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()
.
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,
...
)
data_findcols(
data,
pattern = NULL,
starts_with = NULL,
ends_with = NULL,
ignore_case = FALSE,
...
)
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,
...
)
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.
A data frame.
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("")
,
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. find_columns(iris, select = c("Species", "Test"))
will just
return "Species"
.
See select
, however, column names matched by the pattern
from exclude
will be excluded instead of selected. If NULL
(the default),
excludes no columns.
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.
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.
Toggle warnings.
Arguments passed down to other functions. Mostly not used yet.
A regular expression (as character string), representing the
pattern to be matched in the in column names. Can also be one of the
following select-helpers: starts_with("")
, end_with("")
, regex("")
,
contains("")
, or a range using :
.
Character string, containing the string to be
matched in the column names. starts_with
finds matches at the beginning
of column names, ends_with
finds matches at the end of column names.
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)
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()
# Find columns names by pattern
find_columns(iris, starts_with("Sepal"))
find_columns(iris, ends_with("Width"))
find_columns(iris, regex("\\."))
find_columns(iris, c("Petal.Width", "Sepal.Length"))
# starts with "Sepal", but not allowed to end with "width"
find_columns(iris, starts_with("Sepal"), exclude = contains("Width"))
# 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)
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