Relocate (reorder) columns of a data frame
Source:R/data_relocate.R
, R/data_remove.R
data_relocate.Rd
data_relocate()
will reorder columns to specific positions, indicated by
before
or after
. data_reorder()
will instead move selected columns to
the beginning of a data frame. Finally, data_remove()
removes columns
from a data frame. All functions support select-helpers that allow flexible
specification of a search pattern to find matching columns, which should
be reordered or removed.
Usage
data_relocate(
data,
select,
before = NULL,
after = NULL,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
data_reorder(
data,
select,
exclude = NULL,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
data_remove(
data,
select = NULL,
exclude = NULL,
ignore_case = FALSE,
regex = FALSE,
verbose = FALSE,
...
)
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"
), a character vector of variable names (e.g.,c("col1", "col2", "col3")
), or a character vector of variable names including ranges specified via:
(e.g.,c("col1:col3", "col5")
),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()
.starts_with()
,ends_with()
, andcontains()
accept several patterns, e.gstarts_with("Sep", "Petal")
.regex()
can be used to define regular expression patterns.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.extract_column_names(iris, select = c("Species", "Test"))
will just return"Species"
.- before, after
Destination of columns. Supplying neither will move columns to the left-hand side; specifying both is an error. Can be a character vector, indicating the name of the destination column, or a numeric value, indicating the index number of the destination column. If
-1
, will be added before or after the last column.- 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.
- exclude
See
select
, however, column names matched by the pattern fromexclude
will be excluded instead of selected. IfNULL
(the default), excludes no columns.
See also
Add a prefix or suffix to column names:
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
# Reorder columns
head(data_relocate(iris, select = "Species", before = "Sepal.Length"))
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1 setosa 5.1 3.5 1.4 0.2
#> 2 setosa 4.9 3.0 1.4 0.2
#> 3 setosa 4.7 3.2 1.3 0.2
#> 4 setosa 4.6 3.1 1.5 0.2
#> 5 setosa 5.0 3.6 1.4 0.2
#> 6 setosa 5.4 3.9 1.7 0.4
head(data_relocate(iris, select = "Species", before = "Sepal.Width"))
#> Sepal.Length Species Sepal.Width Petal.Length Petal.Width
#> 1 5.1 setosa 3.5 1.4 0.2
#> 2 4.9 setosa 3.0 1.4 0.2
#> 3 4.7 setosa 3.2 1.3 0.2
#> 4 4.6 setosa 3.1 1.5 0.2
#> 5 5.0 setosa 3.6 1.4 0.2
#> 6 5.4 setosa 3.9 1.7 0.4
head(data_relocate(iris, select = "Sepal.Width", after = "Species"))
#> Sepal.Length Petal.Length Petal.Width Species Sepal.Width
#> 1 5.1 1.4 0.2 setosa 3.5
#> 2 4.9 1.4 0.2 setosa 3.0
#> 3 4.7 1.3 0.2 setosa 3.2
#> 4 4.6 1.5 0.2 setosa 3.1
#> 5 5.0 1.4 0.2 setosa 3.6
#> 6 5.4 1.7 0.4 setosa 3.9
# which is same as
head(data_relocate(iris, select = "Sepal.Width", after = -1))
#> Sepal.Length Petal.Length Petal.Width Species Sepal.Width
#> 1 5.1 1.4 0.2 setosa 3.5
#> 2 4.9 1.4 0.2 setosa 3.0
#> 3 4.7 1.3 0.2 setosa 3.2
#> 4 4.6 1.5 0.2 setosa 3.1
#> 5 5.0 1.4 0.2 setosa 3.6
#> 6 5.4 1.7 0.4 setosa 3.9
# Reorder multiple columns
head(data_relocate(iris, select = c("Species", "Petal.Length"), after = "Sepal.Width"))
#> Sepal.Length Sepal.Width Species Petal.Length Petal.Width
#> 1 5.1 3.5 setosa 1.4 0.2
#> 2 4.9 3.0 setosa 1.4 0.2
#> 3 4.7 3.2 setosa 1.3 0.2
#> 4 4.6 3.1 setosa 1.5 0.2
#> 5 5.0 3.6 setosa 1.4 0.2
#> 6 5.4 3.9 setosa 1.7 0.4
# which is same as
head(data_relocate(iris, select = c("Species", "Petal.Length"), after = 2))
#> Sepal.Length Sepal.Width Species Petal.Length Petal.Width
#> 1 5.1 3.5 setosa 1.4 0.2
#> 2 4.9 3.0 setosa 1.4 0.2
#> 3 4.7 3.2 setosa 1.3 0.2
#> 4 4.6 3.1 setosa 1.5 0.2
#> 5 5.0 3.6 setosa 1.4 0.2
#> 6 5.4 3.9 setosa 1.7 0.4
# Reorder columns
head(data_reorder(iris, c("Species", "Sepal.Length")))
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1 setosa 5.1 3.5 1.4 0.2
#> 2 setosa 4.9 3.0 1.4 0.2
#> 3 setosa 4.7 3.2 1.3 0.2
#> 4 setosa 4.6 3.1 1.5 0.2
#> 5 setosa 5.0 3.6 1.4 0.2
#> 6 setosa 5.4 3.9 1.7 0.4
# Remove columns
head(data_remove(iris, "Sepal.Length"))
#> Sepal.Width Petal.Length Petal.Width Species
#> 1 3.5 1.4 0.2 setosa
#> 2 3.0 1.4 0.2 setosa
#> 3 3.2 1.3 0.2 setosa
#> 4 3.1 1.5 0.2 setosa
#> 5 3.6 1.4 0.2 setosa
#> 6 3.9 1.7 0.4 setosa
head(data_remove(iris, starts_with("Sepal")))
#> Petal.Length Petal.Width Species
#> 1 1.4 0.2 setosa
#> 2 1.4 0.2 setosa
#> 3 1.3 0.2 setosa
#> 4 1.5 0.2 setosa
#> 5 1.4 0.2 setosa
#> 6 1.7 0.4 setosa