poorman (version 0.2.6)

select: Subset columns using their names and types

Description

Select (and optionally rename) variables in a data.frame, using a concise mini-language that makes it easy to refer to variables based on their name (e.g. a:f selects all columns from a on the left to f on the right). You can also use predicate functions like is.numeric() to select variables based on their properties.

Usage

select(.data, ...)

Value

An object of the same type as .data. The output has the following properties:

• Rows are not affected.

• Output columns are a subset of input columns, potentially with a different order. Columns will be renamed if new_name = old_name form is used.

• Data frame attributes are preserved.

• Groups are maintained; you can't select off grouping variables.

Arguments

.data

A data.frame.

...

<poor-select> One or more unquoted expressions separated by commas. Variable names can be used as if they were positions in the data frame, so expressions like x:y can be used to select a range of variables.

Details

Overview of selection features

poorman selections implement a dialect of R where operators make it easy to select variables:

• : for selecting a range of consecutive variables.

• ! for taking the complement of a set of variables.

• & and | for selecting the intersection or the union of two sets of variables.

• c() for combining selections.

In addition, you can use selection helpers. Some helpers select specific columns:

• everything(): Matches all variables.

• last_col(): Select last variable, possibly with an offset.

These helpers select variables by matching patterns in their names:

• starts_with(): Starts with a prefix.

• ends_with(): Ends with a suffix.

• contains(): Contains a literal string.

• matches(): Matches a regular expression.

• num_range(): Matches a numerical range like x01, x02, x03.

These helpers select variables from a character vector:

• all_of(): Matches variable names in a character vector. All names must be present, otherwise an out-of-bounds error is thrown.

• any_of(): Same as all_of(), except that no error is thrown for names that don't exist.

This helper selects variables with a function:

• where(): Applies a function to all variables and selects those for which the function returns TRUE.

Examples

# Here we show the usage for the basic selection operators. See the
# specific help pages to learn about helpers like [starts_with()].

# Select variables by name:
mtcars %>% select(mpg)

# Select multiple variables by separating them with commas. Note
# how the order of columns is determined by the order of inputs:
mtcars %>% select(disp, gear, am)

# Rename variables:
mtcars %>% select(MilesPerGallon = mpg, everything())

# The `:` operator selects a range of consecutive variables:
select(mtcars, mpg:cyl)

# The `!` operator negates a selection:
mtcars %>% select(!(mpg:qsec))
mtcars %>% select(!ends_with("p"))

# `&` and `|` take the intersection or the union of two selections:
iris %>% select(starts_with("Petal") & ends_with("Width"))
iris %>% select(starts_with("Petal") | ends_with("Width"))

# To take the difference between two selections, combine the `&` and
# `!` operators:
iris %>% select(starts_with("Petal") & !ends_with("Width"))