Dear useRs: Please provide me with your thoughts on an issue related to the design of a production level system. For example, letÂ’s suppose that I need to run the same R script for a finite sequence of items (e.g., in the energy industry, I may need to asses the profitability of all gas stations in the state of Florida). For each of the items, the R script accesses some remote databases, gets all the necessary information and processes the data locally. If the data for every item in the item list were not corrupted then the R script would easily do its job. However, every now and then, the data for some items is partially missing and the R script returns in such cases an error message (e.g., for a given gas station, 1 month worth of data is missing from a 3 month decision horizon). As of right now, using the error option of options (), whenever an exception happens, the R script sends me an email error message and kills the current R instance. As I already figured it out, this is not necessarily the most efficient way to deal with such exceptions. Ideally, I would like the script to inform me of the data problem but continue the analysis with the remaining of the items. Based on my searches, I believe that try / stop can answer my problem. However, if any of you already implemented such a safe fail system I would really appreciate your taking the time to (1) share with me what you think constitutes the best practices and/or (2) point out to me any online material relevant to the topic. I run various versions of R on Windows and multiple UNIX platforms. The list of items is read from a .csv file and stored in a dataframe.
Thank you. Tudor -- Tudor Dan Bodea Georgia Institute of Technology School of Civil and Environmental Engineering Web: http://www.prism.gatech.edu/~gtg757i ______________________________________________ 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.
