Most commonly used statistical tests and implementation in R
r-statistics.co
Most commonly used statistical tests and implementation in R
1–10 of 34 posts
Re: Most commonly used statistical tests and implementation in R
#2This 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
Re: Most commonly used statistical tests and implementation in R
#3This 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 how all these new environments can work together. From RStudio for exploratory analysis, to Jupyter notebooks for presenting a topic. The other two I haven't really found the superior use-case. The tools in this space are just getting better and better.
1. https://www.rstudio.com/ 2. http://jupyter.org/ 3. http://blog.yhat.com/posts/introducing-rodeo.html 4. http://beakernotebook.com/ 5. https://www.dominodatalab.com/
Re: Most commonly used statistical tests and implementation in R
#4It's also worth looking at the documentation in R for each of the functions too. (can invoke with console with ?chisq.test for example).
For example, the chisq.test has optional built-in Monte Carlo testing, and none of the other functions do, oddly.
Re: Most commonly used statistical tests and implementation in R
#5This 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…
> 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 Jupyter notebooks are now rendered natively on GitHub, which I've made considerable use of: https://github.com/minimaxir/sf-arrests-when-where/blob/mast...
Re: Most commonly used statistical tests and implementation in R
#6Earlier 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…
> 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)
You know, to be completely honest, I've never used it directly. I've always used it on our platform. It's very possible that our engineers already did all that setup so it "just works." I took the original post: http://r-statistics.co/Statistical-Tests-in-R.html and reimplemented it in an R notebook with some simple plots at the end, but yeah, the plotting just sort of works for me. I didn't realize I had an incomplete view of the complexity of getting that working :(
https://app.dominodatalab.com/earino/statistical_tests/view/...
We also render the notebooks. The difference is that we also let you run them :)
Re: Most commonly used statistical tests and implementation in R
#7This 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…
Does R Studio have a bunch of GUI plugins for doing the various common statistics tasks? Because the base R Studio doesn't do much (it's nice for running R, but I don't think that one can do linear regressions etc. via a GUI -- correct me if I'm wrong).
Re: Most commonly used statistical tests and implementation in R
#8Earlier 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…
Does R Studio have a bunch of GUI plugins for doing the various common statistics tasks? Because the base R Studio doesn't do much (it's nice for running R, but I don't think that one can do linear regressions etc. via a GUI -- correct me if I'm wrong).
No that's not really what it does. You can edit code separately from the REPL(which is also directly available), view plots, examine some data objects, view help/command history/etc. Essentially it's like an IDE for the R language, it doesn't turn R into like an SPSS GUI type interface.
Edit: the closest thing I can think of in RStudio to that is installing the manipulate package which allows adding sliders and such to plots for some custom plotting controls.
Re: Most commonly used statistical tests and implementation in R
#9If you want to get a bit more general:
- lm can fit linear models
- anova can be used to compare nested linear models -- is the more complex model significantly better?
At this level you start to see a real language of statistics, as opposed to canned routines.
Julian Faraway has some great books:
Re: Most commonly used statistical tests and implementation in R
#10Earlier quoted context omitted.
Does R Studio have a bunch of GUI plugins for doing the various common statistics tasks? Because the base R Studio doesn't do much (it's nice for running R, but I don't think that one can do linear regressions etc. via a GUI -- correct me if I'm wrong).
No that's not really what it does. You can edit code separately from the REPL(which is also directly available), view plots, examine some data objects, view help/command history/etc. Essentially it's like an IDE for the R language, it doesn't turn R into like an SPSS GUI type interface. Edit: the closest thing I can think of in RStudio to that is installing the manipulate package which allows adding sliders and such…
For custom plotting controls, you can just use shiny. It is awesome. Check out the shiny gallery and you will see that.