Can I read a binary file written by C++ in R?
I have been using Rcpp in my R package and the simulations typically generate a large amount of data. I am planning to write the output to binary files in C++ and then read those back in R. This works if I write as text files but I didn't find a solution with binary files. The program sometimes crashes abruptly if I pass data using many NumericVectors (I am yet to fully understand the memory management using Rcpp).
Can this approach enable me to share larger datasets between C++ and R compared to what is possible by passing vectors? In C++, the maximum vector size is limited by RAM and address bus (may be?) but I think R is able to load larger vectors using swap. Am I correct or misunderstanding the concepts?
Yes you can. But it's "complicated".
You are embarking on a topic called binary serialization. There is a lot of work out there. In essence you are somewhere in the continum between of
minimal: open a file, write out N binary items; then on the other side read N binaries. We did something similar at work years ago where wrote some metadata with <rows,cols,version> and then a binary blob of rows * cols double to attach to a matrix
maximal: use a fully descriptive meta language like Protocol Buffer or MessagePack to describe the binary content, write it in C++ (using the appropriate library) and read in back in R (using the corresponding packages---I am involved with one each: RProtoBuf and RcppMsgPack).
And a lot in between. If you really only need to communicate between C(++) and R you could try the RData / rds format. There is one library: librdata and I experimented with it (and filed some bug reports and made some pull requests). I might start there.
So in short: do some research, figure out what to do and then do it :)
PS If you call C++ via Rcpp from R then you may not need files. We can pass large object back and forth -- the limit may be your RAM.
Just got a Windows box set up with two 64 bit Intel Xeon X5680 3.33 GHz processors (6 cores each) and 12 GB of RAM. I've been using SAS on some large data sets, but it's just too slow, so I want to set up R to do parallel processing. I want to be able to carry out matrix operations, e.g., multiplication and inversion. Most of my data are not huge, 3-4 GB range, but one file is around 50 GB. It's been a while since I used R, so I looked around on the web, including the CRAN HPC, to see what was available. I think a foreach loop and the bigmemory package will be applicable. I came across this post: Is there a package for parallel matrix inversion in R that had some interesting suggestions. I was wondering if anyone has experience with the HIPLAR packages. Looks like hiparlm adds functionality to the matrix package and hiplarb add new functions altogether. Which of these would be recommended for my application? Furthermore, there is a reference to the PLASMA library. Is this of any help? My matrices have a lot of zeros, so I think they could be considered sparse. I didn't see any examples of how to pass data fro R to PLASMA, and looking at the PLASMA docs, it says it does not support sparse matrices, so I'm thinking that I don't need this library. Am I on the right track here? Any suggestions on other approaches?
EDIT: It looks like HIPLAR and package pbdr will not be helpful. I'm leaning more toward bigmemory, although it looks like I/O may be a problem: http://files.meetup.com/1781511/bigmemoryRandLinearAlgebra_BryanLewis.pdf. This article talks about a package vam for virtual associative matrices, but it must be proprietary. Would package ff be of any help here? My R skills are just not current enough to know what direction to pursue. Pretty sure I can read this using bigmemory, but not sure the processing will be very fast.
If you want to use HiPLAR (MAGMA and PLASMA libraries in R), it is only available for Linux at the moment. For this and many other things, I suggest switching your OS to the penguin.
That being said, Intel MKL optimization can do wonders for these sort of operations. For most practical uses, it is the way to go. Python built with MKL optimization for example can process large matrices about 20x faster than IDL, which was designed specifically for image processing. R has similarly shown vast improvements when built with MKL optimization. You can also install R Open from Revolution Analytics, which includes MKL optimization, but I am not sure that it has quite the same effect as building it yourself using Intel tools: https://software.intel.com/en-us/articles/build-r-301-with-intel-c-compiler-and-intel-mkl-on-linux
I would definitely consider the type of operations one is looking to perform. GPU processes are those that lend well to high parallelism (many of the same little computations running at once, as with matrix algebra), but they are limited by bus speeds. Intel MKL optimization is similar in that it can help use all of your CPU cores, but it is really optimized to Intel CPU architecture. Hence, it should provide basic memory optimization too. I think that is the simplest route. HiPLAR is certainly the future, as it is CPU-GPU by design, especially with highly parallel heterogeneous architectures making their way into consumer systems. Most consumer systems today cannot fully utilize this though I think.
Cheers,
Adam
In R usually data is loaded in RAM.
Are there any packages which load the data in disk rather than RAM
Check out the bigmemory package, along with related packages like bigtabulate, bigalgebra, biganalytics, and more. There's also ff, though I don't find it as user-friendly as the bigmemory suite. The bigmemory suite was reportedly partially motivated by the difficulty of using ff. I like it because it required very few changes to my code to be able to access a bigmatrix object: it can be manipulated in almost exactly the same ways as a standard matrix, so my code is very reusable.
There's also support for HDF5 via NetCDF4, in packages like RNetCDF and ncdf. This is a popular, multi-platform, multi-language method for efficient storage and access of large data sets.
If you want basic memory mapping functionality, look at the mmap package.
Yes, the ff package can do that.
You may want to look at the Task View on High-Performance Computing for more details.
I have noticed that R only uses one core while executing one of my programs which requires lots of calculations. I would like to take advantage of my multi-core processor to make my program run faster.
I have not yet investigated the question in depth but I would appreciate to benefit from your comments because I do not have good knowledge in computer science and it is difficult for me to get easily understandable information on that subject.
Is there a package that allows R to automatically use several cores when needed?
I guess it is not that simple.
R can only make use of multiple cores with the help of add-on packages, and only for some types of operation. The options are discussed in detail on the High Performance Computing Task View on CRAN
Update: From R Version 2.14.0 add-on packages are not necessarily required due to the inclusion of the parallel package as a recommended package shipped with R. parallel includes functionality from the multicore and snow packages, largely unchanged.
The easiest way to take advantage of multiprocessors is the multicore package which includes the function mclapply(). mclapply() is a multicore version of lapply(). So any process that can use lapply() can be easily converted to an mclapply() process. However, multicore does not work on Windows. I wrote a blog post about this last year which might be helpful. The package Revolution Analytics created, doSMP, is NOT a multi-threaded version of R. It's effectively a Windows version of multicore.
If your work is embarrassingly parallel, it's a good idea to get comfortable with the lapply() type of structuring. That will give you easy segue into mclapply() and even distributed computing using the same abstraction.
Things get much more difficult for operations that are not "embarrassingly parallel".
[EDIT]
As a side note, Rstudio is getting increasingly popular as a front end for R. I love Rstudio and use it daily. However it needs to be noted that Rstudio does not play nice with Multicore (at least as of Oct 2011... I understand that the RStudio team is going to fix this). This is because Rstudio does some forking behind the scenes and these forks conflict with Multicore's attempts to fork. So if you need Multicore, you can write your code in Rstuido, but run it in a plain-Jane R session.
On this question you always get very short answers. The easiest solution according to me is the package snowfall, based on snow. That is, on a Windows single computer with multiple cores. See also here the article of Knaus et al for a simple example. Snowfall is a wrapper around the snow package, and allows you to setup a multicore with a few commands. It's definitely less hassle than most of the other packages (I didn't try all of them).
On a sidenote, there are indeed only few tasks that can be parallelized, for the very simple reason that you have to be able to split up the tasks before multicore calculation makes sense. the apply family is obviously a logical choice for this : multiple and independent computations, which is crucial for multicore use. Anything else is not always that easily multicored.
Read also this discussion on sfApply and custom functions.
Microsoft R Open includes multi-threaded math libraries to improve the performance of R.It works in Windows/Unix/Mac all OS type. It's open source and can be installed in a separate directory if you have any existing R(from CRAN) installation. You can use popular IDE Rstudio also with this.From its inception, R was designed to use only a single thread (processor) at a time. Even today, R works that way unless linked with multi-threaded BLAS/LAPACK libraries.
The multi-core machines of today offer parallel processing power. To take advantage of this, Microsoft R Open includes multi-threaded math libraries.
These libraries make it possible for so many common R operations, such as matrix multiply/inverse, matrix decomposition, and some higher-level matrix operations, to compute in parallel and use all of the processing power available to reduce computation times.
Please check the below link:
https://mran.revolutionanalytics.com/rro/#about-rro
http://www.r-bloggers.com/using-microsoft-r-open-with-rstudio/
As David Heffernan said, take a look at the Blog of revolution Analytics. But you should know that most packages are for Linux. So, if you use windows it will be much harder.
Anyway, take a look at these sites:
Revolution. Here you will find a lecture about parallerization in R. The lecture is actually very good, but, as I said, most tips are for Linux.
And this thread here at Stackoverflow will disscuss some implementation in Windows.
The package future makes it extremely simple to work in R using parallel and distributed processing. More info here. If you want to apply a function to elements in parallel, the future.apply package provides a quick way to use the "apply" family functions (e.g. apply(), lapply(), and vapply()) in parallel.
Example:
library("future.apply")
library("stats")
x <- 1:10
# Single core
y <- lapply(x, FUN = quantile, probs = 1:3/4)
# Multicore in parallel
plan(multiprocess)
y <- future_lapply(x, FUN = quantile, probs = 1:3/4)
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.