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.
fmean(x, ...)
# S3 method for default
fmean(x, g = NULL, w = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
use.g.names = TRUE, nthreads = .op[["nthreads"]], ...)
# S3 method for 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 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 grouped_df
fmean(x, w = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
use.g.names = FALSE, keep.group_vars = TRUE,
keep.w = TRUE, nthreads = .op[["nthreads"]], ...)a numeric vector, matrix, data frame or grouped data frame (class 'grouped_df').
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.
a numeric vector of (non-negative) weights, may contain missing values.
an integer or quoted operator indicating the transformation to perform:
0 - "replace_NA" | 1 - "replace_fill" | 2 - "replace" | 3 - "-" | 4 - "-+" | 5 - "/" | 6 - "%" | 7 - "+" | 8 - "*" | 9 - "%%" | 10 - "-%%". See TRA.
logical. Skip missing values in x. Defaults to TRUE and implemented at very little computational cost. If na.rm = FALSE a NA is returned when encountered.
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.
integer. The number of threads to utilize. See Details of fsum.
matrix and data.frame method: Logical. TRUE drops dimensions and returns an atomic vector if g = NULL and TRA = NULL.
grouped_df method: Logical. FALSE removes grouping variables after computation.
grouped_df method: Logical. Retain summed weighting variable after computation (if contained in grouped_df).
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.
The weighted mean is computed as sum(x * w) / sum(w). 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.
The (w weighted) mean of x, grouped by g, or (if TRA is used) x transformed by its (grouped, weighted) mean.
## default vector method
mpg <- mtcars$mpg
fmean(mpg) # Simple mean
#> [1] 20.09062
fmean(mpg, w = mtcars$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 3.596562 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
#> 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
#> [ reached 'max' / getOption("max.print") -- omitted 1 rows ]
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
#> carb
#> 1 2.000000
#> 2 1.666667
#> 3 1.428571
#> 4 4.666667
#> 5 2.500000
#> 6 3.083333
#> [ reached 'max' / getOption("max.print") -- omitted 1 rows ]
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 3.596562 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
#> 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
#> [ reached getOption("max.print") -- omitted 1 row ]
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
library(dplyr)
mtcars %>% group_by(cyl,vs,am) %>% fmean() # Ordinary
#> # A tibble: 7 × 11
#> cyl vs am mpg disp hp drat wt qsec gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 4 0 1 26 120. 91 4.43 2.14 16.7 5 2
#> 2 4 1 0 22.9 136. 84.7 3.77 2.94 21.0 3.67 1.67
#> 3 4 1 1 28.4 89.8 80.6 4.15 2.03 18.7 4.14 1.43
#> 4 6 0 1 20.6 155 132. 3.81 2.76 16.3 4.33 4.67
#> 5 6 1 0 19.1 205. 115. 3.42 3.39 19.2 3.5 2.5
#> 6 8 0 0 15.1 358. 194. 3.12 4.10 17.1 3 3.08
#> 7 8 0 1 15.4 326 300. 3.88 3.37 14.6 5 6
mtcars %>% group_by(cyl,vs,am) %>% fmean(hp) # Weighted
#> # A tibble: 7 × 11
#> cyl vs am sum.hp mpg disp drat wt qsec gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 4 0 1 91 26 120. 4.43 2.14 16.7 5 2
#> 2 4 1 0 254 22.7 134. 3.78 2.90 21.1 3.62 1.62
#> 3 4 1 1 564 27.7 93.9 4.08 2.07 18.5 4.20 1.49
#> 4 6 0 1 395 20.4 153. 3.78 2.76 16.2 4.44 4.89
#> 5 6 1 0 461 19.1 202. 3.46 3.39 19.2 3.53 2.60
#> 6 8 0 0 2330 14.8 363. 3.14 4.16 17.1 3 3.21
#> 7 8 0 1 599 15.4 323. 3.84 3.39 14.6 5 6.24
mtcars %>% group_by(cyl,vs,am) %>% fmean(hp, "-") # Weighted Transform
#> # A tibble: 32 × 11
#> # Groups: cyl, vs, am [7]
#> cyl vs am hp mpg disp drat wt qsec gear carb
#> * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 6 0 1 110 0.576 6.65 0.124 -0.137 0.269 -0.443 -0.886
#> 2 6 0 1 110 0.576 6.65 0.124 0.118 0.829 -0.443 -0.886
#> 3 4 1 1 93 -4.88 14.1 -0.230 0.253 0.0696 -0.200 -0.486
#> 4 6 1 0 110 2.30 55.8 -0.375 -0.176 0.271 -0.534 -1.60
#> 5 8 0 0 175 3.87 -3.11 0.00691 -0.719 -0.0649 0 -1.21
#> 6 6 1 0 105 -1.00 22.8 -0.695 0.0691 1.05 -0.534 -1.60
#> 7 8 0 0 245 -0.529 -3.11 0.0669 -0.589 -1.24 0 0.790
#> 8 4 1 0 62 1.71 12.4 -0.0898 0.292 -1.09 0.382 0.382
#> 9 4 1 0 95 0.106 6.46 0.140 0.252 1.81 0.382 0.382
#> 10 6 1 0 123 0.0991 -34.6 0.465 0.0491 -0.869 0.466 1.40
#> # … with 22 more rows
mtcars %>% group_by(cyl,vs,am) %>%
select(mpg,hp) %>% fmean(hp, "-") # Only mpg
#> Adding missing grouping variables: `cyl`, `vs`, `am`
#> # A tibble: 32 × 5
#> # Groups: cyl, vs, am [7]
#> cyl vs am hp mpg
#> * <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 6 0 1 110 0.576
#> 2 6 0 1 110 0.576
#> 3 4 1 1 93 -4.88
#> 4 6 1 0 110 2.30
#> 5 8 0 0 175 3.87
#> 6 6 1 0 105 -1.00
#> 7 8 0 0 245 -0.529
#> 8 4 1 0 62 1.71
#> 9 4 1 0 95 0.106
#> 10 6 1 0 123 0.0991
#> # … with 22 more rows
mtcars %>% fgroup_by(cyl,vs,am) %>% # Equivalent and faster !
fselect(mpg,hp) %>% fmean(hp, "-")
#> 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]