This is a collection of custom tidy functions

tidy_extensions.R

future::plan(future:::multiprocess)

do_and_return = function(tbl, code){
	
	# Execute future
	dummy = future::future(code())

	# Return
	tbl
}



do_parallel_start = function(df, .f, ...){
	
	detect_cores = function(){
		
		if(.Platform$OS.type == "unix")
			system("nproc", intern = TRUE) %>% as.integer %>% sum(-1)
		else if(.Platform$OS.type == "windows")
			parallel::detectCores()  %>% as.integer %>% sum(-1)
		else stop("Your platform type is not recognised")
		
	}
	cores = detect_cores()
	cl <- parallel::makeCluster(cores)
	parallel::clusterExport(cl, lsf.str(envir = parent.env(environment())))
	parallel::clusterExport(cl, ".f", envir =  environment())
	#parallel::clusterEvalQ(cl, .f)
	
	
	parallel::clusterEvalQ(cl, {
		library(tidyverse)
		library(magrittr)
	})
	
	partition_col = enquos(...)
	
	df %>%
		left_join( 
			(.) %>% 
				distinct(!!partition_col[[1]]) %>% 
				mutate(
					part = 1:n() %>% 
						magrittr::divide_by(length((.))) %>%
						magrittr::multiply_by(!!cores) %>% 
						ceiling 
				) 
		)  %>%
		#mutate(`.f` = .f %>% list) %>%
		multidplyr::partition(part, cluster = cl) %>%
		do({
			
			#.f = (.) %>% slice(1) %>% pull(`.f`) %>% `[[` (1)
			
			(.) %>%
				group_by(!!!partition_col) %>%
				do({
					purrr::as_mapper(.f)( (.) )
				}) 
			
			#purrr::as_mapper(.f)((.) %>% group_by(!!!partition_col))
			
		})  %>%
		collect() %>%
		ungroup() %>%
		select(-part)
	
}