fmin, fmax: 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, …)
# S3 method for default
fmax(x, g = NULL, TRA = NULL, na.rm = TRUE,
     use.g.names = TRUE, …)
# S3 method for default
fmin(x, g = NULL, TRA = NULL, na.rm = TRUE,
     use.g.names = TRUE, …)
# S3 method for matrix
fmax(x, g = NULL, TRA = NULL, na.rm = TRUE,
     use.g.names = TRUE, drop = TRUE, …)
# S3 method for matrix
fmin(x, g = NULL, TRA = NULL, na.rm = TRUE,
     use.g.names = TRUE, drop = TRUE, …)
# S3 method for data.frame
fmax(x, g = NULL, TRA = NULL, na.rm = TRUE,
     use.g.names = TRUE, drop = TRUE, …)
# S3 method for data.frame
fmin(x, g = NULL, TRA = NULL, na.rm = TRUE,
     use.g.names = TRUE, drop = TRUE, …)
# S3 method for grouped_df
fmax(x, TRA = NULL, na.rm = TRUE,
     use.g.names = FALSE, keep.group_vars = TRUE, …)
# S3 method for grouped_df
fmin(x, TRA = NULL, na.rm = TRUE,
     use.g.names = FALSE, keep.group_vars = TRUE, …)

Arguments

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.

TRA

an integer or quoted operator indicating the transformation to perform: 1 - "replace_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.

Value

fmax returns the maximum value of x, grouped by g, or (if TRA is used) x transformed by its maximum value, grouped by g. Analogous, fmin returns the minimum value …

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).

This all seamlessly generalizes to grouped computations, which are performed in a single pass (without splitting the data) and therefore extremely fast.

When applied to data frames with groups or drop = FALSE, fmax and fmin preserve all column attributes (such as variable labels) but do not distinguish between classed and unclassed objects. The attributes of the data frame itself are also preserved.

Examples

Run this code

# NOT RUN {
## 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..
# }
# NOT RUN {
 <!-- % No code relying on suggested package -->
## method for grouped data frames - created with dplyr::group_by or fgroup_by
library(dplyr)
mtcars %>% group_by(cyl,vs,am) %>% fmax
mtcars %>% group_by(cyl,vs,am) %>% fmax("%")
mtcars %>% group_by(cyl,vs,am) %>% select(mpg) %>% fmax
# }

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