I seem to be unable to compile RPostgreSQL for Windows x64, and after extensive searching, I've not been able to find a precompiled binary. To get on with my work, I've installed a 32 bit version of Postgre and have been using 32 bit R for all database ops.
I need to do much of my work in 64 bit R, so switching back and forth has become a bit painful, especially since this requires a save() and load() operation each time I need to run a query.
I'm wondering whether it is possible to call one R installation directly from another? For example, could I simply pass queries to my 32 bit R installation and retrieve the result? I think there are other times when the ability to call another R installation would be useful as well.
All I've come up with is using a system() call, either directly to pgsql or to 32-bit R, but this doesn't allow for very efficient transfer of data.
I'd very sincerely appreciate any advice or assistance!
P.S. I'd rather ask how to compile RPostgreSQL for x64, but as I understand the rules here, such a question would be inappropriate since it's not a general question (e.g. I'd need step-by-step instructions since I don't have the requisite skills).
http://wiki.postgresql.org/wiki/64bit_Windows_port
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I'm currently working on an university research related software which uses statistical models in it in order to process some calculations around Item Response Theory. The entire source code was written in Go, whereas it communicates with a Rscript server to run scripts written in R and return the generated results. As expected, the software itself has some dependencies needed to work properly (one of them, as seen before, is to have R/Rscript installed and some of its packages).
Due to the fact I'm new to software development, I can't find a proper way to manage all these dependencies on Windows or Linux (but I'm prioritizing Windows right now). What I was thinking is to have a kind of script which checks if [for example] R is properly installed and, if so, if each used package is also installed. If everything went well, then the software could be installed without further problems.
My question is what's the best way to do anything like that and if it's possible to do the same for other possible dependencies, such as Python, Go and some of its libraries. I'm also open to hear suggestions if installing programming languages locally on the machine isn't the proper way to manage software dependencies, or if there's a most convenient way to do it aside from creating a script.
Sorry if any needed information is missing, I would also like to know.
Thanks in advance
I have written a piece of R-code that performs a numerical computation. Now, I want to implement it into a nice GUI. I know that there are some R-packages, that allow to create GUIs from within R (e.g. gWidgets, RGtk2, ...). However, they seem to be rather limited in the capabilities and complicate to build. So I thought about going the other way round and writing a windowed-program that incorporates my R-code.
Is it possible to write a nice GUI (for example in Visual Basic.NET or Java) that allows to gather some user inputs, call the R-computations and display the results?
I ask for Visual Basic because there is this new R-Open that comes along with Visual Studio which makes me think the two must offer natural ways of collaboration with each other. I also hope that I would be able to compile an exe with it in the end.
Thank you very much for you Help!
Bernd
You can embed R in C++ code. There are examples in the R source code and documentation.
Very briefly, you'll need to build a shared DLL version of R (i.e with the --enable-R-shlib option) from the source code, using the Windows Tools. This is how GUIs like RStudio function.
The R Admin manuals have detailed instructions. The RInside package might make this a bit easier.
With the shared DLL you could probably embed R in other languages (it works for R in Python).
My co-workers would like to make sure that our work in R is platform-independent, specifically that code will run on Linux, Mac, and Windows, and that files created on one system will work on other systems.
Since the issue has come up before in my group, I would appreciate a general answer that will make it easier for me to confidently assure my collaborators that there will not be an issue. E.g., it would help to have a reference other than "because (subject matter expert) said so on SO".
Generally, is there a way to know if any features of R are platform-specific (can I assume that this would be stated in a function's help)?
Are there packages or functions that I can be confident will be platform-independent?
Are there types of packages or functions that I should be wary of?
I have previously asked two questions about the cross-platform readability of files created by R: What are the disadvantages of using .Rdata files compared to HDF5 or netCDF? and Are R objects dumped using `dump` readable cross-platform?
Besides Carl's answer, the obvious way to ensure that your work in platform-independent is to test on all platforms.
Which is precisely what CRAN does with its 3800+ packages, and you have access to logs here.
In short, R really tries hard to be platform-independent, and mostly succeeds. To do so with your code, it is up to you to avoid APIs or tools which introduce dependencies. Look at abstractions like system.file(package="boot") and the functions they use---you can easily abstract file-system "roots", and separators are already taken care of.
Check cran.r-project.org for package listings. Every package has a page which will tell you if it's passed testing for different operating systems. Further, as you suggested, the help files are pretty explicit about OS dependencies.
R is "smart" enough to translate "/" to "\" in pathnames for those poor folks working in Windows.
Generally speaking, graphics access is the area most likely to have platform dependencies. Obviously if you system lacks {X11, ImageMagick, ..} you're stuck anyway.
Besides Carl's and Dirk's comments, you should understand that any package that requires compilation from source (as do many (all?) packages that are on Omegahat, Rforge or r-forge) will need to be done on a machine that has the proper C and Fortran libraries. Some interesting packages depend on GTK+ and Tcl/Tk, and there may be a need to make sure you can get the right versions. The http://r.research.att.com/ page that Simon Urbanek maintains is a useful resource for keeping up with supporting resources for Macs.
Here's the scenario:
I have JBoss serving a web service with JBossWS providing me with a wsdl. I have connected and used it from both .NET and Java so far (and it has been quite easy once I figured it out). I am now trying to do the same with R.
Is there anything out there considered to be "the right way" for doing this? I am not that familiar with R, and my searches have not turned up much, so I figured I'd ask and maybe spare my head and the wall a bit of damage.
I have had good luck using rJava to recreate in R something that works in Java. I use this method for connecting to Amazon's AWS Java SDK for their API with R. This allows me, for example, to transfer files to/from S3 from R without having to recreate the whole connection/handshake/boogieWoogie from R.
If you wanted to go more "pure R" I think you'll have to use some combination of RCurl and the XML package to grab and parse the wsdl.
There are a number of ways:
You could retain your Java approach and use the rJava package around it
You could use RCurl which is used to power a few higher-level packages (accessing Google APIs, say)
I believe there is an older SSOAP package on Omegahat which may help too.
I can't find it anywhere on the web (and I don't want to install it). Is the R language a compiled language? How fast does it run a pre-written script? Does it do any kind of compilation, or just execute instructions line by line?
In most cases R is an interpreted language that runs in a read-evaluate-print loop. There are numerous extensions to R that are written in other languages like C and Fortran where speed or interfacing with native libraries is helpful.
I've often rewritten R code in C++ and made it run 100x faster. Looping is especially inefficient in R.
R is generally an interpreted language. However, package compiler offers bytecode compilation that can improve performance. You can also call compiled code from R.
In terms of how fast, it depends on what you are trying to do and how you are trying to do it. Some looping operations can be very slow. However, in many cases, with well written code, the performance of R scripting is determined by the speed of the underlying internal C-based libraries and system memory read-write speeds, and so R is about as fast as anything else.