I've started teaching myself sage and I'm a bit confused about the naming of some commands in graphics. The most basic command for graphics is perhaps plot with its variants polar_plot, contour_plot, etc. However, I've also seen some variants of plot that are obtained from it by adding postfixes to it, for instance, plot_vector_field.
Does anyone know the reason why some graphical commands belong to the first category (prefix_plot) and some to the second (plot_postfix)? I'm asking this because of there is a good reason for this, then it can help me remember the names more easily, and if there is no special reason this might be something to suggest for changes in future releases of sage as it is open source.
PS This is my first question on stackoverflow and I hope this is the right place for asking it, otherwise please feel free to move it anywhere that you feel it might belong.
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I'm wondering if there are any packages for R which help to visualize workflows/code in a way Alteryx does. I find the visualization of the workflows within Alteryx quite helpful, but manually dragging an dropping the tools onto the canvas and set the parameters just takes so much longer than just writing the code in R. Also some functionally within Alteryx is not yet sufficient and has to be implemented via the R/Python-Tool anyway.
During my search I found this post which goes into the same direction, but the suggested packages don't really match what I am looking for.
Best regards
i am using RGL to produce a panel of multiple figures through the mfrow3d command.
for the most part, the html produced from the call to writeWebGL is exemplary.
the one caveat is that for multiple figures (be it 6 or 16), i have noticed a bit of lag when attempting to manipulate any one of these figures (to pan/zoom/look around).
an example can be found here: http://fluxions.dydx.ie:1338/schiz.html (warning, 100MB html file haha).
i wanted to ask people here if there is anything i can do in terms of using the "reuse" argument that may speed up performance.
additionally, i wanted to ask if there is any benefit to using rglWidgets and if there is a small example someone could provide in porting a writeWebGL call produced from the following:
https://johnmuschelli.com/WebGL_Interactive_Paper/supp_1/supp_1_wrap.Rmd
to rglwidgets (in hopes that the reuse argument in widgets may improve performance due to my use of mfrow3d).
i am not familiar on how to capture a multi-figure layout with multiple calls to contour3d as a scene that widgets can use.
dr duncan murdoch has gotten back to me and said there probably is not a way to do this, so i guess i will close it.
he is very helpful and i thank him for his support.
Is there any way in R to write a macro like one would in SAS? That is, I want to write a macro with some input variable (corresponding to a row in a dataset) so I can quickly make a plot of certain characteristics from said row. Any information regarding a package/method to do so would be greatly appreciated.
R will generate some very, very, very basic code for you. If you have RStudio installed, you can click File > Import Dataset > From ... point to your file and click 'Open'. R will automatically create the code to do the import. Again, this is very basic. You really need to know how to code to do anything useful.
You get out of it what you put into it, so spend some time learning this stuff, and inevitably you'll learn a ton. I've found that it's very helpful to read through people's questions that are posted here, and try to solve the problem yourself. You'll learn a lot that way and you'll see what the current trends are. Reading books is great, of course, but sometimes I feel like some authors are too academic, and in the real world, sometimes it's done differently than what you see in textbooks.
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Closed 11 years ago.
I am participating in a big programming competition tomorrow where I use R.
Time is the main factor (only 2 hours for 7 coding problems).
The problems are very mathematics related.
I would like to write "f" instead of "function" when I define a function.
This can be done and I had the code to do so, but I lost it and cannot find it.
Where do I find sin() functions for degrees input, not radian?
(optional) Is there any algorithm specific task view or libraries.
Any tip for a programming contest?
I prepared the following cheat sheet for the contest:
http://pastebin.com/h5xDLhvg
======== EDIT: ==========
So I finally have time to write down my lessons learned.
The programming contest was a lot of fun, but unfortunately I did not score very well. I was in the top 50%, but my aim was to be in the top 25%.
The main problem was that there was very little time to program, just 2 hours in total. But I had to read the problem descriptions and also I needed some time to paste the results in the web form, etc., so it was more like 90 Minutes of programming.
Hopefully the next contest in December will have extended time, like 3-4 hours. The organizers said that perhaps will be the case.
Also, there was no Internet access at the contest, and my mobile reception was not really working.
The main lesson for me is that you have to use a language you daily use in order to have a real chance. Especially, if there is only about 90 Minutes time to program. Since I use haskell more than R in my daily work, I think R was not the best choice. During the contest I mixed up haskell and R function definitions, and I made too many small typos to program fast enough.
What was great about the contest was, that there was about 20 000 bucks prize money in total for the about 80 participants. So the top 25% participants got from 500 to 1500 bucks each. Further, I think the top 15% get a job right away from one of the sponsor IT firms.
So it's a win-win situation. It's fun, plus you can get prize money. Further the IT firms are more than happy, because they have access to the top programmers.
I used the chance to speak to IT decision makers. One of them was from a larger bank. I boldly suggested that they consider switching to Scala for their development (switchung from Java). And also to consider using R and Haskell. It was fun, and they even said they already looked into Scala!
What was interesting to note was, that one of my best friends scored very good at the competition. He is only 19 years old, but he was well in the top 20% and got 500 bucks prize money. He beat me plus 6 of my colleges, who all have a respectable computer science degree. My friend programs more like hacker style, but he was very fast.
People in the top 10 used:
1) Java
2) C# and
3) C++
(No other programming language in the top 10!).
The only other programming language that scored reasonably well was Ruby, I think.
For the next contest the programming language of choice will probably be haskell. For one reason, it's just easier to find 2 team mates for haskell than for R programming. And up to 3 persons can form a team.
My ideal scenario would be a very light weight framework, where I could use multiple programming languages at once for the contest. That way, the main code can be written in haskell (which all team mates can program in). And some specific functions may be programmed in R, or in Mathematica, or even some other programming language (like python/sage).
This sounds a little bit overkill. But I think it would be very usefull. Like a function that has a matrix as a parameter and returns a matrix. Then this framework work generate automatically a RESTful service from the R code, so I could call the R function from any programming language. The matrix is just passed around as JSON data (or some other serialization). Okay, but this is off topic...
So finally some lessons learned as a bullet list:
don't bring food. you don't have time to eat, and there is a rich buffet afterwards
time is the limiting factor!
if you don't program R for a living, don't use R
look for contests where there is more time (3-4 hourss minimum!)
all in all, the concept of the contest is superb! Both for the participants, but also for the sponsors.
BIG THANKS to the help of 'Iterator' for his post!!
I'm going to answer a related, but different question. No offense, but your original suggestions don't seem very wise for a programming competition. Much of the time spent in such contexts is in devising an answer and in debugging (or, better, avoiding the need to debug).
Instead, I will answer this question: "What are the key resources in R that are useful for rapid prototyping, with a focus on being able to find resources quickly, being able to debug quickly, and being able to investigate data quickly? If I need to use numerical optimization methods and algebra systems, what should I investigate?"
Here are my answers:
Install RStudio or possibly Revolution Analytics' R, depending on which interface seems more appropriate to you. Both are good. The former has a very smooth GUI, the latter has a more intense interface, with more capabilities for managing code. Both have some nice properties over the "community" R regarding being able to look up information and navigate the help libraries quickly.
Get acquainted with example(), identify where to get vignettes and tutorials (from packages' pages on CRAN), and take a brief look at demo().
Use the sos library, and master findFn.
Look at the Task Views on CRAN - be sure you know about the tools for high performance computing (if that is going to be related) and the tools for optimization - it's quite common to need to use some kind of solver, and there's a task view for that.
If your code is running slowly during the prototyping or competition, you'll need to run Rprof(). Take that for a spin first. You may also benefit from using the compiler package if your code involves much iteration. In short: You do not want to wait on the computer. You might also look at foreach and doSMP or doMC if you can parcel the job to different cores. To aggregate results, become familiar with plyr and methods like ldply, as well as standard *apply functions, like lapply and apply; another good one to know is rapply. (If you have lots of stuff to process and it takes some time, look at mclapply or the .parallel argument for the plyr functions.)
On Stack Overflow: browse JD Long's questions - much of what you will discover that you do not know will have been asked by him before you thought to ask it. And there's an answer already there.
Create a number of little code templates for yourself. Master functions so that you don't need to learn these in a rush. Learn how to debug and step through these, using debug() and browser().
If you have to count things, learn how to use the hash package (akin to Perl and Python hash tables) and learn to use digest for keys that are too long to be used for hash (see this question for references)
If you are going to need to plot things, get some basic example plots prepared, using either plot or ggplot2, along with hist, boxplot, and some others. If you don't know ggplot2 already, then postpone, but you should become familiar with it. If you happen to use a lot of data, then be sure you know hexbin. If you will have to interact with data, then get to know iplots and the interesting tools there, such as iplot, ihist, and parallel coordinate plots (ipcp).
Be sure you know how to use lists, data frames, and matrices, including subscripting, lookups of entries based on (row, column) indices. (Again, be sure to investigate plyr for transforming and operating on some of these objects.)
Get acquainted with data.table() - it's exceptionally efficient for a lot of things you might do with data frames and matrices.
If you need to do symbolic mathematics, be sure you know the packages for that or else get another standalone tool for symbolic math. Ryacas is one package that appears to be useful.
Get the PDF of the R in a Nutshell, so that you can rapidly search through it for useful methods. Else, get the book itself. Various other books, such as Venables & Ripley, the R Cookbook, and others may be useful, depending on your experience.
If you've already mastered a good editor (e.g. emacs) or IDE (e.g. Eclipse), stick with it and look for bindings to R. Otherwise, a simple one you can begin using right away is Notepad++. Being able to do block selection is a very useful property in an editor. Being able to search through an entire directory hierarchy of code examples is another useful capability.
If you need to do anything involving database data, you may want to know RSQLite and sqldf, though these may not be relevant to a math competition.
Open a bunch of R instances so that you can try things out. :) [This is actually serious: by having multiple instances running, you can somewhat avoid latency associated with sequentially trying things out, waiting for results, and then debugging the results.]
For (1), you can do something like
f <- function(..., body)
{
dots <- substitute(...)
body <- substitute(body)
f <- function()
formals(f) <- dots
body(f) <- body
environment(f) <- parent.env(environment())
f
}
which lets you write, eg, g <- f(x, y, body=x+y) but I'm not sure how far that gets you.
For (2), you could just do:
sindeg <- function(x) sin(x*pi/180)
As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance.
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