20 Tidying

20.1 Setup

library(tidyverse)
#> ── Attaching core tidyverse packages ──── tidyverse 2.0.0 ──
#> ✔ dplyr     1.1.3     ✔ readr     2.1.4
#> ✔ forcats   1.0.0     ✔ stringr   1.5.0
#> ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
#> ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
#> ✔ purrr     1.0.2     
#> ── Conflicts ────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

20.2 References of `tidyr`

Textbook: R for Data Science,Tidy Data

20.2.1 RStudio Primers: See References in Moodle at the bottom

Tidy Your Data – r4ds: Wrangle, II

The first component, ‘Reshape Data’ deals with pivot_longer and pivot_wider. However, it uses an older version of these functions calls gather and spread.

20.3 Variables, values, and observations: Definitions

A variable is a quantity, quality, or property that you can measure.
A value is the state of a variable when you measure it. The value of a variable may change from measurement to measurement.
An observation or case is a set of measurements made under similar conditions (you usually make all of the measurements in an observation at the same time and on the same object). An observation will contain several values, each associated with a different variable. I’ll sometimes refer to an observation as a case or data point.
Tabular data is a table of values, each associated with a variable and an observation. Tabular data is tidy if each value is placed in its own cell, each variable in its own column, and each observation in its own row.
So far, all of the data that you’ve seen has been tidy. In real-life, most data isn’t tidy, so we’ll come back to these ideas again in Data Wrangling.

20.4 Tidy Data

“Data comes in many formats, but R prefers just one: tidy data.” — Garrett Grolemund

Data can come in a variety of formats, but one format is easier to use in R than the others. This format is known as tidy data. A data set is tidy if:

Each variable is in its own column
Each observation is in its own row
Each value is in its own cell (this follows from #1 and #2)

“Tidy data sets are all alike; but every messy data set is messy in its own way.” — Hadley Wickham

“all happy families are all alike; each unhappy family is unhappy in its own way” - Tolstoy’s Anna Karenina

20.5 `tidyr` Basics

Let us look at the figure in R4DS.

Each variable is in its own column
Each observation is in its own row

20.6 Pivot data from wide to long:

pivot_longer()

pivot_longer(data, cols = <columns to pivot into longer format>,
  names_to = <name of the new character column>, # e.g. "group", "category", "class"
  values_to = <name of the column the values of cells go to>) # e.g. "value", "n"

20.7 Pivot data from long to wide:

pivot_wider() In Console: vignette(“pivot”)

pivot_wider(data, 
  names_from = <name of the column (or columns) to get the name of the output column>,
  values_from = <name of the column to get the value of the output>)