Efficiently Count Observations by Group
fcount.RdA much faster replacement for dplyr::count.
Usage
fcount(x, ..., w = NULL, name = "N", add = FALSE,
sort = FALSE, decreasing = FALSE)
fcountv(x, cols = NULL, w = NULL, name = "N", add = FALSE,
sort = FALSE, ...)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 tofselect. Forfcountv: further arguments passed toGRP(such asdecreasing,na.last,method,effectetc.). 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
fcountthis can also be the (unquoted) name of a column in the data frame.fcountvalso supports a single character name. Note that the corresponding argument indplyr::countis calledwt, but collapse has a global default for weights arguments to be calledw.- name
character. The name of the column containing the count or sum of weights.
dplyr::countit is called"n", but"N"is more consistent with the rest of collapse and data.table.- add
TRUEadds the count column tox. Alternativelyadd = "group_vars"(oradd = "gv"for parsimony) can be used to retain only the variables selected for counting inxand the count.- sort, decreasing
arguments passed to
GRPaffecting the order of rows in the output (ifadd = FALSE), and the algorithm used for counting. In general,sort = FALSEis faster unless data is already sorted by the columns used for counting.
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 N
#> 1 Afghanistan 61
#> 2 Albania 61
#> 3 Algeria 61
#> 4 American Samoa 61
#> 5 Andorra 61
#> 6 Angola 61
#> 7 Antigua and Barbuda 61
#> 8 Argentina 61
#> 9 Armenia 61
#> 10 Aruba 61
#> 11 Australia 61
#> 12 Austria 61
#> 13 Azerbaijan 61
#> 14 Bahamas, The 61
#> 15 Bahrain 61
#> 16 Bangladesh 61
#> 17 Barbados 61
#> 18 Belarus 61
#> 19 Belgium 61
#> 20 Belize 61
#> 21 Benin 61
#> 22 Bermuda 61
#> 23 Bhutan 61
#> 24 Bolivia 61
#> 25 Bosnia and Herzegovina 61
#> 26 Botswana 61
#> 27 Brazil 61
#> 28 British Virgin Islands 61
#> 29 Brunei Darussalam 61
#> 30 Bulgaria 61
#> 31 Burkina Faso 61
#> 32 Burundi 61
#> 33 Cabo Verde 61
#> 34 Cambodia 61
#> 35 Cameroon 61
#> [ reached 'max' / getOption("max.print") -- omitted 181 rows ]
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 61
#> 2 32.962 NA 232080002 9169410 61
#> 3 33.471 NA 112839996 9351441 61
#> 4 33.971 NA 237720001 9543205 61
#> 5 34.463 NA 295920013 9744781 61
#> [ 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")
#> year N
#> 1 1960 216
#> 2 1961 216
#> 3 1962 216
#> 4 1963 216
#> 5 1964 216
#> 6 1965 216
#> 7 1966 216
#> 8 1967 216
#> 9 1968 216
#> 10 1969 216
#> 11 1970 216
#> 12 1971 216
#> 13 1972 216
#> 14 1973 216
#> 15 1974 216
#> 16 1975 216
#> 17 1976 216
#> 18 1977 216
#> 19 1978 216
#> 20 1979 216
#> 21 1980 216
#> 22 1981 216
#> 23 1982 216
#> 24 1983 216
#> 25 1984 216
#> 26 1985 216
#> 27 1986 216
#> 28 1987 216
#> 29 1988 216
#> 30 1989 216
#> 31 1990 216
#> 32 1991 216
#> 33 1992 216
#> 34 1993 216
#> 35 1994 216
#> [ reached 'max' / getOption("max.print") -- omitted 26 rows ]
wlddev |> findex_by(country, year) |> fcountv(add = "group_vars", effect = "year")
#> year N
#> 1 1960 216
#> 2 1961 216
#> 3 1962 216
#> 4 1963 216
#> 5 1964 216
#> 6 1965 216
#> 7 1966 216
#> 8 1967 216
#> 9 1968 216
#> 10 1969 216
#> 11 1970 216
#> 12 1971 216
#> 13 1972 216
#> 14 1973 216
#> 15 1974 216
#> 16 1975 216
#> 17 1976 216
#> 18 1977 216
#> 19 1978 216
#> 20 1979 216
#> 21 1980 216
#> 22 1981 216
#> 23 1982 216
#> 24 1983 216
#> 25 1984 216
#> 26 1985 216
#> 27 1986 216
#> 28 1987 216
#> 29 1988 216
#> 30 1989 216
#> 31 1990 216
#> 32 1991 216
#> 33 1992 216
#> 34 1993 216
#> 35 1994 216
#> [ reached 'max' / getOption("max.print") -- omitted 13141 rows ]
#>
#> Indexed by: country [216] | year [61]