Count duplicate values by column

duplicate_count_colpair() takes a data frame and checks each combination of columns for duplicates. Results are presented in a tibble, ordered by the number of duplicates.

Usage

duplicate_count_colpair(
  data,
  ignore = NULL,
  show_rates = TRUE,
  na.rm = deprecated()
)

Arguments

data

Data frame.

ignore

Optionally, a vector of values that should not be checked for duplicates.

show_rates

Logical. If TRUE (the default), adds columns rate_x and rate_y. See value section. Set show_rates to FALSE for higher performance.

na.rm

[Deprecated] Missing values are never counted in any case.

Value

A tibble (data frame) with these columns –

• x and y: Each line contains a unique combination of data's columns, stored in the x and y output columns.

• count: Number of "duplicates", i.e., values that are present in both x and y.

• total_x, total_y, rate_x, and rate_y (added by default): total_x is the number of non-missing values in the column named under x. Also, rate_x is the proportion of x values that are duplicated in y, i.e., count / total_x. Likewise with total_y and rate_y. The two rate_* columns will be equal unless NA values are present.

Summaries with audit()

There is an S3 method for audit(), so you can call audit() following duplicate_count_colpair(). It returns a tibble with summary statistics.

See also

• duplicate_count() for a frequency table.

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

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

• corrr::colpair_map(), a versatile tool for pairwise column analysis which the present function wraps.

Examples

# Basic usage:
mtcars %>%
  duplicate_count_colpair()
#> # A tibble: 55 × 7
#>    x     y     count total_x total_y rate_x rate_y
#>    <chr> <chr> <int>   <int>   <int>  <dbl>  <dbl>
#>  1 cyl   carb     32      32      32 1      1     
#>  2 vs    am       32      32      32 1      1     
#>  3 gear  carb     27      32      32 0.844  0.844 
#>  4 vs    carb     14      32      32 0.438  0.438 
#>  5 am    carb     13      32      32 0.406  0.406 
#>  6 cyl   gear     11      32      32 0.344  0.344 
#>  7 drat  wt        3      32      32 0.0938 0.0938
#>  8 mpg   qsec      2      32      32 0.0625 0.0625
#>  9 drat  gear      1      32      32 0.0312 0.0312
#> 10 drat  carb      1      32      32 0.0312 0.0312
#> # ℹ 45 more rows

# Summaries with `audit()`:
mtcars %>%
  duplicate_count_colpair() %>%
  audit()
#> # A tibble: 5 × 8
#>   term       mean    sd median   min   max na_count na_rate
#>   <chr>     <dbl> <dbl>  <dbl> <dbl> <dbl>    <dbl>   <dbl>
#> 1 count    2.47   7.38       0     0    32        0       0
#> 2 total_x 32      0         32    32    32        0       0
#> 3 total_y 32      0         32    32    32        0       0
#> 4 rate_x   0.0773 0.231      0     0     1        0       0
#> 5 rate_y   0.0773 0.231      0     0     1        0       0