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    <p>Idea # 1: Why not:</p> <pre><code>Genedata$SPOCK1.202363_at_recode &lt;- factor( Genedata$SPOCK1.202363_at &lt;= 8.2244399999999995, labels=c("0", "1") ) </code></pre> <p>That way you don't need to process the inequality twice. You could skip the <code>factor</code> call if you could accept it as a logical vector:</p> <pre><code>Genedata$SPOCK1.202363_at_recode &lt;- Genedata$SPOCK1.202363_at &lt;= 8.2244399999999995 </code></pre> <p>Idea #2: This would surely be faster for multiple rows for a matrix named "X":</p> <pre><code>X &lt;- (X &lt; rep( colMeans(X), each=nrow(X) ) ) </code></pre> <p>You could do it like this:</p> <pre><code>Genedata[ , cols_to_process] &lt;- ( Genedata[ , cols_to_process] &lt; rep( cutoff_vector, each=nrow(Genedata[ , cols_to_process]) ) ) </code></pre> <p>Notice that this is essentially what @flodel's comment was suggesting, because he was converting columns to rows and depending on argument recycling, and then restoring the original structure. Regarding the parallelization option, I don't think this fairly small dataset warrants the effort. This took about 3 minutes on a 5 year-old machine:</p> <pre><code> dfrm &lt;- data.frame(matrix(rnorm(43000*170), 170) ) cutoff &lt;- rnorm(43000, 0, 0.2) system.time( dfrm[ , 1:43000] &lt;- ( dfrm[ , 1:43000] &lt; rep( cutoff, each=170 ) ) ) # user system elapsed #129.052 57.449 185.660 </code></pre> <p>Converting to a matrix might speed this up considerably:</p> <pre><code> mtx &lt;- matrix(rnorm(43000*170), 170) system.time( mtx &lt;- t(1*(t(mtx) &gt; cutoff) ) ) # user system elapsed # 0.226 0.133 0.358 mtx &lt;- matrix(rnorm(43000*170), 170) system.time( mtx[ , 1:43000] &lt;- ( mtx[ , 1:43000] &lt; rep( cutoff, each=170 ) ) ) # user system elapsed # 0.852 0.001 0.849 #Now do it all at once............ system.time( mtx &lt;- mtx &lt; rep( cutoff, each=170 ) ) # user system elapsed #0.095 0.000 0.094 </code></pre>
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    1. POIncreasing speed for R recoding task
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    1. COI've changed from the three lines to the one. It makes the code more elegant, but doesn't seem to affect performance significantly. I took the labels instead of logical vector, because it is easier for the next step of my script. As for the matrix; I might use the data.table as someone suggested; it seems more straightforward and the performance gain is good. I'm not sure about the last bit of the code you suggested. The cutoff point by which i'm dichotimizing my data changes with each column. Its not just replicating.
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    2. COYes, and the "cutoff point" will change with every column. That's what the rep() call is doing.
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    3. COOK, looking into it, cheers. :) Quick question. Will this work with data.table? And thoughts on data.table vs matrix?
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