Discrete -> continuous

EDA.Rmd

@@ -465,7 +465,7 @@ ggplot(data = smaller) +
   geom_hex(aes(x = carat, y = price))
 ```
 
-Another option is to bin one continuous variable so it acts like a categorical variable. Then you can use one of the techniques for visualising the combination of a discrete and a continuous variable that you learned about. For example, you could bin `carat` and then for each group, display a boxplot:
+Another option is to bin one continuous variable so it acts like a categorical variable. Then you can use one of the techniques for visualising the combination of a categorical and a continuous variable that you learned about. For example, you could bin `carat` and then for each group, display a boxplot:
 
 ```{r}
 ggplot(data = smaller, mapping = aes(x = carat, y = price)) + 

visualize.Rmd

@@ -195,12 +195,13 @@ ggplot(shapes, aes(x, y)) +
       geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))
     ```
     
-1.  Which variables in `mpg` are discrete? Which variables are continuous? 
+1.  Which variables in `mpg` are categorical? Which variables are continuous? 
     (Hint: type `?mpg` to read the documentation for the dataset). How
     can you see this information when you run `mpg`?
 
 1.  Map a continuous variable to `color`, `size`, and `shape`. How do
-    these aesthetics behave differently for discrete vs. continuous variables? 
+    these aesthetics behave differently for categorical vs. continuous
+    variables? 
     
 1.  What happens if you map the same variable across multiple aesthetics? 
     What happens if you map different variables across multiple aesthetics?
@@ -354,7 +355,7 @@ knitr::include_graphics("images/visualization-geoms-3.png")
 knitr::include_graphics("images/visualization-geoms-4.png")
 ```
 
-Many geoms, like `geom_smooth()`, use a single geometric object to display multiple rows of data. For these geoms, you can set the `group` aesthetic to a discrete variable to draw multiple objects. ggplot2 will draw a separate object for each unique value of the grouping variable. In practice, ggplot2 will automatically group the data for these geoms whenever you map an aesthetic to a discrete variable (as in the `linetype` example). It is convenient to rely on this feature because the group aesthetic by itself does not add a legend or distinguishing features to the geoms.
+Many geoms, like `geom_smooth()`, use a single geometric object to display multiple rows of data. For these geoms, you can set the `group` aesthetic to a categorical variable to draw multiple objects. ggplot2 will draw a separate object for each unique value of the grouping variable. In practice, ggplot2 will automatically group the data for these geoms whenever you map an aesthetic to a discrete variable (as in the `linetype` example). It is convenient to rely on this feature because the group aesthetic by itself does not add a legend or distinguishing features to the geoms.
 
 ```{r, fig.width = 3, fig.align = 'default', out.width = "33%"}
 ggplot(data = mpg) +