Fast (Grouped) Maxima and Minima for Matrix-Like Objects
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 = TRUE,
use.g.names = TRUE, ...)
## Default S3 method:
fmin(x, g = NULL, TRA = NULL, na.rm = TRUE,
use.g.names = TRUE, ...)
## S3 method for class 'matrix'
fmax(x, g = NULL, TRA = NULL, na.rm = TRUE,
use.g.names = TRUE, drop = TRUE, ...)
## S3 method for class 'matrix'
fmin(x, g = NULL, TRA = NULL, na.rm = TRUE,
use.g.names = TRUE, drop = TRUE, ...)
## S3 method for class 'data.frame'
fmax(x, g = NULL, TRA = NULL, na.rm = TRUE,
use.g.names = TRUE, drop = TRUE, ...)
## S3 method for class 'data.frame'
fmin(x, g = NULL, TRA = NULL, na.rm = TRUE,
use.g.names = TRUE, drop = TRUE, ...)
## S3 method for class 'grouped_df'
fmax(x, TRA = NULL, na.rm = TRUE,
use.g.names = FALSE, keep.group_vars = TRUE, ...)
## S3 method for class 'grouped_df'
fmin(x, TRA = NULL, na.rm = TRUE,
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:
1 - "replace_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. |
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.
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 ...
## 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
library(dplyr)
mtcars %>% group_by(cyl,vs,am) %>% fmax
mtcars %>% group_by(cyl,vs,am) %>% fmax("%")
mtcars %>% group_by(cyl,vs,am) %>% select(mpg) %>% fmaxPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.