fprod is a generic function that computes the (column-wise) product of all values in x, (optionally) grouped by g and/or weighted by w. The TRA argument can further be used to transform x using its (grouped) product.
fprod(x, ...)# S3 method for default
fprod(x, g = NULL, w = NULL, TRA = NULL, na.rm = TRUE,
use.g.names = TRUE, ...)
# S3 method for matrix
fprod(x, g = NULL, w = NULL, TRA = NULL, na.rm = TRUE,
use.g.names = TRUE, drop = TRUE, ...)
# S3 method for data.frame
fprod(x, g = NULL, w = NULL, TRA = NULL, na.rm = TRUE,
use.g.names = TRUE, drop = TRUE, ...)
# S3 method for grouped_df
fprod(x, w = NULL, TRA = NULL, na.rm = TRUE,
use.g.names = FALSE, keep.group_vars = TRUE, keep.w = TRUE, ...)
a numeric vector, matrix, data.frame or grouped tibble (dplyr::grouped_df).
a numeric vector of (non-negative) weights, may contain missing values.
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.
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.
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 grouped tibbles.
matrix and data.frame method: drop dimensions and return an atomic vector if g = NULL and TRA = NULL.
grouped_df method: Logical. FALSE removes grouping variables after computation.
grouped_df method: Logical. Retain product of weighting variable after computation (if contained in grouped_df).
arguments to be passed to or from other methods.
The product of x, grouped by g, or (if TRA is used) x transformed by its product, grouped by g.
Non-grouped product computations internally utilize long-doubles in C++, for additional numeric precision.
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 base::prod which just runs 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.
The weighted product is computed as prod(x * w). 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)].
When applied to data frame's with groups or drop = FALSE, fprod preserves all column attributes (such as variable labels) but does not distinguish between classed and unclassed objects. The attributes of the data.frame itself are also preserved.
# NOT RUN {
## default vector method
mpg <- mtcars$mpg
fprod(mpg) # Simple product
fprod(mpg, w = mtcars$hp) # Weighted product
fprod(mpg, TRA = "/") # Simple transformation: Divide by product
fprod(mpg, mtcars$cyl) # Grouped product
fprod(mpg, mtcars$cyl, mtcars$hp) # Weighted grouped product
fprod(mpg, mtcars[c(2,8:9)]) # More groups...
g <- GRP(mtcars, ~ cyl + vs + am) # Precomputing groups gives more speed !!
fprod(mpg, g)
fprod(mpg, g, TRA = "/") # Groupwise divide by product
## data.frame method
fprod(mtcars)
fprod(mtcars, TRA = "/")
fprod(mtcars, g)
fprod(mtcars, g, use.g.names = FALSE) # No row-names generated
## matrix method
m <- qM(mtcars)
fprod(m)
fprod(m, TRA = "/")
fprod(m, g) # etc...
## method for grouped tibbles - for use with dplyr
library(dplyr)
mtcars %>% group_by(cyl,vs,am) %>% fprod(hp) # Weighted grouped product
mtcars %>% group_by(cyl,vs,am) %>% fprod(TRA = "/")
mtcars %>% group_by(cyl,vs,am) %>% select(mpg) %>% fprod
# }
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