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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: