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R for Data Science by Garrett Grolemund, Hadley Wickham

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Chapter 7. Tibbles with tibble

Introduction

Throughout this book we work with “tibbles” instead of R’s traditional data.frame. Tibbles are data frames, but they tweak some older behaviors 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 terms tibble and data frame interchangeably; when I want to draw particular attention to R’s built-in data frame, I’ll call them data.frames.

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.

library(tidyverse)

Creating Tibbles

Almost all of the functions that you’ll use in this book produce tibbles, as tibbles are one of the unifying features of the tidyverse. Most other R packages use regular data frames, so you might want to coerce a data frame to a tibble. You can do that with as_tibble():

as_tibble(iris)
#> # A tibble: 150 × 5
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>          <dbl>       <dbl>        <dbl>       <dbl>  <fctr>
#> 1          5.1         3.5          1.4         0.2  setosa
#> 2          4.9         3.0          1.4         0.2  setosa
#> 3          4.7         3.2          1.3         0.2  setosa
#> 4          4.6         3.1          1.5         0.2  setosa
#> 5          5.0         3.6          1.4         0.2  setosa
#> ...

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