Move tibbles to program
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@ -54,7 +54,6 @@ book:
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- part: transform.qmd
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- part: transform.qmd
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chapters:
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chapters:
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- tibble.qmd
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- logicals.qmd
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- logicals.qmd
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- numbers.qmd
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- numbers.qmd
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- strings.qmd
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- strings.qmd
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@ -76,6 +75,7 @@ book:
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chapters:
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chapters:
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- functions.qmd
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- functions.qmd
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- vectors.qmd
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- vectors.qmd
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- tibble.qmd
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- iteration.qmd
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- iteration.qmd
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- part: communicate.qmd
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- part: communicate.qmd
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@ -16,8 +16,6 @@ It's difficult to change base R without breaking existing code, so most innovati
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Here we will describe the **tibble** package, which provides opinionated data frames that make working in the tidyverse a little easier.
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Here we will describe the **tibble** package, which provides opinionated data frames that make working in the tidyverse a little easier.
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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.
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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.
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If this chapter leaves you wanting to learn more about tibbles, you might enjoy `vignette("tibble")`.
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### Prerequisites
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### Prerequisites
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In this chapter we'll explore the **tibble** package, part of the core tidyverse.
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In this chapter we'll explore the **tibble** package, part of the core tidyverse.
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@ -264,3 +262,7 @@ If you hit one of those functions, just use `as.data.frame()` to turn your tibbl
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When might you use it?
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When might you use it?
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6. What option controls how many additional column names are printed at the footer of a tibble?
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6. What option controls how many additional column names are printed at the footer of a tibble?
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## Summary
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If this chapter leaves you wanting to learn more about tibbles, you might enjoy `vignette("tibble")`.
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@ -25,9 +25,6 @@ knitr::include_graphics("diagrams/data-science/transform.png", dpi = 270)
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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.
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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.
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- In @sec-tibbles, you'll learn about the **tibble**, the variant of the data frame that we use in this book.
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You'll learn what makes tibbles different from regular data frames and how you can use them to hand enter data.
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- @sec-logicals teaches you about logical vectors.
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- @sec-logicals teaches you about logical vectors.
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These are simplest type of vector, but are extremely powerful.
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These are simplest type of vector, but are extremely powerful.
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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.
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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.
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