
Find or get columns in a data frame based on search patterns
Source:R/data_find.R
, R/data_select.R
find_columns.Rd
find_columns()
returns column names from a data set that
match a certain search pattern, while get_columns()
returns the found data.
data_select()
is an alias for get_columns()
, and data_find()
is an alias
for find_columns()
.
Usage
find_columns(
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,
...
)
get_columns(
data,
select = NULL,
exclude = NULL,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
data_select(
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
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("")
,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.find_columns(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
find_columns()
returns a character vector with column names that matched
the pattern in select
and exclude
, or NULL
if no matching column name
was found. get_columns()
returns a data frame with matching columns.
Details
Note that there are some limitations when calling this from inside other
functions. The following will work as expected, returning all columns that
start with "Sep"
:
foo <- function(data) {
find_columns(data, select = starts_with("Sep"))
}
foo(iris)
However, this example won't work as expected!
foo <- function(data) {
i <- "Sep"
find_columns(data, select = starts_with(i))
}
foo(iris)
One workaround is to use the regex
argument, which provides at least a bit
more flexibility than exact matching. regex
in its basic usage (as seen
below) means that select
behaves like the contains("")
select-helper, but
can also make the function more flexible by allowing to define complex
regular expression pattern in select
.
foo <- function(data) {
i <- "Sep"
find_columns(data, select = i, regex = TRUE)
}
foo(iris)
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 dataframes:
data_to_long()
,data_to_wide()
,data_rotate()
Functions to recode data:
data_rescale()
,data_reverse()
,data_cut()
,data_recode()
,data_shift()
Functions to standardize, normalize, rank-transform:
center()
,standardize()
,normalize()
,ranktransform()
,winsorize()
Split and merge dataframes:
data_partition()
,data_merge()
Functions to find or select columns:
data_select()
,data_find()
Functions to filter rows:
data_match()
,data_filter()
Examples
# Find columns names by pattern
find_columns(iris, starts_with("Sepal"))
#> [1] "Sepal.Length" "Sepal.Width"
find_columns(iris, ends_with("Width"))
#> [1] "Sepal.Width" "Petal.Width"
find_columns(iris, regex("\\."))
#> [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"
find_columns(iris, c("Petal.Width", "Sepal.Length"))
#> [1] "Petal.Width" "Sepal.Length"
# starts with "Sepal", but not allowed to end with "width"
find_columns(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
find_columns(iris, numeric_mean_35)
#> [1] "Sepal.Length" "Petal.Length"