Replace two hand drawn diagrams with code

visualize.Rmd

@@ -85,10 +85,12 @@ The rest of this chapter will show you how to complete and extend this template
 > "The greatest value of a picture is when it forces us to notice what we
 > never expected to see." --- John Tukey
 
-In the plot below, one group of points seems to fall outside of the linear trend. These cars have a higher mileage than you might expect. How can you explain these cars? 
+In the plot below, one group of points (highlighted in red) seems to fall outside of the linear trend. These cars have a higher mileage than you might expect. How can you explain these cars? 
 
 ```{r, echo = FALSE}
-knitr::include_graphics("images/visualization-1.png")
+ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
+  geom_point() + 
+  geom_point(data = dplyr::filter(mpg, displ > 5, hwy > 20), colour = "red", size = 2.2)
 ```
 
 Let's hypothesize that the cars are hybrids. One way to test this hypothesis is to look at the `class` value for each car. The `class` variable of the `mpg` dataset classifies cars into groups such as compact, midsize, and suv. If the outlying points are hybrids, they should be classified as compact cars or, perhaps, subcompact cars (keep in mind that this data was collected before hybrid trucks and suvs became popular).
@@ -97,8 +99,15 @@ You can add a third variable, like `class`, to a two dimensional scatterplot by
 
 An aesthetic is a visual property of the objects in your plot. Aesthetics include things like the size, the shape, or the color of your points. You can display a point (like the one below) in different ways by changing the values of its aesthetic properties. Since we already use the word "value" to describe data, let's use the word "level" to describe aesthetic properties. Here we change the levels of a point's size, shape, and color to make the point small, triangular, or blue.
 
-```{r, echo = FALSE}
-knitr::include_graphics("images/visualization-2.png")
+```{r, echo = FALSE, asp = 1/4}
+ggplot() +
+  geom_point(aes(1, 1), size = 20) +
+  geom_point(aes(2, 1), size = 10) + 
+  geom_point(aes(3, 1), size = 20, shape = 17) + 
+  geom_point(aes(4, 1), size = 20, colour = "blue") + 
+  scale_x_continuous(NULL, limits = c(0.5, 4.5), labels = NULL) + 
+  scale_y_continuous(NULL, limits = c(0.9, 1.1), labels = NULL) + 
+  theme(aspect.ratio = 1/4)
 ```
 
 You can convey information about your data by mapping the aesthetics in your plot to the variables in your dataset. For example, you can map the colors of your points to the `class` variable to reveal the class of each car.