![]() You will be warned if you attempt to close the window while jobs are still running (on RStudio Desktop).Local jobs run as non-interactive child R processes of your main R process, which means that they will be shut down if R is. Results object: This places all the R objects your job creates into a new environment named yourscript_results. Use this option with caution! The objects created by the job will overwrite, without a warning, any objects that have the same name in your environment. Global environment: This places all the R objects your job creates back in your R session’s global environment. If you’d like to import data from your job back into your R session, you have a couple of choices: Note that this can be slow if you have large objects in your environment.Ĭopy job results: By default, the temporary workspace in which the job runs is not saved. ![]() This is useful because it will allow your job to see all the same variables you can see in the IDE. Run job with copy of global environment: If ticked, this option saves your global environment and loads it into the job’s R session before it runs. However, if you want to feed data from your current R session into the job, or have the job return data to your current R session, change the dialog options as follows: ![]() This is the fastest and safest configuration, good for reproducible scripts that have no side effects. This will give you some options for running your job.īy default, the job will run in a clean R session, and its temporary workspace will be discarded when the job is complete. You can run any R script in a separate session by pulling down the Source menu and choosing Source as Local Job. You can use these to run your scripts in the background while you continue to use the IDE.Ī “local job” is an R script that runs in a separate, dedicated R session. In RStudio 1.2, we’re introducing two new features to keep you productive while your code’s working: local jobs and remote jobs. When your R scripts take a long time to run, it can be difficult to get much done in RStudio while they do, unless you’re willing to juggle multiple instances of RStudio. When you run an R script in RStudio today, the R console waits for it to complete, and you can’t do much with RStudio until the script is finished running. ![]()
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