| Title: | Process Command Line Arguments |
|---|---|
| Description: | Process command line arguments. Processing includes checking, naming, and coercing to numeric, date, or logical. By capturing assumptions about inputs and outputs, the 'command' package can provide scripts with well defined interfaces, leading to safer, more modular data analysis pipelines. |
| Authors: | John Bryant [aut, cre], Bayesian Demography Limited [cph] |
| Maintainer: | John Bryant <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.3.0 |
| Built: | 2026-10-17 09:34:42 UTC |
| Source: | https://github.com/bayesiandemography/command |
Process command line arguments, allowing scripts to behave like functions, with well-defined inputs and outputs. Helps make data analysis workflows more modular, and therefore more transparent, flexible, and reliable.
cmd_assign() Process command line arguments
cmd_assign_collect() Collect unnamed command line arguments
into one character vector
use_renv() Activate a renv project before loading packages
extract_shell() Turn a cmd_assign() call into a shell command
extract_make() Turn a cmd_assign() call into a Makefile rule
shell_script() Create a shell script
makefile() Create a Makefile
Quick Start
How to use cmd_assign()
Modular Workflows for Data Analysis Safe, flexible data analysis workflows
Using command with renv Running pipeline scripts with renv
Maintainer: John Bryant [email protected]
Authors:
John Bryant [email protected]
Other contributors:
Bayesian Demography Limited [copyright holder]
Useful links:
Report bugs at https://github.com/bayesiandemography/command/issues
Assign names to values in the global environment. The values are typically supplied through the command line, but can be supplied interactively.
cmd_assign_quiet() is identical to
cmd_assign() except that it does not print
progress messages to the console.
cmd_assign(...) cmd_assign_quiet(...)cmd_assign(...) cmd_assign_quiet(...)
... |
Name-value pairs. |
cmd_assign() is a user-friendly alternative to commandArgs(),
designed specifically for data analysis workflows.
cmd_assign() can be used to record
and enforce assumptions about inputs and outputs.
This provides scripts with better-defined
interfaces, making them clearer and safer.
cmd_assign() is called
for its side effect, which is to create objects
in the global environment. However, cmd_assign()
also invisibly returns a named list of objects.
cmd_assign() behaves differently depending
on whether it is called interactively, or
from within an R script being run from the command line.
For instance, if the code
cmd_assign(use_log = TRUE)
is run interactively, it creates an object
called use_log with value TRUE.
But if the same code is run from within a script that has been invoked using the command
Rscript tidy_data.R --use_log=FALSE
it creates an object called use_log
with value FALSE.
cmd_assign() is typically called interactively
when a workflow is being developed,
and through the command line when the
workflow is in production.
When used in a script called from the
command line, cmd_assign()
first matches named command line arguments,
and then matches unnamed command line arguments,
in the order in which they are supplied.
If, for instance, the script person.R contains
the lines
cmd_assign( .data = "raw_data.csv", max_age = 85, .out = "person.rds" )
and if person.R is run from the command line using
Rscript person.R \ raw_data.csv \ person.rds \ --max_age=100
then cmd_assign() assigns cmd_assign() names to
command line arguments in the following order:
| order | cmd_assign() name |
command line argument |
| 1 | max_age |
--max_age=100 |
| 2 | .data |
raw_data.csv |
| 3 | .out |
person.rds
|
The values specified in the call to cmd_assign() are
not defaults in the sense of values to fall back on
when no value is supplied. For instance, if we take a script
containing the call
cmd_assign( n_draw = 10, .out = "simulation.rds" )
and run it from the command line without passing a value
for n_draw, then cmd_assign() will throw an error.
It will not set n_draw to 10.
Defaults can hide bugs and make code less predictable.
Values passed at the command line start out as
text strings. cmd_assign() coerces these strings
to have the same class as the corresponding values
in the call to cmd_assign(). For instance,
if script fit.R contains the lines
cmd_assign(
.data = "data/individuals.rds",
impute = TRUE,
date = as.Date("2025-01-01"),
.out = "output/fitted.rds"
)
and if fit.R is run from the command line using
Rscript src/fit.R \ data/individuals.rds \ output/fitted.rds \ --impute=TRUE \ --date=2025-01-01
then cmd_assign() will create the following length-1 vectors
| name | class | value |
.data |
"character" |
"data/individuals.rds" |
impute |
"logical" |
TRUE |
date |
"Date" |
"2025-01-01" |
.out |
"character" |
"output/fitted.rds"
|
When naming arguments to cmd_assign(), it is typically
good practice to use names starting with dots for file paths,
and names without dots for ordinary objects, as in
cmd_assign( .households = "data/households.csv", n_record = 500, sex = "Female", .out = "out/subsample_female.rds" )
Function cmd_assign() does not in fact distinguish between
names with dots and names without dots. But the dot-name convention
is still useful because of the way it calls attention
to the distinction between file paths and
ordinary objects. For instance, a dot-name is a
reminder that the object being named is a reference to
an input or output, rather than the input or output itself.
In contrast, functions extract_make(),
extract_shell(), makefile(), and shell_script()
do treat names with dots differently from
names without dots. See the help for
extract_make(), extract_shell(),
makefile(), and shell_script()
for details.
Command-Line Programs Introduction to Rscript
cmd_assign_collect() Collect unnamed command line arguments
into one character vector
extract_shell() Turn a cmd_assign() call into a shell command
extract_make() Turn a cmd_assign() call into a Makefile rule
shell_script() Create a shell script
makefile() Create a Makefile
use_renv() Activate a renv project before loading packages
Quick Start
How to use cmd_assign()
Modular Workflows for Data Analysis Safe, flexible data analysis workflows.
Using command with renv Running pipeline scripts with renv
Base R function commandArgs() uses a more general,
lower-level approach to processing command line arguments.
(commandArgs() is called internally by cmd_assign().)
littler Alternative to Rscript
if (interactive()) { cmd_assign(.data = "mydata.csv", n_iter = 2000, .out = "results.rds") }if (interactive()) { cmd_assign(.data = "mydata.csv", n_iter = 2000, .out = "results.rds") }
Assign names to values in the global environment,
like cmd_assign(), but without specifying the number
of values in advance. Values for unnamed command
line arguments are collected into a single character vector.
cmd_assign_collect_quiet() is identical to
cmd_assign_collect() except that it does not print
progress messages to the console.
cmd_assign_collect(...) cmd_assign_collect_quiet(...)cmd_assign_collect(...) cmd_assign_collect_quiet(...)
... |
Name-value pairs. |
cmd_assign_collect() is called
for its side effect, which is to create objects
in the global environment. However, cmd_assign_collect()
also invisibly returns a named list of objects.
cmd_assign_collect(), like cmd_assign(),
starts by processing named command line arguments
(i.e. arguments of the form --n_draw=100).
Unlike cmd_assign(), however, cmd_assign_collect()
puts all the values that are left over into a single
character vector. This vector can be any length,
including zero. cmd_assign_collect() must
contain a single name-value pair that is not
used for named command line arguments.
cmd_assign_collect() assigns the name from
this name-value to the character vector.
If, for instance, the script combine.R contains
cmd_assign_collect(
.files = c("data/a.csv", "data/b.csv", "out/combined.rds"),
use_log = TRUE
)
and if combine.R is run from the command line using
Rscript combine.R \ data/a.csv \ data/c.csv \ out/combined.rds \ --use_log=TRUE
then use_log will be assigned the value TRUE
and .files will be assigned the value
c("data/a.csv", "data/c.csv", "out/combined.rds").
The R script in which cmd_assign_collect() is
called has responsibility for processing the
character vector of values from unnamed command
line arguments. The call to cmd_assign_collect() above,
for instance, might be followed by statements such as
.inputs <- head(.files, -1) .out <- tail(.files, 1) inputs <- lapply(.inputs, read.csv)
cmd_assign_collect() is more flexible than cmd_assign(),
in that it does not require the number, name, or type of values
to be specified in advance. But if these things
are in fact known in advance, then it is
good practice to document and test for them,
which is what cmd_assign() does.
cmd_assign() Process command line arguments
with one value per argument
extract_shell() Turn a cmd_assign() or
cmd_assign_collect() call into a shell command
extract_make() Turn a cmd_assign() or
cmd_assign_collect() call into a Makefile rule
shell_script() Create a shell script
makefile() Create a Makefile
Quick Start
How to use cmd_assign()
if (interactive()) { cmd_assign_collect( files = c("data/x.csv", "data/y.csv"), use_log = TRUE ) }if (interactive()) { cmd_assign_collect( files = c("data/x.csv", "data/y.csv"), use_log = TRUE ) }
Extract a call to cmd_assign() or cmd_assign_collect() from an
R script, and turn it into a Makefile rule.
extract_make(path_file, dir_make = NULL)extract_make(path_file, dir_make = NULL)
path_file |
A path from
|
dir_make |
The directory that contains the Makefile. The default is the current working directory. |
The rule may need editing before it is added to a Makefile.
extract_make() is typically called
for its side effect, which is to print a
Makefile rule. However, extract_make()
invisibly returns a text string with the rule.
A Makefile rule produced by extract_make()
normally looks something like this:
out/model.rds: src/model.R \
data/cleaned.rds
Rscript $^ $@ --use_log=TRUE
In this rule
out/model.rds is the "target", i.e. the file that the rule creates;
src/model.R and data/timeseries.rds are "prerequisites",
i.e. files that are used to create the target;
\ is a "line continuation character";
at the start of the third line is a tab,
telling make that the recipe for creating the target from
the starts here;
Rscript is a call to utils::Rscript();
$^ is an automatic variable
meaning "all the prerequisites"
and $@ is an automatic variable meaning "the target",
so that Rscript $^ $@ expands to
Rscript src/model.R data/cleaned.rds out/model.rds; and
--use_log=TRUE is a named argument that
Rscript passes to src/model.R
extract_make() to build a data analysis workflow Step 1. Write the R file that carries out
the step in analysis (eg tidying data, fitting
a model, making a graph.) This file
will contain a call to cmd_assign() or cmd_assign_collect(),
and will appear in the first line of the
Makefile rule. When writing and testing the file,
use cmd_assign() or cmd_assign_collect() interactively.
Step 2. Once the R file is working correctly,
call extract_make(), and add the rule
to your Makefile.
When using extract_make(), it is a good idea to
set the current working directory to the project
directory.
The Makefile normally sets at the top of the project, so that the project folder looks something like this:
Makefile - data/ - src/ - out/ report.qmd
To identify arguments that refer to file names,
extract_make() uses the following rules:
if the call includes arguments whose names start with a dot, then these arguments are assumed to refer to file names;
otherwise, find arguments whose values
actually are file names
(as determined by file.exists()), or that look
like they could be.
Project Management with Make Makefiles in data analysis workflows
GNU make Definitive guide
Command-Line Programs Introduction to Rscript
extract_shell() Shell script equivalent of extract_make()
makefile() Create a Makefile
from calls to cmd_assign()
cmd_assign() Process command line arguments
cmd_assign_collect() Process command line arguments when
the number of arguments is not known in advance
Quick Start
How to use cmd_assign() and cmd_assign_collect()
Modular Workflows for Data Analysis Safe, flexible data analysis workflows
littler Alternative to Rscript
library(withr) with_tempdir({ ## create 'src' directory dir.create("src") ## put an R script containing a call to ## 'cmd_assign' in the 'src' directory writeLines(c("cmd_assign(x = 1, .out = 'out/results.rds')", "results <- x + 1", "saveRDS(results, file = .out)"), con = "src/results.R") ## call 'extract_make()' extract_make(path_file = "src/results.R", dir_make = ".") })library(withr) with_tempdir({ ## create 'src' directory dir.create("src") ## put an R script containing a call to ## 'cmd_assign' in the 'src' directory writeLines(c("cmd_assign(x = 1, .out = 'out/results.rds')", "results <- x + 1", "saveRDS(results, file = .out)"), con = "src/results.R") ## call 'extract_make()' extract_make(path_file = "src/results.R", dir_make = ".") })
Extract a call to cmd_assign() or cmd_assign_collect() from an
R script, and turn it into a shell command.
extract_shell(path_file, dir_shell = NULL)extract_shell(path_file, dir_shell = NULL)
path_file |
Path to the R script containing
the call to |
dir_shell |
The directory that contains the shell script. The default is the current working directory. |
extract_shell() is typically called
for its side effect, which is to print a
shell command. However, extract_shell()
invisibly returns a text string with the command.
The shell command produced by extract_shell()
normally looks something like this:
Rscript src/model.R \ data/cleaned.rds \ out/model.rds \ --use_log=TRUE
In this command
Rscript is a call to utils::Rscript();
\ is a "line continuation character";
data/cleaned.rds and out/model.rds are unnamed
arguments that Rscript passes to src/model.R; and
--use_log=TRUE is a named argument that
Rscript passes to src/model.R
extract_shell() to build a data analysis workflow Step 1. Write an R script that carries out
a step in analysis (eg tidying data, fitting
a model, making a graph.) This script
will contain a call to cmd_assign() or cmd_assign_collect(),
and will be the first argument passed to
Rscript in the shell command.
When writing and testing the script,
use cmd_assign() or cmd_assign_collect() interactively.
Step 2. Once the R script is working correctly,
call extract_shell(), and add the command
to your shell script.
The shell script normally sits at the top level of the project, so that the project folder looks something like this:
workflow.sh - data/ - src/ - out/ report.qmd
To identify arguments that refer to file names,
extract_shell() uses the following rules:
if the call includes arguments whose names start with a dot, then these arguments are assumed to refer to file names;
otherwise, find arguments whose values
actually are file names
(as determined by file.exists()), or that look
like they could be.
Episodes 1–3 of The Unix Shell Introduction to the command line
Command-Line Programs Introduction to Rscript
littler Alternative to Rscript
extract_make() Makefile equivalent of extract_shell()
shell_script() Create a shell script
from calls to cmd_assign()
cmd_assign() Process command line arguments
cmd_assign_collect() Process command line arguments when
the number of arguments is not known in advance
Quick Start
How to use cmd_assign() and cmd_assign_collect()
Modular Workflows for Data Analysis Safe, flexible data analysis workflows
library(withr) with_tempdir({ ## create 'src' directory dir.create("src") ## add an R script containing a call to 'cmd_assign' writeLines(c("cmd_assign(x = 1, .out = 'out/results.rds')", "results <- x + 1", "saveRDS(results, file = .out)"), con = "src/results.R") ## call 'extract_shell()' extract_shell(path_file = "src/results.R", dir_shell = ".") })library(withr) with_tempdir({ ## create 'src' directory dir.create("src") ## add an R script containing a call to 'cmd_assign' writeLines(c("cmd_assign(x = 1, .out = 'out/results.rds')", "results <- x + 1", "saveRDS(results, file = .out)"), con = "src/results.R") ## call 'extract_shell()' extract_shell(path_file = "src/results.R", dir_shell = ".") })
Create a Makefile for a data analysis workflow. The Makefile can include rules extracted from existing R files.
makefile( path_files = NULL, dir_make = NULL, name_make = "Makefile", overwrite = FALSE, quiet = FALSE )makefile( path_files = NULL, dir_make = NULL, name_make = "Makefile", overwrite = FALSE, quiet = FALSE )
path_files |
A path from |
dir_make |
The directory where
|
name_make |
The name of the Makefile.
The default is |
overwrite |
Whether to overwrite
an existing Makefile. Default is |
quiet |
Whether to suppress
progress messages. Default is |
The Makefile is a draft and needs editing before use.
makefile() cannot infer every rule, and a file path
that contains a space or a character special to Make
must be corrected in the dependency line.
To create a Makefile in the files
directory, set files to ".".
To obtain the contents of the Makefile
without creating a file on disk,
creating the file on disk, set
name_make to NULL.
Supplying a value for files is
optional for makefile(),
but compulsory for shell_script().
The output from makefile()
includes some general-purpose Makefile
commands, while the output from
shell_script() is generated entirely
from files.
makefile() is called for its
side effect, which is to create a
file. However, makefile() also
returns a string with the contents of the
Makefile.
Project Management with Make Makefiles in data analysis workflows
GNU make Definitive guide
Command-Line Programs Introduction to Rscript
Creating a Makefile
More on makefile()
extract_make() Turn a cmd_assign() call into a Makefile rule
shell_script() Shell script equivalent of makefile()
cmd_assign() Process command line arguments
Modular Workflows for Data Analysis Safe, flexible data analysis workflows
littler Alternative to Rscript
library(withr) with_tempdir({ ## create 'src' directory dir.create("src") ## put R scripts containing calls to ## 'cmd_assign' in the 'src' directory writeLines(c("cmd_assign(x = 1, .out = 'out/results.rds')", "results <- x + 1", "saveRDS(results, file = .out)"), con = "src/results.R") writeLines(c("cmd_assign(x = 1, .out = 'out/more_results.rds')", "more_results <- x + 2", "saveRDS(more_results, file = .out)"), con = "src/more_results.R") ## call 'makefile()' makefile(path_files = "src", dir_make = ".") ## Makefile has been created list.files() ## print contents of Makefile cat(readLines("Makefile"), sep = "\n") })library(withr) with_tempdir({ ## create 'src' directory dir.create("src") ## put R scripts containing calls to ## 'cmd_assign' in the 'src' directory writeLines(c("cmd_assign(x = 1, .out = 'out/results.rds')", "results <- x + 1", "saveRDS(results, file = .out)"), con = "src/results.R") writeLines(c("cmd_assign(x = 1, .out = 'out/more_results.rds')", "more_results <- x + 2", "saveRDS(more_results, file = .out)"), con = "src/more_results.R") ## call 'makefile()' makefile(path_files = "src", dir_make = ".") ## Makefile has been created list.files() ## print contents of Makefile cat(readLines("Makefile"), sep = "\n") })
Create a shell script for a data analysis workflow consisting of commands extracted from existing R files.
shell_script( path_files, dir_shell = NULL, name_shell = "workflow.sh", overwrite = FALSE, quiet = FALSE )shell_script( path_files, dir_shell = NULL, name_shell = "workflow.sh", overwrite = FALSE, quiet = FALSE )
path_files |
A path from |
dir_shell |
The directory where
|
name_shell |
The name of the shell script.
The default is |
overwrite |
Whether to overwrite
an existing shell script. Default is |
quiet |
Whether to suppress
progress messages. Default is |
shell_script() tries to put the commands in the order
they need to run, by matching each script's output path
with the input paths of the other scripts.
It can get this order wrong, so the shell script may
still need editing.
To create a shell script in the files
directory, set files to ".".
To obtain the contents of the shell script
without creating a file on disk,
creating the file on disk, set
name_shell to NULL.
Supplying a value for files is
compulsory for shell_script(),
but optional for makefile().
The output from shell_script()
is generated entirely from files
while the output from makefile()
also includes some general-purpose Makefile
commands.
shell_script() is called for its
side effect, which is to create a
file. However, shell_script() also
returns a string with the contents of the
shell script.
Episodes 1–3 of The Unix Shell Introduction to the command line
Command-Line Programs Introduction to Rscript
Creating a Shell Script More on shell_script()
extract_shell() Turn a cmd_assign() call into a shell command
makefile() Makefile equivalent of shell_script()
cmd_assign() Process command line arguments
Modular Workflows for Data Analysis Safe, flexible data analysis workflows
littler Alternative to Rscript
library(withr) with_tempdir({ ## create 'src' directory dir.create("src") ## put R scripts containing calls to ## 'cmd_assign' in the 'src' directory writeLines(c("cmd_assign(x = 1, .out = 'out/results.rds')", "results <- x + 1", "saveRDS(results, file = .out)"), con = "src/results.R") writeLines(c("cmd_assign(.results = 'out/results.rds', .out = 'out/more_results.rds')", "results <- readRDS(.results)", "more_results <- results + 2", "saveRDS(more_results, file = .out)"), con = "src/more_results.R") ## the shell script runs 'results.R' first, because ## 'more_results.R' reads the file that 'results.R' writes ## call 'shell_script()' shell_script(path_files = "src", dir_shell = ".") ## shell script has been created list.files() ## print contents of shell script cat(readLines("workflow.sh"), sep = "\n") })library(withr) with_tempdir({ ## create 'src' directory dir.create("src") ## put R scripts containing calls to ## 'cmd_assign' in the 'src' directory writeLines(c("cmd_assign(x = 1, .out = 'out/results.rds')", "results <- x + 1", "saveRDS(results, file = .out)"), con = "src/results.R") writeLines(c("cmd_assign(.results = 'out/results.rds', .out = 'out/more_results.rds')", "results <- readRDS(.results)", "more_results <- results + 2", "saveRDS(more_results, file = .out)"), con = "src/more_results.R") ## the shell script runs 'results.R' first, because ## 'more_results.R' reads the file that 'results.R' writes ## call 'shell_script()' shell_script(path_files = "src", dir_shell = ".") ## shell script has been created list.files() ## print contents of shell script cat(readLines("workflow.sh"), sep = "\n") })
Make sure renv is activated, even if a script is run from
Rscript or littler.
use_renv(project = NULL, quiet = TRUE)use_renv(project = NULL, quiet = TRUE)
project |
Optional path to a project root. If |
quiet |
If |
Package renv
makes workflows reproducible by locking down the versions of
R packages. Normal renv mechanisms can fail, however,
if a script is run from Rscript or littler.
Adding
command::use_renv()
to the top of R scripts fixes the problem.
For more details, see the article Using command with renv.
The project root path (invisibly) if renv was activated
or was already active; otherwise NULL.
cmd_assign() Process command line arguments
cmd_assign_collect() Process command line arguments when
the number of arguments is not known in advance
command::use_renv() ## if renv not used here, no effect ## see article for full examplecommand::use_renv() ## if renv not used here, no effect ## see article for full example