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Julia has advantages over Stata and SPSS as well. The main thing holding it back is its ecosystem. R has better libraries and a much better IDE (RStudio), whil
by zhdc1 6y ago
Julia has advantages over Stata and SPSS as well.
The main thing holding it back is its ecosystem. R has better libraries and a much better IDE (RStudio), while Python is still the best option for machine/deep learning (and general programming).
However, Julia has much better performance. R is arguably easier for someone with little coding experience to learn, but Julia isn't that much more difficult, and it's much more intuitive to code in than Python.
Julia + RStudio + CRAN/BioConductor would take the cake.
- dagw 6y agoJulia has advantages over Stata and SPSS as well. I can't speak for Stata, but literally everybody I know using SPSS use it because of the easy to use GUI that allows quite complex analysis with basically zero programming.
- zhdc1 6y agoI don't intend to downplay either Stata or SPSS for exactly that reason - they're very good at what they do. However, they have a limited scope (which isn't a bad thing), and if you want to go beyond it, you have to turn towards other options (e.g., R for extensibility, or Julia (or C, or whatever) for performance).
- superbcarrot 6y ago> R is arguably easier for someone with little coding experience to learn, but Julia isn't that much more difficult, and it's much more intuitive to code in than Python. Having some experience with all three of these languages, I find Julia much harder than R and less intuitive than Python. The relatively clean syntax isn't enough to make Julia an easy language.