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multidplyr  

A Multi-Process 'dplyr' Backend
View on CRAN: Click here


Download and install multidplyr package within the R console
Install from CRAN:
install.packages("multidplyr")

Install from Github:
library("remotes")
install_github("cran/multidplyr")

Install by package version:
library("remotes")
install_version("multidplyr", "0.1.3")



Attach the package and use:
library("multidplyr")
Maintained by
Hadley Wickham
[Scholar Profile | Author Map]
First Published: 2021-02-08
Latest Update: 2023-03-22
Description:
Partition a data frame across multiple worker processes to provide simple multicore parallelism.
How to cite:
Hadley Wickham (2021). multidplyr: A Multi-Process 'dplyr' Backend. R package version 0.1.3, https://cran.r-project.org/web/packages/multidplyr. Accessed 17 Mar. 2025.
Previous versions and publish date:
0.1.0 (2021-02-08 20:10), 0.1.1 (2021-12-01 22:10), 0.1.2 (2022-09-26 21:40)
Other packages that cited multidplyr R package
View multidplyr citation profile
Other R packages that multidplyr depends, imports, suggests or enhances
Complete documentation for multidplyr
Functions, R codes and Examples using the multidplyr R package
Some associated functions: cluster_call . cluster_utils . default_cluster . multidplyr-package . new_cluster . partition . party_df . reexports . 
Some associated R codes: cluster-call.R . cluster-utils.R . cluster.R . dplyr-dual.R . dplyr-single.R . multidplyr-package.R . partydf.R . utils.R . zzz.R .  Full multidplyr package functions and examples
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