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.
data_unique(
data,
select = NULL,
keep = "best",
exclude = NULL,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE
)A data frame, containing only the chosen duplicates.
A data frame.
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".
The method to be used for duplicate selection, either "best" (the default), "first", or "last".
See select, however, column names matched by the pattern
from exclude will be excluded instead of selected. If NULL (the default),
excludes no columns.
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.
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.
Toggle warnings.
data_duplicated()
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")
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