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    <p>Having written tens of thousands of lines of code in both languages, R is just a lot more idiosyncratic and less consistent than Python. It's really nice for doing quick plots and investigation on a small to medium size dataset, mainly because its built-in dataframe object is nicer than the numpy/scipy equivalent, but you'll find all kinds of weirdness as you do things more complicated than one liners. My advice is to use <a href="http://rpy.sourceforge.net/rpy2.html">rpy2</a> (which unfortunately has a much worse UI than its predecessor, rpy) and just do as little as possible in R with the rest in Python.</p> <p>For example, consider the following matrix code:</p> <pre><code>&gt; u = matrix(1:9,nrow=3,ncol=3) &gt; v = u[,1:2] &gt; v[1,1] [2] 1 &gt; w = u[,1] &gt; w[1,1] Error in w[1, 1] : incorrect number of dimensions </code></pre> <p>How did that fail? The reason is that if you select a submatrix from a matrix which has only one column along any given axis, R "helpfully" drops that column and changes the type of the variable. So w is a vector of integers rather than a matrix: </p> <pre><code>&gt; class(v) [1] "matrix" &gt; class(u) [1] "matrix" &gt; class(w) [1] "integer" </code></pre> <p>To avoid this, you need to actually pass an obscure keyword parameter:</p> <pre><code>&gt; w2 = u[,1,drop=FALSE] &gt; w2[1,1] [3] 1 &gt; class(w2) [1] "matrix" </code></pre> <p>There's a lot of nooks and crannies like that. Your best friend at the beginning will be introspection and online help tools like <code>str</code>,<code>class</code>,<code>example</code>, and of course <code>help</code>. Also, make sure to look at the example code on the <a href="http://addictedtor.free.fr/graphiques/thumbs.php">R Graph Gallery</a> and in Ripley's <a href="http://www.stats.ox.ac.uk/pub/MASS4/">Modern Applied Statistics with S-Plus</a> book. </p> <hr> <p><strong>EDIT</strong>: Here's another great example with factors. </p> <pre><code>&gt; xx = factor(c(3,2,3,4)) &gt; xx [1] 3 2 3 4 Levels: 2 3 4 &gt; yy = as.numeric(xx) &gt; yy [1] 2 1 2 3 </code></pre> <p>Holy cow! Converting something from a factor back to a numeric didn't actually do the conversion you thought it would. Instead it's doing it on the internal enumerated type of the factor. This is a source of hard-to-find bugs for people who aren't aware of this, because it's still returning integers and will in fact actually work <em>some</em> of the time (when the input is already numerically ordered). </p> <p>This is what you actually need to do</p> <pre><code>&gt; as.numeric(levels(xx))[xx] [1] 3 2 3 4 </code></pre> <p>Yeah, sure, that fact is on the <code>factor</code> help page, but you only land up there when you've lost a few hours to this bug. This is another example of how R does not do what you intend. Be very, very careful with anything involving type conversions or accessing elements of arrays and lists.</p>
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    1. CO+1. God I hate that `drop=TRUE` default.
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    2. COI'm sorry, but that's especially a personal opinion. You can indeed not be happy with some defaults, or find it "less idiosyncratic", but for what it's designed R outperforms Python in _my_ personal opinion. I just got used to how R works, and as you most often select a row or column from a matrix to do a **vectorized** calculation on it, the default drop=T isn't all that unlogic.
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    3. COWell, sure, but you can multiply an n x 1 matrix just as well as you can a length n vector. There's no reason to force a type conversion. Anyway, let's just say this is not the behavior in any other matrix language library (including numpy) and is hence relevant to the author's original question re: pitfalls :)
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