Most commonly used statistical tests and implementation in R

Post by sandGorgon » Fri, Jan 01, 2016, 6:49 AM UTC

earino wrote: post #3

hackaflocka wrote: post #2

This is a good resource for those new to R. R has some really good GUI layers now. I struggled and struggled for years trying to learn the command line methods, but it was too much for me. The following do a great job (these are alternatives) - Deducer - R Commander - RKWard

It seems like this list is incomplete without mentioning that both RStudio[1] and Jupyter[2] notebooks now have really first class support for R. There are also two upstatrs, Rodeo[3] and Beaker[4] are doing cool stuff as well. The company I work for, Domino Data Lab[5], let's you fire up a lot of these notebooks in a nice hosted environment on big cloud servers with minimal cost and effort. It's a fun way to learn h…

Post by sandGorgon » Fri, Jan 01, 2016, 7:10 AM UTC

earino wrote: post #12

sandGorgon wrote: post #11

Earlier quoted context omitted.

What do you recommend to build business dashboards in R? By pulling data from an api or sb for example

Two approaches: 1. If you're interested in running a shiny server, use http://rstudio.github.io/shinydashboard/ ! I have used it to build professional high quality dashboards VERY quickly. 2. You can use an API server like Domino's API end points or OpenCPU to expose R APIs and build the interface using JavaScript at plot.ly! This really can be incredibly elegant and you can do really neat dynamic dashboards.

Post by sandGorgon » Fri, Jan 01, 2016, 7:41 AM UTC

earino wrote: post #14

sandGorgon wrote: post #13

Earlier quoted context omitted.

That is great to know! Is opencpu something you recommend for production? I'm just starting to work with analysts who work in R, and I have struggled with the question whether we should wrap existing R code as an api...or port to python. Low volume right now, so not really concerned with performance... But rather that can R deployment play nicely with things like supervisord,etc in production

Well, I really don't want to turn this into a sales pitch, but that's the exact use case for Domino's API endpoints. Check out http://support.dominodatalab.com/hc/en-us/articles/204173149... for an explanation. If you're interested, drop me an email and I can work up an example project for you. Exposing R algorithms as REST endpoints is exactly what it does quite well. As for OpenCPU, I know that the guy who wrote it…

Post by snydly » Fri, Jan 01, 2016, 8:52 AM UTC

ekianjo wrote: post #16

> If the p-Value is less than significance level (ideally 0.05), Erm, no. P=0.05 is borderline meaningless, there could as much as 30% chance you are wrong about the actual difference being there depending on the true probability of the initial hypothesis. P-values should be used with strong caution.

Post by IndianAstronaut » Fri, Jan 01, 2016, 10:50 AM UTC

minimaxir wrote: post #5

earino wrote: post #3

Earlier quoted context omitted.

It seems like this list is incomplete without mentioning that both RStudio[1] and Jupyter[2] notebooks now have really first class support for R. There are also two upstatrs, Rodeo[3] and Beaker[4] are doing cool stuff as well. The company I work for, Domino Data Lab[5], let's you fire up a lot of these notebooks in a nice hosted environment on big cloud servers with minimal cost and effort. It's a fun way to learn h…

> Jupyter[2] notebooks now have really first class support for R. Jupyter and R is a bit iffy since the R kernel is not native. Although the kernel works fine, setting it up has a ton of manually-installed dependencies, and in-line plots flat-out give unexpected output. (I've had to cheat by embeding charts via Markdown. Although that has the benefit of having the charts be responsive) The important perk is that Jupy…