extract_column_names() returns column names from a data set that
match a certain search pattern, while data_select() returns the found data.
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,
...
)
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.
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(""). 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".
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.
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"))
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()
# Find columns names by pattern
extract_column_names(iris, starts_with("Sepal"))
extract_column_names(iris, ends_with("Width"))
extract_column_names(iris, regex("\\."))
extract_column_names(iris, c("Petal.Width", "Sepal.Length"))
# starts with "Sepal", but not allowed to end with "width"
extract_column_names(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
extract_column_names(iris, numeric_mean_35)
# rename returned columns for "data_select()"
head(data_select(mtcars, c(`Miles per Gallon` = "mpg", Cylinders = "cyl")))
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