Count duplicate values

duplicate_count() returns a frequency table. When searching a data frame, it includes values from all columns for each frequency count.

This function is a blunt tool designed for initial data checking. It is not too informative if many values have few characters each.

For summary statistics, call audit() on the results.

Usage

duplicate_count(
  x,
  ignore = NULL,
  locations_type = c("character", "list"),
  numeric_only = deprecated()
)

Arguments

x

Vector or data frame.

ignore

Optionally, a vector of values that should not be counted.

locations_type

String. One of "character" or "list". With "list", each locations value is a vector of column names, which is better for further programming. By default ("character"), the column names are pasted into a string, which is more readable.

numeric_only

[Deprecated] No longer used: All values are coerced to character.

Value

If x is a data frame or another named vector, a tibble with four columns. If x isn't named, only the first two columns appear:

• value: All the values from x.

• frequency: Absolute frequency of each value in x, in descending order.

• locations: Names of all columns from x in which value appears.

• locations_n: Number of columns named in locations.

The tibble has the scr_dup_count class, which is recognized by the audit() generic.

Details

Don't use numeric_only. It no longer has any effect and will be removed in the future. The only reason for this argument was the risk of errors introduced by coercing values to numeric. This is no longer an issue because all values are now coerced to character, which is more appropriate for checking reported statistics.

Summaries with audit()

There is an S3 method for the audit() generic, so you can call audit() following duplicate_count(). It returns a tibble with summary statistics for the two numeric columns, frequency and locations_n (or, if x isn't named, only for frequency).

See also

• duplicate_count_colpair() to check each combination of columns for duplicates.

• duplicate_tally() to show instances of a value next to each instance.

• janitor::get_dupes() to search for duplicate rows.

Examples

# Count duplicate values...
iris %>%
  duplicate_count()
#> # A tibble: 77 × 4
#>    value      frequency locations                  locations_n
#>    <chr>          <int> <chr>                            <int>
#>  1 setosa            50 Species                              1
#>  2 versicolor        50 Species                              1
#>  3 virginica         50 Species                              1
#>  4 0.2               29 Petal.Width                          1
#>  5 3                 27 Sepal.Width, Petal.Length            2
#>  6 1.5               25 Petal.Length, Petal.Width            2
#>  7 1.4               21 Petal.Length, Petal.Width            2
#>  8 1.3               20 Petal.Length, Petal.Width            2
#>  9 5.1               17 Sepal.Length, Petal.Length           2
#> 10 5                 14 Sepal.Length, Petal.Length           2
#> # ℹ 67 more rows

# ...and compute summaries:
iris %>%
  duplicate_count() %>%
  audit()
#> # A tibble: 2 × 8
#>   term         mean    sd median   min   max na_count na_rate
#>   <chr>       <dbl> <dbl>  <dbl> <dbl> <dbl>    <dbl>   <dbl>
#> 1 frequency    9.74 9.92       7     1    50        0       0
#> 2 locations_n  1.64 0.511      2     1     3        0       0

# Any values can be ignored:
iris %>%
  duplicate_count(ignore = c("setosa", "versicolor", "virginica"))
#> # A tibble: 74 × 4
#>    value frequency locations                  locations_n
#>    <chr>     <int> <chr>                            <int>
#>  1 0.2          29 Petal.Width                          1
#>  2 3            27 Sepal.Width, Petal.Length            2
#>  3 1.5          25 Petal.Length, Petal.Width            2
#>  4 1.4          21 Petal.Length, Petal.Width            2
#>  5 1.3          20 Petal.Length, Petal.Width            2
#>  6 5.1          17 Sepal.Length, Petal.Length           2
#>  7 5            14 Sepal.Length, Petal.Length           2
#>  8 2.8          14 Sepal.Width                          1
#>  9 3.2          13 Sepal.Width                          1
#> 10 5.6          12 Sepal.Length, Petal.Length           2
#> # ℹ 64 more rows