fmin-fmax | R Documentation |
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.
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, ...)
x |
a numeric vector, matrix, data frame or grouped data frame (class 'grouped_df'). |
g |
a factor, |
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 |
na.rm |
logical. Skip missing values in |
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. |
keep.group_vars |
grouped_df method: Logical. |
... |
arguments to be passed to or from other methods. If |
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
.
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 ...
Fast Statistical Functions, Collapse Overview
## 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()
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