Keep only one row from all with duplicated IDsSource:
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
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.
data_unique() keeps all columns.
data_unique( data, select = NULL, keep = "best", exclude = NULL, ignore_case = FALSE, regex = FALSE, verbose = TRUE )
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.,
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.
c(1, 3, 5)),
a vector of negative integers, giving the positions counting from the right (e.g.,
one of the following select-helpers:
contains(), a range using
contains()accept several patterns, e.g
or a function testing for logical conditions, e.g.
is.numeric), or any user-defined function that selects the variables for which the function returns
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
-(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, the
excludeargument can be used alternatively. For instance,
-) is equivalent to
-). In case negation should not work as expected, use the
NULL, selects all columns. Patterns that found no matches are silently ignored, e.g.
find_columns(iris, select = c("Species", "Test"))will just return
The method to be used for duplicate selection, either "best" (the default), "first", or "last".
select, however, column names matched by the pattern from
excludewill be excluded instead of selected. If
NULL(the default), excludes no columns.
TRUEand 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.
TRUE, the search pattern from
selectwill 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 = TRUEis 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.
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") #> (2 duplicates removed, with method 'best') #> id item1 item2 item3 #> 1 1 2 2 2 #> 2 2 1 1 1 #> 3 3 1 1 1