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Octave replaced Mathlab.
Python based libraries are somewhere in between.
Julia with Jupyter will probably replace Mathematica, LabVIEW and Mathcad (and unify all of the above) with a powerful native language and environment.
I've regretted that Octave hasn't done for Matlab what R did for S. I understand some of the cirumstances that made this happen, but I'm deeply saddened of the entrenchment that Matlab has in scientific computing. It's getting chipped away little by little at the edges by Python, and to a lesser extent by Julia, but Matlab still is strong. And yes, some uses of Matlab can be replaced by R, but overall the two package…
what are the problem domains ? Do you think something like Python Pandas... and especially Jupyter/IPython can replace it ?
I've regretted that Octave hasn't done for Matlab what R did for S. I understand some of the cirumstances that made this happen, but I'm deeply saddened of the entrenchment that Matlab has in scientific computing. It's getting chipped away little by little at the edges by Python, and to a lesser extent by Julia, but Matlab still is strong. And yes, some uses of Matlab can be replaced by R, but overall the two package…
I've regretted that Octave hasn't done for Matlab what R did for S. I understand some of the cirumstances that made this happen, but I'm deeply saddened of the entrenchment that Matlab has in scientific computing. It's getting chipped away little by little at the edges by Python, and to a lesser extent by Julia, but Matlab still is strong. And yes, some uses of Matlab can be replaced by R, but overall the two package…
Numpy/Scipy (and Julia, maybe) are taking this role. There's really no reason to use Matlab these days.
A lot of package are built from matlab and not for Python, if you are a PhD student you want to use those package, not write your own, if you really really need to write some software you want to write it on top of something that already exists...
It is really sad, but it is the reality...
I have some programming background and really would like to get into statistics. Should I do some R tutorial and throw my weblogs at it to see what I can do? Or is there some awesome learning resource you could share?
I've regretted that Octave hasn't done for Matlab what R did for S. I understand some of the cirumstances that made this happen, but I'm deeply saddened of the entrenchment that Matlab has in scientific computing. It's getting chipped away little by little at the edges by Python, and to a lesser extent by Julia, but Matlab still is strong. And yes, some uses of Matlab can be replaced by R, but overall the two package…
what are the problem domains ? Do you think something like Python Pandas... and especially Jupyter/IPython can replace it ?
Also, mathematicians and statisticians think functionally and the general attitude in python is to do object oriented programming while R is strictly functional programming with a little bit of object programming.
R replaced SPSS. Octave replaced Mathlab. Python based libraries are somewhere in between. Julia with Jupyter will probably replace Mathematica, LabVIEW and Mathcad (and unify all of the above) with a powerful native language and environment.
What does it offer in terms of controls systems/realtime, GUI building, and data flow?
The bigger issue is that while R is liked by statisticians it lacks many of the features for the software development. We run across difficulties with logging, version control of packages, speed, size of docker image, build time etc. But, with these drawbacks I keep coming back because I develop faster and better in R.
What would be a good start to learn this? I have some programming background and really would like to get into statistics. Should I do some R tutorial and throw my weblogs at it to see what I can do? Or is there some awesome learning resource you could share?
I program in R 80% of my day. I have experiences in all the major alternatives but keep returning to R. It has one huge flaw, being slow but otherwise is fantastic to work with and has a vibrant community. The bigger issue is that while R is liked by statisticians it lacks many of the features for the software development. We run across difficulties with logging, version control of packages, speed, size of docker ima…
I recently had to loop through 1.3Gb of data (5000 files) and merge just one column from each file into a new dataset. It did so in ~2 hours. Yet the loop was just ~5 lines of code.