2  The Grammar of Graphics

2.1 The tidyverse packages

Throughout this course, we will be using tidy data principles1 to create several types of visualizations. The main package we will use is the tidyverse, which includes several useful tools for data wrangling, analysis and visualization. The first step then is to install the package! You can do this from the packages vignette in explorer pane in RStudio, or by writing install.packages("tidyverse") into the console pane.

Once the package has been installed, the next step will be to load the library so that we can start using it! Simply write the command below in a script the editor pane and click run, or directly in the console pane and press enter.

After installing tidyverse packages, we will get access to two very important functions which we will be using extensively. The first is the the command ggplot() from the package ggplot2 which will allow us to make plots based on the grammar of graphics. The second is the pipe operator or %>% from the dplyr package, which translates loosely to the phrase “and then”, and which we will use to put several commands and functions together in a pipeline.2

At the top of every R script, you will see the libraries used. For almost every chapter of this book we will use the two libraries below. IMPORTANT: every time you start a new R script or session, you must call on these libraries so R knows which commands you want to use!

library(ggplot2)
library(dplyr)

2.2 The ggplot2 Package

The ggplot2 package is installed and loaded alongside the tidyverse package, though it can – and should – be called on separately. This is a very powerful tool to make print-quality graphs and all sorts of visual outputs. To do this, it draws on the grammar of graphics, which is a concept developed by Leland Wilkinson (Wilkinson 2005). The main idea behind this complex book is that plots can be divided into several elements, each with a specific role to play. ggplot2 has 7 such elements:

  • Data
  • Aesthetics
  • Layers
  • Scales
  • Coordinates
  • Facets
  • Themes

Throughout this chapter, we will focus on the first three (Data, Aesthetics, Layers) which are the minimum requirements to make a basic plot. The element data tells R which vector(s) from your environment are going to be used to draw the plot. The aesthetics element determines which variable(s) will be used and in what capacity. The layers element tells R which type of geometry you wish to draw and in which order.

df %>% 
  ggplot(aes(x=var1,y=var2))+
  geom_point()

In the example above, we are telling R that there is an object df in our environment which has at least two vectors (columns), one called var1 and another var2. We are also telling it that we want var1 to be our x axis and var2 to be our y axis, we define this inside the aes() command either globally for the plot (i.e. inside the ggplot() command) or specifically for a layer (i.e. inside geom_point()). Finally, we are telling R that we want to make a scatter plot by defining the layer geom_point(). Notice that after the ggplot() command and until the end of the graph, we use a \(+\) sign.

2.3 Example

To make our first ggplot plot, we will use the mtcars data set as an example.

data("mtcars")

The cars data set has 32 observations and 11 variables. This data set comes pre-loaded with R and it often used in examples. Let’s see what is inside!

Table 2.1: The Motor Trend Car Road Tests Data Set
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2

Once the data has been loaded, let’s use the pipe operator to do some cleaning. In the code below, we are creating a new object called df - a common way of naming data frames - and filling it with the mtcars data with some modifications. We are asking R to a) take the mtcars data, b) and then %>% select four variables, c) and then %>% give them new name. This pipeline is saved into the new object df.

df <- mtcars %>% 
  select(cyl, mpg, hp, am) %>% 
  rename(cylinders=cyl,
         mileage=mpg,
         horsepower=hp,
         transmission=am)

With this df stored in our environment, we can start making plots. Let’s begin with a histogram that shows the distribution of mileage across the 32 variables in our data set. For this we will use geom_histogram.

df %>% #Our Data 
  ggplot(aes(x= mileage))+ #Our Aesthetics
  geom_histogram() #Our Layer
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

A histogram with default settings

Figure @ref(fig:hist-fig) shows us our very first ggplot, which shows the number of observations at each of the binned levels. From this plot we know that most cars in our data set do around 15 miles per gallon. However, it is not very nice looking! We can improve this by adding more parameters.

You might notice that below the code R is giving us a warning: stat_bin() using bins = 30. Pick better value with binwidth. Here the software is hinting that we might want to change the number of bars (bins) or their width (binwidth) in our plot to make it more informative.3 In figure @ref(fig:hist2-fig) we change the number of bins to 5 inside our geom_histogram layer, and also declare the color of the column fill (darkgray) and the outline (black).

df %>% 
  ggplot(aes(x= mileage))+ 
  geom_histogram(bins = 5, fill="darkgray", color="black")

A nicer looking histogram

  1. These principles are: a) each variable should have its own column, b) each observation should have its own row, and c) each value should have its own cell.↩︎

  2. For Windows users, you can use the RStudio short cut ctrl + shift + m to write this pipe %>% operator.↩︎

  3. Most other software will give you a default based on some parameter such as the Freedman-Diaconis rule, ggplot does not do this, forcing you to experiment with different parameters that best reflect your data.↩︎