Fast (Grouped, Weighted) Mean for Matrix-Like Objects
fmean.Rdfmean is a generic function that computes the (column-wise) mean of x, (optionally) grouped by g and/or weighted by w.
The TRA argument can further be used to transform x using its (grouped, weighted) mean.
Usage
fmean(x, ...)
# Default S3 method
fmean(x, g = NULL, w = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
use.g.names = TRUE, nthreads = .op[["nthreads"]], ...)
# S3 method for class 'matrix'
fmean(x, g = NULL, w = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
use.g.names = TRUE, drop = TRUE, nthreads = .op[["nthreads"]], ...)
# S3 method for class 'data.frame'
fmean(x, g = NULL, w = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
use.g.names = TRUE, drop = TRUE, nthreads = .op[["nthreads"]], ...)
# S3 method for class 'grouped_df'
fmean(x, w = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
use.g.names = FALSE, keep.group_vars = TRUE,
keep.w = TRUE, stub = .op[["stub"]], nthreads = .op[["nthreads"]], ...)Arguments
- x
a numeric vector, matrix, data frame or grouped data frame (class 'grouped_df').
- g
a factor,
GRPobject, atomic vector (internally converted to factor) or a list of vectors / factors (internally converted to aGRPobject) used to groupx.- w
a numeric vector of (non-negative) weights, may contain missing values.
- 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. Skip missing values in
x. Defaults toTRUEand implemented at very little computational cost. Ifna.rm = FALSEaNAis returned when encountered.- 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. See Details of
fsum.- drop
matrix and data.frame method: Logical.
TRUEdrops dimensions and returns an atomic vector ifg = NULLandTRA = NULL.- keep.group_vars
grouped_df method: Logical.
FALSEremoves grouping variables after computation.- keep.w
grouped_df method: Logical. Retain summed weighting variable after computation (if contained in
grouped_df).- stub
character. If
keep.w = TRUEandstub = TRUE(default), the summed weights column is prefixed by"sum.". Users can specify a different prefix through this argument, or set it toFALSEto avoid prefixing.- ...
arguments to be passed to or from other methods. If
TRAis used, passingset = TRUEwill transform data by reference and return the result invisibly.
Details
The weighted mean is computed as sum(x * w) / sum(w), using a single pass in C. If na.rm = TRUE, missing values will be removed from both x and w i.e. utilizing only x[complete.cases(x,w)] and w[complete.cases(x,w)].
For further computational details see fsum, which works equivalently.
Value
The (w weighted) mean of x, grouped by g, or (if TRA is used) x transformed by its (grouped, weighted) mean.
Examples
## default vector method
mpg <- mtcars$mpg
fmean(mpg) # Simple mean
#> [1] 20.09063
fmean(mpg, w = mtcars$hp) # Weighted mean: Weighted by hp
#> [1] 17.97245
fmean(mpg, TRA = "-") # Simple transformation: demeaning (See also ?W)
#> [1] 0.909375 0.909375 2.709375 1.309375 -1.390625 -1.990625 -5.790625
#> [8] 4.309375 2.709375 -0.890625 -2.290625 -3.690625 -2.790625 -4.890625
#> [15] -9.690625 -9.690625 -5.390625 12.309375 10.309375 13.809375 1.409375
#> [22] -4.590625 -4.890625 -6.790625 -0.890625 7.209375 5.909375 10.309375
#> [29] -4.290625 -0.390625 -5.090625 1.309375
fmean(mpg, mtcars$cyl) # Grouped mean
#> 4 6 8
#> 26.66364 19.74286 15.10000
fmean(mpg, mtcars[8:9]) # another grouped mean.
#> 0.0 0.1 1.0 1.1
#> 15.05000 19.75000 20.74286 28.37143
g <- GRP(mtcars[c(2,8:9)])
fmean(mpg, g) # Pre-computing groups speeds up the computation
#> 4.0.1 4.1.0 4.1.1 6.0.1 6.1.0 8.0.0 8.0.1
#> 26.00000 22.90000 28.37143 20.56667 19.12500 15.05000 15.40000
fmean(mpg, g, mtcars$hp) # Grouped weighted mean
#> 4.0.1 4.1.0 4.1.1 6.0.1 6.1.0 8.0.0 8.0.1
#> 26.00000 22.69409 27.68209 20.42405 19.10087 14.82854 15.35259
fmean(mpg, g, TRA = "-") # Demeaning by group
#> [1] 0.4333333 0.4333333 -5.5714286 2.2750000 3.6500000 -1.0250000
#> [7] -0.7500000 1.5000000 -0.1000000 0.0750000 -1.3250000 1.3500000
#> [13] 2.2500000 0.1500000 -4.6500000 -4.6500000 -0.3500000 4.0285714
#> [19] 2.0285714 5.5285714 -1.4000000 0.4500000 0.1500000 -1.7500000
#> [25] 4.1500000 -1.0714286 0.0000000 2.0285714 0.4000000 -0.8666667
#> [31] -0.4000000 -6.9714286
fmean(mpg, g, mtcars$hp, "-") # Group-demeaning using weighted group means
#> [1] 0.57594937 0.57594937 -4.88209220 2.29913232 3.87145923 -1.00086768
#> [7] -0.52854077 1.70590551 0.10590551 0.09913232 -1.30086768 1.57145923
#> [13] 2.47145923 0.37145923 -4.42854077 -4.42854077 -0.12854077 4.71790780
#> [19] 2.71790780 6.21790780 -1.19409449 0.67145923 0.37145923 -1.52854077
#> [25] 4.37145923 -0.38209220 0.00000000 2.71790780 0.44741235 -0.72405063
#> [31] -0.35258765 -6.28209220
## data.frame method
fmean(mtcars)
#> mpg cyl disp hp drat wt qsec
#> 20.090625 6.187500 230.721875 146.687500 3.596563 3.217250 17.848750
#> vs am gear carb
#> 0.437500 0.406250 3.687500 2.812500
fmean(mtcars, g)
#> mpg cyl disp hp drat wt qsec vs am gear
#> 4.0.1 26.00000 4 120.3000 91.00000 4.430000 2.140000 16.70000 0 1 5.000000
#> 4.1.0 22.90000 4 135.8667 84.66667 3.770000 2.935000 20.97000 1 0 3.666667
#> 4.1.1 28.37143 4 89.8000 80.57143 4.148571 2.028286 18.70000 1 1 4.142857
#> 6.0.1 20.56667 6 155.0000 131.66667 3.806667 2.755000 16.32667 0 1 4.333333
#> 6.1.0 19.12500 6 204.5500 115.25000 3.420000 3.388750 19.21500 1 0 3.500000
#> 8.0.0 15.05000 8 357.6167 194.16667 3.120833 4.104083 17.14250 0 0 3.000000
#> 8.0.1 15.40000 8 326.0000 299.50000 3.880000 3.370000 14.55000 0 1 5.000000
#> carb
#> 4.0.1 2.000000
#> 4.1.0 1.666667
#> 4.1.1 1.428571
#> 6.0.1 4.666667
#> 6.1.0 2.500000
#> 8.0.0 3.083333
#> 8.0.1 6.000000
fmean(fgroup_by(mtcars, cyl, vs, am)) # Another way of doing it..
#> cyl vs am mpg disp hp drat wt qsec gear
#> 1 4 0 1 26.00000 120.3000 91.00000 4.430000 2.140000 16.70000 5.000000
#> 2 4 1 0 22.90000 135.8667 84.66667 3.770000 2.935000 20.97000 3.666667
#> 3 4 1 1 28.37143 89.8000 80.57143 4.148571 2.028286 18.70000 4.142857
#> 4 6 0 1 20.56667 155.0000 131.66667 3.806667 2.755000 16.32667 4.333333
#> 5 6 1 0 19.12500 204.5500 115.25000 3.420000 3.388750 19.21500 3.500000
#> 6 8 0 0 15.05000 357.6167 194.16667 3.120833 4.104083 17.14250 3.000000
#> 7 8 0 1 15.40000 326.0000 299.50000 3.880000 3.370000 14.55000 5.000000
#> carb
#> 1 2.000000
#> 2 1.666667
#> 3 1.428571
#> 4 4.666667
#> 5 2.500000
#> 6 3.083333
#> 7 6.000000
head(fmean(mtcars, g, TRA = "-")) # etc..
#> mpg cyl disp hp drat wt
#> Mazda RX4 0.4333333 0 5.000000 -21.66667 0.09333333 -0.1350000
#> Mazda RX4 Wag 0.4333333 0 5.000000 -21.66667 0.09333333 0.1200000
#> Datsun 710 -5.5714286 0 18.200000 12.42857 -0.29857143 0.2917143
#> Hornet 4 Drive 2.2750000 0 53.450000 -5.25000 -0.34000000 -0.1737500
#> Hornet Sportabout 3.6500000 0 2.383333 -19.16667 0.02916667 -0.6640833
#> Valiant -1.0250000 0 20.450000 -10.25000 -0.66000000 0.0712500
#> qsec vs am gear carb
#> Mazda RX4 0.1333333 0 0 -0.3333333 -0.6666667
#> Mazda RX4 Wag 0.6933333 0 0 -0.3333333 -0.6666667
#> Datsun 710 -0.0900000 0 0 -0.1428571 -0.4285714
#> Hornet 4 Drive 0.2250000 0 0 -0.5000000 -1.5000000
#> Hornet Sportabout -0.1225000 0 0 0.0000000 -1.0833333
#> Valiant 1.0050000 0 0 -0.5000000 -1.5000000
## matrix method
m <- qM(mtcars)
fmean(m)
#> mpg cyl disp hp drat wt qsec
#> 20.090625 6.187500 230.721875 146.687500 3.596563 3.217250 17.848750
#> vs am gear carb
#> 0.437500 0.406250 3.687500 2.812500
fmean(m, g)
#> mpg cyl disp hp drat wt qsec vs am gear
#> 4.0.1 26.00000 4 120.3000 91.00000 4.430000 2.140000 16.70000 0 1 5.000000
#> 4.1.0 22.90000 4 135.8667 84.66667 3.770000 2.935000 20.97000 1 0 3.666667
#> 4.1.1 28.37143 4 89.8000 80.57143 4.148571 2.028286 18.70000 1 1 4.142857
#> 6.0.1 20.56667 6 155.0000 131.66667 3.806667 2.755000 16.32667 0 1 4.333333
#> 6.1.0 19.12500 6 204.5500 115.25000 3.420000 3.388750 19.21500 1 0 3.500000
#> 8.0.0 15.05000 8 357.6167 194.16667 3.120833 4.104083 17.14250 0 0 3.000000
#> 8.0.1 15.40000 8 326.0000 299.50000 3.880000 3.370000 14.55000 0 1 5.000000
#> carb
#> 4.0.1 2.000000
#> 4.1.0 1.666667
#> 4.1.1 1.428571
#> 6.0.1 4.666667
#> 6.1.0 2.500000
#> 8.0.0 3.083333
#> 8.0.1 6.000000
head(fmean(m, g, TRA = "-")) # etc..
#> mpg cyl disp hp drat wt
#> Mazda RX4 0.4333333 0 5.000000 -21.66667 0.09333333 -0.1350000
#> Mazda RX4 Wag 0.4333333 0 5.000000 -21.66667 0.09333333 0.1200000
#> Datsun 710 -5.5714286 0 18.200000 12.42857 -0.29857143 0.2917143
#> Hornet 4 Drive 2.2750000 0 53.450000 -5.25000 -0.34000000 -0.1737500
#> Hornet Sportabout 3.6500000 0 2.383333 -19.16667 0.02916667 -0.6640833
#> Valiant -1.0250000 0 20.450000 -10.25000 -0.66000000 0.0712500
#> qsec vs am gear carb
#> Mazda RX4 0.1333333 0 0 -0.3333333 -0.6666667
#> Mazda RX4 Wag 0.6933333 0 0 -0.3333333 -0.6666667
#> Datsun 710 -0.0900000 0 0 -0.1428571 -0.4285714
#> Hornet 4 Drive 0.2250000 0 0 -0.5000000 -1.5000000
#> Hornet Sportabout -0.1225000 0 0 0.0000000 -1.0833333
#> Valiant 1.0050000 0 0 -0.5000000 -1.5000000
## method for grouped data frames - created with dplyr::group_by or fgroup_by
mtcars |> fgroup_by(cyl,vs,am) |> fmean() # Ordinary
#> cyl vs am mpg disp hp drat wt qsec gear
#> 1 4 0 1 26.00000 120.3000 91.00000 4.430000 2.140000 16.70000 5.000000
#> 2 4 1 0 22.90000 135.8667 84.66667 3.770000 2.935000 20.97000 3.666667
#> 3 4 1 1 28.37143 89.8000 80.57143 4.148571 2.028286 18.70000 4.142857
#> 4 6 0 1 20.56667 155.0000 131.66667 3.806667 2.755000 16.32667 4.333333
#> 5 6 1 0 19.12500 204.5500 115.25000 3.420000 3.388750 19.21500 3.500000
#> 6 8 0 0 15.05000 357.6167 194.16667 3.120833 4.104083 17.14250 3.000000
#> 7 8 0 1 15.40000 326.0000 299.50000 3.880000 3.370000 14.55000 5.000000
#> carb
#> 1 2.000000
#> 2 1.666667
#> 3 1.428571
#> 4 4.666667
#> 5 2.500000
#> 6 3.083333
#> 7 6.000000
mtcars |> fgroup_by(cyl,vs,am) |> fmean(hp) # Weighted
#> cyl vs am sum.hp mpg disp drat wt qsec gear
#> 1 4 0 1 91 26.00000 120.30000 4.430000 2.140000 16.70000 5.000000
#> 2 4 1 0 254 22.69409 134.33504 3.779843 2.898169 21.08846 3.618110
#> 3 4 1 1 564 27.68209 93.87482 4.080266 2.067223 18.54041 4.200355
#> 4 6 0 1 395 20.42405 153.35443 3.775949 2.757468 16.19063 4.443038
#> 5 6 1 0 461 19.10087 202.24425 3.455358 3.390868 19.16941 3.533623
#> 6 8 0 0 2330 14.82854 363.10815 3.143090 4.158685 17.08489 3.000000
#> 7 8 0 1 599 15.35259 323.03673 3.839699 3.393706 14.55593 5.000000
#> carb
#> 1 2.000000
#> 2 1.618110
#> 3 1.485816
#> 4 4.886076
#> 5 2.600868
#> 6 3.210300
#> 7 6.237062
mtcars |> fgroup_by(cyl,vs,am) |> fmean(hp, "-") # Weighted Transform
#> cyl vs am hp mpg disp drat
#> Mazda RX4 6 0 1 110 0.57594937 6.645570 0.1240506329
#> Mazda RX4 Wag 6 0 1 110 0.57594937 6.645570 0.1240506329
#> Datsun 710 4 1 1 93 -4.88209220 14.125177 -0.2302659574
#> Hornet 4 Drive 6 1 0 110 2.29913232 55.755748 -0.3753579176
#> Hornet Sportabout 8 0 0 175 3.87145923 -3.108155 0.0069098712
#> Valiant 6 1 0 105 -1.00086768 22.755748 -0.6953579176
#> Duster 360 8 0 0 245 -0.52854077 -3.108155 0.0669098712
#> Merc 240D 4 1 0 62 1.70590551 12.364961 -0.0898425197
#> Merc 230 4 1 0 95 0.10590551 6.464961 0.1401574803
#> Merc 280 6 1 0 123 0.09913232 -34.644252 0.4646420824
#> Merc 280C 6 1 0 123 -1.30086768 -34.644252 0.4646420824
#> Merc 450SE 8 0 0 180 1.57145923 -87.308155 -0.0730901288
#> Merc 450SL 8 0 0 180 2.47145923 -87.308155 -0.0730901288
#> Merc 450SLC 8 0 0 180 0.37145923 -87.308155 -0.0730901288
#> Cadillac Fleetwood 8 0 0 205 -4.42854077 108.891845 -0.2130901288
#> Lincoln Continental 8 0 0 215 -4.42854077 96.891845 -0.1430901288
#> Chrysler Imperial 8 0 0 230 -0.12854077 76.891845 0.0869098712
#> Fiat 128 4 1 1 66 4.71790780 -15.174823 -0.0002659574
#> Honda Civic 4 1 1 52 2.71790780 -18.174823 0.8497340426
#> Toyota Corolla 4 1 1 65 6.21790780 -22.774823 0.1397340426
#> Toyota Corona 4 1 0 97 -1.19409449 -14.235039 -0.0798425197
#> Dodge Challenger 8 0 0 150 0.67145923 -45.108155 -0.3830901288
#> AMC Javelin 8 0 0 150 0.37145923 -59.108155 0.0069098712
#> Camaro Z28 8 0 0 245 -1.52854077 -13.108155 0.5869098712
#> Pontiac Firebird 8 0 0 175 4.37145923 36.891845 -0.0630901288
#> Fiat X1-9 4 1 1 66 -0.38209220 -14.874823 -0.0002659574
#> Porsche 914-2 4 0 1 91 0.00000000 0.000000 0.0000000000
#> Lotus Europa 4 1 1 113 2.71790780 1.225177 -0.3102659574
#> Ford Pantera L 8 0 1 264 0.44741235 27.963272 0.3803005008
#> Ferrari Dino 6 0 1 175 -0.72405063 -8.354430 -0.1559493671
#> Maserati Bora 8 0 1 335 -0.35258765 -22.036728 -0.2996994992
#> Volvo 142E 4 1 1 109 -6.28209220 27.125177 0.0297340426
#> wt qsec gear carb
#> Mazda RX4 -0.13746835 0.26936709 -0.4430380 -0.8860759
#> Mazda RX4 Wag 0.11753165 0.82936709 -0.4430380 -0.8860759
#> Datsun 710 0.25277660 0.06959220 -0.2003546 -0.4858156
#> Hornet 4 Drive -0.17586768 0.27058568 -0.5336226 -1.6008677
#> Hornet Sportabout -0.71868455 -0.06489270 0.0000000 -1.2103004
#> Valiant 0.06913232 1.05058568 -0.5336226 -1.6008677
#> Duster 360 -0.58868455 -1.24489270 0.0000000 0.7896996
#> Merc 240D 0.29183071 -1.08846457 0.3818898 0.3818898
#> Merc 230 0.25183071 1.81153543 0.3818898 0.3818898
#> Merc 280 0.04913232 -0.86941432 0.4663774 1.3991323
#> Merc 280C 0.04913232 -0.26941432 0.4663774 1.3991323
#> Merc 450SE -0.08868455 0.31510730 0.0000000 -0.2103004
#> Merc 450SL -0.42868455 0.51510730 0.0000000 -0.2103004
#> Merc 450SLC -0.37868455 0.91510730 0.0000000 -0.2103004
#> Cadillac Fleetwood 1.09131545 0.89510730 0.0000000 0.7896996
#> Lincoln Continental 1.26531545 0.73510730 0.0000000 0.7896996
#> Chrysler Imperial 1.18631545 0.33510730 0.0000000 0.7896996
#> Fiat 128 0.13277660 0.92959220 -0.2003546 -0.4858156
#> Honda Civic -0.45222340 -0.02040780 -0.2003546 0.5141844
#> Toyota Corolla -0.23222340 1.35959220 -0.2003546 -0.4858156
#> Toyota Corona -0.43316929 -1.07846457 -0.6181102 -0.6181102
#> Dodge Challenger -0.63868455 -0.21489270 0.0000000 -1.2103004
#> AMC Javelin -0.72368455 0.21510730 0.0000000 -1.2103004
#> Camaro Z28 -0.31868455 -1.67489270 0.0000000 0.7896996
#> Pontiac Firebird -0.31368455 -0.03489270 0.0000000 -1.2103004
#> Fiat X1-9 -0.13222340 0.35959220 -0.2003546 -0.4858156
#> Porsche 914-2 0.00000000 0.00000000 0.0000000 0.0000000
#> Lotus Europa -0.55422340 -1.64040780 0.7996454 0.5141844
#> Ford Pantera L -0.22370618 -0.05592654 0.0000000 -2.2370618
#> Ferrari Dino 0.01253165 -0.69063291 0.5569620 1.1139241
#> Maserati Bora 0.17629382 0.04407346 0.0000000 1.7629382
#> Volvo 142E 0.71277660 0.05959220 -0.2003546 0.5141844
#>
#> Grouped by: cyl, vs, am [7 | 5 (3.8) 1-12]
mtcars |> fgroup_by(cyl,vs,am) |>
fselect(mpg,hp) |> fmean(hp, "-") # Only mpg
#> hp mpg
#> Mazda RX4 110 0.57594937
#> Mazda RX4 Wag 110 0.57594937
#> Datsun 710 93 -4.88209220
#> Hornet 4 Drive 110 2.29913232
#> Hornet Sportabout 175 3.87145923
#> Valiant 105 -1.00086768
#> Duster 360 245 -0.52854077
#> Merc 240D 62 1.70590551
#> Merc 230 95 0.10590551
#> Merc 280 123 0.09913232
#> Merc 280C 123 -1.30086768
#> Merc 450SE 180 1.57145923
#> Merc 450SL 180 2.47145923
#> Merc 450SLC 180 0.37145923
#> Cadillac Fleetwood 205 -4.42854077
#> Lincoln Continental 215 -4.42854077
#> Chrysler Imperial 230 -0.12854077
#> Fiat 128 66 4.71790780
#> Honda Civic 52 2.71790780
#> Toyota Corolla 65 6.21790780
#> Toyota Corona 97 -1.19409449
#> Dodge Challenger 150 0.67145923
#> AMC Javelin 150 0.37145923
#> Camaro Z28 245 -1.52854077
#> Pontiac Firebird 175 4.37145923
#> Fiat X1-9 66 -0.38209220
#> Porsche 914-2 91 0.00000000
#> Lotus Europa 113 2.71790780
#> Ford Pantera L 264 0.44741235
#> Ferrari Dino 175 -0.72405063
#> Maserati Bora 335 -0.35258765
#> Volvo 142E 109 -6.28209220
#>
#> Grouped by: cyl, vs, am [7 | 5 (3.8) 1-12]