BY: Split-Apply-Combine Computing

Description

BY is an S3 generic that efficiently applies functions over vectors or matrix- and data frame columns by groups. Similar to dapply it seeks to retain the structure and attributes of the data, but can also output to various standard formats. A simple parallelism is also available.

Usage

BY(x, …)
# S3 method for default
BY(x, g, FUN, …, use.g.names = TRUE, sort = TRUE,
   expand.wide = FALSE, parallel = FALSE, mc.cores = 1L,
   return = c("same", "vector", "list"))
# S3 method for matrix
BY(x, g, FUN, …, use.g.names = TRUE, sort = TRUE,
   expand.wide = FALSE, parallel = FALSE, mc.cores = 1L,
   return = c("same", "matrix", "data.frame", "list"))
# S3 method for data.frame
BY(x, g, FUN, …, use.g.names = TRUE, sort = TRUE,
   expand.wide = FALSE, parallel = FALSE, mc.cores = 1L,
   return = c("same", "matrix", "data.frame", "list"))
# S3 method for grouped_df
BY(x, FUN, …, use.g.names = FALSE, keep.group_vars = TRUE,
   expand.wide = FALSE, parallel = FALSE, mc.cores = 1L,
   return = c("same", "matrix", "data.frame", "list"))

Arguments

a atomic vector, matrix, data frame or alike object.

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.

FUN

a function, can be scalar- or vector-valued.

…

further arguments to FUN.

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.

sort

logical. Sort the groups? Internally passed to GRP or qF, and only effective if g is not already a factor or GRP object.

expand.wide

logical. If FUN is a vector-valued function returning a vector of fixed length > 1 (such as the quantile function), expand.wide can be used to return the result in a wider format (instead of stacking the resulting vectors of fixed length above each other in each output column).

parallel

logical. TRUE implements simple parallel execution by internally calling mclapply instead of lapply.

mc.cores

integer. Argument to mclapply indicating the number of cores to use for parallel execution. Can use detectCores() to select all available cores.

return

an integer or string indicating the type of object to return. The default 1 - "same" returns the same object type (i.e. class and other attributes are retained, just the names for the dimensions are adjusted). 2 - "matrix" always returns the output as matrix, 3 - "data.frame" always returns a data frame and 4 - "list" returns the raw (uncombined) output. Note: 4 - "list" works together with expand.wide to return a list of matrices.

keep.group_vars

grouped_df method: Logical. FALSE removes grouping variables after computation.

Value

X where FUN was applied to every column split by g.

Details

BY is a frugal re-implementation of the Split-Apply-Combine computing paradigm. It is generally faster than tapply, by, aggregate and plyr, and preserves data attributes just like dapply.

It is however principally a wrapper around lapply(split(x, g), FUN, …), that strongly optimizes on attribute checking compared to base R functions. For more details look at the documentation for dapply which works very similar (apart from the splitting performed in BY). For larger tasks requiring split-apply-combine computing on data frames use dplyr, data.table, or try to work with the Fast Statistical Functions.

BY is used internally in collap for functions that are not Fast Statistical Functions.

Examples

Run this code

# NOT RUN {
v <- iris$Sepal.Length   # A numeric vector
f <- iris$Species        # A factor. Vectors/lists will internally be converted to factor

## default vector method
BY(v, f, sum)                                # Sum by species
head(BY(v, f, scale))                        # Scale by species (please use fscale instead)
head(BY(v, f, scale, use.g.names = FALSE))   # Omitting auto-generated names
BY(v, f, quantile)                           # Species quantiles: by default stacked
BY(v, f, quantile, expand.wide = TRUE)       # Wide format

## matrix method
m <- qM(num_vars(iris))
BY(m, f, sum)                          # Also return as matrix
BY(m, f, sum, return = "data.frame")   # Return as data.frame.. also works for computations below
head(BY(m, f, scale))
head(BY(m, f, scale, use.g.names = FALSE))
BY(m, f, quantile)
BY(m, f, quantile, expand.wide = TRUE)
BY(m, f, quantile, expand.wide = TRUE, # Return as list of matrices
   return = "list")

## data.frame method
BY(num_vars(iris), f, sum)             # Also returns a data.fram
BY(num_vars(iris), f, sum, return = 2) # Return as matrix.. also works for computations below
head(BY(num_vars(iris), f, scale))
head(BY(num_vars(iris), f, scale, use.g.names = FALSE))
BY(num_vars(iris), f, quantile)
BY(num_vars(iris), f, quantile, expand.wide = TRUE)
BY(num_vars(iris), f, quantile,        # Return as list of matrices
   expand.wide = TRUE, return = "list")
# }
# NOT RUN {
 <!-- % No code relying on suggested package -->
## grouped data frame method (faster than dplyr only for small data)
library(dplyr)
giris <- group_by(iris, Species)
giris |> BY(sum)                      # Compute sum
giris |> BY(sum, use.g.names = TRUE,  # Use row.names and
             keep.group_vars = FALSE) # remove 'Species' and groups attribute
giris |> BY(sum, return = "matrix")   # Return matrix
giris |> BY(sum, return = "matrix",   # Matrix with row.names
             use.g.names = TRUE)
giris |> BY(quantile)                 # Compute quantiles (output is stacked)
giris |> BY(quantile,                 # Much better, also keeps 'Species'
             expand.wide = TRUE)
# }

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