Find or get columns in a data frame based on search patterns

extract_column_names() returns column names from a data set that match a certain search pattern, while data_select() returns the found data.

Usage

data_select(
  data,
  select = NULL,
  exclude = NULL,
  ignore_case = FALSE,
  regex = FALSE,
  verbose = TRUE,
  ...
)

get_columns(
  data,
  select = NULL,
  exclude = NULL,
  ignore_case = FALSE,
  regex = FALSE,
  verbose = TRUE,
  ...
)

extract_column_names(
  data,
  select = NULL,
  exclude = NULL,
  ignore_case = FALSE,
  regex = FALSE,
  verbose = TRUE,
  ...
)

data_find(
  data,
  select = NULL,
  exclude = NULL,
  ignore_case = FALSE,
  regex = FALSE,
  verbose = TRUE,
  ...
)

find_columns(
  data,
  select = NULL,
  exclude = NULL,
  ignore_case = FALSE,
  regex = FALSE,
  verbose = TRUE,
  ...
)

Arguments

data

A data frame.

select

Variables that will be included when performing the required tasks. Can be either

• a variable specified as a literal variable name (e.g., column_name),

• a string with the variable name (e.g., "column_name"), or a character vector of variable names (e.g., c("col1", "col2", "col3")),

• a formula with variable names (e.g., ~column_1 + column_2),

• a vector of positive integers, giving the positions counting from the left (e.g. 1 or c(1, 3, 5)),

• a vector of negative integers, giving the positions counting from the right (e.g., -1 or -1:-3),

• one of the following select-helpers: starts_with(), ends_with(), contains(), a range using : or regex(""). starts_with(), ends_with(), and contains() accept several patterns, e.g starts_with("Sep", "Petal").

• or a function testing for logical conditions, e.g. is.numeric() (or is.numeric), or any user-defined function that selects the variables for which the function returns TRUE (like: foo <- function(x) mean(x) > 3),

• ranges specified via literal variable names, select-helpers (except regex()) and (user-defined) functions can be negated, i.e. return non-matching elements, when prefixed with a -, e.g. -ends_with(""), -is.numeric or -(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, the exclude argument can be used alternatively. For instance, select=-ends_with("Length") (with -) is equivalent to exclude=ends_with("Length") (no -). In case negation should not work as expected, use the exclude argument instead.

If NULL, selects all columns. Patterns that found no matches are silently ignored, e.g. extract_column_names(iris, select = c("Species", "Test")) will just return "Species".

exclude

See select, however, column names matched by the pattern from exclude will be excluded instead of selected. If NULL (the default), excludes no columns.

ignore_case

Logical, if TRUE and when one of the select-helpers or a regular expression is used in select, ignores lower/upper case in the search pattern when matching against variable names.

regex

Logical, if TRUE, the search pattern from select will be treated as regular expression. When regex = TRUE, select must be a character string (or a variable containing a character string) and is not allowed to be one of the supported select-helpers or a character vector of length > 1. regex = TRUE is comparable to using one of the two select-helpers, select = contains("") or select = regex(""), however, since the select-helpers may not work when called from inside other functions (see 'Details'), this argument may be used as workaround.

verbose

Toggle warnings.

...

Arguments passed down to other functions. Mostly not used yet.

Value

extract_column_names() returns a character vector with column names that matched the pattern in select and exclude, or NULL if no matching column name was found. data_select() returns a data frame with matching columns.

Details

Specifically for data_select(), select can also be a named character vector. In this case, the names are used to rename the columns in the output data frame. See 'Examples'.

Note that it is possible to either pass an entire select helper or only the pattern inside a select helper as a function argument:

foo <- function(data, pattern) {
  extract_column_names(data, select = starts_with(pattern))
}
foo(iris, pattern = "Sep")
foo2 <- function(data, pattern) {
  extract_column_names(data, select = pattern)
}
foo2(iris, pattern = starts_with("Sep"))

This means that it is also possible to use loop values as arguments or patterns:

for (i in c("Sepal", "Sp")) {
  head(iris) |>
    extract_column_names(select = starts_with(i)) |>
    print()
}

However, this behavior is limited to a "single-level function". It will not work in nested functions, like below:

inner <- function(data, arg) {
  extract_column_names(data, select = arg)
}
outer <- function(data, arg) {
  inner(data, starts_with(arg))
}
outer(iris, "Sep")

In this case, it is better to pass the whole select helper as the argument of outer():

outer <- function(data, arg) {
  inner(data, arg)
}
outer(iris, starts_with("Sep"))

See also

• Functions to rename stuff: data_rename(), data_rename_rows(), data_addprefix(), data_addsuffix()

• Functions to reorder or remove columns: data_reorder(), data_relocate(), data_remove()

• Functions to reshape, pivot or rotate data frames: data_to_long(), data_to_wide(), data_rotate()

• Functions to recode data: rescale(), reverse(), categorize(), recode_values(), slide()

• Functions to standardize, normalize, rank-transform: center(), standardize(), normalize(), ranktransform(), winsorize()

• Split and merge data frames: data_partition(), data_merge()

• Functions to find or select columns: data_select(), extract_column_names()

• Functions to filter rows: data_match(), data_filter()

Examples

# Find columns names by pattern
extract_column_names(iris, starts_with("Sepal"))
#> [1] "Sepal.Length" "Sepal.Width" 
extract_column_names(iris, ends_with("Width"))
#> [1] "Sepal.Width" "Petal.Width"
extract_column_names(iris, regex("\\."))
#> [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width" 
extract_column_names(iris, c("Petal.Width", "Sepal.Length"))
#> [1] "Petal.Width"  "Sepal.Length"

# starts with "Sepal", but not allowed to end with "width"
extract_column_names(iris, starts_with("Sepal"), exclude = contains("Width"))
#> [1] "Sepal.Length"

# find numeric with mean > 3.5
numeric_mean_35 <- function(x) is.numeric(x) && mean(x, na.rm = TRUE) > 3.5
extract_column_names(iris, numeric_mean_35)
#> [1] "Sepal.Length" "Petal.Length"

# rename returned columns for "data_select()"
head(data_select(mtcars, c(`Miles per Gallon` = "mpg", Cylinders = "cyl")))
#>                   Miles per Gallon Cylinders
#> Mazda RX4                     21.0         6
#> Mazda RX4 Wag                 21.0         6
#> Datsun 710                    22.8         4
#> Hornet 4 Drive                21.4         6
#> Hornet Sportabout             18.7         8
#> Valiant                       18.1         6