Conditional density plots in R — how to draw a conditional density plot using R. A conditional density plot shows the density of a sample split into groups.

Use cdplot() to draw a conditional density plot using R.

cdplot(x, y,
       plot = TRUE, tol.ylab = 0.05, ylevels = NULL,
       bw = "nrd0", n = 512, from = NULL, to = NULL,
       col = NULL, border = 1, main = "", xlab = NULL, ylab = NULL,
       yaxlabels = NULL, xlim = NULL, ylim = c(0, 1), ...)

There are many potential parameters for cdplot() but the most helpful are:

There are several arguments related to the density() function, which in most cases you’ll never need to alter.

A basic plot requires a factor variable and a numeric:

cdplot(tension ~ breaks, data = warpbreaks)

A conditional density plot

You can use the ylevels argument to alter the order of the plotting of a cdplot():

cdplot(tension ~ breaks, data = warpbreaks, ylevels = 3:1)

Use the ylevels argument to change the order of a conditional density plot

Give customized names to the factor levels via the yaxlabels argument:

cdplot(group ~ weight, data = PlantGrowth,
       yaxlabels = c("Control", "Treatment-1", "Treatment-2"))

Custom factor labels via yaxlabels argument in cdplot

Altering graphical appearance

Only some of the general graphical parameters can be changed in cdplot(), as in the following example.

Use graphical parameters col and border to alter the appearance:

cdplot(spray ~ count, data = InsectSprays,
       col = cm.colors(6), border = "blue")

Basic graphical parameters col and border used to alter a cdplot

If you want to change any other graphical parameters you’ll need to call par() first:

opar <- par(las = 1, cex = 0.8, mar = c(5,7,2,3))

cdplot(feed ~ weight, data = chickwts, ylab = "")
title(ylab = "feed", line = 5)

par(opar)

Use par() to set graphical parameters (other than col, border) in a cdplot

In the preceding example the margins were altered to allow the annotations to “fit”. In the title the annotation was shifted outwards.

This article is partly in support of my book An Introduction to R see the publications page for more information.

• For more articles look at the Tips and Tricks page and look for the various categories or use the search box.
• See also the Knowledge Base where there are other topics related to R and data science.
• An Introduction to R will be published by Pelagic Publishing. See all my books at Pelagic Publishing.
• Visit our other site at GardenersOwn for a more ecological matters.

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A histogram is a standard way to present the distribution of a sample of numbers. A basic histogram is useful but it is easy to add more to a histogram in R to produce an even more informative plot. In this article you’ll see how to add a rug and a strip-chart to a basic historgram using simple R commands.

A basic histogram

It is easy to make a histogram using R with the hist() command. For example:

set.seed(123)
x <- norm(n = 50, mean = 10, sd = 1)
hist(x, col = "skyblue")

Produces a histogram resembling this:

Add a rug

A rug plot can be added to more or less any graphic. The rug() command can add the rug to any side of the plot:

• side = 1 is the bottom axis
• side = 2 is the left axis

You can alter the colour and width of the rug lines using regular graphical parameters:

rug(x, side = 1, col = "blue")

Adds the rug like so:

Add a strip chart

A strip chart can also be added to any chart via the stripchart() command. However, you also need to specify add = TRUE to the command. Giving a bit of jitter helps to separate out points that are coincident:

stripchart(x,
           method = "jitter",
           pch = 23,
           bg = "pink",
           add = TRUE)

The final plot looks like so:

There are many additional options for the stripchart() command; use help(stripchart) in R to find out more. Look out for other articles in our Tips & Tricks pages.

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