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    <h3>Update</h3> <p>Not knowing what you are trying to do, I'll share one more suggestion: Preallocate vectors of the type you want for each column, insert values into those vectors, and then, at the end, create your <code>data.frame</code>.</p> <p>Continuing with Julian's <code>f3</code> (a preallocated <code>data.frame</code>) as the fastest option so far, defined as:</p> <pre><code># pre-allocate space f3 &lt;- function(n){ df &lt;- data.frame(x = numeric(n), y = character(n), stringsAsFactors = FALSE) for(i in 1:n){ df$x[i] &lt;- i df$y[i] &lt;- toString(i) } df } </code></pre> <p>Here's a similar approach, but one where the <code>data.frame</code> is created as the last step.</p> <pre><code># Use preallocated vectors f4 &lt;- function(n) { x &lt;- numeric(n) y &lt;- character(n) for (i in 1:n) { x[i] &lt;- i y[i] &lt;- i } data.frame(x, y, stringsAsFactors=FALSE) } </code></pre> <p><code>microbenchmark</code> from the "microbenchmark" package will give us more comprehensive insight than <code>system.time</code>:</p> <pre><code>library(microbenchmark) microbenchmark(f1(1000), f3(1000), f4(1000), times = 5) # Unit: milliseconds # expr min lq median uq max neval # f1(1000) 1024.539618 1029.693877 1045.972666 1055.25931 1112.769176 5 # f3(1000) 149.417636 150.529011 150.827393 151.02230 160.637845 5 # f4(1000) 7.872647 7.892395 7.901151 7.95077 8.049581 5 </code></pre> <p><code>f1()</code> (the approach below) is incredibly inefficient because of how often it calls <code>data.frame</code> and because growing objects that way is generally slow in R. <code>f3()</code> is much improved due to preallocation, but the <code>data.frame</code> structure itself might be part of the bottleneck here. <code>f4()</code> tries to bypass that bottleneck without compromising the approach you want to take.</p> <hr> <h3>Original answer</h3> <p>This is really not a good idea, but if you wanted to do it this way, I guess you can try:</p> <pre><code>for (i in 1:10) { df &lt;- rbind(df, data.frame(x = i, y = toString(i))) } </code></pre> <p>Note that in your code, there is one other problem:</p> <ul> <li>You should use <code>stringsAsFactors</code> if you want the characters to not get converted to factors. Use: <code>df = data.frame(x = numeric(), y = character(), stringsAsFactors = FALSE)</code></li> </ul>
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    1. COThanks! That solves my problem. Why is this "really not a good idea"? And in what way are x and y mixed in the for loop?
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    2. CO@user2932774, It's incredibly inefficient to grow an object this way in R. An improvement (but still not necessarily the best way) would be to preallocate a `data.frame` of the ultimate size you expect and add the values in with `[` extraction/replacement.
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    3. COThanks, Ananda. I normally go with preallocation, but I disagree that this is really not a good idea. It depends on the situation. In my case, I'm dealing with small data and the alternative will be more time-consuming to code. Plus, this is more elegant code compared to that required to update numerical indices to fill the appropriate portions of the pre-allocated data frame on every iteration. Just curious, what is the "best way" to accomplish this task in your opinion? I would have thought that preallocation would have been best.
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