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Extract all duplicates

Source: R/data_duplicated.R
data_duplicated.Rd

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"), a character vector of variable names (e.g., c("col1", "col2", "col3")), or a character vector of variable names including ranges specified via : (e.g., c("col1:col3", "col5")),

  • for some functions, like data_select() or data_rename(), select can be a named character vector. In this case, the names are used to rename the columns in the output data frame. See 'Details' in the related functions to see where this option applies.

  • 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"). regex() can be used to define regular expression patterns.

  • 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".

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