Discuss ggplot2 call transition

EDA.Rmd

@@ -554,3 +554,28 @@ ggplot(data = diamonds2, mapping = aes(x = cut, y = resid)) +
 ```
 
 Modelling is important because once you have recognised a pattern, a model allows you to make that pattern quantitative and precise, and partition it out from what remains. That supports a powerful interative approach where you indentify a pattern with visualisation, then subtract with a model, allowing you to see the subtler trends that remain. I deliberately chose not to teach modelling yet, because understanding what models are and how they work are easiest once you have some other tools in hand: data wrangling, and programming.
+
+## ggplot2 calls
+
+As we move on from these introductory chapters, we'll transition to a more concise expression of ggplot2 code. So far we've been very explicit, which is helpful when you are learning:
+
+```{r, eval = FALSE}
+ggplot(data = faithful, mapping = aes(x = eruptions)) + 
+  geom_histogram(binwidth = 0.25)
+```
+
+But the first couple of arguments to a function are typically so important that you should know them by heart. The first two arguments to `ggplot()` are `data` and `mapping`, and the first two arguments to `aes()` are `x` and `y`. In the remainder of the book, we won't supply those names. That saves typing and by reducing the amount of boilerplate makes it easier to see what's different between plots (that's a rely important programming concern that we'll come back in [functions]).
+
+```{r, eval = FALSE}
+ggplot(faithful, aes(eruptions)) + 
+  geom_histogram(binwidth = 0.25)
+```
+
+Sometimes we'll turn the end of pipeline of data transformation into a plot. Watch for the transition from `%>%` to `+`. I wish this transition wasn't necessary but unfortunately ggplot2 was created before the pipe was discovered.
+
+```{r, eval = FALSE}
+diamonds %>% 
+  count(cut, clarity) %>% 
+  ggplot(aes(clarity, cut, fill = n)) + 
+  geom_tile()
+```