tidyselect (version 0.1.1)

vars_rename: Select or rename variables

Description

These functions power dplyr::select() and dplyr::rename(). They enable dplyr selecting or renaming semantics in your own functions.

Usage

vars_rename(.vars, ..., .strict = TRUE)
vars_select(.vars, ..., .include = character(), .exclude = character())

Arguments

.vars

A character vector of existing column names.

..., args

Expressions to compute

These arguments are automatically quoted and evaluated in a context where elements of vars are objects representing their positions within vars. They support unquoting and splicing. See vignette("programming") for an introduction to these concepts.

Note that except for :, - and c(), all complex expressions are evaluated outside that context. This is to prevent accidental matching to vars elements when you refer to variables from the calling context.

.strict

If TRUE, will throw an error if you attempt to rename a variable that doesn't exist.

.include, .exclude

Character vector of column names to always include/exclude.

Value

A named character vector. Values are existing column names, names are new names.

Context of evaluation

Quoting verbs usually support references to both objects from the data frame and objects from the calling context. Selecting verbs behave a bit differently.

• Bare names are evaluated in the data frame only. You cannot refer to local objects unless you explicitly unquote them with !!.

• Calls to helper functions are evaluated in the calling context only. You can safely and directly refer to local objects.

Customising error messages

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 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").

See Also

vars_pull()

Examples

# NOT RUN {
# Keep variables
vars_select(names(iris), everything())
vars_select(names(iris), starts_with("Petal"))
vars_select(names(iris), ends_with("Width"))
vars_select(names(iris), contains("etal"))
vars_select(names(iris), matches(".t."))
vars_select(names(iris), Petal.Length, Petal.Width)
vars_select(names(iris), one_of("Petal.Length", "Petal.Width"))

df <- as.data.frame(matrix(runif(100), nrow = 10))
df <- df[c(3, 4, 7, 1, 9, 8, 5, 2, 6, 10)]
vars_select(names(df), num_range("V", 4:6))

# Drop variables
vars_select(names(iris), -starts_with("Petal"))
vars_select(names(iris), -ends_with("Width"))
vars_select(names(iris), -contains("etal"))
vars_select(names(iris), -matches(".t."))
vars_select(names(iris), -Petal.Length, -Petal.Width)

# Rename variables
vars_select(names(iris), petal_length = Petal.Length)
vars_select(names(iris), petal = starts_with("Petal"))

# Rename variables preserving all existing
vars_rename(names(iris), petal_length = Petal.Length)

# You can unquote symbols or quosures
vars_select(names(iris), !! quote(Petal.Length))

# And unquote-splice lists of symbols or quosures
vars_select(names(iris), !!! list(quo(Petal.Length), quote(Petal.Width)))


# When selecting with bare symbols, you can only refer to data
# objects. This avoids ambiguity. If you want to refer to local
# objects, you can explicitly unquote them. They must contain
# variable positions (integers) or variable names (strings):
Species <- 2
vars_select(names(iris), Species)     # Picks up `Species` from the data frame
vars_select(names(iris), !! Species)  # Picks up the local object referring to column 2

# On the other hand, function calls behave the opposite way. They
# are evaluated in the local context only and cannot refer to data
# frame objects. This makes it easy to refer to local variables:
x <- "Petal"
vars_select(names(iris), starts_with(x))  # Picks up the local variable `x`


# The .data pronoun is available:
vars_select(names(mtcars), .data$cyl)
vars_select(names(mtcars), .data$mpg : .data$disp)

# However it isn't available within calls since those are evaluated
# outside of the data context. This would fail if run:
# vars_select(names(mtcars), identical(.data$cyl))


# If you're writing a wrapper around vars_select(), pass the dots
# via splicing to avoid matching dotted arguments to vars_select()
# named arguments (`vars`, `include` and `exclude`):
wrapper <- function(...) {
  vars_select(names(mtcars), !!! quos(...))
}

# This won't partial-match on `vars`:
wrapper(var = cyl)

# This won't match on `include`:
wrapper(include = cyl)


# If your wrapper takes named arguments, you need to capture then
# unquote to pass them to vars_select(). See the vignette on
# programming with dplyr for more on this:
wrapper <- function(var1, var2) {
  vars_select(names(mtcars), !! enquo(var1), !! enquo(var2))
}
wrapper(starts_with("d"), starts_with("c"))
# }