build_drake_graphUse drake_config() instead.
build_drake_graph(plan = read_drake_plan(), targets = plan$target,
envir = parent.frame(), verbose = drake::default_verbose(),
jobs = 1, sanitize_plan = FALSE, console_log_file = NULL,
trigger = drake::trigger(), cache = NULL)workflow plan data frame.
A workflow plan data frame is a data frame
with a target column and a command column.
(See the details in the drake_plan() help file
for descriptions of the optional columns.)
Targets are the objects and files that drake generates,
and commands are the pieces of R code that produce them.
Use the function drake_plan() to generate workflow plan
data frames easily, and see functions plan_analyses(),
plan_summaries(), evaluate_plan(),
expand_plan(), and gather_plan() for
easy ways to generate large workflow plan data frames.
character vector, names of targets to build.
Dependencies are built too. Together, the plan and
targets comprise the workflow network
(i.e. the graph argument).
Changing either will change the network.
environment to use. Defaults to the current
workspace, so you should not need to worry about this
most of the time. A deep copy of envir is made,
so you don't need to worry about your workspace being modified
by make. The deep copy inherits from the global environment.
Wherever necessary, objects and functions are imported
from envir and the global environment and
then reproducibly tracked as dependencies.
logical or numeric, control printing to the console.
Use pkgconfig to set the default value of verbose
for your R session:
for example, pkgconfig::set_config("drake::verbose" = 2).
0 or FALSE: print nothing.
1 or TRUE: print only targets to build.
2: also print checks and cache info.
3: also print any potentially missing items.
4: also print imports and writes to the cache.
maximum number of parallel workers for processing the targets.
If you wish to parallelize the imports and preprocessing as well, you can
use a named numeric vector of length 2, e.g.
make(jobs = c(imports = 4, targets = 8)).
make(jobs = 4) is equivalent to make(jobs = c(imports = 1, targets = 4)).
Windows users should not set jobs > 1 if
parallelism is "mclapply" because
mclapply() is based on forking. Windows users
who use parallelism = "Makefile" will need to
download and install Rtools.
You can experiment with predict_runtime()
to help decide on an appropriate number of jobs.
For details, visit
https://ropenscilabs.github.io/drake-manual/time.html.
deprecated
character scalar,
connection object (such as stdout()) or NULL.
If NULL, console output will be printed
to the R console using message().
If a character scalar, console_log_file
should be the name of a flat file, and
console output will be appended to that file.
If a connection object (e.g. stdout())
warnings and messages will be sent to the connection.
For example, if console_log_file is stdout(),
warnings and messages are printed to the console in real time
(in addition to the usual in-bulk printing
after each target finishes).
Name of the trigger to apply to all targets.
Ignored if plan has a trigger column.
See trigger() for details.
drake cache as created by new_cache().
See also get_cache() and this_cache().
An igraph object.
Deprecated on 2018-11-02.
# NOT RUN {
# See ?drake_config for examples.
# }
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