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Extract all duplicates, for visual inspection. Note that it also contains the first occurrence of future duplicates, unlike duplicated() or dplyr::distinct()). Also contains an additional column reporting the number of missing values for that row, to help in the decision-making when selecting which duplicates to keep.

## Usage

data_duplicated(
data,
select = NULL,
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. find_columns(iris, select = c("Species", "Test")) will just return "Species".

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 dataframe, containing all duplicates.

## See also

data_unique()

## Examples

df1 <- data.frame(
id = c(1, 2, 3, 1, 3),
year = c(2022, 2022, 2022, 2022, 2000),
item1 = c(NA, 1, 1, 2, 3),
item2 = c(NA, 1, 1, 2, 3),
item3 = c(NA, 1, 1, 2, 3)
)

data_duplicated(df1, select = "id")
#>   Row id year item1 item2 item3 count_na
#> 1   1  1 2022    NA    NA    NA        3
#> 4   4  1 2022     2     2     2        0
#> 3   3  3 2022     1     1     1        0
#> 5   5  3 2000     3     3     3        0

data_duplicated(df1, select = c("id", "year"))
#>   Row id year item1 item2 item3 count_na
#> 1   1  1 2022    NA    NA    NA        3
#> 4   4  1 2022     2     2     2        0

# Filter to exclude duplicates
df2 <- df1[-c(1, 5), ]
df2
#>   id year item1 item2 item3
#> 2  2 2022     1     1     1
#> 3  3 2022     1     1     1
#> 4  1 2022     2     2     2