| job | R Documentation |
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., |
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.
Invisibly returns the job id on which you can call other rstudioapi::job*
functions, e.g., rstudioapi::rstudioapi::jobRemove(job_id).
empty(): job::job() but with NULL defaults, i.e., an "empty" job.
Jonas Kristoffer Lindeløv, jonas@lindeloev.dk
export, jobRunScript
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)
}
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