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I certainly agree that coming up the learning curve with R has greatly improved my working knowledge of statistics. However, lately I find myself becoming incr

I certainly agree that coming up the learning curve with R has greatly improved my working knowledge of statistics. However, lately I find myself becoming increasingly frustrated with the language itself as my projects grow in complexity. Of course this could be due to my lack of skill with R or programming in general... Does anyone else have this issue?

golwengaud 16y ago
I did, quite quickly. In particular, R seems to be focused on univariate data; support for multivariate data (things like "lego plot" histograms, or any kind of histogram in more than one variable) is patchy at best. Having said that, I must note that I am new to R, and rejecting anything so quickly makes me very nervous. I suspect that I have not even come close to plumbing the depths, so to speak, of R's capabilities.
- jacobolus 16y ago
  To both of you: I’m far from a statistician, so YMMV, but after trying to work with multivariate data in both R and MATLAB, I really found using numpy/scipy to be substantially nicer. And even better, if you ever decide to do anything else in a program, like munge text or interact with internet services, a general-purpose language like Python is a big advantage.
- wildanimal 16y ago
  You can check out the lattice, ggplot2, scatterplot3d and rgl libraries to see how R handles multivariate data - R is especially well suited for analyzing panel (longitudinal) data with multiple variables.
wildanimal 16y ago
Google apparently runs R on desktops (http://dataspora.com/blog/predictive-analytics-using-r/ http://dataspora.com/blog/predictive-analytics-using-r/) and ports it to Python or C++ once their ideas are tested (for production code). Having said that if your list of functions are growing, try packaging up in a library - there are many tutorials available on the web. If your data is becoming big, try using RHIPE (Hadoop integration), bigmemory, snow, or a number of other packages: http://cran.r-project.org/web/views/HighPerformanceComputing.html http://cran.r-project.org/web/views/HighPerformanceComputing...

golwengaud 16y ago

I did, quite quickly. In particular, R seems to be focused on univariate data; support for multivariate data (things like "lego plot" histograms, or any kind of histogram in more than one variable) is patchy at best. Having said that, I must note that I am new to R, and rejecting anything so quickly makes me very nervous. I suspect that I have not even come close to plumbing the depths, so to speak, of R's capabilities.

jacobolus 16y ago
To both of you: I’m far from a statistician, so YMMV, but after trying to work with multivariate data in both R and MATLAB, I really found using numpy/scipy to be substantially nicer. And even better, if you ever decide to do anything else in a program, like munge text or interact with internet services, a general-purpose language like Python is a big advantage.
wildanimal 16y ago
You can check out the lattice, ggplot2, scatterplot3d and rgl libraries to see how R handles multivariate data - R is especially well suited for analyzing panel (longitudinal) data with multiple variables.

wildanimal 16y ago

Google apparently runs R on desktops (http://dataspora.com/blog/predictive-analytics-using-r/ http://dataspora.com/blog/predictive-analytics-using-r/) and ports it to Python or C++ once their ideas are tested (for production code). Having said that if your list of functions are growing, try packaging up in a library - there are many tutorials available on the web. If your data is becoming big, try using RHIPE (Hadoop integration), bigmemory, snow, or a number of other packages: http://cran.r-project.org/web/views/HighPerformanceComputing.html http://cran.r-project.org/web/views/HighPerformanceComputing...