I'd consider doing the Stanford Statistical Learning class which starts off with teaching you R. https://lagunita.stanford.edu/courses/HumanitiesSciences/Sta...

Also recommend Swirl, which is an interactive tutorial - http://swirlstats.com/students.html

Or you can go straight to Julia, which is a modern statistical package and language without R's quirks http://julialang.org/

If you're eventually going to put your project on the Web, or just want to learn programming the right way, might be better off doing it in python.

I know a reasonable amount of R, but I don't really know any other programming language. I did once go looking at Julia, and had thought to write a few simple things using it, but in the end just ran out of time and did it with R.

Learning R has been one of the most challenging and enjoyable things I've done over the last three years. However, I would be interested to know where Python, Go or Julia would make my life better or easier.

I mostly write R scripts to analyse and graphically display interpretations of data. There are so many contributed libraries, it feels like I am really spoiled for choice.

I also write Shiny web apps. Is there a Python equivalent of Shiny? I have heard of Django, but is that really as easy as just writing a Python script and deploying on a server? I always thought Django was more of a complete framework which also required server side coding.

Anyway, I would genuinely like to know what advantage Python or Julia or Go would provide me with over just continuing with R.

R quirks ... the <- syntax, the ~ formula syntax, looping through a dataframe is horrifically slow when you can't vectorize, multiple legacy object semantics, poor parallel/multiprocessing support, poor support for datasets that don't fit in memory ... upside is any statistical method probably has a decent implementation in R

python - most comprehensive ecosystem and libraries beyond statistics, e.g. Web, numerical and scientific computing, machine learning (Tensorflow), NLP, generally a good language to learn to program in, pretty easy and forgiving while also being reasonably expressive, performant, offering functional as well as object oriented features/styles.

the good folks at plotly are working on a shiny equivalent (dash) but it's not out yet. Django + matplotlib or bokeh or some client-side graphics like plotly.js is potentially powerful but not really as integrated as shiny.

Also check out this article on updates to R 3.4. R tends to be fast enough for most work (I use it regularly on one-off analysis or things that won't ever make it farther than ad-hoc reporting/findings but can't imagine using it in production systems). The listed changes should go a long way towards making R just fast(er) enough for dealing with larger datasets (doesn't help with datasets larger than memory though). For large datasets all the momentum seems to be moving towards Spark (sparklyr is RStudio's SparkR integration. Very much a beta but getting better by the day). On the Python front Dask is awesome for out of memory computation that has no equivalent in R.

Worst case, you can always use MPI with R and run on a Beowulf cluster. Of course that might not help if you want to use a function from a library, and the library itself expects everything to be in memory on one node, but at least it gives you another option for parallelization.

I always thought there were plenty of options for parallel and high(er) performance computing with R.[1]

Data sets which don't entirely fit in memory can be stored pretty much anywhere and queried. Simplest would be an SQLite database. Formats like Feather[2] and fst[3] are also really useful. There are multiple R interfaces for Redis.[4]

R has a library to interact with the Tensorflow API[5] and many other machine learning services.

"Multiple legacy object semantics" doesn't really mean a lot to me. Knowing nothing else, that's just the way R is, plus I don't think there are that many really strange ways of doing things. Mostly there's a discernable pattern across functions.

Sounding like a fanboy sure, but as I said I just don't know anything else and while Julia sounds nice (speed, way of the future etc) it doesn't have the breadth of library support, stability or community that R does yet and I don't see a compelling reason for me personally to spend time learning Python.

Not saying R is best to the exclusion of all else, but for data analysis, generalised scripting and some web based reports/reactive apps, I can't find a compelling reason to switch, and I guess don't really understand the criticism of the language.

[1] https://cran.r-project.org/web/views/HighPerformanceComputin...

[2] https://github.com/wesm/feather

[3] https://github.com/fstpackage/fst

[4] https://github.com/antirez/redis-doc/pull/798/files

[5] https://github.com/rstudio/tensorflow/blob/master/README.md

See The Blub paradox: http://www.paulgraham.com/avg.html

You should learn a few languages even if you don't need them, just for the mind expansion and learning what is necessary and what is your culture's conventions. It's like travelling to a different country, beware the patriot who has never left his home country!

Why do I say that? First of all, R's syntax is quirky and different enough from (Python|Java|C|Ruby|etc.) that you might almost find it harder to learn R if you're already used to something else. Second, aside from the syntax the biggest thing to get used to with R is that it's very much vector oriented. Basically you're always working with vectors, even when you only have what you would otherwise think of as a single scalar value. You just put in a vector of length 1. Anyway, that whole paradigm is different enough from other programming languages, that you might as well just learn it that way from the beginning.

Now to be fair, there are libraries and things that let other languages act and feel a bit more like R, but I'm intentionally not considering those right now, as that would just be one more complication to deal with. And if you are locked in on using R for whatever reason, there's no need to complicate life.

The only other question I would have, is whether or not you absolutely must use R at all. If you have the option to choose your language, you can do pretty much anything that you can do in R, using Python, or Octave, or probably many other languages. If that's an option, then you just need to decide which would be easier / more useful for you. And while I won't take sides in general, I will say that Python may be a little bit easier to learn in general, but then you're back to using external libraries for more of the statistical / numerical stuff.

Just looking to see if I could learn R without knowing much else.

My guess is that you can. R has some quirks, but there's nothing especially scary about it. Depending on how much you already know about statistics, you may find that learning and understanding the math is more difficult than learning to use R.

If you don't know R or Python, I would say that learning Python might work out better for you. Python is a general purpose programming language, whereas R is really good at stats and visualization. Python is also pretty good at this, you can use pandas, matplotlib, and scikit-learn.

My goal is to be able to analyze the next play and possibly give a probability attached to where the coach will call the play.

But that's just the first step. I would probably need to do a lot of different stuff with the data.

My email is mtgprivatelearning@gmail.com

Bruce Eckel had a blog about this, I think you'll find the discussion around R / Python / Mathematica interesting

http://bruceeckel.github.io/2015/02/15/why-not-go-there/

As a starter, you probably need something like dataquest[0] or udacitys[1] data courses.

[0] https://www.dataquest.io [1] www.udacity.com

(I'm kinda decided and want to do a bit of statistcics, but also have one general purpose language I know a bit better)

Python in more general purpose. If you want to do things that are not statistics in the future, python makes more sense.

If you're going to get started with python, get started with python 3.

Using Python for data will help you learn something that can be used for more... but be warned that Dataframes on stuff like Spark don't work with list comprehensions (my favorite Python feature).

Fortran is always fun though. :)

Aside from RStudio, I love R's lispy/apl-ish semantics, and easy integration with Fortran/C++ (and a host of other languages, including Java, Haskell, Ruby, Prolog, Lisp, etc...).

I'm sure Python is great, but R is made for stats, is amazing, and RStudio beats every IDE I've ever used, regardless of language.