title: How to accelerate (parallelize ?) text files into dataframe conversion?
body: <p>I have hundreds of .csv in a folder that I would like to convert into a dataframe in R. I can do this with this code : </p>
  
  <pre><code>file_list &lt;- list.files()
  
  dataset = NULL
  
  for (file in file_list){  
  
      temp_dataset &lt;-read.table(file, header=TRUE, sep=" ")
  
      temp_dataset$symbol &lt;- unlist(strsplit(file,split=".",fixed=T))[1]
  
      dataset&lt;-rbind(dataset, temp_dataset)
      rm(temp_dataset)
  
      print(file)
  }
  </code></pre>
  
  <p>But the bigger the dataframe gets the slower new files are added and it rapidly becomes consequent. My entire folder is only 300mb so it's still manageable by R. Is there a way to accelerate the process ?</p>