11–20 of 126 posts
Mathematica isn't being used at all? That's surprising. Mathematica is wonderful. I wonder what's holding it back? It doesn't seem to have a package manager. Could it be that simple?
If you need to call one of the built-in pieces of Magic (TM) then Mathematica is OK, but if you want to build something new that needs to interface with literally anything outside of Mathematica, then Mathematica is a PITA.
Mathematica isn't being used at all? That's surprising. Mathematica is wonderful. I wonder what's holding it back? It doesn't seem to have a package manager. Could it be that simple?
I'm not sure if Mathematica is on the radar if you are looking for "just" a stats package, it can do way more. I have no idea how it compares price-wise to SAS?
Plenty of free high quality documentation and learning materials around R (just read anything by Hadley)
Package manager. Super easy to find, install, and start using packages.
Open source / Free
Large community of users
Extensive usage by the stats community. (If a new algorithm comes out, chances R there will be an R implementation)
Easy to build and share your own packages via Github.
Easy to link C++ code to your packages.
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I love R, but something about how the language feels syntactically, it's not as pleasurable programming wise compared to something like the Python data stack. But with all of the above advantages, I don't see myself switching to anything else in the future for my data science work, unless I have a really pressing need to. The other thing is that the language is so damn popular that the useR conference was sold out in pre-reg. rounds.. Seriously guys, stop using and learning about R so I can get in the conference....
Mathematica isn't being used at all? That's surprising. Mathematica is wonderful. I wonder what's holding it back? It doesn't seem to have a package manager. Could it be that simple?
All of these languages have libraries that produce the same results, the difficulty is mangling the data into the correct input format. Python's list comprehensions are much, much easier to use than MATLAB matrices, R's data frames, Java's ArrayLists, etc. I'd advise any new graduate student to learn how to plug data into traditional programs, but save yourself a headache and perform your data manipulation in Python. Eventually you can take the leap and do the analysis in Python as well.
I think Python is the biggest hidden gem in statistics. It's had a tremendous impact on machine learning and algorithm development, yet traditional statisticians still rely on SAS/R/Stata/MATLAB. All of these languages have libraries that produce the same results, the difficulty is mangling the data into the correct input format. Python's list comprehensions are much, much easier to use than MATLAB matrices, R's data…
Am I right to believe that there is no way that proprietary scientific software can keep up with open source?
Programming languages, perhaps, are less vulnerable to these issues. And perhaps open source could beat these applications eventually, given perfect competition. But we're not in that world, unfortunately.
R? Everyone doing most anything interesting has been using Python for a while.