title: R list object size larger than corresponding raw data file
body: <p>I've got a CSV file I import into R and then split into several subsets which consitute my list "importedData":</p>
  
  <pre><code>filePath &lt;- "Test.csv"
  rowsPerBatch &lt;- 58
  
  numRows &lt;- length(count.fields(file = filePath, sep = ","))
  readSegment &lt;- function(x) fread(file = filePath, sep = ",", header = TRUE, skip = rowsPerBatch*(x-1), nrows = rowsPerBatch-1)
  importedData &lt;- lapply(1:(numRows/rowsPerBatch), readSegment)
  </code></pre>
  
  <p>The raw CSV file is just 4MB. However, the list object within R is 17.8 MB large. Why is that the case? Is there a way to do the above more memory-efficiently? </p>
  
  <p>I am planning on scaling-up the algoirithm above to handle several dozen CSV files each >200MB. If each of their corresponding list objects in R is 3x their original size, I'm afraid the memory usage will get out of control very quickly.</p>
  
  <p>Thanks!</p>