## 2.1 The grammar of graphics

We start with a discussion of a theoretical framework for data visualization known as “the grammar of graphics.” This framework serves as the foundation for the ggplot2 package which we’ll use extensively in this chapter. Think of how we construct and form sentences in English by combining different elements, like nouns, verbs, articles, subjects, objects, etc. We can’t just combine these elements in any arbitrary order; we must do so following a set of rules known as a linguistic grammar. Similarly to a linguistic grammar, “the grammar of graphics” defines a set of rules for constructing statistical graphics by combining different types of layers. This grammar was created by Leland Wilkinson (Wilkinson 2005) and has been implemented in a variety of data visualization software platforms like R, but also Plotly and Tableau.

### 2.1.1 Components of the grammar

In short, the grammar tells us that:

A statistical graphic is a mapping of data variables to aesthetic attributes of geometric objects.

Specifically, we can break a graphic into the following three essential components:

1. data: the dataset containing the variables of interest.
2. geom: the geometric object in question. This refers to the type of object we can observe in a plot. For example: points, lines, and bars.
3. aes: aesthetic attributes of the geometric object. For example, x/y position, color, shape, and size. Aesthetic attributes are mapped to variables in the dataset.

You might be wondering why we wrote the terms data, geom, and aes in a computer code type font. We’ll see very shortly that we’ll specify the elements of the grammar in R using these terms. However, let’s first break down the grammar with an example.

### 2.1.2 Gapminder data

In February 2006, a Swedish physician and data advocate named Hans Rosling gave a TED talk titled “The best stats you’ve ever seen” where he presented global economic, health, and development data from the website gapminder.org. For example, for data on 142 countries in 2007, let’s consider only a few countries in Table 2.1 as a peek into the data.

TABLE 2.1: Gapminder 2007 Data: First 3 of 142 countries
Country Continent Life Expectancy Population GDP per Capita
Afghanistan Asia 43.8 31889923 975
Albania Europe 76.4 3600523 5937
Algeria Africa 72.3 33333216 6223

Each row in this table corresponds to a country in 2007. For each row, we have 5 columns:

1. Country: Name of country.
2. Continent: Which of the five continents the country is part of. Note that “Americas” includes countries in both North and South America and that Antarctica is excluded.
3. Life Expectancy: Life expectancy in years.
4. Population: Number of people living in the country.
5. GDP per Capita: Gross domestic product (in US dollars).

Now consider Figure 2.1, which plots this for all 142 of the data’s countries.

Let’s view this plot through the grammar of graphics:

1. The data variable GDP per Capita gets mapped to the x-position aesthetic of the points.
2. The data variable Life Expectancy gets mapped to the y-position aesthetic of the points.
3. The data variable Population gets mapped to the size aesthetic of the points.
4. The data variable Continent gets mapped to the color aesthetic of the points.

We’ll see shortly that data corresponds to the particular data frame where our data is saved and that “data variables” correspond to particular columns in the data frame. Furthermore, the type of geometric object considered in this plot are points. That being said, while in this example we are considering points, graphics are not limited to just points. We can also use lines, bars, and other geometric objects.

Let’s summarize the three essential components of the grammar in Table 2.2.

TABLE 2.2: Summary of the grammar of graphics for this plot
data variable aes geom
GDP per Capita x point
Life Expectancy y point
Population size point
Continent color point

### 2.1.3 Other components

There are other components of the grammar of graphics we can control as well. As you start to delve deeper into the grammar of graphics, you’ll start to encounter these topics more frequently. In this book, we’ll keep things simple and only work with these two additional components:

• faceting breaks up a plot into several plots split by the values of another variable (Section 2.6)
• position adjustments for barplots (Section 2.8)

Other more complex components like scales and coordinate systems are left for a more advanced text such as R for Data Science (Grolemund and Wickham 2017). Generally speaking, the grammar of graphics allows for a high degree of customization of plots and also a consistent framework for easily updating and modifying them.

### 2.1.4 ggplot2 package

In this book, we will use the ggplot2 package for data visualization, which is an implementation of the grammar of graphics for R (Wickham, Chang, et al. 2020). As we noted earlier, a lot of the previous section was written in a computer code type font. This is because the various components of the grammar of graphics are specified in the ggplot() function included in the ggplot2 package. For the purposes of this book, we’ll always provide the ggplot() function with the following arguments (i.e., inputs) at a minimum:

• The data frame where the variables exist: the data argument.
• The mapping of the variables to aesthetic attributes: the mapping argument which specifies the aesthetic attributes involved.

After we’ve specified these components, we then add layers to the plot using the + sign. The most essential layer to add to a plot is the layer that specifies which type of geometric object we want the plot to involve: points, lines, bars, and others. Other layers we can add to a plot include the plot title, axes labels, visual themes for the plots, and facets (which we’ll see in Section 2.6).

Let’s now put the theory of the grammar of graphics into practice.

### References

Grolemund, Garrett, and Hadley Wickham. 2017. R for Data Science. First. Sebastopol, CA: O’Reilly Media. https://r4ds.had.co.nz/.

Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, and Dewey Dunnington. 2020. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.

Wilkinson, Leland. 2005. The Grammar of Graphics (Statistics and Computing). First. Secaucus, NJ: Springer-Verlag.