Multidplyr and group_by () and filter ()

Question

I have the following data framework, and I am going to find all IDs that have a different USE, but the same TYPE.

ID <- rep(1:4, each=3)
USAGE <- c("private","private","private","private",
"taxi","private","taxi","taxi","taxi","taxi","private","taxi")
TYPE <- c("VW","VW","VW","VW","MER","VW","VW","VW","VW","VW","VW","VW")
df <- data.frame(ID,USAGE,TYPE)

If I run

df %>% group_by(ID, TYPE) %>% filter(n_distinct(USAGE)>1)

I get the expected result. But my original framework has> 2 million lines. Therefore, I would like to use all my kernels when performing this operation.

I tried this code with multidplyr:

f1 <- partition(df, ID)
f2 <- f1 %>% group_by(ID, TYPE) %>% filter(n_distinct(USAGE)>1)
f3 <- collect(f2)

But then the following message appears:

Warning message: group_indices_.grouped_df ignores extra arguments

after

f1 <- partition(df, ID)

and

Error in checkForRemoteErrors(lapply(cl, recvResult)) : 
  4 nodes produced errors; first error: Evaluation error: object 'f1' not found.

after

f2 <- f1%>% group_by(ID, TYPE) %>% filter(f1, n_distinct(USAGE)>1)

What would be the correct way to implement the entire operation in multidplyr? Many thanks.

Answer 1

partition(). , ​​ , .

library(tidyverse)
library(multidplyr)

fast <- df %>%
  partition(ID, TYPE) %>%
  group_by(ID, TYPE) %>%
  filter(n_distinct(USAGE) > 1) %>%
  collect()

group_indices, , dplyr.

slow <- df %>%
  group_by(ID, TYPE) %>%
  filter(n_distinct(USAGE) > 1)

fast == slow
       ID USAGE TYPE
#[1,] TRUE  TRUE TRUE
#[2,] TRUE  TRUE TRUE
#[3,] TRUE  TRUE TRUE

: ? cluster , .

library(microbenchmark)
library(parallel)

cluster <- create_cluster(cores = detectCores())

fast_func <- function(df) {
  df %>%
    partition(ID, TYPE, cluster = cluster) %>%
    group_by(ID, TYPE) %>%
    filter(n_distinct(USAGE) > 1) %>%
    collect()
}

slow_func <- function(df) {
  slow <- df %>%
    group_by(ID, TYPE) %>%
    filter(n_distinct(USAGE) > 1)
}

microbenchmark(fast_func(df), slow_func(df))
# Unit: milliseconds
# expr       min        lq      mean    median        uq       max neval cld
# fast_func(df) 41.360358 47.529695 55.806609 50.529851 61.459433 133.53045   100   b
# slow_func(df)  4.717761  6.974897  9.333049  7.796686  8.468594  49.51916   100  a 

. fast_func 56 9. - , . , , .

# Embiggen the data
df <- df[rep(seq_len(nrow(df)), each=2000000),] %>% tbl_df()

microbenchmark(fast_func(df), slow_func(df))
# Unit: seconds
# expr       min        lq      mean    median        uq       max neval cld
# fast_func(df) 43.067089 43.781144 50.754600 49.440864 55.308355 65.499095    10   b
# slow_func(df)  1.741674  2.550008  3.529607  3.246665  3.983452  7.214484    10  a 

fast_func ! , , .