One of the most important aspects of the R ecosystem is the ease with which
extensions and new features can be developed and distributed in the form of
packages. The main distribution channel is the Comprehensive R
Archive Network, from which packages can be installed directly from
R. Another popular option is using GitHub repositories from which packages can
also be painlessly installed, e.g. using install_github
from the
devtools
package.
The import
package provides an alternative approach to using external
functionality in R scripts; first however, it is useful to describe the standard
approach to clarify how import
may serve as improvement.
The most common way to include the functionality provided by a package is to use
the library
function:
library(PackageA) library(PackageB) value1 <- function_a(...) # Supposedly this comes from PackageA, value2 <- function_b(...) # and this from PackageB, but who knows?! ...
In some situations this is fine; however there are some subtle shortcomings:
The problem with (1) is that the search path is populated with more
objects than are needed and it is not immediately clear whether name clashes
will occur. Problem (2) refers to the case where packages export
different objects with the same names, say if function_b
is exported in both
PackageA
and PackageB
above. In this case the name will
point to the object from the package attached last. The earlier exposed
objects are said to masked. Even if this is not a problem
when writing the script, an update of packages may cause this problem
later on when executing the script; and tracking down the resulting
errors may be tough and time consuming. Problem (3) may appear unimportant,
but it is not to be underestimated. Code snippets are very commonly shared
and spending time figuring out where functionality comes from is not
a very satisfying nor value-adding activity.
It is possible to unambiguously specify where a function comes from by
prefixing it with ::
every time it used, but this is often
overly verbose and does not provide an easily accessible overview of what
external functionality is used in a script. One may also import single
exported objects, one at a time, using the (double) "colon syntax",
function_a <- PackageA::function_a function_b <- PackageB::function_b
The downside of this approach is that the object is placed
in the user's global work space, rather than being encapsulated somewhere else
in the search path (when using library
to load pkg
, a namespace package:pkg
will be attached in the search path which will contain the exported functions
from pkg
). Another minor point is that one can only import one object at a
time using this approach.
While packages form the backbone of code distribution, another option comes
in the form of scripts, but these are usually task specific and not
commonly used to "bundle" functionality for use in other scripts. In
particular, when source
is used to include contents from one script in
another, once again all objects produced by the script will be "exposed"
and may "over populate" the working environment, masking other objects,
if not only producing some mental clutter. Scope management is therefore not too
comfortable when splitting functionality across files in a modular way.
The import
package sets out to improve the way external functionality
is included in your code by alleviating some of the concerns raised above by
providing an expressive way of importing object from both packages and
scripts. The latter provides a bridge between the package approach to
distribution and simple stand-alone script files. This allows for the use of scripts
as modules, a collection of related object definitions, each of which
may be used at different places without exposing more than necessary.
The package is inspired in part by Python's
from some_module import some_function
syntax, and solves
the two issues raised above. It is also similar to roxygen2
s
@importFrom package function1 function2
for packages. While import
will
also work for package development, the intended use case is when using
external functions R
scripts.
In addition to being able to import objects from packages, import
also allows
you to import objects from other scripts (i.e. a kind of module). This allows
a simple way to distribute and use functionality without the need to write
a full package. One example is a Shiny app, where one can place definitions
in a script and import only the needed objects where they are used. This
avoids workspace clutter and name clashes. For more details see below.
The most basic use case is to import a few functions from package
(here the psych
package):
import::from(psych, geometric.mean, harmonic.mean) geometric.mean(trees$Volume)
The imported objects are placed in a separate entity in the search path which by
default is named "imports". It is therefore also easy to get rid of them again
with detach("imports")
. The main point is that it is clear which functions
will be used and where they come from. It's noteworthy that there is nothing
special going on: the import::from
function is only a convenient wrapper
around getExportedValue
(as is ::
itself) and assign
.
The import
package itself should not to be attached
(don't include it via library
, you will get a warning). Rather, it is designed
to be expressive when using the colon syntax. To import non-exported objects
one must use triple-colon syntax: import:::from(pkg, obj)
.
If any of the import
functions are called regularly, i.e. without preceding
import::
or import:::
, an error is raised. If import
is attached, a
startup message will inform that import
should not be attached.
If one of the function names conflicts with an existing function (such as filter
from the dplyr
package) it is simple to rename it:
import::from(dplyr, select, arrange, keep_when = filter) keep_when(mtcars, hp>250)
This does pretty much what it says: three functions are imported from dplyr
,
two of which will keep their original name, and one which is renamed, e.g. to
avoid name clash with stats::filter
.
You can use .all=TRUE
to import all functions from a package, but rename one of them:
import::from(dplyr, keep_when = filter, .all=TRUE)
To omit a function from the import, use .except
(which takes a character vector):
import::from(dplyr, .except=c("filter", "lag"))
Note that import
tries to be smart about this and assumes that if you are using the
.except
parameter, you probably want to import everything you are not explicitly omitting,
and sets the .all
parameter to TRUE
. You can override this in exceptional cases, but you
seldom need to.
Finally, a more complex example, combining a few different import statements:
import::from(magrittr, "%>%") import::from(dplyr, starwars, select, mutate, keep_when = filter) import::from(tidyr, unnest) import::from(broom, tidy) ready_data <- starwars %>% keep_when(mass < 100) %>% select(name, height, mass, films) %>% unnest(films) %>% mutate( log_mass = log(mass), films=factor(films)) linear_model <- lm(log_mass ~ height + films, data = ready_data) %>% tidy
In the above, it is clear which package provides which functions
(one could e.g. otherwise be tempted to think that tidy
belonged to
tidyr
). Note that ordering is irrelevant, even if tidyr
at some point
exposes a function tidy
after an update, as import
is explicit about
importing.
It also shows that one can import multiple objects in a single
statement, and even rename objects if desired; for example, in the above
one can imagine that filter
from stats
is needed later on, and so
dplyr
's filter
is renamed to avoid confusion. Sometimes, it is
not at all clear what purpose a package has; e.g. the name magrittr
does
not immediately reveal that it's main purpose is to provide the pipe
operator, %>%
.
The import
package allows for importing objects defined in script files,
which we will here refer to as "modules".
The module will be fully evaluated by import
when an import is requested,
after which objects such as functions or data can be imported.
Such modules should be side-effect free, but this is
not enforced.
Attachments are detached (e.g. packages attached by library
)
but loaded namespaces remain loaded. This means that values created
by functions in an attached namespace will work with import
, but
functions to be exported should not rely on such functions (use function
importing in the modules instead).
For example, the file sequence_module.R contains several functions calculating terms of mathematical sequences. It is possible to import from such files, just as one imports from packages:
import::from(sequence_module.R, fibonacci, square, triangular)
Renaming, the .all
, and the .except
parameters work in the same way as for packages:
import::from(sequence_module.R, fib=fibonacci, .except="square")
If a module is modified, import
will
realize this and reload the script if further imports are executed or
re-executed; otherwise additional imports will not cause the script to be
reloaded for efficiency. As the script is loaded in its own environment
(maintained by import
) dependencies are kept (except those exposed through
attachment), as the following small example shows.
Contents of "some_module.R":
## Do not use library() inside a module. This results in a warning, ## and functions relying on ggplot2 will not work. #library(ggplot2) ## This is also not recommended, because it is not clear wether recursively ## imported functions should be available after the module is imported #import::here(qplot, .from = ggplot2) ## This is the recommended way to recursively import functions on which ## module functions depend. The qplot function will be available to ## module functions, but will not itself be available after import import::here(qplot, .from = ggplot2) ## Note this operator overload is not something you want to `source`! `+` <- function(e1, e2) paste(e1, e2) ## Some function relying on the above overload: a <- function(s1, s2) s1 + rep(s2, 3) ## Another value. b <- head(iris, 10) ## A value created using a recursively imported function p <- qplot(Sepal.Length, Sepal.Width, data = iris, color = Species) ## A function relying on a function exposed through attachment: plot_it <- function() qplot(Sepal.Length, Sepal.Width, data = iris, color = Species)
Usage:
import::from(some_module.R, a, b, p, plot_it) ## Works: a("cool", "import") ## The `+` is not affecting anything here, so this won't work: # "cool" + "import" # Works: b p plot_it()
Suppose that you have some related functionality that you wish to bundle, and
that authoring a full package seems excessive or inappropriate
for the specific task, for example bundling related user interface components for a shiny
application. One option with import
is to author a module (script), say as outlined
below:
# File: foo.R # Desc: Functionality related to foos. # Imports from other_resources.R # When recursively importing from another module or package for use by # your module functions, you should always use import::here() rather # than import::from() or library() import::here(fun_a, fun_b, .from = "other_resources.R") internal_fun <- function(...) ... fun_c <- function(...) { ... a <- fun_a(...) i <- internal_fun(...) ... } fun_d <- function(...) ...
Then in another file we need fun_c
:
# File: bar.R # Desc: Functionality related to bars. # Imports from foo.R import::here(fun_c, .from = "foo.R") ...
In the above, only fun_c
is visible inside bar.R
. The
functions on which it depends exist, but are not exposed.
Also, note that imported scripts may themselves import.
Since the desired effect of import::from
inside a module script
is ambiguous, this results in a warning (but the functions will
still be imported into the local environment of the script, just
as with import::here
which only imports are only exposed to
the module itself.
When importing from a module, it is sourced into an environment
managed by import
, and will not be sourced again upon subsequent
imports (unless the file has changed). For example, in a shiny
application, importing some
objects in server.R
and others in ui.R
from the same module will not
cause it to be sourced twice.
The import
package will by default use the current set of library paths, i.e.
the result of .libPaths()
. It is, however, possible to specify a different set
of library paths using the .library
argument in any of the import
functions,
for example to import packages installed in a custom location, or to remove any
ambiguity as to where imports come from.
Note that in versions up to and including 1.3.0
this defaulted to use only the
first entry in the library paths, i.e. .library=.libPaths()[1L]
. We believe
the new default is applicable in a broader set of circumstances, but if this
change causes any issues, we would very much appreciate hearing about it.
When importing from a module (.R file), the directory where import
looks for
the module script can be specified with the with .directory
parameter.
The default is .
(the current working directory).
One can also specify which names to use in the search path and use several to
group imports. Names can be specified either as character literals or as
variables of type character
(for example if the environment needs to be
determined dynamically).
import::from(magrittr, "%>%", "%$%", .into = "operators") import::from(dplyr, arrange, .into = "datatools") import::from(psych, describe, .into=month.name[1]) # Uses env: "January"
The import::into
and import::from
accept the same parameters and achieve the
same result. The the choice between them a matter of preference). If using
custom search path entities actively, one might prefer the alternative syntax
(which does the same but reverses the argument order):
import::into("operators", "%>%", "%$%", .from = magrittr) import::into("datatools", arrange, .from = dplyr) import::into(month.name[1], describe, .from=psych)
Be aware that beginning in version 1.3.0
hidden objects (those with names
prefixed by a period) are supported. Take care to avoid name clashes with
argument names.
If it is desired to import objects directly into the current environment,
this can be accomplished by import::here
. This is particularly
useful when importing inside a function definition, or module scripts as
described here.
import::here("%>%", "%$%", .from = magrittr) import::here(arrange, .from = dplyr)
Instead of specifying a named environment on the search path, by passing a
character
to the .into
parameter, it is possible to directly specify an
environment. The function automatically determines which use case is involved,
based on the mode()
of the .into
parameter (either character
or
environment
).
Prior to version 1.3.0
, non-standard evaluation (NSE) was applied to the
.into
parameter, and it was necessary to surround it with {}
, in order for
it to be treated as an environment
. This is no longer needed, although it is
still allowed (curly brackets are simply ignored).
Examples include:
# Import into the local environment import::into(environment(), "%>%", .from = magrittr) # Import into the global environment, curlies are optional import::into({.GlobalEnv}, "%>%", "%$%", .from = magrittr) # Import into a new environment, mainly useful for python-style imports # (see below) x = import::into(new.env(), "%<>%", .from = magrittr)
The import
package uses non-standard evaluation (NSE) on the .from
and ...
parameters, allowing the names of packages and functions to be listed without
quoting them. This makes some common use-cases very straightforward, but can get
in the way of more programmatic usages.
This is where the .character_only
parameter comes in handy. By setting
.character_only=TRUE
, the non-standard evaluation of the .from
and the ...
parameters is disabled. Instead, the parameters are processed as character
vectors containing the relevant values.
(Previously, NSE was also applied to the .into
parameter, but as of version
1.3.0
this is no longer the case, and all parameters except .from
and ...
are always evaluated in a standard way.)
It is useful to examine some examples of how specifying .character_only=TRUE
can be helpful.
It is not always know in advance which objects to import from a given
package. For example, assume we have a list of objects from the
broom
package that we need to import, we can do it as follows:
objects <- c("tidy", "glance", "augment") import::from("broom", objects, .character_only=TRUE)
This will import the three functions specified in the objects
vector.
It is worth noting that because .character_only
disables non-standard
evaluation on all parameters, the name of the package must now be quoted.
One common use case is when one wants to import all objects except one
or a few, because of conflicts with other packages. Should one, for
example, want to use the stats
versions of the filter()
and lag()
functions, but import all the other functions in the dplyr
package,
one could do it like this:
objects <- setdiff(getNamespaceExports("dplyr"), c("filter","lag")) import::from("dplyr", objects, .character_only=TRUE)
The same approach can be used when the directory of the source file for a module is not known in advance. This can be useful when the original source file is not always run with the original working directory, but one still does not want to specify a hard-coded absolute path, but to determine it at run time:
mymodule <- file.path(mypath, "module.R") import::from(mymodule, "myfunction", .character_only=TRUE)
Again, note that now the name of the function must be quoted because non-standard evaluation is disabled on all parameters.
The here
package is useful in many circumstances like this; it
allows the setting of a "root" directory for a project and by using
the here::here()
function to figure out the correct directory,
regardless of the working directory.
import::from(here::here("src/utils/module.R")), "myfunction", .character_only=TRUE)
Alternatively, if the file name is always the same and it is only the directory
that differs, you could use the .directory
parameter, which always expects standard
evaluation arguments.
import::from(module.R, "myfunction", here::here("src/utils"))
Note that here::here()
has no relation to import::here()
despite
the similarity in names.
Another case where .character_only
comes in handy is when one
wants to import some functions from a URL. While import
does
not allow direct importing from a URL (because of difficult
questions about when a URL target has changes, whether to
download a file and other things), it easy to achieve the desired
result by using the pins
package (whose main purpose is to
resolve such difficult questions). A simple example follows, which
directly imports the myfunc()
function, which is defined in the
plusone_module.R
:
url <- "https://raw.githubusercontent.com/rticulate/import/master/man/examples/plusone_module.R" import::from(pins::pin(url), "myfunc", .character_only=TRUE) myfunc(3) #> [1] 4
A frequent pattern in python imports packages under an alias; all subsequent use of the imported objects then explicitly includes the alias:
import pandas as pd import numpy as np import math as m print(m.pi) print(m.e)
In order to achieve this functionality with the import package, use .into={new.env()}
which assign to a new environment without attaching it. import::from()
returns this environment, so it can be assigned to a variable:
# Import into a new namespace, use $ to access td <- import::from(tidyr, spread, pivot_wider, .into={new.env()}) dp <- import::from(dplyr, .all=TRUE, .into={new.env()}) dp$select(head(cars),dist) #> dist #> 1 2 #> 2 10 #> 3 4 #> 4 22 #> 5 16 #> 6 10 # Note that functions are not visible without dp$ prefix select(head(cars),dist) #> Error in select(head(cars), dist): could not find function "select"
S3 methods work well in local context, but when the method is called from a different environment, it must be registered (in packages, this is done in the NAMESPACE
file). import
can now register methods of form generic.class
or generic.class.name
automatically using the new .S3
argument. By specifying .S3=TRUE
, import
will automatically detect methods for existing or new generics. No need to export and/or register them manually!
Consider following script foo.r
with a generic and two methods:
# foo.r # functions with great foonctionality foo = function(x){ UseMethod("foo", x) } foo.numeric <- function(x){ x + 1 } foo.character <- function(x){ paste0("_", x, "_") }
Now, all we need is to import the foo
generic:
import::from("foo.r", foo, .S3=TRUE) foo(0) # 1 foo("bar") # _bar_
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