News

tidyselect 0.2.3

• Internal fixes in prevision of using tidyselect within dplyr.

• vars_select() and vars_rename() now correctly support unquoting character vectors that have names.

• vars_select() now ignores missing variables.

tidyselect 0.2.2

• dplyr is now correctly mentioned as suggested package.

tidyselect 0.2.1

• - 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 seq().

• c() now supports character vectors the same way as - and seq(). (#37 @gergness)

tidyselect 0.2.0

The main point of this release is to revert a troublesome behaviour introduced in tidyselect 0.1.0. It also includes a few features.

Evaluation rules

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 : and c()) can refer to both registered variables and to objects from the context.

However the semantics for context expressions (any calls other than to : and 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 .data pronoun:

vars_select(names(mtcars), .data$cyl : .data$drat)

New features

• The new select helper last_col() is helpful to select over a custom range: vars_select(vars, 3:last_col()).

• : and - now handle strings as well. This makes it easy to unquote a column name: (!! name) : last_col() or -(!! name).

• vars_select() gains a .strict argument similar to rename_vars(). If set to FALSE, errors about unknown variables are ignored.

• 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_select() (#20).

• vars_rename() is now implemented with the tidy eval framework. Like 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.

Fixes

• one_of() argument vars has been renamed to .vars to avoid spurious matching.

tidyselect 0.1.1

tidyselect is the new home for the legacy functions dplyr::select_vars(), dplyr::rename_vars() and dplyr::select_var().

API changes

We took this opportunity to make a few changes to the API:

• select_vars() and rename_vars() are now vars_select() and vars_rename(). This follows the tidyverse convention that a prefix corresponds to the input type while suffixes indicate the output type. Similarly, select_var() is now vars_pull().

• 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 matched to 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_type to the .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, c("variable", "variables").

Establishing a variable context

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.

New evaluation semantics

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 !!, which is mostly for expert use.

Note that since dplyr 0.7, helper calls (like starts_with()) obey 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).

Reference manual