Description Usage Arguments Details Value Functions Author(s) See Also Examples
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
.
1 2 3 4 5 6 7 8 9 |
... |
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | 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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