3.5 Histograms and Density Plots (lattice)

The classic way to visualize the distribution of any data are histograms. They are closely related to density plots, where the individual data points are not binned into certain classes but a continuous density function is calculated to show the distribution. Both approaches reflect a certain level of abstraction (binning vs. functional representation), therefore a general formulation of which of them is more accepted is hard. In any case, the both achieve exactly the same result, they will show us the distribution of our data.

As is to be expected with lattice, the default plotting routine does not really satisfy the (or maybe better our) aesthetic expectations.

hist_lattice <- histogram(~ price, data = diamonds)
hist_lattice

Figure 3.18: A basic histogram produced with lattice.

This is even worse for the default density plot…

dens_lattice <- densityplot(~ price, data = diamonds)
dens_lattice

Figure 3.19: A basic density plot produced with lattice.

Yet, as we’ve already adjusted our global graphical parameter settings, we can now easily modify this.

hist_lattice <- histogram(~ price | color, 
                          data = diamonds,
                          as.table = TRUE,
                          par.settings = my_theme)

l_his <- hist_lattice

print(l_his)

Figure 3.20: A lattice panel histogram with modified graphical parameter settings.

Now, this is a plot that every journal editor will very likely accept.

Until now, we have seen how to condition our plots according to one factorial variable (diamonds$cut). It is, in theory, possible to condition plots on any number of factorial variable, though more than two is seldom advisable. Two, however, is definitely acceptable and still easy enough to perceive and interpret. In lattice this is generally formulated as

y ~ x | g + f

where g and f are the factorial variables used for the conditioning.

In the below code chunk, we are first creating our plot object. Then, we are using a function called useOuterStrips() which makes sure that the strips that correspond to the conditioning variables are plotted on both the top and the left side of our plot. The default lattice setting is to plot both at the top, which makes the navigation through the plot by the viewer a little more difficult.

Another default setting for density plots in lattice is to plot a point (circle) for each observation of our variable (price) at the bottom of the plot along the x-axis. In our case, as we have a lot of data points, this is not desirable, so we set plot.points = FALSE.

dens_lattice <- densityplot(~ price | cut + color, 
                            data = diamonds,
                            as.table = TRUE,
                            par.settings = my_theme,
                            plot.points = FALSE,
                            between = list(x = 0.2, y = 0.2),
                            scales = list(x = list(rot = 45)))

l_den <- useOuterStrips(dens_lattice)

print(l_den)

Figure 3.21: A panel density plot conditioned according to two variables using lattice.

You may have noticed that the lines of the density plot are plotted in a light shade of blue (cornflowerblue to be precise). It is up to you to change this…

Another thing you may notice when looking at the above plot is that the x-axis labels are rotated by 45 degrees. This one we also leave up to you to figure out… ;-)