See examples for an introduction. See the job website for more examples.
See details for some warnings.
Note that job::empty()
is identical to job::job()
but all arguments default to NULL
.
job( ..., import = "all", packages = .packages(), opts = options(), title = NULL ) empty(..., import = NULL, packages = NULL, opts = NULL, title = NULL)
... | A named or unnamed code block enclosed in curly brackets, |
---|---|
import | Which objects to import into the job.
|
packages | Character vector of packages to load in the job. Defaults to
all loaded packages in the calling environment. |
opts | List of options to overwrite in the job. Defaults to |
title | The job title. You can write e.g., |
Invisibly returns the job id on which you can call other rstudioapi::job*
functions, e.g., rstudioapi::rstudioapi::jobRemove(job_id)
.
This is a wrapper around rstudioapi::jobRunScript
. To control what gets
returned, see export
. By default, all objects that changed during
the job are returned, i.e., job::export("changed")
.
Returning large objects:jobRunScript
is very
slow at importing and exporting large objects. For exporting back into
globalenv()
, it may be faster to saveRDS()
results within the job and
readRDS()
them in your environment.
empty
: job::job()
but with NULL defaults, i.e., an "empty" job.
Jonas Kristoffer Lindeløv, jonas@lindeloev.dk
if (rstudioapi::isAvailable()) { # Unnamed code chunks returns to globalenv() global_var = 5 job::job({ x = rnorm(global_var) print("This text goes to the job console") m = mean(x) }) # later: print(x) print(m) # Named code chunks assign job environment to that name job::job(my_result = { y = rnorm(global_var) sigma = sd(y) }, title = "Title with code: {code}") # later: print(my_result$y) print(my_result$sigma) # Delete everything in the job environment to return nothing. # Useful if text output + file output is primary job::job({ some_cars = mtcars[mtcars$cyl > 4, ] print(mean(some_cars$mpg)) print(summary(some_cars)) # saveRDS(some_cars, "job_result.rds") job::export("none") # return nothing }) # Control imports from calling environment (variables, packages, options) my_df = data.frame(names = c("alice", "bob")) ignore_var = 15 job::job(result2 = { if (exists("ignore_var") == FALSE) print("ignore_var is not set here") names = rep(my_df$names, global_var) }, import = c(global_var, my_df), packages = NULL, opts = list(mc.cores = 3)) # later print(result2$names) }