Chapter 4 Data Visualization

Now that we have learned basic data structures in R, we can now learn about how to do visualize our data. There are several different data visualization tools in R, and we focus on one of the most popular, “Grammar of Graphics”, or known as “ggplot”.

The syntax for ggplot2 will look a bit different than the code we have been writing, with syntax such as:

ggplot(penguins) + aes(x = bill_length_mm) + geom_histogram() 
# Data              Aesthetics               Geometry

The output of all of these functions, such as from ggplot() or aes() are not data types or data structures that we are familiar with…rather, they are graphical information.

You should be worried less about how this syntax is similar to what we have learned in the course so far, but to view it as a new grammar (of graphics!) that you can “layer” on to create more sophisticated plots.

To get started, we will consider these most simple and common plots:

Univariate

Numeric: histogram
Character: bar plots

Bivariate

Numeric vs. Numeric: Scatterplot, line plot
Numeric vs. Character: Box plot

Why do we focus on these common plots? Our eyes are better at distinguishing certain visual features more than others. All of these plots are focused on their position to depict data, which gives us the most effective visual scale.

Image Source: https://www.oreilly.com/library/view/visualization-analysis-and/9781466508910/K14708_C005.xhtml

4.1 Grammar of Graphics

The syntax of the grammar of graphics breaks down into 4 sections.

Data

Mapping to data

Geometry

Additional settings

You add these 4 sections together to form a plot.

4.2 Histogram

ggplot(penguins) + aes(x = bill_length_mm) + geom_histogram()

4.3 Let’s take it apart

You can always try out a ggplot incrementally if you’re not sure what pieces do:

ggplot(penguins)          #data

ggplot(penguins) +                #data
  aes(x = bill_length_mm)  #aesthetics

ggplot(penguins) +                #data
  aes(x = bill_length_mm) + #aesthetics
  geom_histogram()          #geometry

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_bin()`).

4.4 Bar plots

ggplot(penguins) + aes(x = species) + geom_bar()

4.4.1 Scatterplot

ggplot(penguins) + aes(x = bill_length_mm, y = bill_depth_mm) + geom_point()

4.4.2 Multivariate Scatterplot

ggplot(penguins) + aes(x = bill_length_mm, y = bill_depth_mm, color = species) + geom_point()

4.4.3 Multivaraite Scatterplot

ggplot(penguins) + aes(x = bill_length_mm, y = bill_depth_mm) + geom_point() + facet_wrap(~species)

4.4.4 Line plot?

ggplot(penguins) + aes(x = bill_length_mm, y = bill_depth_mm) + geom_line()

4.4.5 Grouped Line plot?

ggplot(penguins) + aes(x = bill_length_mm, y = bill_depth_mm, group = species) + geom_line()

4.4.6 Boxplot

ggplot(penguins) + aes(x = species, y = bill_depth_mm) + geom_boxplot()

4.4.7 Grouped Boxplot

ggplot(penguins) + aes(x = species, y = bill_depth_mm, color = island) + geom_boxplot()

4.4.8 Some additional options

ggplot(data = penguins) + aes(x = bill_length_mm, y = bill_depth_mm, color = species) + geom_point() + labs(x = “Bill Length”, y = “Bill Depth”, title = “Comparison of penguin bill length and bill depth across species”) + scale_x_continuous(limits = c(30, 60))

4.5 Summary of options

data

geom_point: x, y, color, shape

geom_line: x, y, group, color

geom_histogram: x, y, fill

geom_bar: x, fill

geom_boxplot: x, y, fill, color

facet_wrap

labs

scale_x_continuous

scale_y_continuous

scale_x_discrete

scale_y_discrete

Consider the esquisse package to help generate your ggplot code via drag and drop.

An excellent ggplot “cookbook” can be found here.

4.6 Exercises

You can find exercises and solutions on Posit Cloud, or on GitHub.