Partitioning ensures that all observations in a group end up on the same worker. To try and keep the observations on each worker balanced, 'partition()' uses a greedy algorithm that iteratively assigns each group to the worker that currently has the fewest rows.
Dataset to partition, typically grouped. When grouped, all observations in a group will be assigned to the same cluster.
Cluster to use.
library(dplyr) cl <- default_cluster() cluster_library(cl, "dplyr") mtcars2 <- partition(mtcars, cl) mtcars2 %>% mutate(cyl2 = 2 * cyl) mtcars2 %>% filter(vs == 1) mtcars2 %>% group_by(cyl) %>% summarise(n()) mtcars2 %>% select(-cyl)
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