*probabilities*rather than frequencies. So instead of the frequency of a particular price range, you graph the probability that a car selected from the data is in that price range. To do this, you add

`probability = True`

to the arguments. Now the R code looks like this:

```
> hist(Cars93$Price, xlab="Price (x $1,000)", xlim = c(0,70),
main = "Prices of 93 Models of 1993 Cars",probability = TRUE)
```

The result appears here. The y-axis measures *Density* — a concept related to probability. The graph is called a *density plot*.

The point of all this is what you do next. After you create the graph, you can use an additional function called `lines()`

to add a line to the density plot:

```
> lines(density(Cars93$Price))
```

The graph now looks like the following image.

So in base R graphics, you can create a graph and then start adding to it after you see what the initial graph looks like. It’s something like painting a picture of a lake and then adding mountains and trees as you see fit.