```
ggplot(___) +
geom_point(
mapping = aes(x = ___, y = ___,
color = ___,
size = ___),
alpha = ___
)
```

View Interactive VersionFor the `gapminder_2007`

dataset we can plot the GDP per capita `gdpPercap`

vs. the life expectancy as follows:

Input

ggplot(gapminder_2007) + geom_point(aes(x = gdpPercap, y = lifeExp))

To adjust the point size based on the population (`pop`

) of each country we can use:

Input

ggplot(gapminder_2007) + geom_point(aes(x = gdpPercap, y = lifeExp, size = pop))

We see that the point sizes in the plot above do not clearly reflect the population differences in each country. If we compare the point size representing a population of 250 million people with the one displaying 750 million, we can see, that their sizes are not proportional. Instead, the point sizes are *binned* by default. To reflect the actual population differences by the point size we can use the `scale_size_area()`

function instead. The scaling information can be added like any other ggplot object with the `+`

operator:

Input

ggplot(gapminder_2007) + geom_point(aes(x = gdpPercap, y = lifeExp, size = pop)) + scale_size_area(max_size = 10)

Note that we have adjusted the point’s `max_size`

which results in bigger point sizes.