data_addprefix {datawizard} | R Documentation |
Rename columns and variable names
Description
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.
Usage
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)
Arguments
data |
A data frame, or an object that can be coerced to a data frame.
|
pattern |
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).
|
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 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" .
|
exclude |
See select , however, column names matched by the pattern
from exclude will be excluded instead of selected. If NULL (the default),
excludes no columns.
|
ignore_case |
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.
|
regex |
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.
|
verbose |
Toggle warnings and messages.
|
... |
Other arguments passed to or from other functions.
|
replacement |
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.
|
safe |
Do not throw error if for instance the variable to be
renamed/removed doesn't exist.
|
rows |
Vector of row names.
|
Value
A modified data frame.
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
# 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)))
[Package
datawizard version 0.13.0
Index]