I am no R integration expert by any means, but I believe you can have R dump results into a CSV. I would do this and then use that as the standard in which to validate what is imported into Tableau Desktop data.
A web search led me to this page which explains how they validated in Tableau: http://tagteam.harvard.edu/hub_feeds/1981/feed_items/740889
Is this what you were looking for? Or was it preferred to have something a little more systematic rather than a one-time test?
Thanks for the reply, the blog post was a pretty good read, though it doesn't really help me in the current situation. I don't have a problem with validating the code directly in R. I can run this calculation in R and excel, but the numbers would be hard coded, so the numbers would not change when having Tableau filter out odd observations. I could do my best and clean up the data outside of Tableau, but I know that next month, I will get a new data set and I will need to clean it up again. I have created calculated fields that help remove a lot of these observation and I would hope the filter will work on new data as well. With that in respect, I would then need Tableau's R connection to calculation the census based on "clean" data.
Back to the R code, when I run it in R, it will output a vector of the same length as the data that is inputted (a condition that must be met, stated in the R-blogger post you linked above). When I run this code via Tableau's R connection, I am definitely getting something wrong. There are medical services that some patients will be transferred to and apparently they all have a census of 6 patients. When I try to view the underlying data, the R calculation is not present. I suspect that it is not calculating the census for each patient.
I also suspect that I'm not aggregating the data properly, but I'm uncertain on how to do it. Currently, I'm using ATTR() on the .arg's because I want to use every single clean observation. If you have any other searches, I would appreciate it. My own google searches only got me so far.