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An Import Mechanism For R

The import package is intended to simplify the way in which functions from external packages or modules are made available for use in R scripts. Learn more on the package website, by reading vignette("import"), or using the help (?import::from).

Introduction

The typical way of using functionality exposed by a package in R scripts is to load (and attach) the entire package with library() (or require()). This can have the undesirable effect of masking objects in the user’s search path and can also make it difficult and confusing to identify what functionality comes from which package when using several library statements.

The import package provides a simple alternative, allowing the user specify in a concise way exactly which objects. For example, the Hmisc package exposes over four hundred functions. Instead of exposing all of those functions, someone who only needs access to, say the impute() and the nomiss() functions, can import those functions only:

import::from(Hmisc, impute, nomiss)

For more on the motivation behind the package, see vignette(“import”)

Installation

To install import from CRAN:

install.packages("import")

You can also install the development version of import from GitHub using devtools:

devtools::install_github("rticulate/import")

Usage

Importing functions from R packages

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)

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)

Use .all=TRUE to import all functions from a package. If you want to rename one of them, you can still do that:

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 still override this in exceptional cases, but you seldom need to.

These and other examples are discussed in more detail in the Importing from Packages section of the package vignette.

Importing Functions from “Module” Scripts

The import package allows R files to be used as “modules” from which functions are loaded. 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, as well as the .all and .except parameters, work in the same way as for packages:

import::from(sequence_module.R, fib=fibonacci, .except="square")

These and other examples are discussed in more detail in the Importing from Modules section of the package vignette.

Choosing where import looks for packages or modules

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).

Choosing where the imported functions are placed

By default, imported objects are placed in a separate entity in the search path called “imports”. One can also specify which names to use in the search path and use several to group imports:

import::from(magrittr, "%>%", "%$%", .into = "operators") 
import::from(dplyr, arrange, .into = "datatools")

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)

If it is desired to place imported objects in the current environment, use import::here():

More advanced usage

The import package is designed to be simple to use for basic cases, so it uses symbolic evaluation to allow the names of packages, modules and functions to be entered without quotes (except for operators, such as "%>%" which must be quoted). However, this means that it calling a variable containing the name of a module, or a vector of functions to import, will not work. For this use case, you can use the .character_only parameter:

module_name <- "../utils/my_module.R"

# Will not work (import will look for a package called "module_name")
import::from(module_name, foo, bar)

# This will correctly import the foo() and bar() functions from "../utils/my_module.R"
import::from(module_name, foo, bar, .character_only=TRUE)

The .character_only parameter is covered in more detail in the Advanced Usage section of the package vignette, which also describes how you can import from module scripts stored online with the help of the pins package, or achieve python-like imports with the help of {} notation for environments in the .into parameter.

Contributing

Contributions to this project are welcome. Please start by opening an issue or discussion thread. New features are added conservatively based on supply (is anyone willing to contribute an implementation of the feature?), demand (how many people seem to need a new feature?), and last, but not least, by whether a feature can be implemented without breaking backwards compatibility.

(Did we forget to add you? If so, please let us know!)

See also:

  • Some of the use cases for import can now be handled directly in base R using the new exclude and include.only arguments of library() and require()
  • For an interesting but slightly different idea of Python-like modules for R, see the modules package by @klmr.
  • Another approach, focused on treating the use of functions with naming conflicts as explicit errors is the conflicted package by @hadley.