Finding and setting the working directory within a super computer - r

Question

Obviously one can find and set working directories with getwd() and setwd(). This question is a bit more complicated. I'm running two files on a super computer (one is a regular R file which calls the other file (a .stan file). The way I submit work to the super computer is that I have to zip a folder containing the data, the .R file, and the .stan file. I upload this folder, and I pull this folder by setting it as one of the parameters in the super computer. I call the data using the standard read.csv() command and everything is hunky dory.
However, when I call the .stan file from the .R file, it can't access it because it needs to know the working directory.
This is the error I get:
> fit <- stan(file="ace_thresholds.stan", data=stanData, cores = 4)
Error in file(fname, "rt") : cannot open the connection
In addition: Warning messages:
1: In normalizePath(file) :
path[1]="ace_thresholds.stan": No such file or directory
2: In file(fname, "rt") :
cannot open file 'ace_thresholds.stan': No such file or directory
Error in get_model_strcode(file, model_code) :
cannot open model file "ace_thresholds.stan"
Calls: stan -> stan_model -> stanc -> get_model_strcode
Execution halted
When I tried setting the working directory to the unzipped NSG_stan folder (which is what I assumed the wd to be, I received this error:
fit <- stan(file="NSG_stan/ace_thresholds.stan", data=stanData, cores = 4)
Error in file(fname, "rt") : cannot open the connection
In addition: Warning messages:
1: In normalizePath(file) :
path[1]="NSG_stan/ace_thresholds.stan": No such file or directory
2: In file(fname, "rt") :
cannot open file 'NSG_stan/ace_thresholds.stan': No such file or directory
Error in get_model_strcode(file, model_code) :
cannot open model file "NSG_stan/ace_thresholds.stan"
Calls: stan -> stan_model -> stanc -> get_model_strcode
Execution halted
So I tried running print(getwd()) within the script and in the printout I see that the wd is
"/projects/ps-nsg/home/nsguser/ngbw/workspace/NGBW-JOB-RTOOL_TG-EBE9CDBF28BF42AF8CB6EC9355006B3E/NSG_stan"
which means that the working directory will shift with every job. So to accurately set the working directory, I'd need to set it to the current folder within the script. I looked for various posts on how to do this like the following
# install.packages("rstudioapi") # run this if it's your first time using it to install
library(rstudioapi) # load it
# the following line is for getting the path of your current open file
current_path <- getActiveDocumentContext()$path
# The next line set the working directory to the relevant one:
setwd(dirname(current_path ))
# you can make sure you are in the right directory
print( getwd() )
The issue with this is that it's a super computer, so it's difficult to install packages, because every time I want to install something, I have to email the folks associated with the super comp and that all takes time.
I've looked over this thread as well: R command for setting working directory to source file location in Rstudio. Appears to be a lot of dissent over what works. I tried a couple of them, and they didn't work.
setwd(getSrcDirectory()[1])
this.dir <- dirname(parent.frame(2)$ofile)
setwd(this.dir)
I've included the .R file below, in the event that it helps, but I think this is probably a pretty easy answer for someone with a decent amount of coding experience.
.R file (so it's the "ace_thresholds.stan" file that I either need to link to the current wd or to include the code that would set the wd, such that this "ace_thresholds.stan" call would work. Does that make sense?
Thanks much!
dat <- ace.threshold.t2.samp
dat <- subset(dat, !is.na(rw))
dat$condition <- factor(dat$condition)
dat$pid <- factor(dat$pid)
nTotal <- dim(dat)[1]
nCond <- length(unique(dat$condition))
nSubj <- length(unique(dat$pid))
intensity <- dat$rw
condition <- as.numeric(dat$condition)
pid <- as.numeric(dat$pid)
correct <- dat$correct_button == "correct"
chancePerformance <- 1/2
stanData <- list(nTotal=nTotal, nLevels=nCond, nSubj = nSubj, subject = pid, intensity=intensity, level=condition, correct=correct, chancePerformance=chancePerformance)
fit.rw <- stan(file="ace_thresholds.stan", data=stanData, cores = 4, control=list(max_treedepth=15, adapt_delta=0.90))