With fsum, fprod, fmean, fmedian, fmode, fvar, fsd, fmin, fmax, fnth, 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 grouped data frames / tibbles (dplyr) and data.table's.
x suitably aggregated or transformed. Data frame column-attributes and overall attributes are preserved.
## 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,] ...)
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a vector, matrix, data frame or grouped data frame (class 'grouped_df'). | |
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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. |
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a numeric vector of (non-negative) weights, may contain missing values. Supported by fsum, fprod, fmean, fmedian, fnth, fvar, fsd and fmode. |
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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. |
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logical. Skip missing values in x. Defaults to TRUE in all functions and implemented at very little computational cost. Not available for fnobs. |
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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. | |
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matrix and data.frame methods: Logical. TRUE drops dimensions and returns an atomic vector if g = NULL and TRA = NULL. |
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grouped_df method: Logical. FALSE removes grouping variables after computation. By default grouping variables are added, even if not present in the grouped_df. |
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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). |
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arguments to be passed to or from other methods, and extra arguments to some functions, i.e. the algorithm used to compute variances etc. |
Panel-decomposed (i.e. between and within) statistics as well as grouped and weighted skewness and kurtosis are implemented in qsu.
The vector-valued functions and operators fcumsum, 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.
## 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) # ..
\donttest{
## method for grouped data frames - created with dplyr::group_by or fgroup_by
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)
}
## This compares fsum with data.table (2 threads) and base::rowsum # Starting with small data mtcDT <- qDT(mtcars) f <- qF(mtcars$cyl)library(microbenchmark) microbenchmark(mtcDT[, lapply(.SD, sum), by = f], rowsum(mtcDT, f, reorder = FALSE), fsum(mtcDT, f, na.rm = FALSE), unit = "relative")
expr min lq mean median uq max neval cld mtcDT[, lapply(.SD, sum), by = f] 145.436928 123.542134 88.681111 98.336378 71.880479 85.217726 100 c rowsum(mtcDT, f, reorder = FALSE) 2.833333 2.798203 2.489064 2.937889 2.425724 2.181173 100 b fsum(mtcDT, f, na.rm = FALSE) 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 100 a
# Now larger data tdata <- qDT(replicate(100, rnorm(1e5), simplify = FALSE)) # 100 columns with 100.000 obs f <- qF(sample.int(1e4, 1e5, TRUE)) # A factor with 10.000 groups
microbenchmark(tdata[, lapply(.SD, sum), by = f], rowsum(tdata, f, reorder = FALSE), fsum(tdata, f, na.rm = FALSE), unit = "relative")
expr min lq mean median uq max neval cld tdata[, lapply(.SD, sum), by = f] 2.646992 2.975489 2.834771 3.081313 3.120070 1.2766475 100 c rowsum(tdata, f, reorder = FALSE) 1.747567 1.753313 1.629036 1.758043 1.839348 0.2720937 100 b fsum(tdata, f, na.rm = FALSE) 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000 100 a
Please see the documentation of individual functions.
Collapse Overview, Data Transformations, Time Series and Panel Series