Note about data frames in json

lists.Rmd

@@ -158,7 +158,7 @@ embed_jpg("images/pepper-3.jpg", 300)
 
 ## A common pattern of for loops
 
-Lets start by creating a stereotypical list: an eight element list where each element contains a random vector of random length. (You'll learn `rerun()` later.)
+Lets start by creating a stereotypical list: an eight element list where each element contains a random vector of random length. (You'll learn about `rerun()` later.)
 
 ```{r}
 x <- rerun(8, runif(sample(5, 1)))
@@ -522,6 +522,13 @@ You'll see an example of this in the next section, as `transpose()` is particula
 
 It's called transpose by analogy to matrices. When you subset a transposed matrix, you switch indices: `x[i, j]` is the same as `t(x)[j, i]`. It's the same idea when transposing a list, but the subsetting looks a little different: `x[[i]][[j]]` is equivalent to `transpose(x)[[j]][[i]]`. Similarly, a transpose is its own inverse so `transpose(transpose(x))` is equal to `x`.
 
+Tranpose is also useful when working with JSON apis. Many JSON APIs represent data frames in a row-based format, rather than R's column-based format. `transpose()` makes it easy to switch between the two:
+
+```{r}
+df <- dplyr::data_frame(x = 1:3, y = c("a", "b", "c"))
+df %>% transpose() %>% str()
+```
+
 ### Exercises
 
 ## Dealing with failure