title: pull out specific rows from a dataframe to make a new dataframe
body: <p>I have a dataframe that has some NA values.  I want to pull out the rows that have NA, <em>but</em> I don't want to delete them, I want to make a new dataframe containing only those rows that had NA in a specific column.</p>
  
  <p>I have searched for the answer on how to do this but everything I find just tells me how to remove the NA rows. There was a comment to one person where they said rather than removing the rows they don't want, search for how to make a new frame using the rows you do want....but I have not been able to find out how to do that.</p>
  
  <p>My dataframe, Biov_per_genus2, looks like this:</p>
  
  <pre><code>      ID      Code Mag_x Sample Source Count Avg_Biov_cell total_biov_code
  1    Env_102         A   200    102    Env    44  7.962052e+03    
  2    Env_102         A   400    102    Env     1            NA              
  3    Env_102        AA   200    102    Env     2  2.567925e+01    
  4    Env_102        AA   400    102    Env     8  9.664901e+00    
  5    Env_102         B   200    102    Env    46  1.883699e+04    
  6    Env_102        CG   400    102    Env     1            NA              
  7    Env_102        CY   400    102    Env    12  2.188643e+01    
  8    Env_102         D   400    102    Env    21  1.413717e+01    
  9    Env_102         F   400    102    Env     6  8.136725e+02    
  10   Env_102    Group1   200    102    Env     2  2.073616e+02    
  11   Env_102    Group1   400    102    Env    87  9.557676e+00    
  12   Env_102        JJ   200    102    Env    24  5.169177e+03    
  13   Env_102        JJ   400    102    Env    18  5.230752e+02    
  14   Env_102        KK   400    102    Env     1            NA              
  15   Env_102        MC   400    102    Env    32  1.342800e+03    
  16   Env_102         N   400    102    Env     7  1.453212e+02    
  17   Env_102         O   200    102    Env    43  2.035783e+04    
  18   Env_102         O   400    102    Env    10  1.255538e+03    
  19   Env_102     PrevH   200    102    Env     3  3.474356e+05    
  20   Env_102      S-SS   200    102    Env     3  2.458556e+03    
  21   Env_102      S-SS   400    102    Env     3  1.846000e+02    
  22   Env_102        TF   200    102    Env     8            NA              
  23   Env_102         U   200    102    Env     2  6.819019e+02    
  24   Env_102        WG   200    102    Env     1  9.894446e+03    
  25   Env_102         Z   200    102    Env    28  3.133701e+02    
  26   Env_114         A   200    114    Env    34  8.463451e+03    
  27   Env_114        AA   400    114    Env    23  1.027414e+01    
  28   Env_114         B   200    114    Env     6  2.099966e+04    
  29   Env_114        CC   200    114    Env     4            NA              
  30   Env_114        CG   400    114    Env     1  1.000500e+03    
  31   Env_114        CY   400    114    Env    24  3.989823e+01    
  32   Env_114         D   400    114    Env    15  3.602360e+01    
  33   Env_114         E   200    114    Env     4  7.127227e+03    
  34   Env_114         F   400    114    Env    19  3.215944e+02    
  35   Env_114         G   200    114    Env     4  3.106407e+03    
  36   Env_114    Group1   200    114    Env    17  1.664819e+02    
  37   Env_114    Group1   400    114    Env    91  1.020834e+01    
  38   Env_114         J   400    114    Env     1  1.123198e+03    
  39   Env_114        JJ   200    114    Env     6  1.630015e+03    
  40   Env_114        JJ   400    114    Env     3  4.003960e+02    
  41   Env_114        KK   200    114    Env     6            NA              
  42   Env_114        KK   400    114    Env     3            NA              
  43   Env_114    LL/N/O   400    114    Env     8  4.682544e+02    
  44   Env_114        MC   400    114    Env    18  5.718000e+03    
  45   Env_114         N   200    114    Env     1  8.586049e+03    
  46   Env_114         O   200    114    Env    34  1.092983e+04    
  47   Env_114      S-SS   200    114    Env     3  7.149000e+03    
  48   Env_114        TF   200    114    Env    22  1.880243e+02    
  49   Env_114        TF   400    114    Env     1            NA              
  50   Env_114         U   200    114    Env     2  9.306367e+02    
  51   Env_114        WG   200    114    Env     4            NA              
  52   Env_114         Z   200    114    Env    58  2.270314e+02    
  53   Env_125         A   200    125    Env   153  9.614530e+03    
  54   Env_125         A   400    125    Env     6  2.200686e+02     
  </code></pre>
  
  <p>and it goes on for >700 rows.  I want to pull out the rows which have NA in the Avg_Biov_cell column and put all that data into a new dataframe.</p>
  
  <p>Any advice would be appreciated.</p>