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2013-10-20T22:48:58.003
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<code>importr</code> is a pretty high level function, trading performance for ease-of-use. It does a lot beside just loading an R package. It also maps all R objects in that package to Python (rpy2) objects. That effort is lost when doing <code>importr('lattice')</code> in your script, if the result is not used. Beside that, importing packages in R itself is not without a cost (for the larger R packages with S4 class definitions, you this can be noticeable when the script is short). <code>rpy2</code> can't do much about this. Using R variables such as R_PROFILE is possible, but this was not enabled by default before <a href="https://bitbucket.org/lgautier/rpy2/commits/488383323ecaf30ed307ac7e642122e51677c756" rel="nofollow noreferrer">very recently</a>. How to enable it is on SO (<a href="https://stackoverflow.com/questions/6364082/rpy2-importr-fails-with-renviron">here</a>). Now, here <code>importr</code> is taking "only" 25% of the response time. Optimization efforts focusing on this will not be able to make it more than 25% faster (and that's a very optimistic limit). Interpolating data into a string to evaluate it as R code after that is not very optimal (as warned in the <a href="http://rpy.sourceforge.net/rpy2/doc-2.3/html/robjects_rinstance.html?highlight=evaluate#evaluating-a-string-as-r-code" rel="nofollow noreferrer">documentation for rpy2</a> ). Consider calling the R function through rpy2, passing the data as anything exporting the buffer interface (for example).
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1. POPreloading for Importr in Django
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1. COThanks so much. I will dive into what performance optimizations are available. As an aside, rpy2 is wonderful; thank you for your efforts.
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