Hi thank you for your suggestions, but I am not sure if I explained my problem well enough. Lets asume, that I have 30 different script files and 1 script which calls these 30 scripts one after the other by "source". Some of the 30 scripts only contain definitions of functions which are called in other scripts, some only execute code, load, save, and interact with the user via tcl tk. Together they represent a big programm which asks from the user a lot of input, treats and manipulates the input. At the end, the user obtains some results files. As there are many script sources during the execution, there are a lot of different variables initialized etc... Some are only of temporary need, some are necessary for later steps in other scripts. Now the question: Is there any methodology to ensure the running ? For example after every script I could save the whole workingspace into script1.Rdata, delete all variable which are not needed in a later script (perhabs a little bit difficult to manage that, but possible), and continue with the execution.
I don't know if I was able to describe my problem more precisely. Alexander cberry wrote > > Alexander, > > If Tal's suggestion to use caching in Sweave doesn't appeal to you, you > might look at 'R.cache' and other packages mentioned in > > http://cran.r-project.org/web/views/ReproducibleResearch.html > > under 'Caching of R Objects'. > > However, an advantage of the Sweave-like approaches is that you can > generate a brief report that includes the versions of scripts used, > summarizes the data processing, and gives intermediate results for later > inspection and sanity checks. > > HTH, > > Chuck > > Tal Galili <tal.galili@> writes: > >> Hi Alexander, >> Saving full environments is possible, but it is very easy to start >> loosing >> track on where each variable came from. >> You might want to use this process: >> http://www.r-bloggers.com/a-better-way-of-saving-and-loading-objects-in-r/ >> It depends on how many variables you work with, but it might help. >> >> Another way is to do all of the work through Sweave, and combine it with >> caching: http://cran.r-project.org/web/packages/cacheSweave/index.html >> This will ensure that every code chunk will keep the variables you >> created, >> without the need to re-run the code from scratch. >> >> For extracting data from outside sources, I would often use the first >> method, and for analysis I would use the later option. >> >> Good luck, >> Tal >> >> >> ----------------Contact >> Details:------------------------------------------------------- >> Contact me: Tal.Galili@ | 972-52-7275845 >> Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) | >> www.r-statistics.com (English) >> ---------------------------------------------------------------------------------------------- >> >> >> >> >> On Thu, Apr 19, 2012 at 11:15 AM, Alexander >> <juschitz_alexander@>wrote: >> >>> Hello, I am working under R2.11 Windows and currently I work on a big R >>> progjet which executes different R script in a row. Every R script >>> represents a module. As every module depends of the variables created in >>> the >>> modules previously executed, I want to be shure, that I don't create or >>> change a variable in a scriptwithout being aware that this affects the >>> results in a later executed script. Therefore, I was think to save all >>> important variables to keep in a seperate "backup" environment. >>> Everytime a >>> script starts, it loads the variables of the "backup" environment in >>> .GlobalEnv. At the end of the script, I want to add all new obtained >>> variables to the "backup" environment (and check automaticaly if any >>> variables of "backup"environment is going to be overwritten) and clean >>> the >>> workspace .GlobalEnv to start the next script neat and tidy. What do you >>> think of this solution? Does anyone have better ideas or experience to >>> share? >>> >>> Thank you in advance >>> >>> Alexander >>> >>> -- >>> View this message in context: >>> http://r.789695.n4.nabble.com/working-with-environments-to-ensure-code-quality-for-long-R-scripts-tp4570195p4570195.html >>> Sent from the R help mailing list archive at Nabble.com. >>> >>> ______________________________________________ >>> R-help@ mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-help >>> PLEASE do read the posting guide >>> http://www.R-project.org/posting-guide.html >>> and provide commented, minimal, self-contained, reproducible code. >>> >> >> [[alternative HTML version deleted]] >> > > -- > Charles C. Berry Dept of Family/Preventive > Medicine > cberry at ucsd edu UC San Diego > http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901 > > ______________________________________________ > R-help@ mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- View this message in context: http://r.789695.n4.nabble.com/working-with-environments-to-ensure-code-quality-for-long-R-scripts-tp4570195p4571989.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
