A regular scatter plot represents with dots (or any other symbols) the relationship between two given variables. One can plot a third variable to any scatter plot by the mean of the size of the dots or circles representing the data, thus obtain a bubble plot. But one can also plot this third variable by the mean of a gradient of colors applied onto the dots.

We will see how to use ggplot() to code for such a color-graded scatter plot. Here is the dataframe with the three variables:

# dataframe
df <- data.frame(values1, values2, values3)
str(df)
## 'data.frame':    300 obs. of  3 variables:
##  $ values1: num  66.5 64.8 37.5 45.2 51 ...
##  $ values2: num  39.5 32.9 33.6 31.2 22.4 ...
##  $ values3: num  3.27 5.7 4.4 3.83 3.1 ...



We use geom_point() as for any scatter plot, but we need to add aes(color=) to represent the values of the variable values3 as a gradient of colors:

ggplot(df, aes(values1, values2)) +
  geom_point(aes(color = values3))



It is of course possible to tune the look of the plot with, for example, size= and shape=:

ggplot(df, aes(values1, values2)) +
  geom_point(aes(color = values3), size = 2.5, shape = 17)



And it is possible to swap the blue scale by default for another palette such as viridis (read more here about colors):

ggplot(df, aes(values1, values2)) +
  geom_point(aes(color = values3)) +
  scale_color_viridis_c()



Adding plot title, axis titles, ticks, labels and other essential elements

In this section, you will learn how to set/modify all the necessary elements that make a plot complete and comprehensible. Such elements are: