select: Select/rename variables by name

Description

select() keeps only the variables you mention; rename() keeps all variables.

Usage

select(.data, ...)
rename(.data, ...)

Arguments

.data

A tbl. All main verbs are S3 generics and provide methods for tbl_df(), dtplyr::tbl_dt() and dbplyr::tbl_dbi().

...

One or more unquoted expressions separated by commas. You can treat variable names like they are positions.

Positive values select variables; negative values to drop variables. If the first expression is negative, select() will automatically start with all variables.

Use named arguments to rename selected variables.

These arguments are automatically quoted and evaluated in a context where column names represent column positions. They support unquoting and splicing. See vignette("programming") for an introduction to these concepts.

Value

An object of the same class as .data.

Useful functions

As well as using existing functions like : and c(), there are a number of special functions that only work inside select

starts_with(), ends_with(), contains()
matches()
num_range()
one_of()
everything()

To drop variables, use -.

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

Scoped selection and renaming

The three scoped variants of select() (select_all(), select_if() and select_at()) and the three variants of rename() (rename_all(), rename_if(), rename_at()) make it easy to apply a renaming function to a selection of variables.

Tidy data

When applied to a data frame, row names are silently dropped. To preserve, convert to an explicit variable with tibble::rownames_to_column().

Examples

Run this code

# NOT RUN {
iris <- as_tibble(iris) # so it prints a little nicer
select(iris, starts_with("Petal"))
select(iris, ends_with("Width"))

# Move Species variable to the front
select(iris, Species, everything())

df <- as.data.frame(matrix(runif(100), nrow = 10))
df <- tbl_df(df[c(3, 4, 7, 1, 9, 8, 5, 2, 6, 10)])
select(df, V4:V6)
select(df, num_range("V", 4:6))

# Drop variables with -
select(iris, -starts_with("Petal"))


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

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


# Renaming -----------------------------------------
# * select() keeps only the variables you specify
select(iris, petal_length = Petal.Length)

# * rename() keeps all variables
rename(iris, petal_length = Petal.Length)


# Unquoting ----------------------------------------

# Like all dplyr verbs, select() supports unquoting of symbols:
vars <- list(
  var1 = sym("cyl"),
  var2 = sym("am")
)
select(mtcars, !!!vars)

# For convenience it also supports strings and character
# vectors. This is unlike other verbs where strings would be
# ambiguous.
vars <- c(var1 = "cyl", var2 ="am")
select(mtcars, !!vars)
rename(mtcars, !!vars)
# }

Run the code above in your browser using DataCamp Workspace