Reads all *.csv files from working directory and moves the data into a single dataframe

Read-all-csv-files-into-dataframe.R

require(data.table)
require(dplyr)

#Get a list with all csv files from the directory that is set as 'working directory'
filelist = list.files(pattern="*.csv$")

#read all csv files with data.table::fread() and put in df_input_list
df_input_list <- lapply(filelist, fread)

#reading in csv files can also be done using the base R function read.csv(), without needing to load package "data.table":
# df_input_list <- lapply(filelist, read.csv)

#get the filenames, remove extension for use as "id"
names(df_input_list) <- gsub(filelist, pattern="\\..*", replacement="")

#Merge all the dataframes and use the filenames as id    
df_merged <- bind_rows(df_input_list, .id = "id")

1.9 GB of data in 12 files read and merged in less than 10 seconds..You did a real good job. Using read.csv instead of data.table is much slower.

Thanks, I appreciate the feedback!