This is a technical description of the tidyselect syntax.
library(tidyselect)
library(magrittr)
# For better printing
mtcars <- tibble::as_tibble(mtcars)
iris <- tibble::as_tibble(iris)To illustrate the semantics of tidyselect, we’ll use variants of
dplyr::select() and dplyr::rename() that
return the named vector of locations for the selected or renamed
elements:
select_loc <- function(data, ...) {
eval_select(rlang::expr(c(...)), data)
}
rename_loc <- function(data, ...) {
eval_rename(rlang::expr(c(...)), data)
}The tidyselect syntax is all about sets of
variables, internally represented by integer vectors of
locations. For example, c(1L, 2L)
represents the set of the first and second variables, as does
c(1L, 2L, 1L).
If a vector of locations contains duplicates, they are normally treated as the same element, since they represent sets. An exception to this occurs with named elements whose names differ. If the names don’t match, they are treated as different elements in order to allow renaming a variable to multiple names (see section on Renaming variables).
Today, the syntax of tidyselect is generally designed around Boolean
algebra, i.e. we recommend writing
starts_with("a") & !ends_with("z"). Earlier versions of
tidyselect had more of a flavour of set operations, so that you’d write
starts_with("a") - ends_with("b"). While the set operations
are still supported, and is how tidyselect represents variables
internally, we no longer recommend them because Boolean algebra is easy
for people to understand.
Within data-expressions (see Evaluation section), bare names represent their own locations, i.e. a set of size 1. The following expressions are equivalent:
: operator: can be used to select consecutive variables between
two locations. It returns the corresponding sequence of locations.
Because bare names represent their own locations, it is easy to select a range of variables:
The | operator takes the union of two
sets:
iris %>% select_loc(starts_with("Sepal") | ends_with("Width"))
#> Sepal.Length Sepal.Width Petal.Width
#> 1 2 4The & operator takes the
intersection of two sets:
The ! operator takes the complement of
a set:
Taking the intersection with a complement produces a set difference:
c()tidyselect functions can take dots, like
dplyr::select(), or a named argument, like
tidyr::pivot_longer(). In the latter case, the dots syntax
is accessible via c(). In fact ... syntax is
implemented through c(...) and is thus completely
equivalent.
mtcars %>% select_loc(mpg, disp:hp)
#> mpg disp hp
#> 1 3 4
mtcars %>% select_loc(c(mpg, disp:hp))
#> mpg disp hp
#> 1 3 4c(x, y, z) is a equivalent to
x | y | z:
When named inputs are provided in ... or
c(), the selection is renamed. If the inputs are already
named, the outer and inner names are combined with a
... separator:
Otherwise the outer names is propagated to the selected elements according to the following rules:
With data frames, a numeric suffix is appended because columns must be uniquely named.
With normal vectors, the name is simply assigned to all selected inputs.
Combination and propagation can be composed by using nested
c():
Named elements have special rules to determine their identities in a set. Unnamed elements match any names:
a | c(foo = a) is equivalent to
c(foo = a).a & c(foo = a) is equivalent to
c(foo = a).Named elements with different names are distinct:
c(foo = a) & c(bar = a) is equivalent to
c().c(foo = a) | c(bar = a) is equivalent to
c(foo = a, bar = a).Because unnamed elements match any named ones, it is possible to select multiple elements and rename one of them:
Predicate function objects can be supplied as input in an
env-expression, typically with the selection helper
where(). They are applied to all elements of the data, and
should return TRUE or FALSE to indicate
inclusion. Predicates in env-expressions are effectively expanded to the
set of variables that they represent:
iris %>% select_loc(where(is.numeric))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1 2 3 4
iris %>% select_loc(where(is.factor))
#> Species
#> 5
iris %>% select_loc(where(is.numeric) | where(is.factor))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 2 3 4 5
iris %>% select_loc(where(is.numeric) & where(is.factor))
#> named integer(0)We call selection helpers any function that inspects the
currently active variables with peek_vars() and returns a
selection.
peek_vars() returns a character vector of names.Examples of selection helpers are all_of(),
contains(), or last_col(). These selection
helpers are evaluated as env-expressions (see Evaluation section).
The following data types can be returned from selection helpers or
forced via !! or force() (the latter works in
tidyselect because it is treated as an env-expression, see Evaluation
section):
Vectors of locations:
Vectors of names. These are matched and transformed to locations.
Predicate functions. These are applied to all elements to determine inclusion.
tidyselect is not a typical tidy evaluation UI. The main difference is that there is no data masking. In a typical tidy eval function, expressions are evaluated with data-vars first in scope, followed by env-vars:
mask <- function(data, expr) {
rlang::eval_tidy(rlang::enquo(expr), data)
}
foo <- 10
cyl <- 200
# `cyl` represents the data frame column here:
mtcars %>% mask(cyl * foo)
#> [1] 60 60 40 60 80 60 80 40 40 60 60 80 80 80 80 80 80 40 40 40 40 80 80 80 80
#> [26] 40 40 40 80 60 80 40It is possible to bypass the data frame variables by forcing symbols
to be looked up in the environment with !! or
.env:
With tidyselect, there is no such hierarchical data masking. Instead, expressions are evaluated either in the context of the data frame or in the user environment, without overlap. The scope of lookup depends on the kind of expression:
data-expressions are evaluated in the data frame
only. This includes bare symbols, the boolean operators, -,
:, and c(). You can’t refer to
environment-variables in a data-expression:
env-expressions are evaluated in the environment. This includes all calls other than those mentioned above, as well as symbols that are part of those calls. You can’t refer to data-variables in a data-expression:
Because the scoping is unambiguous, you can safely refer to env-vars in an env-expression, without having to worry about potential naming clashes with data-vars:
x <- data.frame(x = 1:3, y = 4:6, z = 7:9)
# `ncol(x)` is an env-expression, so `x` represents the data frame in
# the environment rather than the column in the data frame
x %>% select_loc(2:ncol(x))
#> y z
#> 2 3If you have variable names in a character vector, it is safe to refer
to the env-var containing the names with all_of() because
it is an env-expression:
Note that currently, env-vars are still allowed in some
data-expressions, for compatibility. However this is in the process of
being deprecated and you should see a note recommending to use
all_of() instead. This note will become a deprecation
warning in the future, and then an error.
mtcars %>% select_loc(cyl_pos)
#> Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
#> ℹ Please use `all_of()` or `any_of()` instead.
#> ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> cyl
#> 2Within data-expressions (see Evaluation section), +,
* and / are overridden to cause an error. This
is to prevent confusion stemming from normal data masking usage where
variables can be transformed on the fly:
The select and rename variants take the same types of inputs and have the same type of return value. They have a few important differences.
Unlike eval_select() which can select without renaming,
eval_rename() expects a fully named selection. If one or
several names are missing, an error is thrown.
If the input data is a data frame, tidyselect generally throws an error when duplicate column names are selected, in order to respect the invariant of unique column names.
# Lists can have duplicates
as.list(mtcars) %>% select_loc(foo = mpg, foo = cyl)
#> foo foo
#> 1 2
# Data frames cannot
mtcars %>% select_loc(foo = mpg, foo = cyl)
#> Error in `select_loc()`:
#> ! Names must be unique.
#> ✖ These names are duplicated:
#> * "foo" at locations 1 and 2.A selection can rename a variable to an existing name if the latter is not part of the selection:
mtcars %>% select_loc(cyl, cyl = mpg)
#> Error in `select_loc()`:
#> ! Names must be unique.
#> ✖ These names are duplicated:
#> * "cyl" at locations 1 and 2.
mtcars %>% select_loc(disp, cyl = mpg)
#> disp cyl
#> 3 1This is not possible when renaming.
mtcars %>% rename_loc(cyl, cyl = mpg)
#> Error in `rename_loc()`:
#> ! All renaming inputs must be named.
mtcars %>% rename_loc(disp, cyl = mpg)
#> Error in `rename_loc()`:
#> ! All renaming inputs must be named.However, the name conflict can be solved by renaming the existing variable to another name:
Normally a data frame shouldn’t have duplicate names. However, the
real world is messy and duplicates do happen in the wild. tidyselect
tries to be as permissive as it can with these duplicates so that users
can restore unique names with select() or
rename().
First let’s create a data frame with duplicate names:
If the duplicates are not part of the selection, they are simply ignored:
If the duplicates are selected, this is an error:
dups %>% select_loc(x)
#> Error in `select_loc()`:
#> ! Names must be unique.
#> ✖ These names are duplicated:
#> * "x" at locations 1 and 2.The duplicate names can be repaired by renaming chosen locations:
The tidyselect syntax was inspired by the base::subset()
function written by Peter Dalgaard. The select parameter of
subset.data.frame() is evaluated in a data mask where the
column names are bound to their locations in the data frame. This allows
: to create sequences of variable locations. The locations
can be combined with c(). This selection interface set the
tone for the development of the tidyselect syntax.