%CPU >90%), and thus will be near the top of the list (which will be in decreasing order). Look through the resulting list of PIDs (process IDs, which are unique identifiers for each process) and info for the R process, which will likely be using a great deal of CPU (%CPU eg. the command line.įirst do the following at the command line to obtain a list of processes including R: ps aux | grep R Specifically, I’ll show you how to do this from mac Terminal, i.e.

Today, I’m doing a short post to show you how to get out of this situation by killing the process in R from outside the R environment.

R can “hang” for these and many other reasons. Or, the function you’re using might require a maximum number of iterations to be specified, or else it will use an exhaustive search. Alternatively, there might be an issue with FORTRAN coding. by default) run a function that needs to visit the total number of models possible for your dataset or a certain amount of parameter space. What could be happening is that the process is based on an maximum-likelihood estimation of a parameter that requires convergence, you could have accidentally (e.g.

From time to time, we make mistakes in programming or testing a new R script or function, only to find that R “freezes” and appears to be stuck, or working but giving the impression that it will take an eternity to complete the computation.

Executing a command in r mac os