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"]], ...)
a numeric vector, matrix, data frame or grouped data frame (class 'grouped_df').
GRP object, atomic vector (internally converted to factor) or a list of vectors / factors (internally converted to a
GRP object) used to group
a numeric vector of (non-negative) weights, may contain missing values.
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
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.
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.
logical. Initialize result with
0 instead of
na.rm = TRUE e.g.
fsum(NA, fill = TRUE) returns
0 instead of
integer. The number of threads to utilize. See Details.
matrix and data.frame method: Logical.
TRUE drops dimensions and returns an atomic vector if
g = NULL and
TRA = NULL.
grouped_df method: Logical.
FALSE removes grouping variables after computation.
grouped_df method: Logical. Retain summed weighting variable after computation (if contained in
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.
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
w i.e. utilizing only
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.
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.
w weighted) sum of
x, grouped by
g, or (if
TRA is used)
x transformed by its (grouped, weighted) sum.
## 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$hp) # Weighted grouped sum (total) fsum(mpg, mtcars[c(2,8:9)]) # More groups.. g <- GRP(mtcars, ~ cyl + vs + am) # Precomputing groups gives more speed ! fsum(mpg, g) fmean(mpg, g) == fsum(mpg, g) / fnobs(mpg, g) fsum(mpg, g, TRA = "%") # Percentages by group ## data.frame method fsum(mtcars) fsum(mtcars, TRA = "%") fsum(mtcars, g) fsum(mtcars, g, TRA = "%") ## matrix method m <- qM(mtcars) fsum(m) fsum(m, TRA = "%") fsum(m, g) fsum(m, g, TRA = "%") ## method for grouped data frames - created with dplyr::group_by or fgroup_by library(dplyr) mtcars %>% group_by(cyl,vs,am) %>% fsum(hp) # Weighted grouped sum (total) mtcars %>% fgroup_by(cyl,vs,am) %>% fsum(hp) # Equivalent and faster !! mtcars %>% fgroup_by(cyl,vs,am) %>% fsum(TRA = "%") mtcars %>% fgroup_by(cyl,vs,am) %>% fselect(mpg) %>% fsum()
## This compares fsum with data.table (2 threads) and base::rowsum # Starting with small data mtcDT <- qDT(mtcars) f <- qF(mtcars$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