## Mapping aesthetics to categorical variables

Now let's map both symbol size and shape to `GENDER`

. To map symbol size to levels of a categorical variable, it is helpful to set the variable as a factor using the
`factor()`

command.

Then you set up your plot as before, but control your symbol size by adding a new layer using the plus sign: `+ scale_size_manual(values = c(a, b))`

.

The parameters `a`

and `b`

have a minimum value of 0 and can be as large as you like. You must select values of `a`

and `b`

to produce symbols of the desired size. In the next example, I have chosen symbol sizes of 5 and 7. You may select different sizes, depending on your preferences. You will gain experience very quickly and select the symbol sizes that suit your graphs best. In this case, I introduced some transparency using the `alpha = I()`

syntax. Transparency assists in the interpretation of graphs that involve a large number of points. Enter the following syntax:

**qplot(HEIGHT, WEIGHT_1, data = T, xlab = "HEIGHT (cm)", ylab...**