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(This article was first published on <strong><a href="https://matloff.wordpress.com/2018/11/20/r-python-a-concrete-example/"> Mad (Data) Scientist</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>I like both Python and R, and teach them both, but for data science R is the clear choice. When asked why, I always note (a) written by statisticians for statisticians, (b) built-in matrix type and matrix manipulations, (c) great graphics, both base and CRAN, (d) excellent parallelization facilities, etc. I also like to say that R is “more CS-ish than Python,” just to provoke my fellow computer scientists. <img alt="🙂" src="https://i0.wp.com/s0.wp.com/wp-content/mu-plugins/wpcom-smileys/twemoji/2/72x72/1f642.png?w=456&ssl=1" class="wp-smiley" data-recalc-dims="1"> </p>
<p>But one aspect that I think is huge but probably gets lost when I cite it is R’s row/column/element-name feature. I’ll give an example here.</p>
<p>Today I was dealing with a problem of ID numbers that are nonconsecutive.  My solution was to set up a lookup table. Say we have ID data (5, 12, 13, 9, 12, 5, 6). There are 5 distinct ID values, so we’d like to map these into new IDs 1,2,3,4,5. Here is a simple solution:</p>
<p> </p>
<blockquote>
<p>> x &lt;-c(5,12,13,9,12,5,6)<br>> xuc &lt;-as.character(unique(x))<br>> xuc<br>[1] “5” “12” “13” “9” “6” <br>> xLookup &lt;-1:length(xuc)<br>> names(xLookup) &lt;-xuc<br>> xLookup<br>5 12 13 9 6 <br>1 2 3 4 5</p>
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<p>So, from now on, to do the lookup, I just use as subscript the character from of the original ID, e.g.</p>
<blockquote>
<p>> xLookup[’12’]<br>12 <br>2</p>
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<p>Of course, I did all this within program code. So to change a column of IDs to the new ones, I wrote</p>
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<p> ul[as.character(testSet[,1])])</p>
</blockquote>
<p>Lots of other ways to do this, of course, but it shows how handy the names can be.</p>
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