Find or get columns in a data frame based on search patterns
Source:R/data_select.R
, R/extract_column_names.R
extract_column_names.Rd
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,
...
)
extract_column_names(
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"
), a character vector of variable names (e.g.,c("col1", "col2", "col3")
), or a character vector of variable names including ranges specified via:
(e.g.,c("col1:col3", "col5")
),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
orc(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:
orregex("")
.starts_with()
,ends_with()
, andcontains()
accept several patterns, e.gstarts_with("Sep", "Petal")
.or a function testing for logical conditions, e.g.
is.numeric()
(oris.numeric
), or any user-defined function that selects the variables for which the function returnsTRUE
(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, theexclude
argument can be used alternatively. For instance,select=-ends_with("Length")
(with-
) is equivalent toexclude=ends_with("Length")
(no-
). In case negation should not work as expected, use theexclude
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 fromexclude
will be excluded instead of selected. IfNULL
(the default), excludes no columns.- ignore_case
Logical, if
TRUE
and when one of the select-helpers or a regular expression is used inselect
, ignores lower/upper case in the search pattern when matching against variable names.- regex
Logical, if
TRUE
, the search pattern fromselect
will be treated as regular expression. Whenregex = 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("")
orselect = 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 column 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"
# find range of colum names by range, using character vector
extract_column_names(mtcars, c("cyl:hp", "wt"))
#> [1] "cyl" "disp" "hp" "wt"
# 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