From 47d239b84b2847ae052c39944a87824e219df6dd Mon Sep 17 00:00:00 2001 From: Hadley Wickham Date: Thu, 20 Oct 2022 14:27:58 -0500 Subject: [PATCH] Move tibbles to program --- _quarto.yml | 2 +- tibble.qmd | 6 ++++-- transform.qmd | 3 --- 3 files changed, 5 insertions(+), 6 deletions(-) diff --git a/_quarto.yml b/_quarto.yml index a0b9b80..89c9c1c 100644 --- a/_quarto.yml +++ b/_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 diff --git a/tibble.qmd b/tibble.qmd index cd43d1b..ba48a76 100644 --- a/tibble.qmd +++ b/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")`. diff --git a/transform.qmd b/transform.qmd index f52495a..af879c8 100644 --- a/transform.qmd +++ b/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.