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fndistinct is a generic function that (column-wise) computes the number of distinct values in x, (optionally) grouped by g. It is significantly faster than length(unique(x)). The TRA argument can further be used to transform x using its (grouped) distinct value count.

Usage

fndistinct(x, ...)

# Default S3 method
fndistinct(x, g = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
           use.g.names = TRUE, nthreads = .op[["nthreads"]], ...)

# S3 method for class 'matrix'
fndistinct(x, g = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
           use.g.names = TRUE, drop = TRUE, nthreads = .op[["nthreads"]], ...)

# S3 method for class 'data.frame'
fndistinct(x, g = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
           use.g.names = TRUE, drop = TRUE, nthreads = .op[["nthreads"]], ...)

# S3 method for class 'grouped_df'
fndistinct(x, TRA = NULL, na.rm = .op[["na.rm"]],
           use.g.names = FALSE, keep.group_vars = TRUE, nthreads = .op[["nthreads"]], ...)

Arguments

x

a vector, matrix, data frame or grouped data frame (class 'grouped_df').

g

a factor, GRP object, atomic vector (internally converted to factor) or a list of vectors / factors (internally converted to a GRP object) used to group x.

TRA

an integer or quoted operator indicating the transformation to perform: 0 - "na" | 1 - "fill" | 2 - "replace" | 3 - "-" | 4 - "-+" | 5 - "/" | 6 - "%" | 7 - "+" | 8 - "*" | 9 - "%%" | 10 - "-%%". See TRA.

na.rm

logical. TRUE: Skip missing values in x (faster computation). FALSE: Also consider 'NA' as one distinct value.

use.g.names

logical. Make group-names and add to the result as names (default method) or row-names (matrix and data frame methods). No row-names are generated for data.table's.

nthreads

integer. The number of threads to utilize. Parallelism is across groups for grouped computations and at the column-level otherwise.

drop

matrix and data.frame method: Logical. TRUE drops dimensions and returns an atomic vector if g = NULL and TRA = NULL.

keep.group_vars

grouped_df method: Logical. FALSE removes grouping variables after computation.

...

arguments to be passed to or from other methods. If TRA is used, passing set = TRUE will transform data by reference and return the result invisibly.

Details

fndistinct implements a pretty fast C-level hashing algorithm inspired by the kit package to find the number of distinct values.

If na.rm = TRUE (the default), missing values will be skipped yielding substantial performance gains in data with many missing values. If na.rm = FALSE, missing values will simply be treated as any other value and read into the hash-map. Thus with the former, a numeric vector c(1.25,NaN,3.56,NA) will have a distinct value count of 2, whereas the latter will return a distinct value count of 4.

fndistinct preserves all attributes of non-classed vectors / columns, and only the 'label' attribute (if available) of classed vectors / columns (i.e. dates or factors). When applied to data frames and matrices, the row-names are adjusted as necessary.

Value

Integer. The number of distinct values in x, grouped by g, or (if TRA is used) x transformed by its distinct value count, grouped by g.

Examples

## default vector method
fndistinct(airquality$Solar.R)                   # Simple distinct value count
#> [1] 117
fndistinct(airquality$Solar.R, airquality$Month) # Grouped distinct value count
#>  5  6  7  8  9 
#> 27 28 29 27 27 

## data.frame method
fndistinct(airquality)
#>   Ozone Solar.R    Wind    Temp   Month     Day 
#>      67     117      31      40       5      31 
fndistinct(airquality, airquality$Month)
#>   Ozone Solar.R Wind Temp Month Day
#> 5    21      27   18   18     1  31
#> 6     9      28   16   19     1  30
#> 7    24      29   17   14     1  31
#> 8    24      27   18   19     1  31
#> 9    21      27   19   20     1  30
fndistinct(wlddev)                               # Works with data of all types!
#> country   iso3c    date    year  decade  region  income    OECD   PCGDP  LIFEEX 
#>     216     216      61      61       7       7       4       2    9470   10548 
#>    GINI     ODA     POP 
#>     368    7832   12877 
head(fndistinct(wlddev, wlddev$iso3c))
#>     country iso3c date year decade region income OECD PCGDP LIFEEX GINI ODA POP
#> ABW       1     1   61   61      7      1      1    1    32     60    0  20  60
#> AFG       1     1   61   61      7      1      1    1    18     60    0  60  60
#> AGO       1     1   61   61      7      1      1    1    40     59    3  58  60
#> ALB       1     1   61   61      7      1      1    1    40     59    9  32  60
#> AND       1     1   61   61      7      1      1    1    50      0    0   0  60
#>  [ reached 'max' / getOption("max.print") -- omitted 1 rows ]

## matrix method
aqm <- qM(airquality)
fndistinct(aqm)                                  # Also works for character or logical matrices
#>   Ozone Solar.R    Wind    Temp   Month     Day 
#>      67     117      31      40       5      31 
fndistinct(aqm, airquality$Month)
#>   Ozone Solar.R Wind Temp Month Day
#> 5    21      27   18   18     1  31
#> 6     9      28   16   19     1  30
#> 7    24      29   17   14     1  31
#> 8    24      27   18   19     1  31
#> 9    21      27   19   20     1  30

## method for grouped data frames - created with dplyr::group_by or fgroup_by
airquality |> fgroup_by(Month) |> fndistinct()
#>   Month Ozone Solar.R Wind Temp Day
#> 1     5    21      27   18   18  31
#> 2     6     9      28   16   19  30
#> 3     7    24      29   17   14  31
#> 4     8    24      27   18   19  31
#> 5     9    21      27   19   20  30
wlddev |> fgroup_by(country) |>
             fselect(PCGDP,LIFEEX,GINI,ODA) |> fndistinct()
#>                country PCGDP LIFEEX GINI ODA
#> 1          Afghanistan    18     60    0  60
#> 2              Albania    40     59    9  32
#> 3              Algeria    60     60    3  60
#> 4       American Samoa    17      0    0   0
#> 5              Andorra    50      0    0   0
#> 6               Angola    40     59    3  58
#> 7  Antigua and Barbuda    43     60    0  47
#> 8            Argentina    60     60   29  60
#> 9              Armenia    30     59   20  29
#> 10               Aruba    32     60    0  20
#> 11           Australia    60     59    9   0
#> 12             Austria    60     60   16   0
#> 13          Azerbaijan    30     60    5  29
#> 14        Bahamas, The    60     59    0  41
#>  [ reached 'max' / getOption("max.print") -- omitted 202 rows ]