My work involves building statistical / econometric models using R, SPSS modeler. I am also doing my PhD (part time) in econometrics. In order to do more advanced data / model visualisation I am thinking about to pick up another programming language. Any suggestion will be much appreciated.
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closed as not constructive by whuber♦ Nov 15 '12 at 16:40
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For nice visualizations I'd suggest Matlab, but it is more an alternative to R rather than something completely different. Python is something you could appreciate both for processing and visualization. It is not too quick, but quite flexible and with lots of useful libraries. Easy to learn... All in all, there are lots of options and it really depends on what you are after. If you want some heavy numeric processing abilities, there's C++. Java is also quick and would be good for simpler visualizations as well and easier to learn IMO. You are talking about visualizations and it is not really a programming language thing, I mean there is some software which can produce nice plots etc... Check out Mathematica for example. |
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My take: you will probably gain more in the way of data and model visualization abilities if you a) dig deeper into R (e.g., looking through the R Graph Gallery) and b) read general literature about visualization (Tufte, Cleveland etc.) than if you learn a new programming language. Yes, knowing more programming languages is often helpful, as when you need to speed up a massive data analysis by using C instead of R. But when it comes to visualization specifically, I would say that R already offers most everything you could want to do - you just need to find out how and what you do want. |
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If the big thing is data-visualization as the question is tagged I would say JavaScript. There are tons of really cool ways to visualize data in very pretty ways with JS, one example is d3js.org. |
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For visualisation of models or data, R plus the various additional packages should be sufficient, as @StephenKolassa suggests. If you're going to do econometric things, I guess you might still pick another language either because you want to do a lot of something that R does not offer or make particularly easy, or you want to to make your existing procedures go faster. For the first task, maybe you want to learn how to use BUGS, JAGS or STAN to run MCMC models more easily. For the second you basically want to learn C or C++. It's possible that you'll find it easier to construct and work with arbitrary model-based quantities of interest if you happen to have a posterior sample of all your unknowns to hand. This is a less appreciated advantage of working within a sampling or MCMC-oriented language. Obviously if you're not happy with things Bayesian this is irrelevant. |
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