Fast (Grouped) Maxima and Minima for Matrix-Like Objects
fmin_fmax.Rdfmax and fmin are generic functions that compute the (column-wise) maximum and minimum value of all values in x, (optionally) grouped by g. The TRA argument can further be used to transform x using its (grouped) maximum or minimum value.
Usage
fmax(x, ...)
fmin(x, ...)
# Default S3 method
fmax(x, g = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
use.g.names = TRUE, ...)
# Default S3 method
fmin(x, g = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
use.g.names = TRUE, ...)
# S3 method for class 'matrix'
fmax(x, g = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
use.g.names = TRUE, drop = TRUE, ...)
# S3 method for class 'matrix'
fmin(x, g = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
use.g.names = TRUE, drop = TRUE, ...)
# S3 method for class 'data.frame'
fmax(x, g = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
use.g.names = TRUE, drop = TRUE, ...)
# S3 method for class 'data.frame'
fmin(x, g = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
use.g.names = TRUE, drop = TRUE, ...)
# S3 method for class 'grouped_df'
fmax(x, TRA = NULL, na.rm = .op[["na.rm"]],
use.g.names = FALSE, keep.group_vars = TRUE, ...)
# S3 method for class 'grouped_df'
fmin(x, TRA = NULL, na.rm = .op[["na.rm"]],
use.g.names = FALSE, keep.group_vars = TRUE, ...)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.- 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.
- 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.- ...
arguments to be passed to or from other methods. If
TRAis used, passingset = TRUEwill transform data by reference and return the result invisibly.
Details
Missing-value removal as controlled by the na.rm argument is done at no extra cost since in C++ any logical comparison involving NA or NaN evaluates to FALSE. Large performance gains can nevertheless be achieved in the presence of missing values if na.rm = FALSE, since then the corresponding computation is terminated once a NA is encountered and NA is returned (unlike max and min which just run through without any checks).
For further computational details see fsum.
Value
fmax returns the maximum value of x, grouped by g, or (if TRA is used) x transformed by its (grouped) maximum value. Analogous, fmin returns the minimum value ...
Examples
## default vector method
mpg <- mtcars$mpg
fmax(mpg) # Maximum value
#> [1] 33.9
fmin(mpg) # Minimum value (all examples below use fmax but apply to fmin)
#> [1] 10.4
fmax(mpg, TRA = "%") # Simple transformation: Take percentage of maximum value
#> [1] 61.94690 61.94690 67.25664 63.12684 55.16224 53.39233 42.18289
#> [8] 71.97640 67.25664 56.63717 52.50737 48.37758 51.03245 44.83776
#> [15] 30.67847 30.67847 43.36283 95.57522 89.67552 100.00000 63.42183
#> [22] 45.72271 44.83776 39.23304 56.63717 80.53097 76.69617 89.67552
#> [29] 46.60767 58.11209 44.24779 63.12684
fmax(mpg, mtcars$cyl) # Grouped maximum value
#> 4 6 8
#> 33.9 21.4 19.2
fmax(mpg, mtcars[c(2,8:9)]) # More groups..
#> 4.0.1 4.1.0 4.1.1 6.0.1 6.1.0 8.0.0 8.0.1
#> 26.0 24.4 33.9 21.0 21.4 19.2 15.8
g <- GRP(mtcars, ~ cyl + vs + am) # Precomputing groups gives more speed !
fmax(mpg, g)
#> 4.0.1 4.1.0 4.1.1 6.0.1 6.1.0 8.0.0 8.0.1
#> 26.0 24.4 33.9 21.0 21.4 19.2 15.8
fmax(mpg, g, TRA = "%") # Groupwise percentage of maximum value
#> [1] 100.00000 100.00000 67.25664 100.00000 97.39583 84.57944 74.47917
#> [8] 100.00000 93.44262 89.71963 83.17757 85.41667 90.10417 79.16667
#> [15] 54.16667 54.16667 76.56250 95.57522 89.67552 100.00000 88.11475
#> [22] 80.72917 79.16667 69.27083 100.00000 80.53097 100.00000 89.67552
#> [29] 100.00000 93.80952 94.93671 63.12684
fmax(mpg, g, TRA = "replace") # Groupwise replace by maximum value
#> [1] 21.0 21.0 33.9 21.4 19.2 21.4 19.2 24.4 24.4 21.4 21.4 19.2 19.2 19.2 19.2
#> [16] 19.2 19.2 33.9 33.9 33.9 24.4 19.2 19.2 19.2 19.2 33.9 26.0 33.9 15.8 21.0
#> [31] 15.8 33.9
## data.frame method
fmax(mtcars)
#> mpg cyl disp hp drat wt qsec vs am gear
#> 33.900 8.000 472.000 335.000 4.930 5.424 22.900 1.000 1.000 5.000
#> carb
#> 8.000
head(fmax(mtcars, TRA = "%"))
#> mpg cyl disp hp drat wt qsec vs
#> Mazda RX4 61.94690 75 33.89831 32.83582 79.10751 48.30383 71.87773 0
#> Mazda RX4 Wag 61.94690 75 33.89831 32.83582 79.10751 53.00516 74.32314 0
#> Datsun 710 67.25664 50 22.88136 27.76119 78.09331 42.77286 81.26638 100
#> Hornet 4 Drive 63.12684 75 54.66102 32.83582 62.47465 59.27360 84.89083 100
#> Hornet Sportabout 55.16224 100 76.27119 52.23881 63.89452 63.42183 74.32314 0
#> Valiant 53.39233 75 47.66949 31.34328 55.98377 63.79056 88.29694 100
#> am gear carb
#> Mazda RX4 100 80 50.0
#> Mazda RX4 Wag 100 80 50.0
#> Datsun 710 100 80 12.5
#> Hornet 4 Drive 0 60 12.5
#> Hornet Sportabout 0 60 25.0
#> Valiant 0 60 12.5
fmax(mtcars, g)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 4.0.1 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 4.1.0 24.4 4 146.7 97 3.92 3.190 22.90 1 0 4 2
#> 4.1.1 33.9 4 121.0 113 4.93 2.780 19.90 1 1 5 2
#> 6.0.1 21.0 6 160.0 175 3.90 2.875 17.02 0 1 5 6
#> 6.1.0 21.4 6 258.0 123 3.92 3.460 20.22 1 0 4 4
#> 8.0.0 19.2 8 472.0 245 3.73 5.424 18.00 0 0 3 4
#> 8.0.1 15.8 8 351.0 335 4.22 3.570 14.60 0 1 5 8
fmax(mtcars, g, use.g.names = FALSE) # No row-names generated
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 2 24.4 4 146.7 97 3.92 3.190 22.90 1 0 4 2
#> 3 33.9 4 121.0 113 4.93 2.780 19.90 1 1 5 2
#> 4 21.0 6 160.0 175 3.90 2.875 17.02 0 1 5 6
#> 5 21.4 6 258.0 123 3.92 3.460 20.22 1 0 4 4
#> 6 19.2 8 472.0 245 3.73 5.424 18.00 0 0 3 4
#> 7 15.8 8 351.0 335 4.22 3.570 14.60 0 1 5 8
## matrix method
m <- qM(mtcars)
fmax(m)
#> mpg cyl disp hp drat wt qsec vs am gear
#> 33.900 8.000 472.000 335.000 4.930 5.424 22.900 1.000 1.000 5.000
#> carb
#> 8.000
head(fmax(m, TRA = "%"))
#> mpg cyl disp hp drat wt qsec vs
#> Mazda RX4 61.94690 75 33.89831 32.83582 79.10751 48.30383 71.87773 0
#> Mazda RX4 Wag 61.94690 75 33.89831 32.83582 79.10751 53.00516 74.32314 0
#> Datsun 710 67.25664 50 22.88136 27.76119 78.09331 42.77286 81.26638 100
#> Hornet 4 Drive 63.12684 75 54.66102 32.83582 62.47465 59.27360 84.89083 100
#> Hornet Sportabout 55.16224 100 76.27119 52.23881 63.89452 63.42183 74.32314 0
#> Valiant 53.39233 75 47.66949 31.34328 55.98377 63.79056 88.29694 100
#> am gear carb
#> Mazda RX4 100 80 50.0
#> Mazda RX4 Wag 100 80 50.0
#> Datsun 710 100 80 12.5
#> Hornet 4 Drive 0 60 12.5
#> Hornet Sportabout 0 60 25.0
#> Valiant 0 60 12.5
fmax(m, g) # etc..
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 4.0.1 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 4.1.0 24.4 4 146.7 97 3.92 3.190 22.90 1 0 4 2
#> 4.1.1 33.9 4 121.0 113 4.93 2.780 19.90 1 1 5 2
#> 6.0.1 21.0 6 160.0 175 3.90 2.875 17.02 0 1 5 6
#> 6.1.0 21.4 6 258.0 123 3.92 3.460 20.22 1 0 4 4
#> 8.0.0 19.2 8 472.0 245 3.73 5.424 18.00 0 0 3 4
#> 8.0.1 15.8 8 351.0 335 4.22 3.570 14.60 0 1 5 8
## method for grouped data frames - created with dplyr::group_by or fgroup_by
mtcars |> fgroup_by(cyl,vs,am) |> fmax()
#> cyl vs am mpg disp hp drat wt qsec gear carb
#> 1 4 0 1 26.0 120.3 91 4.43 2.140 16.70 5 2
#> 2 4 1 0 24.4 146.7 97 3.92 3.190 22.90 4 2
#> 3 4 1 1 33.9 121.0 113 4.93 2.780 19.90 5 2
#> 4 6 0 1 21.0 160.0 175 3.90 2.875 17.02 5 6
#> 5 6 1 0 21.4 258.0 123 3.92 3.460 20.22 4 4
#> 6 8 0 0 19.2 472.0 245 3.73 5.424 18.00 3 4
#> 7 8 0 1 15.8 351.0 335 4.22 3.570 14.60 5 8
mtcars |> fgroup_by(cyl,vs,am) |> fmax("%")
#> cyl vs am mpg disp hp drat wt
#> Mazda RX4 6 0 1 100.00000 100.00000 62.85714 100.00000 91.13043
#> Mazda RX4 Wag 6 0 1 100.00000 100.00000 62.85714 100.00000 100.00000
#> Datsun 710 4 1 1 67.25664 89.25620 82.30088 78.09331 83.45324
#> Hornet 4 Drive 6 1 0 100.00000 100.00000 89.43089 78.57143 92.91908
#> Hornet Sportabout 8 0 0 97.39583 76.27119 71.42857 84.45040 63.42183
#> Valiant 6 1 0 84.57944 87.20930 85.36585 70.40816 100.00000
#> Duster 360 8 0 0 74.47917 76.27119 100.00000 86.05898 65.81858
#> Merc 240D 4 1 0 100.00000 100.00000 63.91753 94.13265 100.00000
#> Merc 230 4 1 0 93.44262 95.97819 97.93814 100.00000 98.74608
#> Merc 280 6 1 0 89.71963 64.96124 100.00000 100.00000 99.42197
#> Merc 280C 6 1 0 83.17757 64.96124 100.00000 100.00000 99.42197
#> Merc 450SE 8 0 0 85.41667 58.43220 73.46939 82.30563 75.03687
#> Merc 450SL 8 0 0 90.10417 58.43220 73.46939 82.30563 68.76844
#> Merc 450SLC 8 0 0 79.16667 58.43220 73.46939 82.30563 69.69027
#> Cadillac Fleetwood 8 0 0 54.16667 100.00000 83.67347 78.55228 96.79204
#> Lincoln Continental 8 0 0 54.16667 97.45763 87.75510 80.42895 100.00000
#> Chrysler Imperial 8 0 0 76.56250 93.22034 93.87755 86.59517 98.54351
#> Fiat 128 4 1 1 95.57522 65.04132 58.40708 82.75862 79.13669
#> Honda Civic 4 1 1 89.67552 62.56198 46.01770 100.00000 58.09353
#> Toyota Corolla 4 1 1 100.00000 58.76033 57.52212 85.59838 66.00719
#> Toyota Corona 4 1 0 88.11475 81.86776 100.00000 94.38776 77.27273
#> Dodge Challenger 8 0 0 80.72917 67.37288 61.22449 73.99464 64.89676
#> AMC Javelin 8 0 0 79.16667 64.40678 61.22449 84.45040 63.32965
#> Camaro Z28 8 0 0 69.27083 74.15254 100.00000 100.00000 70.79646
#> Pontiac Firebird 8 0 0 100.00000 84.74576 71.42857 82.57373 70.88864
#> Fiat X1-9 4 1 1 80.53097 65.28926 58.40708 82.75862 69.60432
#> Porsche 914-2 4 0 1 100.00000 100.00000 100.00000 100.00000 100.00000
#> Lotus Europa 4 1 1 89.67552 78.59504 100.00000 76.47059 54.42446
#> Ford Pantera L 8 0 1 100.00000 100.00000 78.80597 100.00000 88.79552
#> Ferrari Dino 6 0 1 93.80952 90.62500 100.00000 92.82051 96.34783
#> Maserati Bora 8 0 1 94.93671 85.75499 100.00000 83.88626 100.00000
#> Volvo 142E 4 1 1 63.12684 100.00000 96.46018 83.36714 100.00000
#> qsec gear carb
#> Mazda RX4 96.70975 80 66.66667
#> Mazda RX4 Wag 100.00000 80 66.66667
#> Datsun 710 93.51759 80 50.00000
#> Hornet 4 Drive 96.14243 75 25.00000
#> Hornet Sportabout 94.55556 100 50.00000
#> Valiant 100.00000 75 25.00000
#> Duster 360 88.00000 100 100.00000
#> Merc 240D 87.33624 100 100.00000
#> Merc 230 100.00000 100 100.00000
#> Merc 280 90.50445 100 100.00000
#> Merc 280C 93.47181 100 100.00000
#> Merc 450SE 96.66667 100 75.00000
#> Merc 450SL 97.77778 100 75.00000
#> Merc 450SLC 100.00000 100 75.00000
#> Cadillac Fleetwood 99.88889 100 100.00000
#> Lincoln Continental 99.00000 100 100.00000
#> Chrysler Imperial 96.77778 100 100.00000
#> Fiat 128 97.83920 80 50.00000
#> Honda Civic 93.06533 80 100.00000
#> Toyota Corolla 100.00000 80 50.00000
#> Toyota Corona 87.37991 75 50.00000
#> Dodge Challenger 93.72222 100 50.00000
#> AMC Javelin 96.11111 100 50.00000
#> Camaro Z28 85.61111 100 100.00000
#> Pontiac Firebird 94.72222 100 50.00000
#> Fiat X1-9 94.97487 80 50.00000
#> Porsche 914-2 100.00000 100 100.00000
#> Lotus Europa 84.92462 100 100.00000
#> Ford Pantera L 99.31507 100 50.00000
#> Ferrari Dino 91.06933 100 100.00000
#> Maserati Bora 100.00000 100 100.00000
#> Volvo 142E 93.46734 80 100.00000
#>
#> Grouped by: cyl, vs, am [7 | 5 (3.8) 1-12]
mtcars |> fgroup_by(cyl,vs,am) |> fselect(mpg) |> fmax()
#> cyl vs am mpg
#> 1 4 0 1 26.0
#> 2 4 1 0 24.4
#> 3 4 1 1 33.9
#> 4 6 0 1 21.0
#> 5 6 1 0 21.4
#> 6 8 0 0 19.2
#> 7 8 0 1 15.8