readFiles now uses the furrr package for parallel processing. Updated readFiles documentation future::plan now reexported

Author: jasenfinch

DESCRIPTION

@@ -21,7 +21,9 @@ Imports:
     lubridate,
     ggplot2,
     ggthemes,
-    yaml
+    yaml,
+    future,
+    furrr
 License: GPL-3
 Encoding: UTF-8
 RoxygenNote: 7.1.1

NAMESPACE

@@ -6,6 +6,7 @@ export(binneRlyse)
 export(detectClusterType)
 export(detectInfusionScans)
 export(detectParameters)
+export(plan)
 export(plotChromFromFile)
 export(readBinningParameters)
 export(readFiles)
@@ -52,6 +53,8 @@ importFrom(dplyr,summarise)
 importFrom(dplyr,summarise_all)
 importFrom(dplyr,ungroup)
 importFrom(dplyr,vars)
+importFrom(furrr,future_map)
+importFrom(future,plan)
 importFrom(ggplot2,aes)
 importFrom(ggplot2,element_text)
 importFrom(ggplot2,facet_wrap)
@@ -85,7 +88,6 @@ importFrom(parallel,clusterExport)
 importFrom(parallel,detectCores)
 importFrom(parallel,makeCluster)
 importFrom(parallel,parLapply)
-importFrom(parallel,parLapplyLB)
 importFrom(parallel,stopCluster)
 importFrom(purrr,map)
 importFrom(purrr,map_chr)

R/getPeaks.R

@@ -5,12 +5,10 @@
 
 sampProcess <- function(file,scans,dp){
 
-    `%>%` <- getFromNamespace('%>%','magrittr')
-    
-    pl <- binneR::getFile(file,scans) %>%
-        dplyr::mutate(mz = round(mz,dp)) %>% 
-        dplyr::group_by(polarity,mz) %>% 
-        dplyr::summarise(intensity = sum(intensity)/length(scans)) 
+    pl <- getFile(file,scans) %>%
+        mutate(mz = round(mz,dp)) %>% 
+        group_by(polarity,mz) %>% 
+        summarise(intensity = sum(intensity)/length(scans)) 
 
     return(pl)
 }

R/readFiles.R

@@ -9,47 +9,44 @@
 #' @param clusterType the type of cluster to use for parallel processing
 #' @return A list containing peak lists for the relevant scans with combined 
 #' scan ranges for each present mode in the data file. 
+#' @details 
+#' Parallel processing is managed by the \code{future} package. This can be specified using the \code{plan() function}. See the example below and \code{?future::plan} for details on how this can be specified.
 #' @author Jasen Finch
-#' @importFrom parallel makeCluster parLapplyLB stopCluster
+#' @examples 
+#' ## Example file path
+#' file_paths <- metaboData::filePaths('FIE-HRMS',
+#'                                     'BdistachyonEcotypes')[1]
+#' 
+#' ## Optionally use declare parallel processing options
+#' plan(future::multisession,workers = 2)
+#'                                                                         
+#' ## Process example file
+#' res <- readFiles(file_paths,
+#'                  dp = 2,
+#'                  scans = 6:17)
+#' @importFrom furrr future_map
 #' @importFrom dplyr bind_rows 
 #' @importFrom tidyr spread
-#' @examples 
-#' res <- readFiles(
-#'     metaboData::filePaths('FIE-HRMS',
-#'                           'BdistachyonEcotypes')[1],
-#'                           dp = 2,
-#'                           scans = 6:17)
 #' @export
 
 readFiles <- function(files,
 											dp, 
-											scans, 
-											nCores = 1, 
-											clusterType = detectClusterType()){ 
-	
-	clust <- makeCluster(nCores, type = clusterType) 
+											scans){ 
 
-	clusterExport(clust, 'sampProcess')
-	
-	pl <- parLapplyLB(clust,
-										files,
-										fun = sampProcess,
+	pl <- future_map(files,
+										sampProcess,
 										scans = scans,
 										dp = dp) %>%
 		set_names(files) %>%
 		bind_rows(.id = 'file') %>%
 		mutate(mz = str_c(polarity,mz)) %>%
 		split(.$polarity) %>%
-		parLapplyLB(cl = clust,function(x){
-			
-			`%>%` <- getFromNamespace('%>%','magrittr')
+		future_map(~{
 
-			x <- spread(x,key = 'mz',value = 'intensity',fill = 0) %>%
+			.x %>%
+				spread(key = 'mz',value = 'intensity',fill = 0) %>%
 				ungroup() %>%
 				select(-file,-polarity)
-			
-			return(x)
 		})
-	stopCluster(clust)
 	return(pl)
 }  

R/reexports.R

@@ -1,3 +1,8 @@
 #' @rdname reexports
 #' @export
-magrittr::`%>%`
+magrittr::`%>%`
+
+#' @rdname reexports
+#' @importFrom future plan
+#' @export
+future::plan