Package: multidplyr 0.1.3.9000

Hadley Wickham

multidplyr: A Multi-Process 'dplyr' Backend

Partition a data frame across multiple worker processes to provide simple multicore parallelism.

Authors:Hadley Wickham [aut, cre], Posit Software, PBC [cph, fnd]

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multidplyr.pdf |multidplyr.html✨
multidplyr/json (API)
NEWS

# Install multidplyr in R:
install.packages('multidplyr', repos = c('https://tidyverse.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/tidyverse/multidplyr/issues

On CRAN:

dplyrmultiprocess

13 exports 638 stars 7.99 score 26 dependencies 4 dependents 905 downloads

Last updated 8 months agofrom:c8867038955f66aadf70b37234598ff691704461

Exports:%>%cluster_assigncluster_assign_eachcluster_assign_partitioncluster_callcluster_copycluster_librarycluster_rmcluster_senddefault_clusternew_clusterpartitionparty_df

Dependencies:BHcallrclicrayondplyrfansigenericsgluelifecyclemagrittrpillarpkgconfigprocessxpsqsR6RApiSerializeRcppRcppParallelrlangstringfishtibbletidyselectutf8vctrswithr

An introduction to multidplyr

Rendered frommultidplyr.Rmdusingknitr::rmarkdownon Jun 01 2024.

Last update: 2021-02-03
Started: 2015-11-10

Readme and manuals

Help Manual

Help pageTopics
Call a function on each node of a clustercluster_call cluster_send
Cluster utitility functionscluster_assign cluster_assign_each cluster_assign_partition cluster_copy cluster_library cluster_rm cluster_utils
Create a new cluster with sensible defaults.new_cluster
Partition data across workers in a clusterpartition
A `party_df` partitioned data frameparty_df