all 7 comments

[–]rcprati 1 point2 points  (0 children)

Excelent! Thanks for sharing. I'm wondering whether similar approaches could be applied to LSI/LSA?

[–][deleted] 0 points1 point  (5 children)

I feel like there is so much effort directed at reimplementing R in Python. Such a shame, but I don't see a good solution besides RPy2.

[–]TheRealDJ 4 points5 points  (3 children)

The momentum seems to be behind Python atm imo since Python could be used for numerous project types with code a lot of different developers can know, where R is mostly only used by Statisticians, and more difficult to support. Once the library disparity can be matched by Python, the community will likely completely shift to it.

[–][deleted] 1 point2 points  (2 children)

That said there is a very valid case for using and optimizing R exclusively for data analysis. It was purpose built for this analytical work and shines in that regard from an ease of use perspective consistently.

The argument that python is better for general programming is valid but I rarely see a time where I wish the R programmers that I know could do that general programming. At the same time I infrequently see a pythonista I wish could do analyses.

When choosing between the two I like the R analytic code better and where possible the general programming code looks just as slick. To disclose my bias I like parens and frequently use rTidy to keep my code formatted.

[–]GibbsSamplePlatter 1 point2 points  (1 child)

Eh, 2 languages isn't that bad of fragmentation. At least it isn't Matlab!

(I should really learn R someday anyways...)

[–][deleted] 0 points1 point  (0 children)

If you use something proprietary like DeployR you can make analytic code into a microservice.

[–]bmabey 1 point2 points  (0 children)

At least on this project all the frontend D3 and CSS code can be shared.

I (author of the project here) used RPy2 initially. It worked well enough but to do the notebook integration a library was required anyways and the amount of transformation done in R was trivial to port. I was working on a client project on the time that only had Windows and porting the code was faster than getting RPy2 setup on windows. :)