multiplyr: Data Manipulation with Parellelism and Shared Memory Matrices
In multiplyr: Data Manipulation with Parallelism and Shared Memory Matrices

Description Major differences from dplyr Standard dplyr-like functions Parallel functions Additional data frame functions Data manipulation adjuncts

Provides a new form of data frame backed by shared memory matrices and a way to manipulate them. Upon creation these data frames are shared across multiple local nodes to allow for simple parallel processing. Run the following command for a more thorough explanation: vignette("basics")

summarise with dplyr will return a single number, but here it will return N values depending on how many nodes there are. Typically you should follow summarise with reduce, which is run locally.

`arrange`	Sort data
`distinct`	Select unique rows or unique combinations of variables
`filter`	Filter data
`group_by`	Group data
`group_sizes`	Return size of groups
`groupwise`	Use grouped data (also known as `ungroup`)
`mutate`	Change values of existing variables (and create new ones)
`n_groups`	Return number of groups
`rename`	Rename variables
`rowwise`	Use data as individual rows
`select`	Retain only specified variables
`slice`	Select rows by position
`summarise`	Summarise data
`transmute`	Change variables and drop all others

`partition_even`	Partition data evenly amongst cluster nodes
`partition_group`	Partition data so that each group is wholly on a node
`within_group`	Execute code within a group
`within_node`	Execute code within a group

`Multiplyr`	Create new parallel data frame
`define`	Define new variables
`nsa`	No strings attached mode
`reduce`	Summarise locally only
`regroup`	Return to grouped data
`undefine`	Delete variables

`between`	Tests whether elements of a vector lie between two values (inclusively)
`cumall`	Cumulative all
`cumany`	Cumulative any
`cummean`	Cumulative mean
`first`	Returns first value in vector
`last`	Returns last value in vector
`lag`	Offset x backwards by n
`lead`	Offset x forwards by n
`n`	Number of items in current group
`nth`	Return the nth item from a vector