Relocate (reorder) columns of a data frame

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")),

• for some functions, like data_select() or data_rename(), select can be a named character vector. In this case, the names are used to rename the columns in the output data frame. See 'Details' in the related functions to see where this option applies.

• 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"). regex() can be used to define regular expression patterns.

• 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".

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 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.

...

Arguments passed down to other functions. Mostly not used yet.

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.

Value

A data frame with reordered 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