Smoothed density scatter plots are a good way of visualizing large datasets. In this recipe, we will learn how to create them using the smoothScatter() function.
For this recipe, we don't need to load any additional libraries. We just need to type the recipe in the R prompt or run it as a script.
We will use the smoothScatter() function that is part of the base graphics library. We will use an example from the help file that can be accessed from the R prompt with the help command:
n <- 10000 x <- matrix(rnorm(n), ncol=2) y <- matrix(rnorm(n, mean=3, sd=1.5), ncol=2) smoothScatter(x,y)
The smoothScatter() function produces a smoothed color density representation of the scatter plot, which is obtained through a kernel density estimate. We passed the x and y variables that represented the data to be plotted. The gradient of the blue color shows us the density of the data points, with most points in the center of the graph. The dots in the outer light blue circles are outliers.
We can pass a number of arguments to smoothScatter() in order to adjust the smoothing, for example, nbin for specifying the number of equally spaced grid points for the density estimation and nrpoints to specify how many points are to be shown as dots. In addition, we can also pass standard arguments such as xlab, ylab, pch, and cex in order to modify axis and plot symbol characteristics.