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Count Combinations and Totals for Logical Columns in Data Frame

count_lgl.Rd

Functions to count the number of ocurrences of unique combinations of values across all logical columns in a data frame and the total number of TRUE values for each logical column.

Usage

count_lgl(df, .newcol = n, .arrange_by = NULL)

sum_lgl(df, wt = NULL)

Arguments

df

a data frame, or a data frame extension (e.g. a tibble).

.newcol

quoted name to be assigned to the new count column; default n.

.arrange_by

<data-masking> quoted name of a column for ordering results; default NULL.

wt

frequency weights. Can be NULL or the name of a numeric variable column; default NULL.

  • If NULL (the default), counts the number of rows with TRUE values for each logical column.

  • If a variable, computes sum(wt) for rows with TRUE values for each logical column.

Value

count_lgl() returns a data frame comprising unique combinations of the logical columns of df and an additional column of the counts of ocurrences of each of these combinations. sum_lgl() returns a named numeric vector of totals for each logical column.

Details

Function count_lgl() counts the number of ocurrences of unique combinations of values across all logical columns in a data frame using summarise in package dplyr.

Function sum_lgl() counts the total number of TRUE values for each logical column in a data frame using matrix multiplication.

These functions may be useful for counting combinations or tallying totals of keywords flagged as TRUE in logical columns added to a data frame using kwd_cols.

See also

summarise.

Other logical-cols: kwd_cols(), list_lgl()

Examples

## Following on from kwd_cols() examples… 
car_names <- data.frame(Response = rownames(mtcars)) |>
  kwd_cols(, c("Mazda", "Merc", "Toyota", "Volkswagen", "X", "450"))
car_names
#>               Response Mazda  Merc Toyota Volkswagen     X   450
#> 1            Mazda RX4  TRUE FALSE  FALSE      FALSE  TRUE FALSE
#> 2        Mazda RX4 Wag  TRUE FALSE  FALSE      FALSE  TRUE FALSE
#> 3           Datsun 710 FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 4       Hornet 4 Drive FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 5    Hornet Sportabout FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 6              Valiant FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 7           Duster 360 FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 8            Merc 240D FALSE  TRUE  FALSE      FALSE FALSE FALSE
#> 9             Merc 230 FALSE  TRUE  FALSE      FALSE FALSE FALSE
#> 10            Merc 280 FALSE  TRUE  FALSE      FALSE FALSE FALSE
#> 11           Merc 280C FALSE  TRUE  FALSE      FALSE FALSE FALSE
#> 12          Merc 450SE FALSE  TRUE  FALSE      FALSE FALSE  TRUE
#> 13          Merc 450SL FALSE  TRUE  FALSE      FALSE FALSE  TRUE
#> 14         Merc 450SLC FALSE  TRUE  FALSE      FALSE FALSE  TRUE
#> 15  Cadillac Fleetwood FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 16 Lincoln Continental FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 17   Chrysler Imperial FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 18            Fiat 128 FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 19         Honda Civic FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 20      Toyota Corolla FALSE FALSE   TRUE      FALSE FALSE FALSE
#> 21       Toyota Corona FALSE FALSE   TRUE      FALSE FALSE FALSE
#> 22    Dodge Challenger FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 23         AMC Javelin FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 24          Camaro Z28 FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 25    Pontiac Firebird FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 26           Fiat X1-9 FALSE FALSE  FALSE      FALSE  TRUE FALSE
#> 27       Porsche 914-2 FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 28        Lotus Europa FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 29      Ford Pantera L FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 30        Ferrari Dino FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 31       Maserati Bora FALSE FALSE  FALSE      FALSE FALSE FALSE
#> 32          Volvo 142E FALSE FALSE  FALSE      FALSE FALSE FALSE

(carname_counts <- count_lgl(car_names, .arrange_by = desc(n)))
#>   Mazda  Merc Toyota Volkswagen     X   450  n
#> 1 FALSE FALSE  FALSE      FALSE FALSE FALSE 20
#> 2 FALSE  TRUE  FALSE      FALSE FALSE FALSE  4
#> 3 FALSE  TRUE  FALSE      FALSE FALSE  TRUE  3
#> 4  TRUE FALSE  FALSE      FALSE  TRUE FALSE  2
#> 5 FALSE FALSE   TRUE      FALSE FALSE FALSE  2
#> 6 FALSE FALSE  FALSE      FALSE  TRUE FALSE  1
sum_lgl(car_names)
#>      Mazda       Merc     Toyota Volkswagen          X        450 
#>          2          7          2          0          3          3 
sum_lgl(carname_counts, wt = "n")
#>      Mazda       Merc     Toyota Volkswagen          X        450 
#>          2          7          2          0          3          3 

car_names |> count_lgl(.newcol = subtotals) |> print_lf() |> sum_lgl(wt = "subtotals")
#>   Mazda  Merc Toyota Volkswagen     X   450 subtotals
#> 1 FALSE FALSE  FALSE      FALSE FALSE FALSE        20
#> 2 FALSE FALSE  FALSE      FALSE  TRUE FALSE         1
#> 3 FALSE FALSE   TRUE      FALSE FALSE FALSE         2
#> 4 FALSE  TRUE  FALSE      FALSE FALSE FALSE         4
#> 5 FALSE  TRUE  FALSE      FALSE FALSE  TRUE         3
#> 6  TRUE FALSE  FALSE      FALSE  TRUE FALSE         2
#> 
#>      Mazda       Merc     Toyota Volkswagen          X        450 
#>          2          7          2          0          3          3 

rm(car_names, carname_counts)