collapse (version 1.2.1)

A1-fast-statistical-functions: Fast (Grouped, Weighted) Statistical Functions for Matrix-Like Objects

Description

With fsum, fprod, fmean, fmedian, fmode, fvar, fsd, fmin, fmax, ffirst, flast, fNobs and fNdistinct, collapse presents a coherent set of extremely fast and flexible statistical functions (S3 generics) to perform column-wise, grouped and weighted computations on atomic vectors, matrices and data.frames, with special support for dplyr grouped tibbles and data.table's.

Notes: (1) Panel-decomposed (i.e. between and within) statistics as well as grouped and weighted skewness and kurtosis are implemented in qsu. (2) The vector-valued functions and operators fscale/STD, fbetween/B, fHDbetween/HDB, fwithin/W, fHDwithin/HDW, flag/L/F, fdiff/D/Dlog and fgrowth/G are documented under Data Transformations and Time-Series and Panel-Series. These functions also support plm::pseries and plm::pdata.frame's.

Value

x aggregated. data.frame column-attributes and overall attributes are preserved.

Usage

## All functions (FUN) follow a common syntax in 4 methods:
FUN(x, ...)
## Default S3 method:
FUN(x, g = NULL, [w = NULL,] TRA = NULL, [na.rm = TRUE,]
    use.g.names = TRUE, ...)
## S3 method for class 'matrix'
FUN(x, g = NULL, [w = NULL,] TRA = NULL, [na.rm = TRUE,]
    use.g.names = TRUE, drop = TRUE, ...)
## S3 method for class 'data.frame'
FUN(x, g = NULL, [w = NULL,] TRA = NULL, [na.rm = TRUE,]
    use.g.names = TRUE, drop = TRUE, ...)
## S3 method for class 'grouped_df'
FUN(x, [w = NULL,] TRA = NULL, [na.rm = TRUE,]
    use.g.names = FALSE, keep.group_vars = TRUE, [keep.w = TRUE,] ...)

Arguments

x		a vector, matrix, data.frame or grouped tibble (dplyr::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.
	w
a numeric vector of (non-negative) weights, may contain missing values. Supported by fsum, fprod, fmean, fvar, fsd and fmode.
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 in all functions and implemented at very little computational cost. Not available for fNobs.
	use.g.names
make group-names and add to the result as names (vector method) or row-names (matrix and data.frame method). No row-names are generated for data.tables and (default) grouped tibbles.
drop		matrix and data.frame methods: Drop dimensions and return an atomic vector if g = NULL and TRA = NULL.
		keep.group_vars
	grouped_df method: Logical. FALSE removes grouping variables after computation. By default grouping variables are added, even if not present in the grouped_df.
	keep.w
grouped_df method: Logical. TRUE (default) also aggregates weights and saves them in a column, FALSE removes weighting variable after computation (if contained in grouped_df).
...		arguments to be passed to or from other methods, and extra arguments to some functions, i.e. the algorithm used to compute variances etc.

Details

Please see the documentation of individual functions.

See Also

Collapse Overview, Data Transformations, Time-Series and Panel-Series

Examples

# NOT RUN {
## default vector method
mpg <- mtcars$mpg
fsum(mpg)                         # Simple sum
fsum(mpg, TRA = "/")              # Simple transformation: divide all values by the sum
fsum(mpg, mtcars$cyl)             # Grouped sum
fmean(mpg, mtcars$cyl)            # Grouped mean
fmean(mpg, w = mtcars$hp)         # Weighted mean, weighted by hp
fmean(mpg, mtcars$cyl, mtcars$hp) # Grouped mean, weighted by hp
fsum(mpg, mtcars$cyl, TRA = "/")  # Proportions / division by group sums
fmean(mpg, mtcars$cyl, mtcars$hp, # Subtract weighted group means, see also ?fwithin
      TRA = "-")

## data.frame method
fsum(mtcars)
fsum(mtcars, TRA = "%")                 # This computes percentages
fsum(mtcars, mtcars[c(2,8:9)])           # Grouped column sum
g <- GRP(mtcars, ~ cyl + vs + am)        # Here precomputing the groups!
fsum(mtcars, g)                          # Faster !!
fmean(mtcars, g, mtcars$hp)
fmean(mtcars, g, mtcars$hp, "-")         # demeaning by weighted group means...
fmean(fgroup_by(mtcars, cyl, vs, am), hp, "-")  # another way of doing it...


fmode(wlddev, drop = FALSE)              # Compute statistical modes of variables in this data
fmode(wlddev, wlddev$income)             # grouped statistical modes ..

## matrix method
m <- qM(mtcars)
fsum(m)
fsum(m, g) # ...

## method for grouped tibbles - for use with dplyr
library(dplyr)
mtcars %>% group_by(cyl,vs,am) %>% select(mpg,carb) %>% fsum
mtcars %>% fgroup_by(cyl,vs,am) %>% fselect(mpg,carb) %>% fsum # equivalent and faster !!
mtcars %>% fgroup_by(cyl,vs,am) %>% fsum(TRA = "%")
mtcars %>% fgroup_by(cyl,vs,am) %>% fmean(hp)         # weighted grouped mean, save sum of weights
mtcars %>% fgroup_by(cyl,vs,am) %>% fmean(hp, keep.group_vars = FALSE)
# }