Split-Apply-Combine (Divide and conquer) in a remote tibble or dbplyr class

[bwcastillo]

Hi, I have this question

Does there exist any way to apply a "Split-Apply-Combine" (divide and conquer algorithm) method to a dbplyr object class? I found the Replyr package but it method wasn't work.

Currently I am connected with Rpostgres library to a Postgresql data base. So, in this way I am querying my data hosted in a postgres database, now just I need to apply the "Split-Apply-Combine" method (Divide and Conquer Algorithm) to get some results.

I am working in this way

fun_connect<-function(){dbConnect(RPostgres::Postgres(),dbname = 'database', 
                                  host = 'localhost', # i.e. 'ec2-54-83-201-96.compute-1.amazonaws.com'
                                  port = 5432, # or any other port specified by your DBA
                                  user = 'postgres',
                                  password = 'secretPassword',
                                  options="-c search_path=schema")}

#Activo función
conn <- fun_connect()

#Conecto y llamo a BBDD "censo"
dbtable<-tbl(conn, "table")

Replyr approach: dont'work

dbtable %>% replyr::replyr_split('columSplit') %>% 
  lapply(funToApply) %>% 
  replyr::replyr_bind_rows()

I am searching something like the local "Split-Apply-Combine" approach (this is just a prototype example):

library(tidyverse)

dfClassic<-data.frame(a=c(123,234,345,65,76,678,567,43,234),
                        b=c("a","b","a","b","c","d","c","c",a"))

split.data.frame(dfClassic, dfClassic$b)%>%
map(.,~data.frame(sum(a)))%>%
bind_rows()

In the dbplyr github issues, the people recommend me use do() function, I tried the next:

dbtable%>% group_by("anycolumn") %>% do(mean(as.numeric("columnToApplyAFunction"))) %>% collect()

but this return me:

|======================================================================================= |100% ~0 s remaining
Error: No more ticks
Run `rlang::last_error()` to see where the error occurred.
In addition: There were 50 or more warnings (use warnings() to see the first 50)

Thanks for you help.
Regards!