fmin-fmax {collapse}

Fast (Grouped) Maxima and Minima for Matrix-Like Objects

Description

fmax 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, GRP object, atomic vector (internally converted to factor) or a list of vectors / factors (internally converted to a GRP object) used to group x.

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 to TRUE and implemented at very little computational cost. If na.rm = FALSE a NA is 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. TRUE drops dimensions and returns an atomic vector if g = NULL and TRA = NULL.

keep.group_vars

grouped_df method: Logical. FALSE removes grouping variables after computation.

...

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.

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 ...

See Also

Fast Statistical Functions, Collapse Overview

Examples

## default vector method
mpg <- mtcars$mpg
fmax(mpg)                         # Maximum value
fmin(mpg)                         # Minimum value (all examples below use fmax but apply to fmin)
fmax(mpg, TRA = "%")              # Simple transformation: Take percentage of maximum value
fmax(mpg, mtcars$cyl)             # Grouped maximum value
fmax(mpg, mtcars[c(2,8:9)])       # More groups..
g <- GRP(mtcars, ~ cyl + vs + am) # Precomputing groups gives more speed !
fmax(mpg, g)
fmax(mpg, g, TRA = "%")           # Groupwise percentage of maximum value
fmax(mpg, g, TRA = "replace")     # Groupwise replace by maximum value

## data.frame method
fmax(mtcars)
head(fmax(mtcars, TRA = "%"))
fmax(mtcars, g)
fmax(mtcars, g, use.g.names = FALSE) # No row-names generated

## matrix method
m <- qM(mtcars)
fmax(m)
head(fmax(m, TRA = "%"))
fmax(m, g) # etc..

## method for grouped data frames - created with dplyr::group_by or fgroup_by
mtcars |> fgroup_by(cyl,vs,am) |> fmax()
mtcars |> fgroup_by(cyl,vs,am) |> fmax("%")
mtcars |> fgroup_by(cyl,vs,am) |> fselect(mpg) |> fmax()