Chapter 5 Creating Some Basic Plots

Reference: R graphics cookbook (https://r-graphics.org/)

  • The base R contains many basic methods for producing graphics. We will learn some of them in this chapter. These plotting functions are good for very quick exploration of data.

  • For more elegant plots, we will use the package ggplot2 (next chapter).

  • We will use some simple datasets in base R to illustrate how to create some basic plots in this chapter.

5.1 Scatter Plot

Let’s take a look at the mtcars dataset. This dataset comes with base R.

head(mtcars) # this is a data frame
##                    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) # display the structure of the data frame
## '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 ...

mpg: miles/gallon wt: weight (1000lbs)

Scatter plot with base graphics

# x-axis: mtcars$wt
# y-axis: mtcars$mpg
plot(x = mtcars$wt, y = mtcars$mpg) # "x =", "y =" are optional

You can produce the same plot with plot(mtcars$wt, mtcars$mpg)

Scatter plot with base graphics (when you only have \(x\) and \(y\) but not a dataset)

set.seed(1)
x <- rnorm(100, 0, 1)
y <- 2 * x + rnorm(100, 0, 1)
plot(x, y)

5.2 Line Graph

The dataset pressure (also in base R) contains the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

Line graph with base graphics

# the only difference from a scatter plot is that we add type="l"
plot(pressure$temperature, pressure$pressure, type = "l")  #l = line

Line graph with base graphics with points

plot(pressure$temperature, pressure$pressure, type = "l")
points(pressure$temperature, pressure$pressure) # add some points

Line graph with base graphics with another line and points (with color)

plot(pressure$temperature, pressure$pressure, type = "l")
points(pressure$temperature, pressure$pressure)
# the additional line may not have a physical meaningful
# just an illustration on how to add a line with base graphics
lines(pressure$temperature, pressure$pressure / 2, col = "red") 
points(pressure$temperature, pressure$pressure / 2, col = "red")

Colors in R You can go to https://www.r-graph-gallery.com/ggplot2-color.html and read more about colors in R.

For example, you can specify the color by name, rgb, number and hex code.

plot(pressure$temperature, pressure$pressure, type = "l", col = rgb(0.1, 0.2, 0.5, 1))
lines(pressure$temperature, pressure$pressure / 2, type = "l", col = 2)
lines(pressure$temperature, pressure$pressure * 2, col = "#8B2813")
lines(pressure$temperature, pressure$pressure * 3, col = "cornflowerblue")

5.3 Bar Chart

Two types of bar charts:

  1. Bar chart of values. x-axis: discrete variable, y-axis: numeric data (not necessarily count data)
  2. Bar chart of count. x-axis: discrete variable, y-axis: count of cases in the discrete variable

Remark: for histogram: x-axis = continuous variable, y-axis = count of cases in the interval.

The BOD data set has 6 rows and 2 columns giving the biochemical oxygen demand versus time in an evaluation of water quality.

str(BOD) 
## 'data.frame':    6 obs. of  2 variables:
##  $ Time  : num  1 2 3 4 5 7
##  $ demand: num  8.3 10.3 19 16 15.6 19.8
##  - attr(*, "reference")= chr "A1.4, p. 270"

Bar chart of values with base graphics

# names.arg = a vector of names to be plotted below each bar or group of bars.
barplot(BOD$demand, names.arg = BOD$Time)

Bar chart of counts with base graphics

In the dateset mtcars, cylis the number of cylinders in the car. The possible values are \(4, 6\), and \(8\). We first find the count of each unique value in mtcars$cyl:

table(mtcars$cyl)
## 
##  4  6  8 
## 11  7 14

To plot the bar chart, we use

barplot(table(mtcars$cyl))

5.4 Histogram

mpg in mtcars is the miles/gallon of the car. It is a continuous variable.

Histogram with base graphics

hist(mtcars$mpg)

Histogram with base graphics

# Specify approximate number of bins with "breaks"
hist(mtcars$mpg, breaks = 10)

Remark: different bin widths will give you histograms with different looks.

5.5 Box Plot

A box plot (or box-and-whisker plot) display the dateset based on the five-number summary (minimum, maximum, sample median, first (\(Q_1\)) and third (\(Q_3\)) quartiles).

IQR = Interquartile range = \(Q_3 - Q_1\)

A box plot usually includes two parts, a box and a set of whiskers. The box is drawn from the first quartile to third quartile with a horizontal line drawn in the middle denoting the median. The boundaries of the whiskers is often based on the 1.5 IQR value. From above \(Q_3\), a distance of \(1.5\) times the IQR is measured out and a whisker is drawn up to the largest observed data point from the dataset that falls within this distance. Similar for the lower one. All other data points outside the buondary of the whiskers are plotted as outliers.

Let’s take a look at another dataset ToothGrowth. In particular, supp is a factor.

str(ToothGrowth)
## 'data.frame':    60 obs. of  3 variables:
##  $ len : num  4.2 11.5 7.3 5.8 6.4 10 11.2 11.2 5.2 7 ...
##  $ supp: Factor w/ 2 levels "OJ","VC": 2 2 2 2 2 2 2 2 2 2 ...
##  $ dose: num  0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...

Box plot with basic graphics (using plot)

# if x is a factor, use the following code
plot(x = ToothGrowth$supp, y = ToothGrowth$len)

Box plot with basic graphics (using boxplot)

# len ~ supp is an example of a "formula" (y ~ x)
boxplot(len ~ supp, data = ToothGrowth)

Box plot with basic graphics + interaction of two variables on x-axis (using boxplot)

boxplot(len ~ supp + dose, data = ToothGrowth)

5.6 Plotting a function curve

curve(x^3 - 5 * x, from = -4, to = 4)

Alternatively:

x <- seq(-4, 4, len = 1000)
plot(x, x^3 - 5 * x, type = "l")

Plotting a built-in function

curve(dnorm(x), from = -4, to = 4)

Plotting a self-defined function

my_function <- function(x) {
  1 / (1 + exp(-x + 10))
}

curve(my_function, from = 0, to = 20)

Plotting a function with additional arguments

curve(dnorm(x, mean = 2, sd = 3), from = -4, to = 4)

5.7 More on plots with base R

5.7.1 Multi-frame plot

To create a 3x2 multi-frame plot. Use par(mfrow = c(3, 2)).

set.seed(1)
x <- rnorm(100, 50, 5)
y <- x + rnorm(100, 2, 2)

# create a 2x2 multi-frame plot
par(mfrow=c(2, 2))
hist(x)
hist(y,breaks = 10)
plot(x, y)
boxplot(x, y)

5.7.2 Type of Plot

Option Type
type = "p" Points (default)
type = "l" Lines connecting the data points
type = "b" Points and non-overlapping lines
type = "h" Height lines
type = "o" Points and overlapping lines 
par(mfrow=c(3, 2))
x <- -5:5
y <- x^2
plot(x, y)
plot(x, y, type = "p")
plot(x, y, type = "l")
plot(x, y, type = "b")
plot(x, y, type = "h")
plot(x, y, type = "o")

5.7.3 Parameters of a plot

Parameter Meaning
type See Type of Plot
main Title
sub Subtitle
xlab x-axis label
ylab y-axis label
xlim x-axis range
ylim y-axis range
pch Symbol of data points
col Color of data points
lty Type of the line

To illustrate some of the components:

set.seed(1)
x <- rnorm(100, 50, 15)
y <- x + rnorm(100, 2, 13)

plot(x, y, col = "red", pch = 15, main = "This is the title", xlim = c(0, 100),
     ylim = c(0,100), xlab = "name of x-axis", ylab = "name of y-axis")

5.7.4 Elements on plot

Function Description
abline(c, m) plot the line y = mx +c
abline(h = a) plot the line y = a
abline(v = b) plot the line x = b
lines(x, y) line joining points with coordinates (x,y) 
set.seed(1)
x <- rnorm(100, 50, 15)
y <- x + rnorm(100, 2, 13)
plot(x, y)
# connect the points (20, 20), (30, 80), (40, 40) with a line
lines(x = c(20, 30, 40),y = c(20, 80, 40), col = "red")
abline(v = 60, col = "blue")