Tibble tweaks

tibble.Rmd

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-Throughout this book we work with "tibbles" instead of R's traditional data.frame. Tibbles _are_ data frames, but they tweak some older behaviours to make life a little easier. R is an old language, and some things that were useful 10 or 20 years ago now get in your way. It's difficult to change base R without breaking existing code, so most innovation occurs in packages. Here we will describe the __tibble__ package, which provides opinionated data frames that make working in the tidyverse a little easier. 
+Throughout this book we work with "tibbles" instead of R's traditional `data.frame`. Tibbles _are_ data frames, but they tweak some older behaviours to make life a little easier. R is an old language, and some things that were useful 10 or 20 years ago now get in your way. It's difficult to change base R without breaking existing code, so most innovation occurs in packages. Here we will describe the __tibble__ package, which provides opinionated data frames that make working in the tidyverse a little easier. In most places, I'll use the term tibble and data frame interchangeably; when I want to draw particular attention to R's build-in data frame, I'll call them `data.frame`s. 
 
 If this chapter leaves you wanting to learn more about tibbles, you might enjoy `vignette("tibble")`.
 
@@ -60,9 +60,9 @@ tribble(
 
 I often add a comment (the line starting with `#`), to make it really clear where the header is.
 
-## Tibbles vs. data frames
+## Tibbles vs. data.frame
 
-There are two main differences in the usage of a data frame vs a tibble: printing and subsetting.
+There are two main differences in the usage of a tibble vs. a classic `data.frame`: printing and subsetting.
 
 ### Printing
 
@@ -125,18 +125,16 @@ df[[1]]
 
 To use these in a pipe, you'll need to use the special placeholder `.`:
 
-```{r, include = FALSE}
-library(magrittr)
-```
-
 ```{r}
 df %>% .$x
 df %>% .[["x"]]
 ```
 
+Compared to a `data.frame`, tibbles are more strict: they never do partial matching, and they will generate a warning if the column you are trying to access does not exist.
+
 ## Interacting with older code
 
-Some older functions don't work with tibbles. If you encounter one of these functions, use `as.data.frame()` to turn a tibble back to a data frame:
+Some older functions don't work with tibbles. If you encounter one of these functions, use `as.data.frame()` to turn a tibble back to a `data.frame`:
 
 ```{r}
 class(as.data.frame(tb))
@@ -149,6 +147,17 @@ The main reason that some older functions don't work with tibble is the `[` func
 1.  How can you tell if an object is a tibble? (Hint: try printing `mtcars`,
     which is a regular data frame). 
 
+1.  Compare and contrast the following operations on a `data.frame` and 
+    equivalent tibble. What is different? Why might the default data frame
+    behaviours cause you frustration?
+
+    ```{r, eval = FALSE}
+    df <- data.frame(abc = 1, xyz = "a")
+    df$x
+    df[, "xyz"]
+    df[, c("abc", "xyz")]
+    ```
+
 1.  Practice referring to non-syntactic names by:
 
     1.  Plotting a scatterplot of `1` vs `2`.