data_unique: Keep only one row from all with duplicated IDs

In datawizard: Easy Data Wrangling and Statistical Transformations

Keep only one row from all with duplicated IDs

Description

From all rows with at least one duplicated ID, keep only one. Methods for selecting the duplicated row are either the first duplicate, the last duplicate, or the "best" duplicate (default), based on the duplicate with the smallest number of NA. In case of ties, it picks the first duplicate, as it is the one most likely to be valid and authentic, given practice effects.

Contrarily to dplyr::distinct(), data_unique() keeps all columns.

Usage

data_unique(
  data,
  select = NULL,
  keep = "best",
  exclude = NULL,
  ignore_case = FALSE,
  regex = FALSE,
  verbose = TRUE
)

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. extract_column_names(iris, select = c("Species", "Test")) will just return "Species".
keep	The method to be used for duplicate selection, either "best" (the default), "first", or "last".
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.

Value

A data frame, containing only the chosen duplicates.

See Also

data_duplicated()

Examples

df1 <- data.frame(
  id = c(1, 2, 3, 1, 3),
  item1 = c(NA, 1, 1, 2, 3),
  item2 = c(NA, 1, 1, 2, 3),
  item3 = c(NA, 1, 1, 2, 3)
)

data_unique(df1, select = "id")

datawizard documentation built on Oct. 6, 2024, 1:08 a.m.