Data Visualization With R

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dataCrunch Data Visualization With R

dataCrunchCourse Material
Slide 2
All the material related to this course are available at our Website
Slides can be viewed at SlideShare
Scripts can be downloaded from GitHub
Videos can be viewed on our Youtube Channel

dataCrunchLearning Objectives
Slide 3
By the end of this module,
→ Understand
➢ What is data visualization?
➢ Why visualize data?
➢ Graphics Systems In R
→ Be able to:
➢ Build basic plot
➢ Add title, labels & text annotations
➢ Modify Axis Ranges
➢ Modify colors, font style & size
➢ Combine multiple plots
➢ Save graphs in multiple formats

dataCrunchWhat is data visualization?
Slide 4
Data visualization is the representation of data in graphical format

dataCrunchWhy visualize data?
Slide 5
● Explore: Visualization helps in exploring and explaining patterns and trends.
● Detect: Patterns or anomalies in data can be detected by looking at graphs .
● Make Sense: Possible to make sense of large amount of data efficiently and in time.
● Communicate: Easy to communicate and share the insights from the data.

dataCrunchWhy visualize data?
Slide 6
A picture is worth a thousand words.
> by(mtcars$mpg, mtcars$cyl, summary)
mtcars$cyl: 4
Min. 1st Qu. Median Mean 3rd Qu. Max.
21.40 22.80 26.00 26.66 30.40 33.90
-----------------------------------------------
mtcars$cyl: 6
17.80 18.65 19.70 19.74 21.00 21.40
-----------------------------------------------
mtcars$cyl: 8
10.40 14.40 15.20 15.10 16.25 19.20

dataCrunchR Graphics System
Slide 7
R has 3 main packages for data visualization:
● Graphics
It is part of R installation and is the fundamental package for visualizing data. It has a lot of
good features and we can create all the basic plots using this package.
● ggplot2
ggplot2 was created by Hadley Wickham and is based on the Grammar Of Graphics written by
Leland Wilkinson. It has a structured approach to data visualization and builds upon the
features available in Graphics and Lattice packages.
● Lattice
The Lattice package is inspired by Trellis Graphics and was created by Deepayan Sarkar who
is part of the R core group. It is a very powerful data visualization system with an emphasis on
multivariate data.

dataCrunch
Slide 8
Plot Basics

dataCrunchplot()
Slide 9
The plot() function is the fundamental tool to build plots in the Graphics package. It is a
generic function and creates the appropriate plot based on the input received from the user. In
this section, we will explore the plot() function by using different types of data and observing
the corresponding plots created. We will use the mtcars data set throughout this section.
The documentation for the plot() function and the mtcars data set can be viewed using the
help function.
1
2
3
4
help(plot)
help(mtcars)

dataCrunchmtcars
Slide 10
Let us take a quick look at the mtcars data set as we will be using it throughout this section:
> head(mtcars)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
> str(mtcars)
'data.frame': 32 obs. of 11 variables:
$ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
$ disp: num 160 160 108 258 360 ...
$ hp : num 110 110 93 110 175 105 245 62 95 123 ...
$ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt : num 2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num 16.5 17 18.6 19.4 17 ...
$ vs : num 0 0 1 1 0 1 0 1 1 1 ...
$ am : num 1 1 1 0 0 0 0 0 0 0 ...
$ gear: num 4 4 4 3 3 3 3 4 4 4 ...
$ carb: num 4 4 1 1 2 1 4 2 2 4 ...

dataCrunchExplore plot()
Slide 11
Next, we will begin exploring the plot() function. The following data will be used as an input:
● Case 1: One continuous variable
● Case 2: One categorical variable
● Case 3: Two continuous variables
● Case 4: Two categorical variables
● Case 5: One continuous & one categorical variable
● Case 6: One categorical & one continuous variable
Case 5 and 6 might look similar but the difference lies in the variables being assigned to the X and Y
axis.

dataCrunchCase 1: One continuous variable
Slide 12
We will use the variable mpg (Miles Per Gallon) for this example.
# plot a single continuous variable
plot(mtcars$mpg)
The plot() function creates a
Scatter Plot when a single
continuous variable is used as the
input. We cannot infer anything
from the above plot as it
represents the data points of the
mpg variable in the XY
coordinate. Let us plot a
categorical variable and see what
happens.

dataCrunchCase 2: One categorical variable
Slide 13
Let us use the cyl (number of cylinders) variable for this data as we need a categorical
variable. But before that we need to convert it to type factor using as.factor
# check the data type of cyl
class(mtcars$cyl)
[1] "numeric"
# coerce to type factor
mtcars$cyl <- as.factor(mtcars$cyl)
# plot a single categorical variable
plot(mtcars$cyl)
The plot() function creates a bar
plot when the data is categorical in
nature.

dataCrunchCase 3: Two continuous variables
Slide 14
Till now we had used only one variable as the input but from this example, we will be using two
variables; one for the X axis and another for the Y axis. In this example, we will look at the
relationship between the displacement and mileage of the cars. The disp and mpg variables
are used and disp is plotted on X axis while mpg is plotted on the Y axis.
# plot two continuous variables
plot(mtcars$disp, mtcars$mpg)
A Scatter plot is created when we use
two continuous variables as the input
for the plot function but in this case,
we can interpret the plot as it
represents the relationship between
two variables.

dataCrunchCase 4: Two categorical variables
Slide 15
In this example, we will use two categorical variables am (transmission type) and cyl (number
of cylinders). We will convert am to type factor before creating the plot. Transmission type will
be plotted on X axis and number of cylinders on Y axis.
# coerce am to type factor
mtcars$am <- as.factor(mtcars$am)
# coerce cyl to type factor
# plot two categorical variables
plot(mtcars$am, mtcars$cyl)
A stacked bar plot is created when
we use two categorical variables as
the input for the plot function. In the
next two examples, we will use both
continuous and categorical variables.

dataCrunchCase 5: Continuous/Categorical Variables
Slide 16
In this example, we will plot a categorical variable cyl on the X axis and a continuous variable
mpg on the Y axis.
# categorical/continuous variables
plot(mtcars$cyl, mtcars$mpg)
A box plot is created when we use a
categorical variable and continuous
variable as input for the plot function.
But in this case, the categorical
variable was plotted on the X axis
and the continuous variable on the Y
axis. What happens if we flip this?

dataCrunchCase 6: Categorical/Continuous Variables
Slide 17
In this example, the continuous variable is plotted on the X axis and the categorical variable on
the Y axis.
# continuous vs categorical variables
plot(mtcars$mpg, mtcars$cyl)
A scatter plot is created but since the
Y axis variable is discrete, we can
observe lines of points for each level
of the discrete variable. We can
compare the range of the X axis
variable for each level of the Y axis
variable.

dataCrunch
Title, Axis Labels & Range
Slide 18

dataCrunchTitle & Labels
Slide 19
In this section, we will learn to enhance the plot by adding/modifying the following features:
● Title
● Subtitle
● Axis Labels
● Axis Range

dataCrunchIntroduction
Slide 20
In the previous section, we created plots which did not have any title or labels. A new user of a plot
should know what the X and Y axis represent as well as the primary information being communicated
by the plot. The title, axis labels and range play an important role in making the plot holistic.
There are two ways of adding the above mentioned elements to a plot:
● Use the relevant arguments within the plot() function.
● Use the title() function.
We will explore both the methods one by one and you can choose the method that you find most
convenient. Let us begin by adding the features using the plot() function.

dataCrunchSyntax
Slide 21
Feature Argument Value Example
Title main String “Scatter Plot”
Subtitle sub String “Displacement vs Miles Per Gallon”
X Axis Label xlab String “Displacement”
Y Axis Label ylab String “Miles Per Gallon”
X Axis Range xlim Numeric Vector c(0, 500)
Y Axis Range ylim Numeric Vector c(0, 50)

dataCrunchTitle
Slide 22
# create a basic plot and add a title
plot(mtcars$disp, mtcars$mpg,
main = “Displacement vs Miles Per Gallon”)
Description
Use the main argument in the plot()
function to add a title to the plot. The title
must be enclosed in double quotes as it is a
string.
Code

dataCrunchSubtitle
Slide 23
# create a basic plot and add a subtitle
sub = “Displacement vs Miles Per Gallon”)
Description
Use the sub argument in the plot()
function to add a subtitle to the plot. The
subtitle must be enclosed in double quotes
as it is a string.
Code

dataCrunchAxis Labels
Slide 24
# create a basic plot and add axis labels
xlab = "Displacement",
ylab = "Miles Per Gallon")
Description
In all plots created till now, the axis labels
appear as mtcars$disp and mtcars$mpg.
It is not the most elegant way of labeling the
axis and it would make more sense to use
appropriate names. Use the xlab and ylab
arguments in the plot() function to add the
X and Y axis labels.
Code

dataCrunchAxis Range
Slide 25
# create a basic plot and modify axis range
xlim = c(0, 600),
ylim = c(0, 50))
Description
The range of the axis can be modified using
the xlim and ylim arguments in the
plot() function. The lower and upper limit
for both the axis must be mentioned in a
numeric vector.
Code

dataCrunchSo far...
Slide 26
# create a plot with title, subtitle, axis
labels and range
main = "Displacement vs Miles Per Gallon",
sub = "Scatter Plot",
ylab = "Miles Per Gallon",
xlim = c(0, 600), ylim = c(0, 50))
Description
Let us create a plot that has all the features
that we have learnt so far:
Code

dataCrunchtitle() Function
Slide 27
# create a basic plot
# add title and label using title() function
title(main = "Displacement vs Miles Per Gallon",
Description
We will add/modify the same set of features but
using the title() function instead of the
arguments in the plot() function but we will
continue to use the plot() function for creating the
basic plot.
Code
Oops.. We have a problem. If you have observed
carefully, the title() function has overwritten the
default axis labels created by the plot() function.

dataCrunchtitle() Function
Slide 28
plot(mtcars$disp, mtcars$mpg, ann = FALSE)
# add title and label using title() function
Description
Use the ann argument in the plot() function and
set it to FALSE to ensure that the plot() function
does not add the default labels.
Code
Note: The range of the axis cannot be modified
using the title() function.

dataCrunchColor
Slide 30
In this section, we will learn to add colors to the following using the col argument:
● Plot Symbol
● Title & Subtitle
● Axis
● Axis Labels
● Foreground

dataCrunchSyntax
Slide 31
Feature Argument Value Example
Symbol col
String
Hexadecimal
RGB
"blue"
Title col.main "#0000ff"
Subtitle col.sub rgb(0, 0, 1)
Axis col.axis "red"
Label col.lab "#ff0000"
Foreground fg rgb(1, 0, 0)
The col argument can be used along with main, sub, axis and lab arguments to specify the color of
the title, subtitle, axes and the labels.

dataCrunchColor: Symbol
Slide 32
# modify color of the plot
col= "red")
OR
col = "#ff0000")
OR
col = rgb(1, 0, 0))
Description
Let us begin by adding color to the symbol in the
plot using the col argument in the plot()
function.
Code

dataCrunchColor: Title
Slide 33
# modify the color of the title
col.main = "blue")
OR
col.main = "#0000ff")
OR
col.main = rgb(0, 0, 1))
Description
The color of the title can be modified by using the
col.main argument in the plot() function.
Code

dataCrunchColor: Subtitle
Slide 34
# modify the color of the subtitle
sub= "Displacement vs Miles Per Gallon",
col.sub = "blue")
OR
col.sub = "#0000ff")
OR
col.sub = rgb(0, 0, 1))
Description
The color of the subtitle can be modified by using
the col.sub argument in the plot() function.
Code

dataCrunchColor: Axis
Slide 35
# modify the color of the axis
col.axis = "blue")
OR
col.axis = "#0000ff")
OR
col.axis = rgb(0, 0, 1))
Description
The color of the axis can be modified by using the
col.axis argument in the plot() function.
Code

dataCrunchColor: Labels
Slide 36
# modify the color of the labels
col.lab = "blue")
Description
The color of the labels can be modified by using the
col.lab argument in the plot() function.
Code

dataCrunchColor: Foreground
Slide 37
# modify the color of the foreground
fg= "red")
Description
The color of the foreground can be modified by
using the fg argument in the plot() function.
Code

dataCrunchColor: Using title() function
Slide 38
# Create a basic plot
plot(mtcars$disp, mtcars$mpg, ann= FALSE)
# modify color using title() function
col.main = "blue", col.lab = "red")
Description
The colors of the title, subtitle and the labels can be
modified using the title() function as well. Let us
try it out:
Code

dataCrunchFont
Slide 40
The font argument can be used along with main, sub, axis and lab arguments to specify the font of
the title, subtitle, axes and the labels.
Feature Argument
Title font.main
Subtitle font.sub
Axis font.axis
Labels font.lab
The font argument takes values from 1 - 5. The font type represented by each value is shown in the
above table.
Value Font Type
1 Plain
2 Bold
3 Italic
4 Bold Italic
5 Symbol

dataCrunchFont: Title
Slide 41
# modify the font of the title
font.main = 1)
Description
The font of the title can be modified using the font.
main argument in the plot() function.
Code

dataCrunchFont: Title
Slide 42
The below plot depicts the appearance of the title when different options for font type are applied:

dataCrunchFont: Subtitle
Slide 43
# modify the font of the subtitle
font.sub = 3)
Description
The font of the subtitle can be modified using the
font.sub argument in the plot() function.
Code

Slide 44
The below plot depicts the appearance of the subtitle when different options for font type are applied:

dataCrunchFont: Axis
Slide 45
# modify the font of the axis
font.axis = 3)
Description
The font of the axis can be modified using the
font.axis argument in the plot() function.
Code

dataCrunchFont: Axis
Slide 46
The below plot depicts the appearance of the axis when different options for font type are applied:

dataCrunchFont: Labels
Slide 47
# modify the font of the labels
font.lab = 3)
Description
The font of the labels can be modified using the
font.lab argument in the plot() function.
Code

dataCrunchFont: Labels
Slide 48
The below plot depicts the appearance of the labels when different options for font type are applied:

dataCrunchFont Size
Slide 50
The cex argument can be used along with main, sub, axis and lab arguments to specify the font size
of the title, subtitle, axes and the labels.
Feature Argument
Title cex.main
Subtitle cex.sub
Axis cex.axis
Labels cex.lab
The values taken by the cex argument are relative to 1 i.e the default size is represented by 1 and if
the supplied value is less than 1, the size of the font will be relatively smaller, and if the supplied value
is greater than 1, the size of the font will be relatively bigger.

dataCrunchFont Size: Title
Slide 51
# modify the font size of the title
main = "Scatter Plot",
cex.main = 1.5)
Description
The font size of the title can be modified using the
cex.main argument in the plot() function.
Code

dataCrunchFont Size: Title
Slide 52
The below plot depicts the appearance of the title when different options for font size are applied:

dataCrunchFont Size: Subtitle
Slide 53
# modify the font size of the subtitle
sub= "Scatter Plot",
cex.sub = 1.5)
Description
The font size of the subtitle can be modified using
the cex.sub argument in the plot() function.
Code

Slide 54
The below plot depicts the appearance of the subtitle when different options for font size are applied:

dataCrunchFont Size: Axis
Slide 55
# modify the font size of the axis
cex.axis = 1.5)
Description
The font size of the axis can be modified using the
cex.axis argument in the plot() function.
Code

dataCrunchFont Size: Axis
Slide 56
The below plot depicts the appearance of the axis when different options for font size are applied:

dataCrunchFont Size: Labels
Slide 57
# modify the font size of the labels
cex.lab = 1.5)
Description
The font size of the labels can be modified using the
cex.lab argument in the plot() function.
Code

dataCrunchFont Size: Labels
Slide 58
The below plot depicts the appearance of the subtitle when different options for font size are applied:

dataCrunch
Text Annotations
Slide 59

dataCrunchText Annotations: Objectives
Slide 60
In this section, we will learn to:
Add text annotations to the plots using
● text() function
● mtext() function

dataCrunchText Annotations: Introduction
Slide 61
The text() and the mtext() functions allow the user to add text annotations to the plots. While the
text() function places the text inside the plot, the mtext() function places the text on the margins of
the plot.
Below is the syntax for both the functions:
# the text function
text(x, y = NULL, labels = seq_along(x), adj = NULL,
pos = NULL, offset = 0.5, vfont = NULL,
cex = 1, col = NULL, font = NULL, ...)
# the mtext function
mtext(text, side = 3, line = 0, outer = FALSE, at = NA,
adj = NA, padj = NA, cex = NA, col = NA, font = NA, ...)
Let us explore each function and its arguments one by one:

dataCrunchText Annotations: text()
Slide 62
To add text annotations using the text() function, the following 3 arguments must be supplied:
● x: x axis coordinate
● y: y axis coordinate
● text: the text to be added to the plot
Below is a simple example:
# add text
text(340, 30, "Sample Text")
The text appears at the coordinates (340, 30) on the
plot. Ensure that the text is enclosed in double quotes
and the coordinates provided are within the range of
the X and Y axis variable.

dataCrunchtext(): Color
Slide 63
# modify the color of the text
text(340, 30, "Sample Text", col = "red")
Description
The color of the text can be modified using the col
argument in the text() function.
Code

dataCrunchtext(): Color
Slide 64
The below plot depicts the appearance of the text when different options for col are applied:

dataCrunchtext(): Font
Slide 65
# modify the font of the text
text(340, 30, "Sample Text", font = 2)
Description
The font of the text can be modified using the font
Code

dataCrunchtext(): Font
Slide 66
The below plot depicts the appearance of the text when different options for font are applied:

dataCrunchtext(): Font Family
Slide 67
# modify the font family of the text
text(340, 30, "Sample Text", family = mono)
Description
The font family of the text can be modified using the
family argument in the text() function.
Code

dataCrunchtext(): Font Family
Slide 68
The below plot depicts the appearance of the text when different options for font family are applied:

dataCrunchtext(): Font Size
Slide 69
# modify the size of the text
text(340, 30, "Sample Text", cex = 1.5)
Description
The size of the text can be modified using the cex
Code

dataCrunchtext(): Font Size
Slide 70
The below plot depicts the appearance of the text when different options for font size are applied:

dataCrunchmtext(): Introduction
Slide 71
The mtext() function places text annotations on the margins of the plot instead of placing them
inside the plot. It allows the user to modify the location of the text in multiple ways and we will
explore them one by one.
Below is a simple example:
# add text
mtext("Sample Text")
As you can see, the text is placed on
the margin of the plot and not inside
the plot. Next, we will learn to
specify the margin where the text
should be placed.

dataCrunchmtext(): Margin
Slide 72
# specify the margin on which the text should appear
mtext("Sample Text", side = 1)
Description
The margin on which we want to place the
text can be specified using the side
argument. It takes 4 values from 1-4 each
representing one side of the plot.
Code

dataCrunchmtext(): Margin Options
Slide 73
The side argument can be used to specify the margin on which the text should be placed.
side Margin
1 Bottom
2 Left
3 Top
4 Right

dataCrunchmtext(): Margin
Slide 74
The below plot depicts the appearance of the text when different options for side are applied:

dataCrunchmtext(): Line
Slide 75
# place the text away from the margin
mtext("Sample Text", line = 1)
Description
The line argument places the text at the
specified distance from the margin. The default
value is 0 and as the value increases the text
is placed farther from the margin and outside
the plot, and as the value decreases the text is
placed inside the plot and farther from the
margin.
Code

Slide 76
# place the text away from the plot
mtext("Sample Text", line = -1)
Description
The line argument places the text inside the
plot when the values is less than zero.
Code

Slide 77
The below plot depicts the appearance of the text when different options for line are applied:

dataCrunchmtext(): adj
Slide 78
# align the text to the left
mtext("Sample Text", adj= 0)
Description
The adj argument is used for horizontal
alignment of the text. If set to 0, the text will be
left aligned and at 1, it will be right aligned.
Code

Slide 79
# align the text to the right
mtext("Sample Text", adj= 1)
Description
When the value is set to 1, the text will be right
aligned.
Code

Slide 80
The below plot depicts the appearance of the text when different options for text() are applied:

dataCrunchLayout: Objectives
Slide 82
In this section, we will learn to:
Combine multiple graphs in a single frame using the following functions:
● par() function
● layout() function

dataCrunchLayout: Introduction
Slide 83
Often, it is useful to have multiple plots in the same frame as it allows us to get a comprehensive view
of a particular variable or compare among different variables. The Graphics package offers two
methods to combine multiple plots.
The par() function can be used to set graphical parameters regarding plot layout using the mfcol and
mfrow arguments. The layout() function serves the same purpose but offers more flexibility by
allowing us to modify the height and width of rows and columns.

dataCrunchLayout: par()
Slide 84
The par() function allows us to customize the graphical parameters(title, axis, font, color, size) for a
particular session. For combining multiple plots, we can use the graphical parameters mfrow and
mfcol. These two parameters create a matrix of plots filled by rows and columns respectively. Let us
combine plots using both the above parameters.
Option Description Arguments
mfrow Fill by rows Number of rows and columns
mfcol Fill by columns Number of rows and columns

dataCrunchLayout: par(mfrow)
Slide 85
(a) mfrow
mfrow combines plots filled by rows i.e it takes two arguments, the number of rows and number of
columns and then starts filling the plots by row. Below is the syntax for mfrow:
Let us begin by combining 4 plots in 2 rows and 2 columns:
# mfrow syntax
mfrow(number of rows, number of columns)

dataCrunchRecipe 1: Code
Slide 86
Let us begin by combining 4 plots in 2 rows and 2 columns. The plots will be filled by rows as we are using
the mfrow function:
# store the current parameter settings in init
init <- par(no.readonly=TRUE)
# specify that 4 graphs to be combined and filled by rows
par(mfrow = c(2, 2))
# specify the graphs to be combined
plot(mtcars$mpg)
hist(mtcars$mpg)
boxplot(mtcars$mpg)
# restore the setting stored in init
par(init)

dataCrunchRecipe 1: Plot
Slide 87

Slide 88
Combine 2 plots in 1 row and 2 columns.
hist(mtcars$mpg)
boxplot(mtcars$mpg)
par(init)

Slide 89

Slide 90
Combine 2 plots in 2 rows and 1 column
hist(mtcars$mpg)
boxplot(mtcars$mpg)
par(init)

Slide 91

Slide 92
Combine 3 plots in 1 row and 3 columns
hist(mtcars$mpg)
boxplot(mtcars$mpg)
par(init)

Slide 93

Slide 94
Combine 3 plots in 3 rows and 1 column
hist(mtcars$mpg)
boxplot(mtcars$mpg)
par(init)

Slide 95

dataCrunchLayout: par(mfcol)
Slide 96
(a) mfcol
mfcol combines plots filled by columns i.e it takes two arguments, the number of rows and number of
columns and then starts filling the plots by columns. Below is the syntax for mfrow:
Let us begin by combining 4 plots in 2 rows and 2 columns:
# mfcol syntax
mfcol(number of rows, number of columns)

Slide 97
Combine 4 plots in 2 rows and 2 columns
# specify that 4 graphs to be combined and filled by columns
par(mfcol = c(2, 2))
plot(mtcars$mpg)
hist(mtcars$mpg)
boxplot(mtcars$mpg)
par(init)

Slide 98

dataCrunchSpecial Cases
Slide 99
What happens if we specify lesser or more number of graphs? In the next two examples, we will
specify lesser or more number of graphs than we ask the par() function to combine. Let us see
what happens in such instances:
Case 1: Lesser number of graphs specified
We will specify that 4 plots need to be combined in 2 rows and 2 columns but provide only 3
graphs.
Case 2: Extra graph specified
We will specify that 4 plots need to be combined in 2 rows and 2 columns but specify 6 graphs
instead of 4.

dataCrunchSpecial Case 1: Code
Slide 100
hist(mtcars$mpg)
boxplot(mtcars$mpg)
par(init)

dataCrunchSpecial Case 1: Plot
Slide 101

dataCrunchSpecial Case 2: Code
Slide 102
plot(mtcars$mpg)
hist(mtcars$mpg)
boxplot(mtcars$mpg)
boxplot(mtcars$mpg)
par(init)

dataCrunchSpecial Case 2: Plot
Slide 103
Frame 1 Frame 2

r-squaredCombining Graphs: layout()
Slide 104
At the core of the layout() function is a matrix. We communicate the structure in which the plots
must be combined using a matrix. As such, the layout function is more flexible compared to the par()
function.
Let us begin by combining 4 plots in a 2 row/2 column structure. We do this by creating a layout using
the matrix function.
Option Description Value
matrix Matrix specifying location of plots Matrix
widths Width of columns Vector
heights Heights of Rows Vector

Slide 105
Combine 4 plots in 2 rows/2 columns filled by rows
# specify the layout
# 4 plots to be combined in 2 row/ 2 columns and arranged by row
layout(matrix(c(1, 2, 3, 4), nrow = 2, byrow = TRUE))
# specify the 4 plots
hist(mtcars$mpg)
boxplot(mtcars$mpg)
plot(mtcars$mpg)

Slide 106

Slide 107
Combine 4 plots in 2 rows/2 columns filled by columns
To fill the plots by column, we specify byrow = FALSE in the matrix.
# specify the layout
# 4 plots to be combined in 2 row/ 2 columns and filled by columns
layout(matrix(c(1, 2, 3, 4), nrow = 2, byrow = FALSE))
hist(mtcars$mpg)
boxplot(mtcars$mpg)
plot(mtcars$mpg)

Slide 108

Slide 109
The magic of the layout() function begins here. We want to combine 3 plots and the first plot should occupy both
the columns in row 1 and the next 2 plots should be in row 2. If you look at the matrix below, 1 is specified twice
and since the matrix is filled by row, it will occupy both the columns in the first row. Similarly the first plot will occupy
the entire first row. It will be crystal clear when you see the plot.
# specify the matrix
> matrix(c(1, 1, 2, 3), nrow = 2, byrow = TRUE)
[,1] [,2]
[1,] 1 1
[2,] 2 3
hist(mtcars$mpg)
boxplot(mtcars$mpg)

Slide 110

Slide 111
The plots must be filled by rows and the third plot must occupy both the columns of the second row while the other
two plots will be placed in the first row. The matrix would look like this:
[,1] [,2]
[1,] 1 2
[2,] 3 3
hist(mtcars$mpg)
boxplot(mtcars$mpg)

Slide 112

Slide 113
The plots must be filled by columns and the first plot must occupy both the rows of the first column
while the other two plots will be placed in the second column in two rows. The matrix would look
like this:
> matrix(c(1, 1, 2, 3), nrow = 2, byrow = FALSE)
[,1] [,2]
[1,] 1 2
[2,] 1 3
# 3 plots to be combined in 2 row/ 2 columns and arranged by columns
hist(mtcars$mpg)
boxplot(mtcars$mpg)

Slide 114

Slide 115
The plots must be filled by columns and the first plot must occupy both the rows of the second
column while the other two plots will be placed in the first column in two rows. The matrix would
look like this:
> matrix(c(1, 2, 3, 3), nrow = 2, byrow = FALSE)
[,1] [,2]
[1,] 1 3
[2,] 2 3
boxplot(mtcars$mpg)
hist(mtcars$mpg)

Slide 116

dataCrunchlayout(): Widths
Slide 117
Widths
In all the layouts created so far, we have kept the size of the rows and columns equal. What if you
want to modify the width and height of the columns and rows? The widths and heights arguments
in the layout() function address the above mentioned issue. Let us check them out one by one:
The widths argument is used for specifying the width of the columns. Based on the number of
columns in the layout, you can specify the width of each column. Let us look at some examples.

Slide 118
Width of the 2nd column is twice the width of the 1st column
[,1] [,2]
[1,] 1 3
[2,] 2 4
layout(matrix(c(1, 2, 3, 4), nrow = 2, byrow = TRUE), widths = c(1, 3))
# specify the plots
hist(mtcars$mpg)
boxplot(mtcars$mpg)
plot(mtcars$mpg)

Slide 119

Slide 120
Width of the 2nd column is twice that of the first and last column
> matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, byrow = TRUE)
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 4 5 6
# 6 plots to be combined in 2 row/ 3 columns and filled by rows
layout(matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, byrow = TRUE), widths = c(1, 2, 1))
# specify the plots
hist(mtcars$mpg)
boxplot(mtcars$mpg)
plot(mtcars$mpg)
hist(mtcars$mpg)
boxplot(mtcars$mpg)

Slide 121

dataCrunchlayout(): Heights
Slide 122
Heights
The heights arguments is used to modify the height of the rows and based on the number of
rows specified in the layout, we can specify the height of each row.
Height of the 2nd row is twice that of the first row
# 4 plots to be combined in 2 row/ 2 columns and filled by rows
layout(matrix(c(1, 2, 3, 4), nrow = 2, byrow = TRUE), heights= c(1, 2))
hist(mtcars$mpg)
boxplot(mtcars$mpg)
plot(mtcars$mpg)

Slide 123

Slide 124
Height of the 3rd row is thrice that of the 1st and 2nd row
[,1] [,2]
[1,] 1 2
[2,] 3 4
[3,] 5 6
# 6 plots to be combined in 3 row/ 2 columns and arranged by rows
layout(matrix(c(1, 2, 3, 4, 5, 6), nrow = 3, byrow = TRUE), heights= c(1, 1, 3))
# specify the plots
hist(mtcars$mpg)
boxplot(mtcars$mpg)
plot(mtcars$mpg)
hist(mtcars$mpg)
boxplot(mtcars$mpg)

Slide 125

dataCrunchPutting it all together...
Slide 126
Before we end this section, let us combine plots using both the widths and heights option.
[,1] [,2]
[1,] 1 2
[2,] 3 4
[3,] 5 6
# 6 plots to be combined in 3 row/ 2 columns and arranged by rows
layout(matrix(c(1, 2, 3, 4, 5, 6), nrow = 3, byrow = TRUE), heights= c(1, 2, 1),
widths = c(2, 1))
hist(mtcars$mpg)
boxplot(mtcars$mpg)
plot(mtcars$mpg)
hist(mtcars$mpg)
boxplot(mtcars$mpg)

dataCrunch
Saving Graphs
Slide 128

dataCrunchSaving Graphs
Slide 129
Once created, the graphs can be saved in multiple formats. The below table lists the different formats in
which the graphs can be downloaded.
Type Function
pdf pdf("graph_name.pdf")
png png("graph_name.png")
jpeg jpeg("graph_name.jpeg")
bmp bmp("graph_name.bmp")
postscript postscript("graph_name.ps")
win.metafile win.metafile("graph_name.wmf")

dataCrunchSaving Graphs: Example
Slide 130
Use the dev.off() to return output to the terminal after the graph has been saved in the required
format.
# saving graph in pdf file
pdf("plot_pdf.pdf")
dev.off()
# saving graph in png file
png("plot_png.png")
dev.off()
# saving graph in jpeg file
jpeg("plot_jpeg.jpg")
dev.off()
# saving graph in bmp file
bmp("plot_bmp.bmp")
dev.off()
# saving graph in postscript file
postscript("plot_postscript.ps")
dev.off()
# saving graph in windows metafile
win.metafile("plot_wmf.wmf")
dev.off()

dataCrunchNext Steps...
Slide 131
Learn to
→ Create basic Line Graph
→ Add multiple lines
→ Modify line type/color
→ Add legend

dataCrunchNext Steps...
Slide 132
Visit dataCrunch for
tutorials on:
→ R Programming
→ Business Analytics
→ Data Visualization
→ Web Applications
→ Package Development
→ Git & GitHub

Data Visualization With R

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