In this tutorial, we explore the plot function from the Graphics package. We look at the different types of plots generated as the data is varied.

1. www.r-squared.in/git-hub
dataCrunch Data Visualization With R: Introduction

2. 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

3. dataCrunch
Slide 3
Plot Basics

4. dataCrunchplot()
Slide 4
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 inputting 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.
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help(plot)
help(mtcars)

5. dataCrunchmtcars
Slide 5
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 ...

6. dataCrunchExplore plot()
Slide 6
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.

7. dataCrunchCase 1: One continuous variable
Slide 7
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.

8. dataCrunchCase 2: One categorical variable
Slide 8
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.

9. dataCrunchCase 3: Two continuous variables
Slide 9
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.

10. dataCrunchCase 4: Two categorical variables
Slide 10
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
mtcars$cyl <- as.factor(mtcars$cyl)
# 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.

11. dataCrunchCase 5: Continuous/Categorical Variables
Slide 11
In this example, we will plot a categorical variable cyl on the X axis and a continuous variable
mpg on the Y axis.
# coerce cyl to type factor
mtcars$cyl <- as.factor(mtcars$cyl)
# categorical vs 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?

12. dataCrunchCase 6: Categorical/Continuous Variables
Slide 12
In this example, the continuous variable is plotted on the X axis and the categorical variable on
the Y axis.
# coerce cyl to type factor
mtcars$cyl <- as.factor(mtcars$cyl)
# 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.

13. dataCrunch
Slide 13
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