title: Big Data Process and Analysis in R
body: <p>I know this is not a new concept by any stretch in R, and I have browsed the High Performance and Parallel Computing Task View.  With that said, I am asking this question from a point of ignorance as I have no formal training in Computer Science and am entirely self taught.  </p>
  
  <p>Recently I collected data from the Twitter Streaming API and currently the raw JSON sits in a 10 GB text file.  I know there have been great strides in adapting R to handle big data, so how would you go about this problem?  Here are just a handful of the tasks that I am looking to do:</p>
  
  <ol>
  <li>Read and process the data into a data frame </li>
  <li>Basic descriptive analysis, including text mining (frequent terms, etc.)</li>
  <li>Plotting</li>
  </ol>
  
  <p>Is it possible to use R entirely for this, or will I have to write some Python to parse the data and throw it into a database in order to take random samples small enough to fit into R.</p>
  
  <p>Simply, any tips or pointers that you can provide will be greatly appreciated.  Again, I won't take offense if you describe solutions at a 3rd grade level either.</p>
  
  <p>Thanks in advance. </p>