Plots as data summary

While we can use tables and statistics to summarize data, it is often use to visually summarize data. This script describes how to produce some common summary plots.

Histogram

The simplest plot is a histogram, which shows the frequencies of different values in a distribution. Drawing a basic histogram in R is easy. First, let's generate a random vector:

set.seed(1)
a <- rnorm(30)

Then we can draw a histogram of the data:

hist(a)

This isn't the most attractive plot, though, and we can easily make it look different:

hist(a, col = "gray20", border = "lightgray")

Density plot

Another approach to summarizing the distribution of a variable is a density plot. This visualization is basically a “smoothed” histogram and it's easy to plot, too.

plot(density(a))

Clearly, the two plots give us similar information. We can even overlay them. Doing so requires a few modifications to our code, though.

hist(a, freq = FALSE, col = "gray20", border = "lightgray")
lines(density(a), col = "red", lwd = 2)

Barplot

One of the simplest data summaries is a barplot. Like a histogram, it shows bars. But those bars are statistics rather than just counts (though they could be counts). We can make a barplot from a vector of numeric values:

b <- c(3, 4.5, 5, 8, 3, 6)
barplot(b)

The result is something visually very similar to the histogram. We can easily label the bars by specifying a names.arg parameter:

barplot(b, names.arg = letters[1:6])

We can also turn the plot on its side, if that looks better:

barplot(b, names.arg = letters[1:6], horiz = TRUE)

We can also create a stacked barplot by providing a matrix rather than a vector of input data. Let's say we have counts of two types of objects (e.g., coins) from three groups:

d <- rbind(c(2, 4, 1), c(6, 1, 3))
d

##      [,1] [,2] [,3]
## [1,]    2    4    1
## [2,]    6    1    3

barplot(d, names.arg = letters[1:3])

Instead of stacking the bars of each type, we can present them side by side using the beside parameter:

barplot(d, names.arg = letters[1:3], beside = TRUE)

Dotchart

Rather than waste a lot of ink on bars, we can see the same kinds of relationships in dotcharts.

dotchart(b, labels = letters[1:6])

As we can see, the barplot and the dotchart communicate the same information in more or less the same way:

layout(matrix(1:2, nrow = 1))
barplot(b, names.arg = letters[1:6], horiz = TRUE, las = 2)
dotchart(b, labels = letters[1:6], xlim = c(0, 8))

Boxplot

It is often helpful to describe the distribution of those data with a box plot. The boxplot describes any continuous vector of data by showing the five number summary and any outliers:

boxplot(a)

It can also compare distributions in two or more groups:

e <- rnorm(100, 1, 1)
f <- rnorm(100, 2, 4)
boxplot(e, f)

We can also use a “formula” description of data if one of our variables describes which group our observations fall into:

g1 <- c(e, f)
g2 <- rep(c(1, 2), each = 100)
boxplot(g1 ~ g2)

As we can see, both of these last two plots are identical. They're just different ways of telling boxplot what to plot.

Scatterplot

When we want to describe the relationships among variables, we often want a scatterplot.

x1 <- rnorm(1000)
x2 <- rnorm(1000)
x3 <- x1 + x2
x4 <- x1 + x3

We can draw a scatterplot in one of two ways. (1) Naming vectors as sequential arguments:

plot(x1, x2)

(2) Using a “formula” interface:

plot(x2 ~ x1)

We can plot the relationship between x1 and the three other variables:

layout(matrix(1:3, nrow = 1))
plot(x1, x2)
plot(x1, x3)
plot(x1, x4)

We can also use the pairs function to do this for all relationships between all variables:

pairs(~x1 + x2 + x3 + x4)

This allows us to visualize a lot of information very quickly.