Is it possible to call Stata functions from R?
Not directly, i.e. there is no package I am aware of that implements a bridge.
You can always call external programs using system() but that is neither elegant nor efficient. That said, you could prepare data in R, write it out, call Stata and then read the results in; see help(system).
There's now an RStata package on CRAN that bridges R and Stata.
The real problem is that Stata doesn't have an interactive interpreter you can pass arguments to.
Dirk is right; you can just go ahead and write the data to a common format
(if size is large and speed is an issue, fixed width is safe), but you can also just use .dta throughout the process, using read.dta in R and natively reading in Stata.
Also, in R you can call to the system() you can pass a do file or a string containing a bunch of Stata commands.
So, generally, trying to use Stata for this or that task may or may not be worth it, especially if an R equivalent is close by.
Related
I have defined some R functions in R studio which has some complicated scripts and a lot of readlines. I can run them successfully in R studio. Is there any way, like macros to transfer these user-defined functions to SAS 9.4 to use? I am not pretty familiar with SAS programming so it is better just copy the R functions into SAS and use it directly. I am trying to figure out how to do the transformation. Thank you!
You can't natively run R code in SAS, and you probably wouldn't want to. R and SAS are entirely different concepts, SAS being closer to a database language while R is a matrix language. Efficient R approaches are terrible in SAS, and vice versa. (Try a simple loop in R and you'll find SAS is orders of magnitude faster; but try matrix algebra in R instead).
You can call R in SAS, though. You need to be in PROC IML, SAS's matrix language (which may be a separate license from your SAS); once there, you use submit / R to submit the code to R. You need the RLANG system option to be set, and you may need some additional details set up on your SAS box to make sure it can see your R installation, and you need R 3.0+. You also need to be running SAS 9.22 or newer.
If you don't have R available through IML, you can use x or call system, if those are enabled and you have access to R through the command line. Alternately, you can run R by hand separately from SAS. Either way you would use a CSV or similar file format to transfer data back and forth.
Finally, I recommend seeing if there's a better approach in SAS for the same problem you solved in R. There usually is, and it's often quite fast.
Q. How does one write an R package function that references an R package dataset in a way that is simple/friendly/efficient for an R user. For example, how does one handle afunction() that calls adataset from a package?
What I think may not be simple/friendly/efficient:
User is required to run
data(adataset) before running
afunction(...)
or else receiving an Error: ... object 'adataset' not found. I have noticed some packages have built-in datasets that can be called anytime the package is loaded, for example, iris, which one can call without bringing it to the Global Environment.
Possible options which I have entertained:
Write data(NamedDataSet) directly into the function. Is this a bad idea. I thought perhaps it could be, looking at memory and given my limiting understanding of function environments.
Code the structure of the dataset directly into the function. I think this works depending on the size of the data but it makes me wonder about how to go about proper documentation in the package.
Change Nothing. Given a large enough dataset, maybe it does not make sense to implement a way different from reading it before calling the function.
Any comments are appreciated.
You might find these resources about writing R data packages useful:
the "External Data" section of R Packages
Creating an R data package
Creating an R data package (overview)
In particular, take note of the DESCRIPTION file and usage of the line LazyData: true. This is how datasets are made available without having to use data(), as in the iris example that you mention.
In Matlab, without any coding, I can create a matrix, open up its spreadsheet, and copy multiple columns of values from Excel and paste them into the spreadsheet. I can then right click this matrix and plot it instantly.
I've tried googling for how to do the equivalent in R, and everything seems to involve creating a function iterating over each value with a for loop. This seems a bit cumbersome, is there an equivalent simple way to do this in RStudio?
Thanks.
You certainly can have a similar functionality by using R's integration with a clipboard. In particular, standard R functions that provide support for clipboard operations include connection functions (base package), such as file(), url(), pipe() and others, clipboard text transfer functions (utils package), such as readClipboard(), writeClipboard(), as well as data import functions (base package), which use connection argument, such as scan() or read.table().
This functionality differs from platform to platform. In particular, for Windows platform, you need to use connection name clipboard, for Mac platform (OS X) - you can use pipe("pbpaste") (see this StackOverflow discussion for more details and alternative methods). It appears that Kmisc package offers a platform-independent approach to that functionality, however, I haven't used it so far, so, can't really confirm that it works as expected. See this discussion for details.
The following code is a simplest example of how you would use the above-mentioned functionality:
read.table("clipboard", sep="\t", header=header, ...)
An explanation and further examples are available in this blog post. As far as plotting the imported data goes, RStudio not only allows you to use standard R approaches, but also adds an element of interactivity via its bundled manipulate package. See this post for more details and examples.
Is there a convenient way to call R functions from Julia?
If so, what mechanisms for doing so exist? (Potentially ranging from simply calling an R script from the shell & hand-coding the I/O to/from Julia, to interacting with an R environment over multiple Julia calls with Julia DataFrames being seamlessly converted to/from R DataFrames).
Calling R scripts and handcoding I/O is the best way to work with R for the moment. We have functions for reading the RDA binary format that R likes and should add some tools for working with it more easily and also writing data in that format, which will speed up I/O considerably relative to passing CSV files around -- which I've done in the past.
Converting between R and Julia DataFrames could be done, but would be quite costly as Julia isn't using a binary representation of data (e.g. NA) that's nearly equivalent to R's. So you'd need to do some non-trivial work to make this work in a way that would be substantially more efficient than using the RDA binary format.
One thing that would be really nice is to build solid Thrift bindings for both R and Julia and then call back and forth using those bindings.
For calling out to R from within Julia, the RCall package is currently your best bet. For calling out to Julia from within R, try the RJulia package. Both are a bit in the works.
I would like to run some Matlab scripts. Nevertheless we don't have the Matlab licence so it is necessary a conversion from Matlab to R language. Unfortunately I'm totally new in Matlab but not in R. Is it possible to read Matlab scripts using R or is there an easy way to translate Matlab scripts in R?
Rewriting from one language to another can be a painstaking process, especially because your have to take great care that the outcomes of both sets of codes are the same. I see roughly four approaches:
Digest the goal of the scripts, put aside the matlab code, and rewrite in R
Try and mimic the matlab code in R
Run the matlab code in octave, and interface with R
Run the code in Octave entirely
These are roughly in order of amount of work. If you just want to get the Matlab code working, definitely use Octave, which should run the code with minimal changes. If you want to convert the code to R, and continue developing in R, I would go for the first option. In that way you can leverage the real strenghts of R, as R is quite different (link with info, comparison R and matlab). But it does take the largest amount of time. Even if you reimplement in R, I would recommend getting the code running in Octave to be able to see if your results in R fit with the Matlab code.