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Does someone have an implementation of Iterative Closest Point (ICP) algorithm for two dimensions (2D) in R?
Here is an attempt in c#
Iterative Closest Point Implementation
Here is a more general question
iterative closest point library
This is to match two sets of points through translation and scaling.
Spacedman's comment is probably best. You might also take a look at http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=12627&objectType=file for a matlab implementation. Assuming it works ok, translating Matlab to R code is relatively easy.
This is somewhat of an answer in the form of a non-answer.
There are many variants of ICP. The design choices are at least partially organized by the late 90's Ph.D. work of Pulli and by Rusinkiewicz & Levoy. If you're going to be using ICP for anything remotely important (translation: "more than just a class assignment"), you should understand the tradeoffs.
Thus, it's probably best to take one of the existing implementations and port it to R.
3 Years too late, but there is the function icpmat in the package Morpho by the same guy who wrote Rvcg. I don't know which variant is implemented though.
Link:
https://github.com/zarquon42b/Morpho
There is a self-contained (as far as I can tell) C++ implementation of ICP here. Maybe you can create your own R wrapper around this C++ code.
I am aware that Leland Wikinson's ideas, as exposed in his book "The Grammar of
Graphics" underlie ggplot2 implementation in R.
But are there other implementations of the same ideas in other statistical packages (SAS or other)?
In SPSS, the Chart Builder was built upon the same foundation. I could be wrong, but I think SPSS implemented it as "GPL." IMHO, Hadley's ggplot2 is much easier to learn and there are mounds of examples online; I haven't seen many examples of graphics built with GPL.
Not to mention, R can be obtained for the price of free.
It is not exactly the same, but SAS has (since 9.2) the ODS Graphics system in place. This graphics system is also based on the same foundation, and if you look at the examples here, you immediately notice the similarities in layout and buildup of these graphs and the ones in ggplot2.
THe idea is here to just get the graphs from the analysis, so you specify the content of the graph at the same time you specify your analysis. Then there is the template language for the ODS graphics to allow you to create your custom graphs. This is something I still miss a bit in ggplot2. But ggplot2 is quite a lot easier.
It seems IBM does some visualization tools with grammar of graphics inside.
They say their backend -- Rapidly Adaptive Visualization Engine (RAVE) -- is based on it.
And recently I found this overview-article about VizJSON -- a language to describe charts, which is apparently some variation of JSON. (I don't really know about SPSS, Many Eyes and the connection between them and other IBM's software. Probably SPSS is the back-end for Many Eyes... Probably VizJSON is the next step to their GPL... Whatever -- it is closed proprietary stuff anyway)
Also there is D3.js. It is open, BSD license. It is a javascript library. Here "javascript" does not mean "web only": you can make SVG files with your plots (and probably they will or already do support more). But it means that you need to know bunch of Web technologies: HTML, Javascript, DOM, CSS etc (+ maybe javascript's package manager..). And also people say it is quite a low-level library.
There is a more high-level tool, based on D3.js -- Vega.
I am not very savvy in these tools and cannot be totally sure about this information ;)
Python now has its own ggplot port.
Also, Tableau is a visualization system based firmly on the Grammar of Graphics (Wilkinson himself works there now). But I'm not sure if this counts, since it's not part of a pre-existing statistical package.
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.
This question already has answers here:
Closed 13 years ago.
Possible Duplicates:
Best IDE / TextEditor for R
Recommendations for Windows text editor for R
Dear All,
I teach a large introductory R course (about 100 students), and would like to recommend suitable text editors for R. The students who attend this course are first year mathematics undergraduates doing their very first course in R. They have never programmed in any language before.
For the vast majority of them, it wouldn't beneficial for them to learn to use a 'complex editor' - by this I mean emacs and vi.
What I would like to do is recommend simple text editors that are
free
can be easily installed on their laptops by users with little computer knowledge
have R syntax highlighting.
available for Windows or Mac.
For windows I've found:
TINN-R
Notepad ++ with the R plugin
Are there any others that I've missed for Windows?
There are a few threads that deal with R text editors:
Best IDE / TextEditor for R
Recommendations for Windows text editor for R
Which IDE for R in Linux?
but these are a bit too complicated for my purpose.
Edits
Following comments from Shane and others I've reworded the question.
Given that you don't have an major specific requirements (like an object browser), it's probably best to use what you're already using as much as possible. Something like Textpad is very simple and can do syntax highlighting.
Here are a few more pointers:
First of all, the R console that ships with Windows has it's own script editor. Just go File > New Script. It's very easy to use and you can execute code by highlighting it. If you just want something simple, I would stick with that.
I use Eclipse (with StatET) on Windows, and I have used it on a Mac too. It's great if you want an extensive IDE (syntax highlighting, integrated console, SVN, etc.) with a small learning curve.
JGR is also very good and platform independent.
Sciviews (which has Tinn-R) has several other options, including SciViews-K which is an R extension for Komodo.
Two others worth mentioning are Rattle and Rkward.
Emacs and VIM have a bigger learning curve, but they're also very powerful, especially if you're already using them for something else.
I see, this question is distinguished from prior ones by asking for a recommendation specific to "Intro to R" students. For the Mac portion of your question, i would suggest TextMate, for two reasons. First, the default answer "just use the Aqua R.app Gui" that R ships with, has minimal syntax highlighting and doesn't allow you to save and insert R commands (not that i'm aware of at least). Both of those things make learning a new language less painful and more efficient. But that might not justify the overhead of learning an editor while learning a new language at the same time.
No doubt others here will recommend TM, but they might not mention TextMate's tiered learning curve, i.e., someone who has never seen TM before can, after a 45-min tutorial, launch an interactive R session from it and use it to save/retrieve R commands "snippets". TM is not free, but it's around $50 with academic discount i believe. I would recommend three bundles for R use in TM, (i) R.app; (ii) R.daemon; and (iii) R, all of which are in the TM svn repository.
As always, emacs is an option: R in Emacs
This may not be the best option because of the learning curve with emacs though.
I haven't used it for R but TextMate on the Mac is awesome and they have an R bundle.
I haven't used it myself, but there is an Eclipse plug-in for R (which should work on Windows and Mac).
Because someone already mentioned Emacs, of course there's VIM with R plugin, don't know how many of those there are, but I found at least one with a quick google. VIM might have an even steeper learning curve than Emacs though.
That said. I think Emacs and VIM will both handle pretty much any language out there, so let the flame war begin!
I use Vim myself but I'm quite certain that both Vim and Emacs would be a bad choice for a student course.
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Closed 10 years ago.
Does anyone know a good online resource for example of R code?
The programs do not have to be written for illustrative purposes, I am really just looking for some places where a bunch of R code has been written to give me a sense of the syntax and capabilities of the language?
Edit: I have read the basic documentation on the main site, but was wondering if there was some code samples or even programs that show how R is used by different people.
Why not look at www.r-project.org under documentation and read at least the introduction? The language is sufficiently different from what you're used to that just looking at code samples won't be enough for you to pick it up. (At least, not beyond basic calculator-like functionality.)
If you want to look a bit deeper, you might want to look at CRAN: an online collection of R modules with source code: cran.r-project.org
I just found this question and thought I would add a few resources to it. I really like the Quick-R site:
http://www.statmethods.net/
Muenchen has written a book about using R if you come from SAS or SPSS. Originally it was an 80 page online doc that Springer encouraged him to make a 400+ page book out of. The original short form as well as the book are here:
http://rforsasandspssusers.com/
You've probably already seen these, but worth listing:
http://cran.r-project.org/doc/manuals/R-intro.pdf
http://cran.r-project.org/doc/contrib/Owen-TheRGuide.pdf
http://cran.r-project.org/doc/contrib/Kuhnert+Venables-R_Course_Notes.zip
I don't want to sound like a trite RTFM guy, but the help files generally have great short snips of working code as examples. I'm no R pro so I end up having to deconstruct the examples to understand them. That process, while tedious, is really useful.
Good luck!
EDIT: well I hesitated to be self linking (it feels a bit masturbatory) but here's my own list of R resources with descriptions and comments on each: http://www.cerebralmastication.com/?page_id=62
The Rosetta Code project shows R compared to other languages.
How about CRAN? You've got over a thousand packages of code to choose from.
The simplest way of seeing code, is to
install R
type "help.start()" or look at online documentation, to get names of functions
type the function name at the prompt
This will print the source code right at the prompt, and illustrate all manner of odd and interesting syntax corners.
The Learning R blog has a lot of good examples. Lately, the author has been doing a visualization series, comparing Lattice and ggplot2.
It is hard to google r, because of it being too short a name. Try http://rseek.org/, which provides an r-customized Google search instead. Search on examples, code in repositories, etc.
Some simple examples can be found at Mathesaurus - if you know e.g. Python or Matlab, look at the respective comparison charts to find the R idioms that correspond to your familiar idioms in the other language.
I use the R Graph Gallery. It has been a lot of help on graphing itself. Lots of good examples.
#R on Freenode has also been very useful.
http://had.co.nz/ggplot2/ has a lot of graphics with example code. And you only need one package to create almost every graph you need.
There is also the R Wiki which is slowly growing.
As you probably know, R and S are pretty similar (apart from the cost!).
I use to use both, and I highly recommend S Poetry.
I can also highly recommend the M.J. Crawley book, and the shorter Venables & Ripley one.
here are links to the R project group on Linkedin. I put together this list of links and a lot of people have found it useful (some have also made very useful additions)
Use Google Code Search with command "lang:r" and your keyword(s)
Steve McIntyre at http://www.climateaudit.org/ is a big fan of R and often posts working code.
There is a scripts category, and the Statistics and R lists some other resources