Fast (Grouped, Weighted) Product for Matrix-Like Objects
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, weighted) product.
fprod(x, ...)
## Default S3 method:
fprod(x, g = NULL, w = NULL, TRA = NULL, na.rm = TRUE,
use.g.names = TRUE, ...)
## S3 method for class 'matrix'
fprod(x, g = NULL, w = NULL, TRA = NULL, na.rm = TRUE,
use.g.names = TRUE, drop = TRUE, ...)
## S3 method for class 'data.frame'
fprod(x, g = NULL, w = NULL, TRA = NULL, na.rm = TRUE,
use.g.names = TRUE, drop = TRUE, ...)
## S3 method for class 'grouped_df'
fprod(x, w = NULL, TRA = NULL, na.rm = TRUE,
use.g.names = FALSE, keep.group_vars = TRUE, keep.w = TRUE, ...)x |
a numeric vector, matrix, data frame or grouped data frame (class 'grouped_df'). |
g |
a factor, |
w |
a numeric vector of (non-negative) weights, may contain missing values. |
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 |
na.rm |
logical. Skip missing values in |
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. |
drop |
matrix and data.frame method: Logical. |
keep.group_vars |
grouped_df method: Logical. |
keep.w |
grouped_df method: Logical. Retain product of weighting variable after computation (if contained in |
... |
arguments to be passed to or from other methods. |
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 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 frames 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.
The (w weighted) product of x, grouped by g, or (if TRA is used) x transformed by its product, grouped by g.
## 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) head(fprod(mtcars, TRA = "/")) fprod(mtcars, g) fprod(mtcars, g, use.g.names = FALSE) # No row-names generated ## matrix method m <- qM(mtcars) fprod(m) head(fprod(m, TRA = "/")) fprod(m, g) # etc.. ## method for grouped data frames - created with dplyr::group_by or fgroup_by library(dplyr) mtcars %>% group_by(cyl,vs,am) %>% fprod(hp) # Weighted grouped product mtcars %>% fgroup_by(cyl,vs,am) %>% fprod(hp) # Equivalent and faster mtcars %>% fgroup_by(cyl,vs,am) %>% fprod(TRA = "/") mtcars %>% fgroup_by(cyl,vs,am) %>% fselect(mpg) %>% fprod
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