(This article was first published on

**Mollie's Research Blog**, and kindly contributed to R-bloggers)Building on the basic histogram with a density plot, we can add measures of central tendency (in this case, mean and median) and a legend.

Like last time, we’ll use the beaver data from the *datasets* package.

hist(beaver1$temp, # histogram

col = "peachpuff", # column color

border = "black",

prob = TRUE, # show densities instead of frequencies

xlim = c(36,38.5),

ylim = c(0,3),

xlab = "Temperature",

main = "Beaver #1")

lines(density(beaver1$temp), # density plot

lwd = 2, # thickness of line

col = "chocolate3")

Next we’ll add a line for the mean:

abline(v = mean(beaver1$temp),

col = "royalblue",

lwd = 2)

And a line for the median:

abline(v = median(beaver1$temp),

col = "red",

lwd = 2)

And then we can also add a legend, so it will be easy to tell which line is which.

legend(x = "topright", # location of legend within plot area

c("Density plot", "Mean", "Median"),

col = c("chocolate3", "royalblue", "red"),

lwd = c(2, 2, 2))

All of this together gives us the following graphic:

In this example, the mean and median are very close, as we can see by using *median()* and *mode()*.

> mean(beaver1$temp)

[1] 36.86219

> median(beaver1$temp)

[1] 36.87

We can do like we did in the previous post and graph beaver1 and beaver2 together by adding a layout line and changing the limits of x and y. The full code for this is available in a gist.

Here’s the output from that code:

To

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