title: Count number of non-NA values for every column in a dataframe
body: <p>I have a big dataset that contains a lot of <code>NA</code>s and some non-Na values.
  At the moment I count my non-<code>NA</code> values for each column like this:</p>
  
  <pre><code> attach(df)
   1000 - (sum(is.na(X1)))
   1000 - (sum(is.na(X2)))
   1000 - (sum(is.na(X3)))
   1000 - (sum(is.na(X4)))
   1000 - (sum(is.na(X5)))
   ...
   detach(df)
  </code></pre>
  
  <p>So my overall length of my observations - the sum of my <code>NA</code> values.</p>
  
  <p>Is there a faster way which uses less code lines and typing effort and gives me fast overview of all the columns and numbers of non-<code>NA</code> values?</p>
  
  <p>Like a for loop or something?</p>
  
  <p>I am looking for something like this:</p>
  
  <pre><code>  X1     Amount of Non-Na-Values
    X2     ...
    X3     ...
    X4
    X5
    X6  
  </code></pre>
  
  <p>Thank you :)</p>