R: Relocate (reorder) columns of a data frame

data_relocate

R Documentation

Relocate (reorder) columns of a data frame

Description

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"`), 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. `find_columns(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.

Examples