If you work with text data in R, the gregexpr() function is essential for pattern matching. It finds all occurrences of a pattern within a string. Key parameters include pattern, text, ignore.case, perl, fixed, and useBytes. You can match characters, ignore case, use advanced regex, and search fixed strings.
For 5 years, the ROracle #rstats package provided by #Oracle to CRAN has been broken on Windows. Some nice fellow has finally caved and posted a fork that actually works. Good for him! But it exposes an underlying problem,
@obrl_soil
As much as I don't like Oracle (mostly because of what they have done in the past), I havet o agree that CRAN's review process suffers from single-person-blockage. All your submissions come down to decision of one person. The #rOpenSci process is more democratic and way more transparent, but it is excruciatingly lengthy. I personally like a middle ground approach (transparent communication, more than one moderator for package submission, quick responses)
When I read R code and see people have very liberally used tons of packages like there is no tomorrow, I cringe. This is bad when they are doing it in research. Basically what they are doing is addicting their analysis and research to tons of packages. Like any other addition, when they don't get their fix, it's gonna hurt, and gonna hurt bad. Their research's reproducibility is close to non-existence as none of those packages will be around for ever. Pick dependencies carefully.
@eliocamp
I didn't say you should not have any dependency. What I said is that be vigelant and careful about what your are including. If you are writing a hobby code or some homework, it is fine, but if you need to publish the code and others should be able to run it on their mashine for decades in future, be careful. I personally give positive edge to #rOpenSci packages.
On the same note, using {renv} or {packrat} is super essential if a reproducibility is important. @LeafyEricScott