I'm currently using Ubuntu, R, and JAGS. I'd like to explore a few WinBUGS examples that are available on the web, such as those available on the website accompanying the book, Bayesian Modeling Using WinBUGS: An introduction.
However, the analyses are stored in the odc format e.g., this one.
I can open the file with a text editor and it does show the model syntax in plain text and in some cases data and so forth. However, I was wondering:
Is there an existing R function that extracts important elements of a WinBUGS odc file?
I would recommend compile odcread and use it to convert .odc files to text files and read it with any text editor.
for f in *.odc; do
odcread ./"$f" > ./"${f%.odc}.txt"
done
The best way is to go and install WinBUGS.
See http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml
You can then open any of these directly there, and copy them for use wherever you want.
Related
I have been practicing with tabulizer package in R and have following problem. Unfortunately I can't offer reproducible example, as pdf is firms property, but I will describe problem in detail.
I'm trying to read PDF that has start and end date in upperright corner. When I open PDF they look normal
Start: 01-Mar-2018
End: 31-Mar-2018
Now the fun part. When I highlight them and use Ctrl+C to copy them here is result when pasted to R.
:tttt: 11-rrr-8118
tt:: 11-rrr-8118
This is exactly same kind of nonsense that extract_text(path, pages=1) will give. A lot of t::ttttt:ttt... My question is that is there some security in this PDF or do I just need to figure out correct encoding or because this PDF is automatically created from system, there is some weird notation to everything?
I figured it out. This PDF is mainly created by metadata (didn't know) and great tool in R for accessing metadata in PDFs is pdftools.
library(pdftools)
pdf_info(path.pdf)
and you can wrangle out all the important metadata bits.
I am new to R and have worked for a while as follows. I have the code writen in a word document, then I copy and paste the document with the code into R as to have the code run which works fine, however when the code is long (hundred pages) it takes a significant amount of time in R to start making the code run. This seems rather not a very effective working procedure and I am sure there are other forms to compile the R code.
On another hand one of then that comes to my mind is to import the content of word into R which I am unsure how to do. Have tried with read.table but it does not work, have look on internet as to how to import data, however most explanations are all for data tables etc or internet files in the form of data tables and similar. I have tried saving the document into csv. however word does not include csv have tried with Rich text format and XML package but again the instructions from the packages are for importing tables and similars. I am wondering if there is an effective way for R to import a word document as is in the word document.
Thank you
It's hard to say what the easiest solution would be, without examining the word document. Assuming it only contains code and nothing else, it should be pretty easy to convert it all to plain text from within Word. You can do that by going to File -> Save As, and use 'plain text' under 'Save as type'.
Then edit the filename extension to .R from .txt, download a proper text editor (I can recommend RStudio for R), and open your code in it. Then you will be able to run the code from inside the editor without using copy / paste.
No, read table won't do it.
Microsoft Word has its own format, which includes a lot of meta data over and above the text you enter into it. You'll need a reader/parser that understands the Word format.
A Java developer would use a library like Apache POI to read and parse it into word tokens and n-grams.
Look for Natural Language Processing tools, like this R module:
http://cran.r-project.org/web/views/NaturalLanguageProcessing.html
Plain and simple: is there a way to read (not run) .sas files on osx in order to rewrite old SAS programs in another language, e.g. R? Note I do not refer to reading sas data files – I know there are several ways, I am just interested in reading old SAS code.
.sas files containing SAS code should just be a text file. You can use any text editor that you like to open and modify these files. Since the system probably doesn't have .sas files associated with any particular program you can either use the "Open with" option when "right-clicking" on the file or you could open the editor first and then open the file from within the editor.
TextEdit will work. Another editor that I like is Komodo Edit.
If I load data(mtcars) it comes with a very neat codebook that I can call using ?mtcars.
I'm interested to document my data in the same way and, furthermore, save that neat codebook as a pdf.
Is it possible to save the 'content' of ?mtcars and how is it created?
Thanks, Eric
P.S. I did read this thread.
update 2012-05-14 00:39:59 PDT
I am looking for a solution using only R; unfortunately I cannot rely on other software (e.g. Tex)
update 2012-05-14 09:49:05 PDT
Thank you very much everyone for the many answers.
Reading these answers I realized that I should have made my priorities much clearer. Therefore, here is a list of my priorities in regard to this question.
R, I am looking for a solution that is based exclusively on R.
Reproducibility, that the codebook can be part of a automated script.
Readability, the text should be easy to read.
Searchability, a file that can be open with any standard software and searched (this is why I thought pdf would be a good solution, but this is overruled by 1 through 3).
I am currently labeling my variables using label() from the Hmisc package and might end up writing a .txt codebook using Label() from the same package.
(I'm not completely sure what you're after, but):
Like other package documentation, the file for mtcars is an .Rd file. You can convert it into other formats (ASCII) than pdf, but the usual way of producing a pdf does use pdflatex.
However, most information in such an .Rd file is written more or less by hand (unless you use yet another R package like roxygen/roxygen2 help you to generate parts of it automatically.
For user-data, usually Noweb is much more convenient.
.Rnw -Sweave-> -> .tex -pdflatex-> pdf is certainly the most usual way with such files.
However, you can use it e.g. with Openoffice (if that is installed) or use it with plain ASCII files instead of TeX.
Have a look at package knitr which may be easier with pure-ASCII files. (I'm not an expert, just switching over from Sweave)
If html is an option, both Sweave and knitr can work with that.
I don't know how to get the pdf of individual data sets but you can build the pdf of the entire datasets package from the LaTeX version using:
path <- find.package('datasets')
system(paste(shQuote(file.path(R.home("bin"), "R")),"CMD",
"Rd2pdf",shQuote(path)))
I'm not sure on this but it only makes sense you'd have to have some sort of LaTeX program like MikTex. Also I'm not sure how this will work on different OS as mine is windows and this works for me.
PS this is only a partial answer to your question as you want to do this for your data, but if nothing else it may get the ball rolling.
The help page that is displayed when entering ?mtcars is generated from an .Rd file, which is a LaTeX-like file that is used for all of R's help pages. Although .Rd files are LaTeX-like, you don't actually need to know LaTeX to read or write them. The actual mtcars.Rd file is available here: http://commondatastorage.googleapis.com/jthetzel-public/mtcars.Rd , which can be viewed with any text editor.
.Rd files included in the ./man directory of a package are converted to .html files when installing the package. They are converted by functions in the "tools" package.. If you would like functionality like ?mtcars for your datasets, you would need to create a package for them. That might sound complicated if you have never created a package before, but it is easy enough to learn and will make you a better R programmer. There are a number of examples of dataset-only packages on CRAN, for example msProstate: http://cran.r-project.org/web/packages/msProstate/index.html . Consider downloading the package source to see how it is organized.
For more information on creating your own packages, writing .Rd files, and building packages:
http://cran.r-project.org/doc/manuals/R-exts.html, especially "1.1.5 Data in packages".
Edit
And if you want to convert the .Rd file in your package to a .pdf, you can do so when building your package, but you will need a LaTeX compiler. If you are on Windows, see here: http://cran.r-project.org/bin/windows/Rtools/ .
You can't create a PDF with just R; you need to use other software that creates PDFs.
You could use a combination of utils::promptData, tools::Rd2HTML, and a simple custom function to open the created HTML file in the users' browser.
It would probably be easier to just make a package containing your data sets. Look at the "datasets" package for an example.
It looks like that if you want to generate a pdf, an external tool like LaTeX is always needed. I would recommend using a simple ASCII text format to generate such a file. In principle the .Rd files are also ASCII text, but I do not find them particularly readable.
Instead, I would recommend using a plain text ASCII format such as Markdown (which is e.g. used on StackOverflow) to write the text file. Such a file is already much more readable than an .Rd formatted file, and as a bonus it can quite easily be processed into a PDF should you choose to do so later on. The knitr package I think is capable of generating PDF files from Markdown sources. In addition, knitr allows you to mix in R code in the Markdown text. This code can be evaluated and the results (even figures) added to the resulting PDF.
In practice you can use sprintf to generate character vectors that you can pipe to a file in order to dynamically generate the markdown text. Just write the template one time, and mark the places for the text you want to add later like this:
base_text = "
First header
============
This document was generated on %s, by %s.
"
text_forfile = sprintf(text, some_date, some_name)
Just dump the text in text_forfile to a .md file and your done, no external tools needed. See this post on SO for how dump text to a file.
my question(s) might be less general than the title suggests. I am running R on Mac OS X with a MySQL database to store the data. I have been working with the Komodo / Sciviews-R for some time. Recently I had the need for auto-generated reports and looked into Sweave. I guess StatET / Eclipse appears to be the "standard" solution for Sweavers.
1) Is it reasonable to switch from Komodo to StatET Eclipse? I tried StatET before but chose Komodo over StatET because I liked the calltip / autosuggest and the more convenient config from Komodo so much.
2) What´s a reasonable workflow to generate Sweave files? Usually I develop my R code first and then care about the report later. I just learned today that there is one file in Sweave that contains R code and Latex code at once and that from this file the .tex document is created. While the example files look handily and can't really imagine how to enter my 250 + lines of R code to a file and mixed it up with Latex.
Is it possible to just enter the qplot() and ggplot() statements to a such a document and source the functionality like database connection and intermediate results somehow?
Or is it just a matter of being used to the mix of Latex and R code?
Thx for any suggestions, hints, links and back-to-the-roots-shout-outs…
You've asked several questions, so here's several answers;
Is StatEt/Eclipse the right way to do Sweave ?
Not nessarily (note: I'm an avid StatEt/Eclipse user, and use it for both pure R and Sweave/R and love it, I haven't used Komodo / sciviews-R). You should be able to run the sweave command from any R command line which will generate a .tex file. You can then turn the .tex file into something readable (like pdf) from any tex environment.
What's a good Sweave workflow ?
When I have wanted to turn an r script into a sweave report I generaly start with an empty sweave template and copy/paste my entire R script into a sweave R block just after the title, i.e;
<<label=myEntireRScript, echo=false, include=false>>
#Insert code here
myTable<-dataframe(...)
myPlot<-qplot(....)
#
Then I go through and find the parts I want to report. For instance, if i want to put a table into the report, I'll cut the R block and put an xtable block in, and the same for variables and plots.
<<label=myEntireRScript, echo=false, include=false>>=
#Insert code here
#
Put any text I want before my table here, maybe with a \Sexpr{print(variable)} named variable
<<label=myTable, result=Tex>>=
myTable<-dataframe(...)
print(xtable(mytable,...),...)
#
Any text I want before my figure
<label=myplot, result=figure>>=
myPlot<-qplot(....)
print(qplot)
#
You may want to look at these related SO posts. The rest of my post relates to your question 2.
When creating reports with Sweave, I usually keep most of the R code and the report text separate. If the R code is fast to run, then I prefer I will include something like the following at the start of the .Rnw file:
<<>>
source('/path/to/script.r')
#
On the other hand, if the R code takes a long time, I will often include something like the following at the end of the R script:
Sweave('/path/to/report.Rnw'); system('pdflatex report.tex')
That way, I can re-generate the report quickly, without needing to run all the R code again. Then, the only work R has to do in the Sweave file is print tables, make graphs and maybe extract a few figures.
Like nullglob, I prefer to keep the R and Sweave files separate, but I prefer to save the workspace with save.image() rather than to source() the file. This avoids running the R calculations with each .Rnw file compiling (and I always end up tinkering with the typesetting more than I'd like).
My general work flow is to do each paper/project in it's own folder with it's own R file(s). When the calculation side is "done", I save.image() to store all the workspace variables as-is.
Then, in the .Rnw file in the same directory I set the working directory with setwd() and load all variables with load(".Rdata"). Of course, you can change the name you use for your workspace, but I do one workspace per folder and keep the default name. Oh, and if you tinker with the R file, be sure save the workspace image and watch out for variables that linger in the workspace and .Rnw file, but are no longer part of the R file... this is where the save.image() approach can cause some headaches.
I am on a Mac and I suggest TextMate if you're mildly geeky and emacs/ess if you're really geeky. I use vim and command line R, but emacs/ess works best for most. If you're in this for the long haul, I doubt you'll regret learning emacs/ess for R, Sweave, and LaTeX.