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While I think Python is great, I think the author discounts R too casually. I share his frustrations about R, but what's nice is R (well, S) was designed to be a statistical computing tool, and it does anything related to that quite well. Especially for data analysis, which is where I spend a lot of my time (perhaps most) in the whole model building process, R is amazing. Also, R is very usable out of the box for mat…
I'm always surprised how much people hate the syntax of R. I primarily work in Python, but I use R once or twice a week... and the syntax seems very clean to me. Can someone give mee an example of what you dislike with R's syntax?
I'm even more surprised to hear complaints about the documentation in R. The help files in R are much more complete and well organized than docstrings in the python libraries we use. Even the web usually lacks anything as useful as what I get from the vignettes function in my R interpreter.
And if you are lucky enough to have free-ish access to MATLAB, here's a free, BSD, open-source, github repo'd machine learning toolbox to help you get started:
http://www.newfolderconsulting.com/prt/
Full disclosure: I'm involved.
Earlier quoted context omitted.
What's your background? (i.e. what do you already know?)
Probably not much. I started working with bigger data some months ago and now I notice that I need some real techniques and not just my "ok try this and this". I'm coding in C (where I "create" the data (numerical integration of stochastic differential equations)) and Python (plotting). I need methods/algorithms/techniques to analyse the data "on the fly" because I can't save it all (it's too much data).
>I found the syntax baffling, the documentation copious, but written for mathematicians instead of hackers I'm always surprised how much people hate the syntax of R. I primarily work in Python, but I use R once or twice a week... and the syntax seems very clean to me. Can someone give mee an example of what you dislike with R's syntax? I'm even more surprised to hear complaints about the documentation in R. The help…
If you are going to use python for ML. Use the python package from Entthought [ http://www.enthought.com/products/epd_free.php ] It has most of the libraries, out of the box.
>I found the syntax baffling, the documentation copious, but written for mathematicians instead of hackers I'm always surprised how much people hate the syntax of R. I primarily work in Python, but I use R once or twice a week... and the syntax seems very clean to me. Can someone give mee an example of what you dislike with R's syntax? I'm even more surprised to hear complaints about the documentation in R. The help…
Does anyone have any experience with Octave and how it compares to the Python setup that OP suggests? What are the benefits/pitfalls?
>I found the syntax baffling, the documentation copious, but written for mathematicians instead of hackers I'm always surprised how much people hate the syntax of R. I primarily work in Python, but I use R once or twice a week... and the syntax seems very clean to me. Can someone give mee an example of what you dislike with R's syntax? I'm even more surprised to hear complaints about the documentation in R. The help…
Personally it's not so much syntax as the confusing data model that gets me in R. So many different but very similar data types - lists, data frames, matrices, tables, vectors - all very similar but slightly different syntax, very frequently converted silently from one to the other when you call functions but resulting in strange quirks that are extremely hard to debug at the other end. The combination of loose data…
A list in R is a list in Python. A data frame in R is a date frame in the pandas library. A matrix in R is a matrix in numpy. A vector in R is a 1-dimensional ndarray in numpy.
But Python adds dictionaries, tuples, iterators, sets, and a bunch of other data types that aren't used in R.
R's lists and vectors are relatively similar... but you could say the same thing about numpy's matrix and ndarray. You could probably say the same thing about python's sets, tuples and lists.
To be honest, I'd have said the strength of python is that it has many more data types than R... rather than fewer data types.
Does anyone have any experience with Octave and how it compares to the Python setup that OP suggests? What are the benefits/pitfalls?
I learned Octave first, then R, then Python.
Octave to me feels like Python + NumPy. I'd say Octave has more in common with R than with Python.
Given the choice between Octave and R, I'd choose R for the more robust user community and incredibly diverse and thorough selections of libraries.