![]() ggplot2 will automatically assign a unique level of the aesthetic (here a unique color) to each unique value of the variable, a process known as scaling. To map an aesthetic to a variable, associate the name of the aesthetic to the name of the variable inside aes(). (If you prefer British English, like Hadley, you can use colour instead of color.) Here we change the levels of a point’s size, shape, and color to make the point small, triangular, or blue: Since we already use the word “value” to describe data, let’s use the word “level” to describe aesthetic properties. You can display a point (like the one below) in different ways by changing the values of its aesthetic properties. Aesthetics include things like the size, the shape, or the color of your points. An aesthetic is a visual property of the objects in your plot. You can add a third variable, like class, to a two dimensional scatterplot by mapping it to an aesthetic. If the outlying points are hybrids, they should be classified as compact cars or, perhaps, subcompact cars (keep in mind that this data was collected before hybrid trucks and SUVs became popular). The class variable of the mpg dataset classifies cars into groups such as compact, midsize, and SUV. ![]() One way to test this hypothesis is to look at the class value for each car. Let’s hypothesize that the cars are hybrids. ggplot2 looks for the mapped variables in the data argument, in this case, mpg. The mapping argument is always paired with aes(), and the x and y arguments of aes() specify which variables to map to the x and y axes. This defines how variables in your dataset are mapped to visual properties. You’ll learn a whole bunch of them throughout this chapter.Įach geom function in ggplot2 takes a mapping argument. ggplot2 comes with many geom functions that each add a different type of layer to a plot. The function geom_point() adds a layer of points to your plot, which creates a scatterplot. You complete your graph by adding one or more layers to ggplot(). So ggplot(data = mpg) creates an empty graph, but it’s not very interesting so I’m not going to show it here. The first argument of ggplot() is the dataset to use in the graph. ggplot() creates a coordinate system that you can add layers to. ![]() With ggplot2, you begin a plot with the function ggplot(). Does this confirm or refute your hypothesis about fuel efficiency and engine size? In other words, cars with big engines use more fuel. See our full R Tutorial Series and other blog posts regarding R programming.The plot shows a negative relationship between engine size ( displ) and fuel efficiency ( hwy). David holds a doctorate in applied statistics. His company, Sigma Statistics and Research Limited, provides both on-line instruction and face-to-face workshops on R, and coding services in R. In the next blog post, we will look at diagnosing our regression model in R.Ībout the Author: David Lillis has taught R to many researchers and statisticians. By the way – lm stands for “linear model”.įinally, we can add a best fit line (regression line) to our plot by adding the following text at the command line: abline(98.0054, 0.9528)Īnother line of syntax that will plot the regression line is: abline(lm(height ~ bodymass)) We see that the intercept is 98.0054 and the slope is 0.9528. Now let’s perform a linear regression using lm() on the two variables by adding the following text at the command line: lm(height ~ bodymass) Call: In the above code, the syntax pch = 16 creates solid dots, while cex = 1.3 creates dots that are 1.3 times bigger than the default (where cex = 1). ![]() Copy and paste the following code into the R workspace: plot(bodymass, height, pch = 16, cex = 1.3, col = "blue", main = "HEIGHT PLOTTED AGAINST BODY MASS", xlab = "BODY MASS (kg)", ylab = "HEIGHT (cm)") We can enhance this plot using various arguments within the plot() command. We can now create a simple plot of the two variables as follows: plot(bodymass, height) Copy and paste the following code to the R command line to create the bodymass variable. Now let’s take bodymass to be a variable that describes the masses (in kg) of the same ten people. Copy and paste the following code to the R command line to create this variable. We take height to be a variable that describes the heights (in cm) of ten people. Today let’s re-create two variables and see how to plot them and include a regression line.
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