This question is not directly about what is open or closed recursion as we could see on this question, but it's more specifically about this talk which is referenced in many placed.
Inside we can see expression such as:
(fun(x1:τ1)⇒e)⇓(fun(x1:τ1)⇒e)
My question is: what does the ⇓ stand for?
I looked around this internet and didn't found anything particular about that, taking into account that googling a symbol is always tedious.
Not sure if it does answer your question, but from reading various material online I think ⇓ means evaluates to or reduced to.
For expression:
(fun(x1:τ1)⇒e)⇓(fun(x1:τ1)⇒e)
it means, function fun which takes x1 of type τ1 as an argument can be reduced to the same function.
May be this question on SE might help you.
Related
I have a function that I'm trying to add to a package. I am generating the documentation via devtools::document(). The .Rd files for this and ~70 other functions are generated successfully, but this one function is not added to the namespace.
The file can be found at the following link, and perhaps importantly, is called truncate.distribution.r. I have many other functions with periods in the names, so I am almost certain that is not the problem.
However, as I was going through the NAMESPACE, I noticed this line S3method(truncate,distribution), and wondered if the similar name was a coincidence (i.e. comma, as opposed to period). I tried removing the period from the name, and re-generating the documentation and NAMESPACE, and it all worked just fine - that is, the function is exported with the package.
While the altered name works, I would like to learn why it failed and how I can prevent similar failures in the future. Also, I like the original name. :)
Anyone have any thoughts? Much appreciated.
So you guys are right, and it had to do with the periods in the names. The particular file that cause the problem for us was one where the first part of the name ("truncate."...) was already a base function, thus being interpreted as S3methods.
Thanks to all who answered. I'm not sure why I'm getting voted down - knowing not to use periods in function names is not inherent knowledge, nor is it expressly forbidden anywhere I looked (i.e. Hadley's, or Google's Style Guides).
I found one other potential cause of similar grief. My last line of Roxygen code had a typo in the #' #export command resulting in the same poor behavior
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Closed 9 years ago.
I'm writing an annual report for uni, in which I would like to detail how my usage of R has increased over the past year. I'm looking metrics that I can use to describe my usage of R. Some possible metrics to describe usage:
number of lines of code in history
number of errors
hours spent using program
number of times a particular function has been called
number of plots made
So my question is: can I extract any of the above from R, or can I extract any other metrics which would demonstrate my usage of R?
First, I'm not sure that this question is at all suited to Stack Overflow. Second, I think that the metrics you've identified are not really suitable. Let's look at the ones you've shortlisted so far:
Number of lines of code in history
You make a lot of tweaks to your code. They accumulate in your history. Your history now has a lot of lines of code. Does that reflect positively of your usage of R? Or, you like to write code like the following in R:
temp <- 0
for (i in 1:10) {
temp <- temp + i
}
print(temp)
while a person familiar with R would just write sum(1:10). One line versus five. Can we really say that number of lines is a good metric?
Number of errors
Maybe there is some merit to this. But are you going to classify errors in some way? Is a missing or misplaced bracket forgivable? What about times when no error or warning is issued but R behaves in a way that you might not have expected, thus leading to unexpected results (for example, assuming that numeric(0) and factor(0) would behave the same way). See here for some R gotchas, several of which won't provide any indication of an error, but would certainly lead to erroneous analysis. How would they be analyzed with this metric?
Number of hours spent using the program
Again, debatable. How do you measure the number of hours? Time spent coding? Time the computer spends processing your code? Time it took you to figure out how to program your problem?
Number of times a particular function has been called
I don't understand this metric at all. Do more obscure functions get a higher weight (for example, if you are one of those who use vapply while the rest of the schmucks use sapply, do you get bonus points for using vapply because it can be safer (and sometimes faster) to use?)
Number of plots made
Sorry, but again, I don't understand this metric at all. First of all, not all plots are created equally! There are several in the data visualization field who feel that a lot of software ruined data visualization because some software (a very popular spreadsheet program, in particular) made it so easy for people to quickly make gaudy plots. With R, they are less gaudy by default, but that in itself doesn't make it good. So, if you're just measuring the number of plots churned out without some other criteria for quality assessment, then I'm not sure how this metric is useful.
And, from your comment to your question:
Actually...stack overflow reputation points might be as good as anything!
Eh... The only time I really use R is to answer questions on Stack Overflow (unfortunately true). At the same time, almost all my reputation points here are from the questions I've answered in the R tag. Sure, there are some users here that I would really trust, but sometimes, I don't even trust myself, so I don't know if that's a good indicator of your usage of R.
Lots of users have also complained that Stack Overflow voting is totally wacky, so I'm not sure that you really can use "reputation" as a valid measure of skill. For example, there's an ongoing discussion among regular users here that answers to "easy" questions get voted up very quickly (because they are easy to verify, often without even running the code) while answers to "complicated" questions don't yield votes proportional to the effort taken to answer the question. Case in point: Why the heck do I have a "Guru" badge for an answer that is essentially a reordered version of data already easily available with two minutes on Google. I'm not particularly proud of that answer, and it certainly doesn't say anything about my "usage" of R.
Now, to make this so that it might qualify as an answer and not just an extended comment on your question itself, the biggest thing that I would consider valid, but not sure how to measure it, would be something like how active you are in the R community. There are many ways to get involved with R, from writing or contributing to packages, filing bug reports, conducting workshops to help others make the switch to R, and so on.
I'm not suggesting that you need to write a book, as several others here have done, or to become a legendary package developer with a cult of underscore followers, but you can take small steps. For instance, although I'm a writing teacher, I have held workshops for students and written a few "getting started tips" just to introduce them to using R, so they can consider adding it to their toolkit. Many other users here regularly blog about their experiences working with R and, again, as this is part of a community, they learn a lot in the process.
Finally, a couple of more ideas:
#PaulHiemstra suggested in his comment that you could "mention the percentage of your programming work you do in R." I would extend that concept as follows: (1) try to measure how much of your work overall is done in R and tools complementary to R (obvious ones like Sweave/knitr/LaTeX come to mind), and (2) try to measure how much of an impact using R has had on improving your overall skills (with the logic being that good programming is often accompanied by logical thought, careful organization, good documentation, and so on).
Related to the previous point, try to see how your usage of R has changed with time. Has your behavior changed from manually redoing the same steps to writing functions yet? Have you then gone back and adapted those functions so that, instead of solving a specific problem you had at a given point in time, they can be used more generally by a larger audience? These are pretty significant changes, particularly if you had started from scratch with the language, and they can be a bit more meaningful than the ideas you presented in your question.
So, to summarize, a lot of the somewhat easily quantifiable things that you've identified in your question will probably lead to very meaningless analysis. I feel that the qualitative inputs you make would be much more valuable.
Another metric: Get an old and complex (don't know if you have one) code and redo it from 0. Use the difference of computation time as metric.
This question already has answers here:
Closed 10 years ago.
Possible Duplicate:
View the source of an R package
I want to see the source code of stats::reorder.
This answer seems not apply to built in packages which are compiled to bytecode:
> stats::reorder
function (x, ...)
UseMethod("reorder")
>bytecode: 0x103321718<
>environment: namespace:stats<
This has nothing to do with reorder being compiled to bytecode and everything to do with it being a generic function.
My answer here elaborates on this.
But specifically for this situation if you want to see the code you can use
# Find what methods are available for reorder
methods(reorder)
# Attempt to check out the code for reorder.default
reorder.default
# Use getAnywhere to view code regardless of if it is exported
getAnywhere(reorder.default)
As others have said, you want methods(reorder). But for your mode general question, the best way is to download the source code of R, and search the code with grep. You can also browse the code online but it's not always obvious in which file a particular function might live.
This isn't a matter of compilation, what you're seeing is the result of the fact that reorder is written to do different things depending on the class of what you want to reorder. There are separate reorder functions for different possible options, and you can list them by calling methods(reorder). You can then examine the source of whichever one is appropriate.
This question already has answers here:
Speed up the loop operation in R
(10 answers)
Closed 9 years ago.
I'm fairly new to R, and one thing that has struck me is that it's running fairly slow. Is there any documentation for optimizing R? For example, optimizing Python is described very good here. In my particular case I'm interested in optimizing R for batch jobs.
I have tried Googling for an answer of course, but it's not exactly easy to Google for R info since R is a pretty generic little search pattern.
For start, you should take a look at R Inferno by Patric Burns.
Than the best idea is to ask more detailed questions here.
Yes, R is a bit awkward for a search term, so try RSiteSearch("performance") within R - this will search within lots of R docs sources.
a simple google search on 'efficient programming in r' reveals the following excellent resources. the first resource is great as it provides a comparison of the bad, good and best ways of programming a task in R. the second resource is more generic.
http://perswww.kuleuven.be/~u0044882/Research/slidesR.pdf
http://www.bioconductor.org/help/course-materials/2010/BioC2010/EfficientRProgramming.pdf
if you are looking at more specific areas of optimizing your R code, specify it more clearly and i am sure you will find an expert here !!
"It's running fairly slow" is very vague. There are many techniques for using R in the most efficient way, the general rule is "avoid loops, and vectorize" - but there is so much more such as ensuring objects are pre-allocated rather than resized on the fly.
It really depends on what you are doing, so please be more specific. The standard documentation has plenty of tips for the basics and your question does not really give opportunity for someone to do any more than regurgitate those.
When standard R really is limited for your needs you can write directly in a compiled language such as C, or use advanced interfaces such as Rcpp. For other tools and techniques that extend beyond the basic R toolkit consult the "High Performance Computing" Task View on CRAN.
Basically I have created two MATLAB functions which involve some basic signal processing and I need to describe how these functions work in a written report. It specifically requires me to describe the algorithms using mathematical notation.
Maths really isn't my strong point at all, in fact I'm quite surprised I've even been able to develop the functions in the first place. I'm quite worried about the situation at the moment, it's the last section of writing I need to complete but it is crucially important.
What I want to know is whether I'm going to have to grab a book and teach myself mathematical notation in a very short space of time or is there possibly an easier/quicker way to learn? (Yes I know reading a book should be simple enough, but maths + short time frame = major headache + stress)
I've searched through some threads on here already but I really don't know where to start!
Although your question is rather vague, and I have no idea what sorts of algorithms you have coded that you are trying to describe in equation form, here are a few pointers that may help:
Check the MATLAB documentation: If you are using built-in MATLAB functions, they will sometimes give an equation in the documentation that describes what they are doing internally. Some examples are the functions CONV, CORRCOEF, and FFT. If the function is rather complicated, it may not have an equation but instead have links to some papers describing the algorithm, which may themselves have equations for the algorithm. An example is the function HILBERT (which you can also find equations for on Wikipedia).
Find some lists of common mathematical symbols: Some standard symbols used to represent common mathematical operations can be found here.
Look at some sample pseudocode to see how it's done: For algorithms you yourself have coded up, you'll have to write them out in equation or pseudocode form. A paper that I've used often in my work is Templates for the Solution of Linear Systems, and it has some examples of pseudocode that may be helpful to you. I would suggest first looking at the list of symbols used in that paper (on page iv) to see some typical notations used to represent various mathematical operations. You can then look at some of the examples of pseudocode throughout the rest of the document, such as in the box on page 8.
I suggest that you learn a little bit of LaTeX and investigate Matlab's publish feature. You only need to learn enough LaTeX to write mathematical expressions. Then you have to write Matlab comments in your source file in LaTeX, but only for the bits you want to look like high-quality maths. Finally, open the Matlab editor on your .m file, and select File | Publish.
See Very Quick Intro to LaTeX and check your Matlab documentation for publish.
In addition to the answers already here, I would strongly advise using words in addition to forumlae in your report to describe the maths that you are presenting.
If I were marking a student's report and they explained the concepts of what they were doing correctly, but had poor or incorrect mathematical notation to back it up: this would lose them some marks, but would hopefully not impede my understanding of the hard work they've put in.
If they had poor/wrong maths, with no explanation of what they meant to say, this could jeapordise my understanding of their entire project and cost them a passing grade.
The reason you haven't found any useful threads is because most of the time, people are trying to turn maths into algorithms, not vice versa!
Starting from an arbitrary algorithm, sometimes pseudo-code, along with suitable comments, is the clearest (and possibly only) representation.