Title: | Multiple, Unpacking, and Destructuring Assignment |
---|---|
Description: | Provides a %<-% operator to perform multiple, unpacking, and destructuring assignment in R. The operator unpacks the right-hand side of an assignment into multiple values and assigns these values to variables on the left-hand side of the assignment. |
Authors: | Nathan Teetor [aut, cre], Paul Teetor [ctb] |
Maintainer: | Nathan Teetor <[email protected]> |
License: | MIT + file LICENSE |
Version: | 0.1.0.9000 |
Built: | 2024-11-01 11:16:48 UTC |
Source: | https://github.com/r-lib/zeallot |
destructure
is used during unpacking assignment to coerce an object
into a list. Individual elements of the list are assigned to names on the
left-hand side of the unpacking assignment expression.
destructure(x)
destructure(x)
x |
An R object. |
If x
is atomic destructure
expects length(x)
to be 1. If a vector with
length greater than 1 is passed to destructure
an error is raised.
New implementations of destructure
can be very simple. A new
destructure
implementation might only strip away the class of a custom
object and return the underlying list structure. Alternatively, an object
might destructure into a nested set of values and may require a more
complicated implementation. In either case, new implementations must return a
list object so %<-%
can handle the returned value(s).
# data frames become a list of columns destructure( data.frame(x = 0:4, y = 5:9) ) # strings are split into list of characters destructure("abcdef") # dates become list of year, month, and day destructure(Sys.Date()) # create a new destructure implementation shape <- function(sides = 4, color = "red") { structure( list(sides = sides, color = color), class = "shape" ) } ## Not run: # cannot destructure the shape object yet c(sides, color) %<-% shape() ## End(Not run) # implement `destructure` for shapes destructure.shape <- function(x) { list(x$sides, x$color) } # now we can destructure shape objects c(sides, color) %<-% destructure(shape()) sides # 4 color # "red" c(sides, color) %<-% destructure(shape(3, "green")) sides # 3 color # 'green'
# data frames become a list of columns destructure( data.frame(x = 0:4, y = 5:9) ) # strings are split into list of characters destructure("abcdef") # dates become list of year, month, and day destructure(Sys.Date()) # create a new destructure implementation shape <- function(sides = 4, color = "red") { structure( list(sides = sides, color = color), class = "shape" ) } ## Not run: # cannot destructure the shape object yet c(sides, color) %<-% shape() ## End(Not run) # implement `destructure` for shapes destructure.shape <- function(x) { list(x$sides, x$color) } # now we can destructure shape objects c(sides, color) %<-% destructure(shape()) sides # 4 color # "red" c(sides, color) %<-% destructure(shape(3, "green")) sides # 3 color # 'green'
Assign values to name(s).
x %<-% value value %->% x
x %<-% value value %->% x
x |
A name structure, see section below. |
value |
A list of values, vector of values, or R object to assign. |
%<-%
and %->%
invisibly return value
.
These operators are used primarily for their assignment side-effect.
%<-%
and %->%
assign into the environment in which they
are evaluated.
the basics
At its simplest, the name structure may be a single variable name, in which
case %<-%
and %->%
perform regular assignment, x
%<-% list(1, 2, 3)
or list(1, 2, 3) %->% x
.
To specify multiple variable names use a call to c()
, for example
c(x, y, z) %<-% c(1, 2, 3)
.
When value
is neither an atomic vector nor a list, %<-%
and
%->%
will try to destructure value
into a list before assigning
variables, see destructure()
.
object parts
Like assigning a variable, one may also assign part of an object, c(x,
x[[1]]) %<-% list(list(), 1)
.
nested names
One can also nest calls to c()
when needed, c(x, c(y, z))
. This nested
structure is used to unpack nested values,
c(x, c(y, z)) %<-% list(1, list(2, 3))
.
collector variables
To gather extra values from the beginning, middle, or end of value
use a
collector variable. Collector variables are indicated with a ...
prefix, c(...start, z) %<-% list(1, 2, 3, 4)
.
skipping values
Use .
in place of a variable name to skip a value without raising an error
or assigning the value, c(x, ., z) %<-% list(1, 2, 3)
.
Use ...
to skip multiple values without raising an error or assigning the
values, c(w, ..., z) %<-% list(1, NA, NA, 4)
.
default values
Use =
to specify a default value for a variable, c(x, y = NULL)
%<-% tail(1, 2)
.
When assigning part of an object a default value may not be specified because
of the syntax enforced by R. The following would raise an "unexpected '=' ..."
error, c(x, x[[1]] = 1) %<-% list(list())
.
For more on unpacking custom objects please refer to
destructure()
.
# basic usage c(a, b) %<-% list(0, 1) a # 0 b # 1 # unpack and assign nested values c(c(e, f), c(g, h)) %<-% list(list(2, 3), list(3, 4)) e # 2 f # 3 g # 4 h # 5 # can assign more than 2 values at once c(j, k, l) %<-% list(6, 7, 8) # assign columns of data frame c(erupts, wait) %<-% faithful erupts # 3.600 1.800 3.333 .. wait # 79 54 74 .. # assign only specific columns, skip # other columns c(mpg, cyl, disp, ...) %<-% mtcars mpg # 21.0 21.0 22.8 .. cyl # 6 6 4 .. disp # 160.0 160.0 108.0 .. # skip initial values, assign final value TODOs <- list("make food", "pack lunch", "save world") c(..., task) %<-% TODOs task # "save world" # assign first name, skip middle initial, # assign last name c(first, ., last) %<-% c("Ursula", "K", "Le Guin") first # "Ursula" last # "Le Guin" # simple model and summary mod <- lm(hp ~ gear, data = mtcars) # extract call and fstatistic from # the summary c(modcall, ..., modstat, .) %<-% summary(mod) modcall modstat # unpack nested values w/ nested names fibs <- list(1, list(2, list(3, list(5)))) c(f2, c(f3, c(f4, c(f5)))) %<-% fibs f2 # 1 f3 # 2 f4 # 3 f5 # 5 # unpack first numeric, leave rest c(f2, fibcdr) %<-% fibs f2 # 1 fibcdr # list(2, list(3, list(5))) # swap values without using temporary variables c(a, b) %<-% c("eh", "bee") a # "eh" b # "bee" c(a, b) %<-% c(b, a) a # "bee" b # "eh" # unpack `strsplit` return value names <- c("Nathan,Maria,Matt,Polly", "Smith,Peterson,Williams,Jones") c(firsts, lasts) %<-% strsplit(names, ",") firsts # c("Nathan", "Maria", .. lasts # c("Smith", "Peterson", .. # handle missing values with default values parse_time <- function(x) { strsplit(x, " ")[[1]] } c(hour, period = NA) %<-% parse_time("10:00 AM") hour # "10:00" period # "AM" c(hour, period = NA) %<-% parse_time("15:00") hour # "15:00" period # NA # right operator list(1, 2, "a", "b", "c") %->% c(x, y, ...chars) x # 1 y # 2 chars # list("a", "b", "c") # magrittr chains, install.packages("magrittr") for this example if (requireNamespace("magrittr", quietly = TRUE)) { library(magrittr) c("hello", "world!") %>% paste0("\n") %>% lapply(toupper) %->% c(greeting, subject) greeting # "HELLO\n" subject # "WORLD!\n" }
# basic usage c(a, b) %<-% list(0, 1) a # 0 b # 1 # unpack and assign nested values c(c(e, f), c(g, h)) %<-% list(list(2, 3), list(3, 4)) e # 2 f # 3 g # 4 h # 5 # can assign more than 2 values at once c(j, k, l) %<-% list(6, 7, 8) # assign columns of data frame c(erupts, wait) %<-% faithful erupts # 3.600 1.800 3.333 .. wait # 79 54 74 .. # assign only specific columns, skip # other columns c(mpg, cyl, disp, ...) %<-% mtcars mpg # 21.0 21.0 22.8 .. cyl # 6 6 4 .. disp # 160.0 160.0 108.0 .. # skip initial values, assign final value TODOs <- list("make food", "pack lunch", "save world") c(..., task) %<-% TODOs task # "save world" # assign first name, skip middle initial, # assign last name c(first, ., last) %<-% c("Ursula", "K", "Le Guin") first # "Ursula" last # "Le Guin" # simple model and summary mod <- lm(hp ~ gear, data = mtcars) # extract call and fstatistic from # the summary c(modcall, ..., modstat, .) %<-% summary(mod) modcall modstat # unpack nested values w/ nested names fibs <- list(1, list(2, list(3, list(5)))) c(f2, c(f3, c(f4, c(f5)))) %<-% fibs f2 # 1 f3 # 2 f4 # 3 f5 # 5 # unpack first numeric, leave rest c(f2, fibcdr) %<-% fibs f2 # 1 fibcdr # list(2, list(3, list(5))) # swap values without using temporary variables c(a, b) %<-% c("eh", "bee") a # "eh" b # "bee" c(a, b) %<-% c(b, a) a # "bee" b # "eh" # unpack `strsplit` return value names <- c("Nathan,Maria,Matt,Polly", "Smith,Peterson,Williams,Jones") c(firsts, lasts) %<-% strsplit(names, ",") firsts # c("Nathan", "Maria", .. lasts # c("Smith", "Peterson", .. # handle missing values with default values parse_time <- function(x) { strsplit(x, " ")[[1]] } c(hour, period = NA) %<-% parse_time("10:00 AM") hour # "10:00" period # "AM" c(hour, period = NA) %<-% parse_time("15:00") hour # "15:00" period # NA # right operator list(1, 2, "a", "b", "c") %->% c(x, y, ...chars) x # 1 y # 2 chars # list("a", "b", "c") # magrittr chains, install.packages("magrittr") for this example if (requireNamespace("magrittr", quietly = TRUE)) { library(magrittr) c("hello", "world!") %>% paste0("\n") %>% lapply(toupper) %->% c(greeting, subject) greeting # "HELLO\n" subject # "WORLD!\n" }
zeallot provides a %<-%
operator to perform multiple
assignment in R. To get started with zeallot be sure to read over the
introductory vignette on unpacking assignment,
vignette('unpacking-assignment')
.
Maintainer: Nathan Teetor [email protected]
Other contributors:
Paul Teetor [contributor]