I have a markdown script with several code chunks and providing this script together with my package.
I want to include unit testing for these code chunks or any way to make sure, my markdown script is always running.
Has anyone tried something before or can recommend a way of testing the markdown script?
I use a function called runAllChunks to run R code from an RMD file. I stole the function from knitr: run all chunks in an Rmarkdown document. That might be helpful in your situation.
I am editing a .rnw file in R Studio. This is a project summary. For the project, I used both R code and bash script.
For the R code, I am using the typical syntax to actually execute the code in the file, as such:
<<>>=
Code in between these symbols executes
#
However, for the bash scripts, I do not want them to actually execute, but I want to include them in the same .rnw file so that a reader can view them and copy/paste them etc. Is there such a syntax to make non-executable code look like code separated from the text, while not actually running it?
I suppose you are looking for eval = FALSE:
<<eval=FALSE>>=
some code here
#
However, you could always just use the "verbatim" environment, which would be more appropriate for non-R code.
\begin{verbatim}
some code here
\end{verbatim}
Perhaps a simple question, but I was wondering if there was a function that would reconvert .md files back to the .Rmd file that originally created it, as I seem to have lost my original .Rmd file and would rather not just copy and paste the code sections. The .md file was originally created using the knit to html function in RStudio.
I am not aware of a tool that does this out of the box. However, you could probably use a combination of the linux command line tools grep and sed to get the desired effect. I think this could be quite a lot of work to get working in a general sense, so copy/pasting in your case would be the fastest way.
I'm trying to write a Bash script in Ubuntu 10.04 that opens a Python file which exports a CSV, and then runs the following Rscript with the goal of exporting a HTML with plots from Dashboard.Rmd:
require(knitr)
setwd('/home/sensors/Desktop/')
knit2html('Dashboard.Rmd')
browseURL('Dashboard.html')
Dashboard.Rmd is an R markdown that calls read.csv on the csv from the first step, makes a data frame and creates plots, but that part's working fine. According to this, I figure that Rscript should replicate the action of pressing "Knit HTML" in R Studio. However, the html it creates is identical to the last time Knit HTML was pressed; i.e. even if the CSV is different, the html doesn't reflect the change.
I also tried using a separate line for knit and markdownToHTML with the same effect. It seems like it doesn't source the code from the Rmd when performing knit. It does update the html properly when I enter the commands from that Rscript into the console of R Studio with Dashboard.Rmd open. However I'm not sure how to translate that into a Bash script. I also tried knit2html with envir=new.env(), envir=R_GlobalEnv, and envir=parent.frame() with no luck. Any help would be appreciated!
So it turns out that this was an artifact of cache=TRUE -- the HTML file was not changed because everything was cached.
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.