper
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Figure 2.4: Add line of best fit
2.1.3 grouping
In addition to mapping variables to the x and y axes, variables
can be mapped to the color, shape, size,
transparency, and other visual characteristics of geometric
objects. This allows groups of observations to be
superimposed in a single graph.

Let’s add sex to the plot and represent it by color.
# indicate sex using color
mapping = aes(x = exper,
y = wage,
color = sex)) +
geom_point(alpha = .7,
size = 3) +
geom_smooth(method = "lm",
se = FALSE,
size = 1.5)
0
10
20
0 20 40
exper
w
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sex
F

M
The color = sex option is placed in the aes function, because we
are mapping a variable to an aesthetic.
The geom_smooth option (se = FALSE) was added to suppresses
the confidence intervals.
It appears that men tend to make more money than women.
Additionally, there may be a stronger relation-
ship between experience and wages for men than than for
women.
$0
$5
$10
$15
$20
$25
0 10 20 30 40 50
exper
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sex
F
M
Figure 2.5: Change colors and axis labels
2.1.4 scales
Scales control how variables are mapped to the visual
characteristics of the plot. Scale functions (which start
with scale_) allow you to modify this mapping. In the next plot,
we’ll change the x and y axis scaling, and
the colors employed.
# modify the x and y axes and specify the colors to be used
mapping = aes(x = exper,
y = wage,
color = sex)) +
geom_point(alpha = .7,
size = 3) +
geom_smooth(method = "lm",
se = FALSE,
size = 1.5) +
…
W35BatsmanAverageDismissalsVirat Kohli1005Kedar
Jadhav621Rohit Sharma51.910Shikhar Dhawan34.412MS
Dhoni24.779

Data visualization with r rob kabacoff2018 09-032

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