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copied!<p>This is one way:</p> <pre><code># create some random data df <- data.frame(y=rnorm(100),x1=rnorm(100), x2=rnorm(100),x3=rnorm(100)) # introduce random NA's df[round(runif(10,1,100)),]$x1 <- NA df[round(runif(10,1,100)),]$x2 <- NA df[round(runif(10,1,100)),]$x3 <- NA # this does the actual work... # assumes data is in columns 2:4, but can be anywhere for (i in 2:4) {df <- df[!is.na(df[,i]),]} </code></pre> <p>And here's another, using <code>sapply(...)</code> and <code>Reduce(...)</code>:</p> <pre><code>xx <- data.frame(!sapply(df[2:4],is.na)) yy <- Reduce("&",xx) zz <- df[yy,] </code></pre> <p>The first statement "applies" the function <code>is.na(...)</code> to columns 2:4 of <code>df</code>, and inverts the result (we want <code>!NA</code>). The second statement applies the logical <code>&</code> operator to the columns of <code>xx</code> in succession. The third statement extracts only rows with <code>yy=T</code>. Clearly this can be combined into one horrifically complicated statement.</p> <pre><code>zz <-df[Reduce("&",data.frame(!sapply(df[2:4],is.na))),] </code></pre> <p>Using <code>sapply(...)</code> and <code>Reduce(...)</code> can be faster if you have very many columns.</p> <p>Finally, most modeling functions have parameters that can be set to deal with NA's directly (without resorting to all this). See, for example the <code>na.action</code> parameter in <a href="http://stat.ethz.ch/R-manual/R-patched/library/stats/html/lm.html" rel="nofollow">lm(...)</a>.</p>

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