R - reading multiple data files at once

I am looking at parallel processing in R and was wondering if I could read in multiple txt files in parallel versus doing it sequentially. Reason for this is I have a shiny application and I want to cut down on the loading time and a large chunk is coming from loading the files.

Current situation:

Shipments_Raw <- read.delim('/srv/samba/share/SAP data/_zmrosales_ship_month.txt', fill = TRUE) ShipmentsYear_Raw <- read.delim('/srv/samba/share/SAP data/_zmrosales_ship_year.txt', fill = TRUE) Open_Raw <- read.delim('/srv/samba/share/SAP data/_zmrosales_wip.txt', fill = TRUE) WIP_Short_Raw <- read.delim('/srv/samba/share/SAP data/_zmro_short.txt', fill = TRUE) WIP_RTQT_Raw <- read.delim('/srv/samba/share/SAP data/_zmro_sno_tasks_year.txt', fill = TRUE) Invoiced_Raw <- read.delim('/srv/samba/share/SAP data/_zmrosales_inv.txt', fill = TRUE) 

I have seen examples of running in parallel but they all end with combining all of the files. Each file I import, I want as a separate dataframe.

Here are some examples:

How do you read in multiple .txt files into R?

https://www.r-bloggers.com/import-all-text-files-in-a-folder-with-parallel-execution/

Ideal situation (although I know this isn't the code):

RunParallel { Shipments_Raw <- read.delim('/srv/samba/share/SAP data/_zmrosales_ship_month.txt', fill = TRUE) ShipmentsYear_Raw <- read.delim('/srv/samba/share/SAP data/_zmrosales_ship_year.txt', fill = TRUE) Open_Raw <- read.delim('/srv/samba/share/SAP data/_zmrosales_wip.txt', fill = TRUE) WIP_Short_Raw <- read.delim('/srv/samba/share/SAP data/_zmro_short.txt', fill = TRUE) WIP_RTQT_Raw <- read.delim('/srv/samba/share/SAP data/_zmro_sno_tasks_year.txt', fill = TRUE) Invoiced_Raw <- read.delim('/srv/samba/share/SAP data/_zmrosales_inv.txt', fill = TRUE) } 

After comment from below

 tic <- Sys.time() Shipments_Raw <- read.delim('/srv/samba/share/SAP data/_zmrosales_ship_month.txt', fill = TRUE) ShipmentsYear_Raw <- read.delim('/srv/samba/share/SAP data/_zmrosales_ship_year.txt', fill = TRUE) Open_Raw <- read.delim('/srv/samba/share/SAP data/_zmrosales_wip.txt', fill = TRUE) WIP_Short_Raw <- read.delim('/srv/samba/share/SAP data/_zmro_short.txt', fill = TRUE) WIP_RTQT_Raw <- read.delim('/srv/samba/share/SAP data/_zmro_sno_tasks_year.txt', fill = TRUE) Invoiced_Raw <- read.delim('/srv/samba/share/SAP data/_zmrosales_inv.txt', fill = TRUE) toc <- Sys.time() Sequential <- toc - tic tic <- Sys.time() file <- c("/srv/samba/share/SAP data//_zmrosales_ship_month.txt", "/srv/samba/share/SAP data//_zmrosales_ship_year.txt", "/srv/samba/share/SAP data//_zmrosales_inv.txt", "/srv/samba/share/SAP data//_zmrosales_wip.txt", "/srv/samba/share/SAP data//_zmro_short.txt", "/srv/samba/share/SAP data//_zmro_sno_tasks_year.txt") x2 <- lapply(file, data.table::fread) Shipments_Raw <- as.data.frame(x2[1]) ShipmentsYear_Raw <- as.data.frame(x2[2]) Invoiced_Raw <- as.data.frame(x2[3]) Open_Raw <- as.data.frame(x2[4]) WIP_Short_Raw <- as.data.frame(x2[5]) WIP_RTQT_Raw <- as.data.frame(x2[6]) toc <- Sys.time() Lapply <- toc - tic Sequential Lapply 

Difference in time:

> Sequential Time difference of 6.011156 secs > Lapply Time difference of 0.8015034 secs