fsum is a generic function that computes the (column-wise) sum of all values in x, (optionally) grouped by g and/or weighted by w (e.g. to calculate survey totals). The TRA argument can further be used to transform x using its (grouped, weighted) sum.

fsum(x, ...)

# S3 method for default
fsum(x, g = NULL, w = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
     use.g.names = TRUE, fill = FALSE, nthreads = .op[["nthreads"]], ...)

# S3 method for matrix
fsum(x, g = NULL, w = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
     use.g.names = TRUE, drop = TRUE, fill = FALSE, nthreads = .op[["nthreads"]], ...)

# S3 method for data.frame
fsum(x, g = NULL, w = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
     use.g.names = TRUE, drop = TRUE, fill = FALSE, nthreads = .op[["nthreads"]], ...)

# S3 method for grouped_df
fsum(x, w = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
     use.g.names = FALSE, keep.group_vars = TRUE,
     keep.w = TRUE, fill = FALSE, nthreads = .op[["nthreads"]], ...)

Arguments

x

a numeric vector, matrix, data frame or grouped data frame (class '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.

TRA

an integer or quoted operator indicating the transformation to perform: 0 - "replace_NA" | 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.

fill

logical. Initialize result with 0 instead of NA when na.rm = TRUE e.g. fsum(NA, fill = TRUE) returns 0 instead of NA.

nthreads

integer. The number of threads to utilize. See Details.

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.

keep.w

grouped_df method: Logical. Retain summed weighting variable after computation (if contained in grouped_df).

...

arguments to be passed to or from other methods. If TRA is used, passing set = TRUE will transform data by reference and return the result invisibly.

Details

Missing-value removal as controlled by the na.rm argument is done very efficiently by simply skipping them in the computation (thus setting na.rm = FALSE on data with no missing values doesn't give extra speed). 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 sum which just runs through without any checks).

The weighted sum (e.g. survey total) is computed as sum(x * w), but in one pass and about twice as efficient. If na.rm = TRUE, missing values will be removed from both x and w i.e. utilizing only x[complete.cases(x,w)] and w[complete.cases(x,w)].

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

When applied to data frames with groups or drop = FALSE, fsum preserves all column attributes. The attributes of the data frame itself are also preserved.

Since v1.6.0 fsum explicitly supports integers. Integers are summed using the long long type in C which is bounded at +-9,223,372,036,854,775,807 (so ~4.3 billion times greater than the minimum/maximum R integer bounded at +-2,147,483,647). If the value of the sum is outside +-2,147,483,647, a double containing the result is returned, otherwise an integer is returned. With groups, an integer overflow error is provided if the sum in any group is outside +-2,147,483,647. Data should be coerced to double beforehand in such cases.

Multithreading, added in v1.8.0, applies at the column-level unless g = NULL and nthreads > NCOL(x). Parallelism over groups is not available because sums are computed simultaneously within each group. nthreads = 1L uses a serial version of the code, not parallel code running on one thread. This serial code is always used with less than 100,000 obs (length(x) < 100000 for vectors and matrices), because parallel execution itself has some overhead.

Value

The (w weighted) sum of x, grouped by g, or (if TRA is used) x transformed by its (grouped, weighted) sum.

Examples

## default vector method
mpg <- mtcars$mpg
fsum(mpg)                         # Simple sum
fsum(mpg, w = mtcars$hp)          # Weighted sum (total): Weighted by hp
fsum(mpg, TRA = "%")              # Simple transformation: obtain percentages of mpg
fsum(mpg, mtcars$cyl)             # Grouped sum
fsum(mpg, mtcars$cyl, mtcars$hp)  # Weighted grouped sum (total)
fsum(mpg, mtcars[c(2,8:9)])       # More groups..
g <- GRP(mtcars, ~ cyl + vs + am) # Precomputing groups gives more speed !
fsum(mpg, g)
fmean(mpg, g) == fsum(mpg, g) / fnobs(mpg, g)
fsum(mpg, g, TRA = "%")           # Percentages by group

## data.frame method
fsum(mtcars)
fsum(mtcars, TRA = "%")
fsum(mtcars, g)
fsum(mtcars, g, TRA = "%")

## matrix method
m <- qM(mtcars)
fsum(m)
fsum(m, TRA = "%")
fsum(m, g)
fsum(m, g, TRA = "%")

## method for grouped data frames - created with dplyr::group_by or fgroup_by
library(dplyr)
mtcars %>% group_by(cyl,vs,am) %>% fsum(hp)  # Weighted grouped sum (total)
mtcars %>% fgroup_by(cyl,vs,am) %>% fsum(hp) # Equivalent and faster !!
mtcars %>% fgroup_by(cyl,vs,am) %>% fsum(TRA = "%")
mtcars %>% fgroup_by(cyl,vs,am) %>% fselect(mpg) %>% fsum()

Benchmark


## 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