Awesome

conflicted

The goal of conflicted is to provide an alternative conflict resolution strategy. R’s default conflict resolution system gives precedence to the most recently loaded package. This can make it hard to detect conflicts, particularly when introduced by an update to an existing package. conflicted takes a different approach, making every conflict an error and forcing you to choose which function to use.

Thanks to @krlmlr for this neat idea! This code was previously part of the experimental strict package, but I decided improved conflict resolution is useful by itself and worth its own package.

Installation

# install.packages("pak")
pak::pak("r-lib/conflicted")

Usage

To use conflicted, all you need to do is load it:

library(conflicted)
library(dplyr)

filter(mtcars, cyl == 8)
#> Error:
#> ! [conflicted] filter found in 2 packages.
#> Either pick the one you want with `::`:
#> • dplyr::filter
#> • stats::filter
#> Or declare a preference with `conflicts_prefer()`:
#> • `conflicts_prefer(dplyr::filter)`
#> • `conflicts_prefer(stats::filter)`

As suggested, you can either namespace individual calls:

dplyr::filter(mtcars, am & cyl == 8)
#>                 mpg cyl disp  hp drat   wt qsec vs am gear carb
#> Ford Pantera L 15.8   8  351 264 4.22 3.17 14.5  0  1    5    4
#> Maserati Bora  15.0   8  301 335 3.54 3.57 14.6  0  1    5    8

Or declare a session-wide preference:

conflicts_prefer(dplyr::filter())
#> [conflicted] Will prefer dplyr::filter over any other package.
filter(mtcars, am & cyl == 8)
#>                 mpg cyl disp  hp drat   wt qsec vs am gear carb
#> Ford Pantera L 15.8   8  351 264 4.22 3.17 14.5  0  1    5    4
#> Maserati Bora  15.0   8  301 335 3.54 3.57 14.6  0  1    5    8

I recommend declaring preferences directly underneath the corresponding library call:

library(dplyr)
conflicts_prefer(dplyr::filter)

You can ask conflicted to report any conflicts in the current session:

conflict_scout()
#> 1 conflict
#> • `lag()`: dplyr and stats

Functions surrounded by [] have been chosen using one of the built-in rules. Here filter() has been selected because of the preference declared above; the set operations have been selected because they follow the superset principle and extend the API of the base equivalents.

How it works

Loading conflicted creates a new “conflicted” environment that is attached just after the global environment. This environment contains an active binding for any object that is exported by multiple packages; the active binding will throw an error message describing how to disambiguate the name. The conflicted environment also contains bindings for library() and require() that suppress conflict reporting and update the conflicted environment with any new conflicts.

Alternative approaches

It is worth comparing conflicted to box and import. Both packages provide strict alternatives to library(), giving much finer control over what functions are added to the search path.

# box expects you to either namespace all package functions or to load them explicitly
box::use(dplyr)
dplyr$filter(mtcars, cyl == 8)
# or:
box::use(dplyr[select, arrange, dplyr_filter = filter])
dplyr_filter(mtcars, cyl == 8)

# import expects you to explicitly load functions
import::from(dplyr, select, arrange, dplyr_filter = filter)
dplyr_filter(mtcars, cyl == 8)

These require more upfront work than conflicted, in return for greater precision and control.

Since conflicted was created base R also improved its tools for managing search path conflicts. See the blog post by Luke Tierney for details. The main difference is that base R requires up front conflict resolution of all functions when loading a package; conflicted only reports problems as you use conflicted functions.

Code of Conduct

Please note that the conflicted project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.