title: R: Plot a subset of principal component variables when you have too many variables
body: <p>I am new to using Vegan for ecosystem level analysis. </p>
  
  <p>I have a dataset with over 4,000 taxa across ten sites, and another with 37 chem-based observations from all ten sites. </p>
  
  <p>I have analyzed both sets of data using prcomp from the Vegan package, and plotted the results using biplot(df) and as expected it look awesome for the chemistry data but produce (as expected) a ridiculous unreadable plot for the taxa data. </p>
  
  <p>I've read in one website that you can plot a biplot based on loading values of those variables that are driving the plot - in their example they plotted only the top 10 loading variables from their prcomp results for clarity, but they omitted to show how they did it. (See <a href="http://www.pmc.ucsc.edu/~mclapham/Rtips/ordination.htm" rel="nofollow">http://www.pmc.ucsc.edu/~mclapham/Rtips/ordination.htm</a>)</p>
  
  <p>After hours of reading and searching for how to plot based on loading (rotation) values I can't find an answer. Can anyone help me figure out how to narrow my variables to those that are the most important instead of looking at all 4,000?</p>
  
  <p>Thank you in advance.
  Ina</p>