A backend for the selecting functions of the 'tidyverse'. It makes it easy to implement select-like functions in your own packages in a way that is consistent with other 'tidyverse' interfaces for selection.
The tidyselect package is the backend of functions like
dplyr::pull() as well as several tidyr verbs. It allows you to
create selecting verbs that are consistent with other tidyverse packages.
tidyselect is on CRAN. You can also install the development version from github with:
This is a maintenance release for compatibility with rlang 0.3.0.
Fixed a warning that occurred when a vector of column positions was
vars_select() or functions depending on it such as
tidyr::gather() (#43 and tidyverse/tidyr#374).
Fixed compatibility issue with rlang 0.2.0 (#51).
Internal fixes in prevision of using
vars_rename() now correctly support unquoting
character vectors that have names.
vars_select() now ignores missing variables.
dplyris now correctly mentioned as suggested package.
- now supports character vectors in addition to strings. This
makes it easy to unquote column names to exclude from the set:
vars <- c("cyl", "am", "disp", "drat")vars_select(names(mtcars), - !!vars)
last_col() now issues an error when the variable vector is empty.
last_col() now returns column positions rather than column names
for consistency with other helpers. This also makes it compatible
with functions like
c() now supports character vectors the same way as
The main point of this release is to revert a troublesome behaviour introduced in tidyselect 0.1.0. It also includes a few features.
The special evaluation semantics for selection have been changed
back to the old behaviour because the new rules were causing too
much trouble and confusion. From now on data expressions (symbols
and calls to
c()) can refer to both registered variables
and to objects from the context.
However the semantics for context expressions (any calls other than
c()) remain the same. Those expressions are evaluated
in the context only and cannot refer to registered variables.
If you're writing functions and refer to contextual objects, it is still a good idea to avoid data expressions. Since registered variables are change as a function of user input and you never know if your local objects might be shadowed by a variable. Consider:
n <- 2 vars_select(letters, 1:n)
Should that select up to the second element of
letters or up to
the 14th? Since the variables have precedence in a data expression,
this will select the 14 first letters. This can be made more robust
by turning the data expression into a context expression:
vars_select(letters, seq(1, n))
You can also use quasiquotation since unquoted arguments are guaranteed to be evaluated without any user data in scope. While equivalent because of the special rules for context expressions, this may be clearer to the reader accustomed to tidy eval:
vars_select(letters, seq(1, !! n))
Finally, you may want to be more explicit in the opposite direction.
If you expect a variable to be found in the data but not in the
context, you can use the
vars_select(names(mtcars), .data$cyl : .data$drat)
The new select helper
last_col() is helpful to select over a
- now handle strings as well. This makes it easy to
unquote a column name:
(!!name) : last_col() or
vars_select() gains a
.strict argument similar to
rename_vars(). If set to
FALSE, errors about unknown variables
vars_select() now treats
NULL as empty inputs. This follows a
trend in the tidyverse tools.
vars_rename() now handles variable positions (integers or round
doubles) just like
vars_rename() is now implemented with the tidy eval framework.
vars_select(), expressions are evaluated without any user
data in scope. In addition a variable context is now established so
you can write rename helpers. Those should return a single round
number or a string (variable position or variable name).
has_vars() is a predicate that tests whether a variable context
has been set (#21).
The selection helpers are now exported in a list
vars_select_helpers. This is intended for APIs that embed the
helpers in the evaluation environment.
varshas been renamed to
.varsto avoid spurious matching.
tidyselect is the new home for the legacy functions
We took this opportunity to make a few changes to the API:
rename_vars() are now
vars_rename(). This follows the tidyverse convention that a prefix
corresponds to the input type while suffixes indicate the output
select_var() is now
The arguments are now prefixed with dots to limit argument matching
issues. While the dots help, it is still a good idea to splice a
list of captured quosures to make sure dotted arguments are never
vars_select()'s named arguments:
vars_select(vars, !!! quos(...))
Error messages can now be customised. For consistency with dplyr,
error messages refer to "columns" by default. This assumes that the
variables being selected come from a data frame. If this is not
appropriate for your DSL, you can now add an attribute
.vars vector to specify alternative names. This must be a
character vector of length 2 whose first component is the singular
form and the second is the plural. For example,
tidyselect provides a few more ways of establishing a variable context:
scoped_vars() sets up a variable context along with an an exit
hook that automatically restores the previous variables. It is the
preferred way of changing the variable context.
with_vars() takes variables and an expression and evaluates the
latter in the context of the former.
poke_vars() establishes a new variable context. It returns the
previous context invisibly and it is your responsibility to restore
it after you are done. This is for expert use only.
current_vars() has been renamed to
peek_vars(). This naming is a
reference to peek and poke
from legacy languages.
The evaluation semantics for selecting verbs have changed. Symbols are
now evaluated in a data-only context that is isolated from the calling
environment. This means that you can no longer refer to local variables
unless you are explicitly unquoting these variables with
is mostly for expert use.
Note that since dplyr 0.7, helper calls (like
the opposite behaviour and are evaluated in the calling context
isolated from the data context. To sum up, symbols can only refer to
data frame objects, while helpers can only refer to contextual
objects. This differs from usual R evaluation semantics where both
the data and the calling environment are in scope (with the former
prevailing over the latter).