Safe and intuitive functions to rename variables or rows in
data frames. data_rename()
will rename column names, i.e. it facilitates
renaming variables data_addprefix()
or data_addsuffix()
add prefixes
or suffixes to column names. data_rename_rows()
is a convenient shortcut
to add or rename row names of a data frame, but unlike row.names()
, its
input and output is a data frame, thus, integrating smoothly into a possible
pipe-workflow.
data_addprefix(
data,
pattern,
select = NULL,
exclude = NULL,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)data_addsuffix(
data,
pattern,
select = NULL,
exclude = NULL,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
data_rename(
data,
pattern = NULL,
replacement = NULL,
safe = TRUE,
verbose = TRUE,
...
)
data_rename_rows(data, rows = NULL)
A modified data frame.
A data frame, or an object that can be coerced to a data frame.
Character vector. For data_rename()
, indicates columns that
should be selected for renaming. Can be NULL
(in which case all columns
are selected). For data_addprefix()
or data_addsuffix()
, a character
string, which will be added as prefix or suffix to the column names. For
data_rename()
, pattern
can also be a named vector. In this case, names
are used as values for the replacement
argument (i.e. pattern
can be a
character vector using <new name> = "<old name>"
and argument replacement
will be ignored then).
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 and messages.
Other arguments passed to or from other functions.
Character vector. Indicates the new name of the columns
selected in pattern
. Can be NULL
(in which case column are numbered
in sequential order). If not NULL
, pattern
and replacement
must be
of the same length. If pattern
is a named vector, replacement
is ignored.
Do not throw error if for instance the variable to be renamed/removed doesn't exist.
Vector of row names.
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()
# Add prefix / suffix to all columns
head(data_addprefix(iris, "NEW_"))
head(data_addsuffix(iris, "_OLD"))
# Rename columns
head(data_rename(iris, "Sepal.Length", "length"))
# data_rename(iris, "FakeCol", "length", safe=FALSE) # This fails
head(data_rename(iris, "FakeCol", "length")) # This doesn't
head(data_rename(iris, c("Sepal.Length", "Sepal.Width"), c("length", "width")))
# use named vector to rename
head(data_rename(iris, c(length = "Sepal.Length", width = "Sepal.Width")))
# Reset names
head(data_rename(iris, NULL))
# Change all
head(data_rename(iris, replacement = paste0("Var", 1:5)))
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