Introduction

In this section we will cover plots that can be used to describe the distribution of variables in our dataset and in the case of scatter plots, assess if there is a relationship between two variables.

For this section we will continue to use the Iris dataset from the previous section.

Visualising Distributions

There are two common methods to visualise the distribution of a single variable: using boxplots or histograms - both portray the same information.

To make the plot, simply pass the variable (column) of interest to the hist() or boxplot() function:

boxplot(iris$Sepal.Width, horizontal = TRUE)
plot(density(iris$Sepal.Width))
hist(iris$Sepal.Width)

I have included a density plot to help you interpret the plots:

Visualising Relationships

To assess the relationship between two continuous variables, use a scatterplot. Simple pass the two variables to the plot() function which accepts as inputs vectors to plot x and y coordinates.

plot(iris$Petal.Length, iris$Petal.Width)

We can see that there is a positive relationship between the two variables - that is to say, as Petal.Width increases, so does Petal.Length. They have a positive correlation.

Visualising Nested Groups

In the previous example using boxplots, we viewed the distribution of one variable Sepal.Width for every sample in the Iris dataset. What if we wanted to compare Sepal.Width across the 3 flower species?

We will use the library ggpubr to do this - the code is relatively intuitive. By passing the column Species to the x-axis, the plot will automatically divide our data according to the three flower species:

ggboxplot(iris, x = "Species", y = "Sepal.Width", bxp.errorbar = TRUE)

For histograms and scatter plots, use the parameter facet.by to create faceted plots - one plot for each grouping variable in a panel.

gghistogram(iris, x = "Sepal.Width", y = "..density..", facet.by = "Species")

ggscatter(iris, x = "Petal.Length", y = "Petal.Width", facet.by = "Species")

Enhancing Plots

Adding colors to plots make groups instantly stand out. To do this in ggpubr, you will need to specify the fill parameter with the grouping variable. This will fill in your histogram bars/boxplots with a unique color according to the number of unique groups in the variable (for the iris dataset this is ‘Setosa’, ‘Versicolor’ and ‘Virginica’).

gghistogram(iris, x = "Sepal.Width", y = "..density..", add_density = T, fill = "Species", add = "median")

ggboxplot(iris, x = "Species", y = "Sepal.Width", fill = "Species", bxp.errorbar = T)

Note

For scatterplots, use color instead of fill.

ggscatter(iris, x = "Petal.Length", y = "Petal.Width", color = "Species")

Worksheet

Copy and paste the contents of the following R Markdown document into your RStudio Cloud workspace and work through the questions.