19 Transforming

The dplyr is a package to transform data. It can combine data as well. We will treat the second feature late in Chapter ??. The package dplyr is a part of the tidyverse packages, and you do not need to install it separately.

library(tidyverse)
#> ── Attaching core tidyverse packages ──── tidyverse 2.0.0 ──
#> ✔ dplyr     1.1.2     ✔ readr     2.1.4
#> ✔ forcats   1.0.0     ✔ stringr   1.5.0
#> ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
#> ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
#> ✔ purrr     1.0.1     
#> ── 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

19.1 dplyr Overview

dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges:

• select() picks variables based on their names.
• filter() picks cases based on their values.
• mutate() adds new variables that are functions of existing variables
• summarise() reduces multiple values down to a single summary.
• arrange() changes the ordering of the rows.
• group_by() takes an existing tbl and converts it into a grouped tbl.

You can learn more about them in vignette(“dplyr”). As well as these single-table verbs, dplyr also provides a variety of two-table verbs, which you can learn about in vignette(“two-table”).

If you are new to dplyr, the best place to start is the data transformation chapter in R for data science.

19.2 select: Subset columns using their names and types

Helper Function	Use	Example
-	Columns except	select(babynames, -prop)
:	Columns between (inclusive)	select(babynames, year:n)
contains()	Columns that contains a string	select(babynames, contains(“n”))
ends_with()	Columns that ends with a string	select(babynames, ends_with(“n”))
matches()	Columns that matches a regex	select(babynames, matches(“n”))
num_range()	Columns with a numerical suffix in the range	Not applicable with babynames
one_of()	Columns whose name appear in the given set	select(babynames, one_of(c(“sex”, “gender”)))
starts_with()	Columns that starts with a string	select(babynames, starts_with(“n”))

19.3 filter: Subset rows using column values

Logical operator	tests	Example
>	Is x greater than y?	x > y
>=	Is x greater than or equal to y?	x >= y
<	Is x less than y?	x < y
<=	Is x less than or equal to y?	x <= y
==	Is x equal to y?	x == y
!=	Is x not equal to y?	x != y
is.na()	Is x an NA?	is.na(x)
!is.na()	Is x not an NA?	!is.na(x)

19.4 arrange and Pipe %>%

• arrange() orders the rows of a data frame by the values of selected columns.

Unlike other dplyr verbs, arrange() largely ignores grouping; you need to explicitly mention grouping variables (`or use .by_group = TRUE) in order to group by them, and functions of variables are evaluated once per data frame, not once per group.

• pipes in R for Data Science.

19.5 mutate

• Create, modify, and delete columns

• Useful mutate functions

  • +, -, log(), etc., for their usual mathematical meanings

  • lead(), lag()

  • dense_rank(), min_rank(), percent_rank(), row_number(), cume_dist(), ntile()

  • cumsum(), cummean(), cummin(), cummax(), cumany(), cumall()

  • na_if(), coalesce()### group_by() and summarise()

19.6 group_by

19.7 summarise or summarize

19.7.0.1 Summary functions

So far our summarise() examples have relied on sum(), max(), and mean(). But you can use any function in summarise() so long as it meets one criteria: the function must take a vector of values as input and return a single value as output. Functions that do this are known as summary functions and they are common in the field of descriptive statistics. Some of the most useful summary functions include:

1. Measures of location - mean(x), median(x), quantile(x, 0.25), min(x), and max(x)
2. Measures of spread - sd(x), var(x), IQR(x), and mad(x)
3. Measures of position - first(x), nth(x, 2), and last(x)
4. Counts - n_distinct(x) and n(), which takes no arguments, and returns the size of the current group or data frame.
5. Counts and proportions of logical values - sum(!is.na(x)), which counts the number of TRUEs returned by a logical test; mean(y == 0), which returns the proportion of TRUEs returned by a logical test.

• if_else(), recode(), case_when()