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As a PhD student and researcher, I often hear friends and colleagues say that they want to learn R, but that the learning curve is so steep that they can't seem to get started. It's true that learning any tool as powerful as R can be confusing at first, especially if you are not accustomed to typing commands in a terminal. That said, there are TONS of resources available for learning R. This post describes some of the resources that I have found most useful in my jouRney.
XLConnect is a powerful package that allows R users to read and write Excel files in a highly integrated manner from within R. It uses the Apache POI API (see the http://poi.apache.org/ webpage) as the underlying interface, so you may expect us to extend the current functionality of XLConnect accordingly in the future. One of the great things about XLConnect is its portability. You can use it across various platforms and operating systems - be it Windows 32 bit, Linux 64 bit or a Mac, you can run your favorite piece of XLConnect-code on all of them. XLConnect works with both xls and the newer xml-based xlsx formats. It works with OpenOffice* and it even works if you don’t have Excel installed at all.
Note: this is a contributed article for the ICSA Bulletin and the basic idea can be summarized in this picture. It is hard to convince people to think about reproducible research (RR). There are two parts of difficulties: (1) tools used to be for experts only and (2) it is still common practice to copy and paste. For some statisticians, RR is almost equivalent to Sweave (R + LaTeX).
Overview The knitr package was designed to be a transparent engine for dynamic report generation with R, solve some long-standing problems in Sweave, and combine features in other add-on packages into one package ( knitr ≈ Sweave + cacheSweave + pgfSweave + weaver + animation::saveLatex + R2HTML::RweaveHTML + highlight::HighlightWeaveLatex + 0.2 * brew + 0.1 * SweaveListingUtils + more).
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