Efficiently Count Observations by Group

A much faster replacement for dplyr::count.

Usage

fcount(x, ..., w = NULL, name = "N", add = FALSE,
      sort = FALSE, decreasing = FALSE, drop = TRUE)

fcountv(x, cols = NULL, w = NULL, name = "N", add = FALSE,
        sort = FALSE, drop = TRUE, ...)

Arguments

x: a data frame or list-like object, including 'grouped_df' or 'indexed_frame'. Atomic vectors or matrices can also be passed, but will be sent through qDF.
...: for fcount: names or sequences of columns to count cases by - passed to fselect. For fcountv: further arguments passed to GRP (such as decreasing, na.last, method, effect etc.). Leaving this empty will count on all columns.
cols: select columns to count cases by, using column names, indices, a logical vector or a selector function (e.g. is_categorical).
w: a numeric vector of weights, may contain missing values. In fcount this can also be the (unquoted) name of a column in the data frame. fcountv also supports a single character name. Note that the corresponding argument in dplyr::count is called wt, but collapse has a global default for weights arguments to be called w.
name: character. The name of the column containing the count or sum of weights. dplyr::count it is called "n", but "N" is more consistent with the rest of collapse and data.table.
add: TRUE adds the count column to x. Alternatively add = "group_vars" (or add = "gv" for parsimony) can be used to retain only the variables selected for counting in x and the count.
sort, decreasing: arguments passed to GRP affecting the order of rows in the output (if add = FALSE), and the algorithm used for counting. In general, sort = FALSE is faster unless data is already sorted by the columns used for counting.
drop: logical. FALSE retains zero-count rows for unobserved combinations of factor levels (analogous to dplyr::count(..., .drop = FALSE)); applies only when at least one of the counted columns is a factor. See GRP (drop argument of GRP.default).

Value

If x is a list, an object of the same type as x with a column (name) added at the end giving the count. Otherwise, if x is atomic, a data frame returned from qDF(x) with the count column added. By default (add = FALSE) only the unique rows of x of the columns used for counting are returned.

Examples

fcount(mtcars, cyl, vs, am)
#>   cyl vs am  N
#> 1   6  0  1  3
#> 2   4  1  1  7
#> 3   6  1  0  4
#> 4   8  0  0 12
#> 5   4  1  0  3
#> 6   4  0  1  1
#> 7   8  0  1  2
fcountv(mtcars, cols = .c(cyl, vs, am))
#>   cyl vs am  N
#> 1   6  0  1  3
#> 2   4  1  1  7
#> 3   6  1  0  4
#> 4   8  0  0 12
#> 5   4  1  0  3
#> 6   4  0  1  1
#> 7   8  0  1  2
fcount(mtcars, cyl, vs, am, sort = TRUE)
#>   cyl vs am  N
#> 1   4  0  1  1
#> 2   4  1  0  3
#> 3   4  1  1  7
#> 4   6  0  1  3
#> 5   6  1  0  4
#> 6   8  0  0 12
#> 7   8  0  1  2
fcount(mtcars, cyl, vs, am, add = TRUE)
#>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb  N
#> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4  3
#> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4  3
#> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1  7
#> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1  4
#> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 12
#>  [ reached 'max' / getOption("max.print") -- omitted 27 rows ]
fcount(mtcars, cyl, vs, am, add = "group_vars")
#>                     cyl vs am  N
#> Mazda RX4             6  0  1  3
#> Mazda RX4 Wag         6  0  1  3
#> Datsun 710            4  1  1  7
#> Hornet 4 Drive        6  1  0  4
#> Hornet Sportabout     8  0  0 12
#> Valiant               6  1  0  4
#> Duster 360            8  0  0 12
#> Merc 240D             4  1  0  3
#> Merc 230              4  1  0  3
#> Merc 280              6  1  0  4
#> Merc 280C             6  1  0  4
#> Merc 450SE            8  0  0 12
#> Merc 450SL            8  0  0 12
#> Merc 450SLC           8  0  0 12
#> Cadillac Fleetwood    8  0  0 12
#> Lincoln Continental   8  0  0 12
#> Chrysler Imperial     8  0  0 12
#>  [ reached 'max' / getOption("max.print") -- omitted 15 rows ]

## With grouped data
mtcars |> fgroup_by(cyl, vs, am) |> fcount()
#>   cyl vs am  N
#> 1   4  0  1  1
#> 2   4  1  0  3
#> 3   4  1  1  7
#> 4   6  0  1  3
#> 5   6  1  0  4
#> 6   8  0  0 12
#> 7   8  0  1  2
mtcars |> fgroup_by(cyl, vs, am) |> fcount(add = TRUE)
#>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb  N
#> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4  3
#> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4  3
#> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1  7
#> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1  4
#> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 12
#>  [ reached 'max' / getOption("max.print") -- omitted 27 rows ]
#> 
#> Grouped by:  cyl, vs, am  [7 | 5 (3.8) 1-12] 
mtcars |> fgroup_by(cyl, vs, am) |> fcount(add = "group_vars")
#>                     cyl vs am  N
#> Mazda RX4             6  0  1  3
#> Mazda RX4 Wag         6  0  1  3
#> Datsun 710            4  1  1  7
#> Hornet 4 Drive        6  1  0  4
#> Hornet Sportabout     8  0  0 12
#> Valiant               6  1  0  4
#> Duster 360            8  0  0 12
#> Merc 240D             4  1  0  3
#> Merc 230              4  1  0  3
#> Merc 280              6  1  0  4
#> Merc 280C             6  1  0  4
#> Merc 450SE            8  0  0 12
#> Merc 450SL            8  0  0 12
#> Merc 450SLC           8  0  0 12
#> Cadillac Fleetwood    8  0  0 12
#> Lincoln Continental   8  0  0 12
#> Chrysler Imperial     8  0  0 12
#>  [ reached 'max' / getOption("max.print") -- omitted 15 rows ]
#> 
#> Grouped by:  cyl, vs, am  [7 | 5 (3.8) 1-12] 

## With indexed data: by default counting on the first index variable
wlddev |> findex_by(country, year) |> fcount()
#>       country iso3c       date year decade     region     income  OECD PCGDP
#> 1 Afghanistan   AFG 1961-01-01 1960   1960 South Asia Low income FALSE    NA
#> 2 Afghanistan   AFG 1962-01-01 1961   1960 South Asia Low income FALSE    NA
#> 3 Afghanistan   AFG 1963-01-01 1962   1960 South Asia Low income FALSE    NA
#> 4 Afghanistan   AFG 1964-01-01 1963   1960 South Asia Low income FALSE    NA
#> 5 Afghanistan   AFG 1965-01-01 1964   1960 South Asia Low income FALSE    NA
#>   LIFEEX GINI       ODA     POP N
#> 1 32.446   NA 116769997 8996973 1
#> 2 32.962   NA 232080002 9169410 1
#> 3 33.471   NA 112839996 9351441 1
#> 4 33.971   NA 237720001 9543205 1
#> 5 34.463   NA 295920013 9744781 1
#>  [ reached 'max' / getOption("max.print") -- omitted 13171 rows ]
#> 
#> Indexed by:  country [216] | year [61] 
wlddev |> findex_by(country, year) |> fcount(add = TRUE)
#>       country iso3c       date year decade     region     income  OECD PCGDP
#> 1 Afghanistan   AFG 1961-01-01 1960   1960 South Asia Low income FALSE    NA
#> 2 Afghanistan   AFG 1962-01-01 1961   1960 South Asia Low income FALSE    NA
#> 3 Afghanistan   AFG 1963-01-01 1962   1960 South Asia Low income FALSE    NA
#> 4 Afghanistan   AFG 1964-01-01 1963   1960 South Asia Low income FALSE    NA
#> 5 Afghanistan   AFG 1965-01-01 1964   1960 South Asia Low income FALSE    NA
#>   LIFEEX GINI       ODA     POP N
#> 1 32.446   NA 116769997 8996973 1
#> 2 32.962   NA 232080002 9169410 1
#> 3 33.471   NA 112839996 9351441 1
#> 4 33.971   NA 237720001 9543205 1
#> 5 34.463   NA 295920013 9744781 1
#>  [ reached 'max' / getOption("max.print") -- omitted 13171 rows ]
#> 
#> Indexed by:  country [216] | year [61] 
# Use fcountv to pass additional arguments to GRP.pdata.frame,
# here using the effect argument to choose a different index variable
wlddev |> findex_by(country, year) |> fcountv(effect = "year")
#>       country iso3c       date year decade     region     income  OECD PCGDP
#> 1 Afghanistan   AFG 1961-01-01 1960   1960 South Asia Low income FALSE    NA
#> 2 Afghanistan   AFG 1962-01-01 1961   1960 South Asia Low income FALSE    NA
#> 3 Afghanistan   AFG 1963-01-01 1962   1960 South Asia Low income FALSE    NA
#> 4 Afghanistan   AFG 1964-01-01 1963   1960 South Asia Low income FALSE    NA
#> 5 Afghanistan   AFG 1965-01-01 1964   1960 South Asia Low income FALSE    NA
#>   LIFEEX GINI       ODA     POP N
#> 1 32.446   NA 116769997 8996973 1
#> 2 32.962   NA 232080002 9169410 1
#> 3 33.471   NA 112839996 9351441 1
#> 4 33.971   NA 237720001 9543205 1
#> 5 34.463   NA 295920013 9744781 1
#>  [ reached 'max' / getOption("max.print") -- omitted 13171 rows ]
#> 
#> Indexed by:  country [216] | year [61] 
wlddev |> findex_by(country, year) |> fcountv(add = "group_vars", effect = "year")
#>       country iso3c       date year decade     region     income  OECD PCGDP
#> 1 Afghanistan   AFG 1961-01-01 1960   1960 South Asia Low income FALSE    NA
#> 2 Afghanistan   AFG 1962-01-01 1961   1960 South Asia Low income FALSE    NA
#> 3 Afghanistan   AFG 1963-01-01 1962   1960 South Asia Low income FALSE    NA
#> 4 Afghanistan   AFG 1964-01-01 1963   1960 South Asia Low income FALSE    NA
#> 5 Afghanistan   AFG 1965-01-01 1964   1960 South Asia Low income FALSE    NA
#>   LIFEEX GINI       ODA     POP N
#> 1 32.446   NA 116769997 8996973 1
#> 2 32.962   NA 232080002 9169410 1
#> 3 33.471   NA 112839996 9351441 1
#> 4 33.971   NA 237720001 9543205 1
#> 5 34.463   NA 295920013 9744781 1
#>  [ reached 'max' / getOption("max.print") -- omitted 13171 rows ]
#> 
#> Indexed by:  country [216] | year [61]

Efficiently Count Observations by Group

Usage

Arguments

Value

See also

Examples