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.
I'm a R newbie.
is there a way i can calculate
(x+x^2+x^3)^2
in R?
so i will get the result:
x^6+2 x^5+3 x^4+2 x^3+x^2
I get an Error: object 'x' not found.
Thanks!
R isn't well suited for this. Some interface packages to languages and libraries that are better at this do exist, such as rSymPy, which allows you to access the SymPy Python library for symbolic mathematics (you'll need to install both). In a similar vein, Ryacas links to the yacas algebra system.
Those interfaces are useful if you need symbolic manipulation as part of an R workflow. Otherwise, consider using the original tools. The ones above are open source and freely available, while other free use alternatives also exist, such as the proprietary web based Wolfram Alpha (for limited use).
I would like to convert an ARIMA model developed in R using the forecast library to Java code. Note that I need to implement only the forecasting part. The fitting can be done in R itself. I am going to look at the predict function and translate it to Java code. I was just wondering if anyone else had been in a similar situation before and managed to successfully use a Java library for the same.
Along similar lines, and perhaps this is a more general question without a concrete answer; What is the best way to deal with situations where in model building can be done in Matlab/R but the prediction/forecasting needs to be done in Java/C++? Increasingly, I have been encountering such a situation over and over again. I guess you have to bite the bullet and write the code yourself and this is not generally as hard as writing the fitting/estimation yourself. Any advice on the topic would be helpful.
You write about 'R or Matlab' to 'C++ or Java'. This gives 2 x 2 choices which is too many degrees of freedom for my taste. So allow me to concentrate on C++ as the target.
Let's consider a simpler case: Prototyping in R, and deploying in C++. If and when the R package you use is actually implemented in C or C++, this becomes pretty easy. You "merely" need to disentangle the routine you are after from its other dependencies (header files, defines, data structures, ...) and provide it with the data and parameters needed. I have done that in the past for production systems.
Here, you talk about the forecast package. This happens to depend on the RcppArmadillo package which itself brings the nice Armadillo C++ library to R. So chances are you can in fact re-write this as a self-contained unit.
Armadillo is also interesting when you want to port Matlab to C++ as it is written to help with exactly that task in mind. I have ported some relatively extensive Matlab code to C++ and reaped a substantial speed gain.
I'm not sure whether this is possible in R, but in Matlab you can interact with your Matlab code from Java - see http://www.cs.virginia.edu/~whitehouse/matlab/JavaMatlab.html. This would enable you to leave all the forecasting code in Matlab and have e.g. an interface written in Java.
Alternatively, you might want to have predictive code written in Java so that you can produce a model and then distribute a program that uses the model without having a dependency on Matlab. The Matlab compiler maybe be useful here, but I've never used it.
A final simple way of interacting messily between Matlab and Java would be (on linux) using pseudoterminals where you would have a pty/tty pair to interface Java and Matlab. In this case you would send data from Java to Matlab, and have Matlab return the forecasting results. I expect this would also work in R, but I don't know the syntax.
In general though, reimplementing the code is a decent solution and probably quicker than learning how to interface java+matlab or create Matlab libraries.
Some further information on the answer given by Richante: Matlab has some really nice capabilities for interop with compiled languages such as C/C++, C#, and Java. In your particular case you might find the toolbox Matlab Builder JA to be particularly relevant. It allows you to export your Matlab code directly to Java, meaning you can directly call code that you've constructed during your model-building phase in Matlab from Java.
More information from the Mathworks here.
I am also concerned with converting "R to Java" so will speak to that part.
As Vincent Zooneykind said in his comment - the PMML library in R makes sense for model export in general but "forecast" is not a supported library as of yet.
An alternative is to use something like https://www.opencpu.org/ to make a call to R from your java program. It surfaces the R code on a http server. Can then just call it with parameters as with a normal http call and return what is neede using java.net.HttpUrlConnection or a choice of http libraries available in Java.
Pros: Separation of concerns, no need to re-write the R code
Cons: Invoking an R server in your live process so need to make sure that is handled robustly
I frequently find myself doing some analysis in R and then wanting to make a quick map. The standard plot() function does a reasonable job of quick, but I quickly find that I need to go to ggplot2 when I want to make something that looks nice or has more complex symbology requirements. Ggplot2 is great, but is sometimes cumbersome to convert a SpatialPolygonsDataFrame into the format required by Ggplot2. Ggplot2 can also be a tad slow when dealing with large maps that require specific projections.
It seems like I should be able to use Mapnik to plot spatial objects directly from R, but after exhausting my Google-fu, I cannot find any evidence of bindings. Rather than assume that such a thing doesn't exist, I thought I'd check here to see if anyone knows of an R - Mapnik binding.
The Mapnik FAQ explicitly mentions Python bindings -- as does the wiki -- with no mention of R, so I think you are correct that no (Mapnik-sponsored, at least) R bindings currently exist for Mapnik.
You might get a more satisfying (or at least more detailed) answer by asking on the Mapnik users list. They will know for certain if any projects exist to make R bindings for Mapnik, and if not, your interest may incite someone to investigate the possibility of generating bindings for R.
I would write the SpatialWotsitDataFrames to Shapefiles and then launch a Python Mapnik script. You could even use R to generate the Python script (package 'brew' is handy for making files from templates and inserting values form R).
I will be analysing vast amount of network traffic related data shortly, and will pre-process the data in order to analyse it. I have found that R and SPSS are among the most popular tools for statistical analysis. I will also be generating quite a lot of graphs and charts. Therefore, I was wondering what is the basic difference between these two softwares.
I am not asking which one is better, but just wanted to know what are the difference in terms of workflow between the two (besides the fact that SPSS has a GUI). I will be mostly working with scripts in either case anyway so I wanted to know about the other differences.
Here is something that I posted to the R-help mailing list a while back, but I think that it gives a good high level overview of the general difference in R and SPSS:
When talking about user friendlyness
of computer software I like the
analogy of cars vs. busses:
Busses are very easy to use, you just
need to know which bus to get on,
where to get on, and where to get off
(and you need to pay your fare). Cars
on the other hand require much more
work, you need to have some type of
map or directions (even if the map is
in your head), you need to put gas in
every now and then, you need to know
the rules of the road (have some type
of drivers licence). The big advantage
of the car is that it can take you a
bunch of places that the bus does not
go and it is quicker for some trips
that would require transfering between
busses.
Using this analogy programs like SPSS
are busses, easy to use for the
standard things, but very frustrating
if you want to do something that is
not already preprogrammed.
R is a 4-wheel drive SUV (though
environmentally friendly) with a bike
on the back, a kayak on top, good
walking and running shoes in the
pasenger seat, and mountain climbing
and spelunking gear in the back.
R can take you anywhere you want to go
if you take time to leard how to use
the equipment, but that is going to
take longer than learning where the
bus stops are in SPSS.
There are GUIs for R that make it a bit easier to use, but also limit the functionality that can be used that easily. SPSS does have scripting which takes it beyond being a mere bus, but the general phylosophy of SPSS steers people towards the GUI rather than the scripts.
I work at a company that uses SPSS for the majority of our data analysis, and for a variety of reasons - I have started trying to use R for more and more of my own analysis. Some of the biggest differences I have run into include:
Output of tables - SPSS has basic tables, general tables, custom tables, etc that are all output to that nifty data viewer or whatever they call it. These can relatively easily be transported to Word Documents or Excel sheets for further analysis / presentation. The equivalent function in R involves learning LaTex or using a odfWeave or Lyx or something of that nature.
Labeling of data --> SPSS does a pretty good job with the variable labels and value labels. I haven't found a robust solution for R to accomplish this same task.
You mention that you are going to be scripting most of your work, and personally I find SPSS's scripting syntax absolutely horrendous, to the point that I've stopped working with SPSS whenever possible. R syntax seems much more logical and follows programming standards more closely AND there is a very active community to rely on should you run into trouble (SO for instance). I haven't found a good SPSS community to ask questions of when I run into problems.
Others have pointed out some of the big differences in terms of cost and functionality of the programs. If you have to collaborate with others, their comfort level with SPSS or R should play a factor as you don't want to be the only one in your group that can work on or edit a script that you wrote in the future.
If you are going to be learning R, this post on the stats exchange website has a bunch of great resources for learning R: https://stats.stackexchange.com/questions/138/resources-for-learning-r
The initial workflow for SPSS involves justifying writing a big fat cheque. R is freely available.
R has a single language for 'scripting', but don't think of it like that, R is really a programming language with great data manipulation, statistics, and graphics functionality built in. SPSS has 'Syntax', 'Scripts' and is also scriptable in Python.
Another biggie is that SPSS squeezes its data into a spreadsheety table structure. Dealing with other data structures is probably very hard, but comes naturally to R. I wouldn't know where to start handling network graph type data in SPSS, but there's a package to do it for R.
Also with R you can integrate your workflow with your reporting by using Sweave - you write a document with embedded bits of R code that generate plots or tables, run the file through the system and out comes the report as a PDF. Great for when you want to do a weekly report, or you do a body of work and then the boss gives you an updated data set. Re-run, read it over, its done.
But you know, your call...
Well, are you a decent programmer? If you are, then it's worthwhile to learn R. You can do more with your data, both in terms of manipulation and statistical modeling, than you can with SPSS, and your graphs will likely be better too. On the other hand, if you've never really programmed before, or find the idea of spending several months becoming a programmer intimidating, you'll probably get more value out of SPSS. The level of stuff that you can do with R without diving into its power as a full-fledged programming language probably doesn't justify the effort.
There's another option -- collaborate. Do you know someone you can work with on your project (you don't say whether it's academic or industry, but either way...), who knows R well?
There's an interesting (and reasonably fair) comparison between a number of stats tools here
http://anyall.org/blog/2009/02/comparison-of-data-analysis-packages-r-matlab-scipy-excel-sas-spss-stata/
I work with both in a company and can say the following:
If you have a large team of different people (not all data scientists), SPSS is useful because it is plain (relatively) to understand. For example, if users are going to run a model to get an output (sales estimates, etc), SPSS is clear and easy to use.
That said, I find R better in almost every other sense:
R is faster (although, sometimes debatable)
As stated previously, the syntax in SPSS is aweful (I can't stress this enough). On the other hand, R can be painful to learn, but there are tons of resources online and in the end it pays much more because of the different things you can do.
Again, like everyone else says, the sky is the limit with R. Tons of packages, resources and more importantly: indepedence to do as you please. In my organization we have some very high level functions that get a lot done. The hard part is creating them once, but then they perform complicated tasks that SPSS would tangle in a never ending web of canvas. This is specially true for things like loops.
It is often overlooked, but R also has plenty of features to cooperate between teams (github integration with RStudio, and easy package building with devtools).
Actually, if everyone in your organization knows R, all you need is to maintain a basic package on github to share everything. This of course is not the norm, which is why I think SPSS, although a worst product, still has a market.
I have not data for it, but from my experience I can tell you one thing:
SPSS is a lot slower than R. (And with a lot, I really mean a lot)
The magnitude of the difference is probably as big as the one between C++ and R.
For example, I never have to wait longer than a couple of seconds in R. Using SPSS and similar data, I had calculations that took longer than 10 minutes.
As an unrelated side note: In my eyes, in the recent discussion on the speed of R, this point was somehow overlooked (i.e., the comparison with SPSS). Furthermore, I am astonished how this discussion popped up for a while and silently disappeared again.
There are some great responses above, but I will try to provide my 2 cents. My department completely relies on SPSS for our work, but in recent months, I have been making a conscious effort to learn R; in part, for some of the reasons itemized above (speed, vast data structures, available packages, etc.)
That said, here are a few things I have picked up along the way:
Unless you have some experience programming, I think creating summary tables in CTABLES destroys any available option in R. To date, I am unaware package that can replicate what can be created using Custom Tables.
SPSS does appear to be slower when scripting, and yes, SPSS syntax is terrible. That said, I have found that scipts in SPSS can always be improved but using the EXECUTE command sparingly.
SPSS and R can interface with each other, although it appears that it's one way (only when using R inside of SPSS, not the other way around). That said, I have found this to be of little use other than if I want to use ggplot2 or for some other advanced data management techniques. (I despise SPSS macros).
I have long felt that "reporting" work created in SPSS is far inferior to other solutions. As mentioned above, if you can leverage LaTex and Sweave, you will be very happy with your efficient workflows.
I have been able to do some advanced analysis by leveraging OMS in SPSS. Almost everything can be routed to a new dataset, but I have found that most SPSS users don't use this functionality. Also, when looking at examples in R, it just feels "easier" than using OMS.
In short, I find myself using SPSS when I can't figure it out quickly in R, but I sincerely have every intention of getting away from SPSS and using R entirely at some point in the near future.
SPSS provides a GUI to easily integrate existing R programs or develop new ones. For more info, see the SPSS Community on IBM Developer Works.
#Henrik, I did the same task you have mentioned (C++ and R) on SPSS. And it turned out that SPSS is faster compared to R on this one. In my case SPSS is aprox. 7 times faster. I am surprised about it.
Here is a code I used in SPSS.
data list free
/x (f8.3).
begin data
1
end data.
comp n = 1e6.
comp t1 = $time.
loop #rep = 1 to 10.
comp x = 1.
loop #i=1 to n.
comp x = 1/(1+x).
end loop.
end loop.
comp t2 = $time.
comp elipsed = t2 - t1.
form elipsed (f8.2).
exe.
Check out this video why is good to combine SPSS and R...
Link
http://bluemixanalytics.wordpress.com/2014/08/29/7-good-reasons-to-combine-ibm-spss-analytics-and-r/
If you have a compatible copy of R installed, you can connect to it from IBM SPSS Modeler and carry out model building and model scoring using custom R algorithms that can be deployed in IBM SPSS Modeler. You must also have a copy of IBM SPSS Modeler - Essentials for R installed. IBM SPSS Modeler - Essentials for R provides you with tools you need to start developing custom R applications for use with IBM SPSS Modeler.
The truth is: both packages are useful if you do data analysis professionally. Sure, R / RStudio has more statistical methods implemented than SPSS. But SPSS is much easier to use and gives more information per each button click. And, therefore, it is faster to exploit whenever a particular analysis is implemented in both R and SPSS.
In the modern age, neither CPU nor memory is the most valuable resource. Researcher's time is the most valuable resource. Also, tables in SPSS are more visually pleasing, in my opinion.
In summary, R and SPSS complement each other well.