Move tibbles to program

_quarto.yml

@@ -54,7 +54,6 @@ book:
 
     - part: transform.qmd
       chapters:
-        - tibble.qmd
         - logicals.qmd
         - numbers.qmd
         - strings.qmd
@@ -76,6 +75,7 @@ book:
       chapters:
         - functions.qmd
         - vectors.qmd
+        - tibble.qmd
         - iteration.qmd
 
     - part: communicate.qmd

tibble.qmd

@@ -16,8 +16,6 @@ It's difficult to change base R without breaking existing code, so most innovati
 Here we will describe the **tibble** package, which provides opinionated data frames that make working in the tidyverse a little easier.
 In most places, we use the term tibble and data frame interchangeably; when we want to draw particular attention to R's built-in data frame, we'll call them `data.frame`s.
 
-If this chapter leaves you wanting to learn more about tibbles, you might enjoy `vignette("tibble")`.
-
 ### Prerequisites
 
 In this chapter we'll explore the **tibble** package, part of the core tidyverse.
@@ -264,3 +262,7 @@ If you hit one of those functions, just use `as.data.frame()` to turn your tibbl
     When might you use it?
 
 6.  What option controls how many additional column names are printed at the footer of a tibble?
+
+## Summary
+
+If this chapter leaves you wanting to learn more about tibbles, you might enjoy `vignette("tibble")`.

transform.qmd

@@ -25,9 +25,6 @@ knitr::include_graphics("diagrams/data-science/transform.png", dpi = 270)
 
 You can read these chapters as you need them; they're designed to be largely standalone so that they can be read out of order.
 
--   In @sec-tibbles, you'll learn about the **tibble**, the variant of the data frame that we use in this book.
-    You'll learn what makes tibbles different from regular data frames and how you can use them to hand enter data.
-
 -   @sec-logicals teaches you about logical vectors.
     These are simplest type of vector, but are extremely powerful.
     You'll learn how to create them with numeric comparisons, how to combine them with Boolean algebra, how to use them in summaries, and how to use them for condition transformations.