11  Dependencies: What does your package need?

Second edition

You are reading the work-in-progress second edition of R Packages. This chapter is undergoing heavy restructuring and may be confusing or incomplete.

11.1 Introduction

A dependency is a code that your package needs to run. Dependencies are managed by two files. The DESCRIPTION manages dependencies at the package level; i.e. what packages needs to be installed for your package to work. R has a rich set of ways to describe different types of dependencies. A key point is whether a dependency is needed by regular users or is only needed for development tasks or optional functionality.

The flip-side is that your package will also be a dependency of other people’s scripts and packages. The job of the NAMESPACE is to define what functions you make available for others to use. The package namespace (as recorded in the NAMESPACE file) is one of the more confusing parts of building a package. It’s a fairly advanced topic, and by-and-large, not that important if you’re only developing packages for yourself. However, understanding namespaces is vital if you plan to submit your package to CRAN. This is because CRAN requires that your package plays nicely with other packages. When you first start using namespaces, it’ll seem like a lot of work for little gain. However, having a high quality namespace helps encapsulate your package and makes it self-contained. This ensures that other packages won’t interfere with your code, that your code won’t interfere with other packages, and that your package works regardless of the environment in which it’s run.

11.2 Run-time vs develop-time dependencies

Packages listed in Imports are needed by your users at runtime. The following lines indicate that your package absolutely needs both dplyr and tidyr to work.

Imports:
    dplyr,
    tidyr

Packages listed in Suggests are either needed for development tasks or might unlock optional functionality for your users. The lines below indicate that, while your package can take advantage of ggplot2 and testthat, they’re not absolutely required:

Suggests:
    ggplot2,
    testthat

For example, the withr package is very useful for writing tests that clean up after themselves. Such usage is compatible with listing withr in Suggests, since regular users don’t need to run the tests. But sometimes a package might also use withr in its own functions, perhaps to offer its own with_*() and local_*() functions. In that case, withr should be listed in Imports.

Both Imports and Suggests take a comma-separated list of package names. We recommend putting one package on each line, and keeping them in alphabetical order. A non-haphazard order makes it easier for humans to parse this field and appreciate changes. The easiest way to add a package to Imports or Suggests is with usethis::use_package(). If the dependencies are already in alphabetical order, use_package() will keep it that way. In general, it can be nice to run usethis::use_tidy_description() regularly, which orders and formats DESCRIPTION fields according to a fixed standard.

Imports and Suggests differ in the strength and nature of dependency:

  • Imports: packages listed here must be present for your package to work. Any time your package is installed, those packages will also be installed, if not already present. devtools::load_all() also checks that all packages in Imports are installed.

    Adding a package to Imports ensures it will be installed, but it does not mean that it will be attached along with your package, i.e. it does not do the equivalent of library(otherpkg)1. Inside your package, the best practice is to explicitly refer to external functions using the syntax package::function(). This makes it very easy to identify which functions live outside of your package. This is especially useful when you read your code in the future.

    If you use a lot of functions from another package, this is rather verbose. There’s also a minor performance penalty associated with :: (on the order of 5µs, so it will only matter if you call the function millions of times). You’ll learn about alternative ways to make functions in other packages available inside your package in Section 11.5.4.

  • Suggests: your package can use these packages, but doesn’t require them. You might use suggested packages for example datasets, to run tests, build vignettes, or maybe there’s only one function that needs the package.

    Packages listed in Suggests are not automatically installed along with your package. This means that you can’t assume the package is available unconditionally. Below we show various ways to handle these checks.

If you add packages to DESCRIPTION with usethis::use_package(), it will also remind you of the recommended way to call them.

usethis::use_package("dplyr") # Default is "Imports"
#> ✔ Adding 'dplyr' to Imports field in DESCRIPTION
#> • Refer to functions with `dplyr::fun()`

usethis::use_package("ggplot2", "Suggests")
#> ✔ Adding 'ggplot2' to Suggests field in DESCRIPTION
#> • Use `requireNamespace("ggplot2", quietly = TRUE)` to test if package is installed
#> • Then directly refer to functions with `ggplot2::fun()`

11.2.1 Guarding the use of a suggested package

Inside a function in your own package, check for the availability of a suggested package with requireNamespace("pkg", quietly = TRUE). There are two basic scenarios:

# the suggested package is required 
my_fun <- function(a, b) {
  if (!requireNamespace("pkg", quietly = TRUE)) {
    stop(
      "Package \"pkg\" must be installed to use this function.",
      call. = FALSE
    )
  }
  # code that includes calls such as pkg::f()
}

# the suggested package is optional; a fallback method is available
my_fun <- function(a, b) {
  if (requireNamespace("pkg", quietly = TRUE)) {
    pkg::f()
  } else {
    g()
  }
}

The rlang package has some useful functions for checking package availability. Here’s how the checks around a suggested package could look if you use rlang:

# the suggested package is required 
my_fun <- function(a, b) {
  rlang::check_installed("pkg", reason = "to use `my_fun()`")
  # code that includes calls such as pkg::f()
}

# the suggested package is optional; a fallback method is available
my_fun <- function(a, b) {
  if (rlang::is_installed("pkg")) {
    pkg::f()
  } else {
    g()
  }
}

These rlang functions have handy features for programming, such as vectorization over pkg, classed errors with a data payload, and, for check_installed(), an offer to install the needed package in an interactive session.

Suggests isn’t terribly relevant for packages where the user base is approximately equal to the development team or for packages that are used in a very predictable context. In that case, it’s reasonable to just use Imports for everything. Using Suggests is mostly a courtesy to external users or to accommodate very lean installations. It can free users from downloading rarely needed packages (especially those that are tricky to install) and lets them get started with your package as quickly as possible.

Another common place to use a suggested package is in an example and here we often guard with require() (but you’ll also see requireNamespace() used for this). This example is from ggplot2::coord_map().

#' @examples
#' if (require("maps")) {
#'   nz <- map_data("nz")
#'   # Prepare a map of NZ
#'   nzmap <- ggplot(nz, aes(x = long, y = lat, group = group)) +
#'     geom_polygon(fill = "white", colour = "black")
#'  
#'   # Plot it in cartesian coordinates
#'   nzmap
#' }

An example is basically the only place where we would use require() inside a package.

Another place you might use a suggested package is in a vignette. The tidyverse team generally writes vignettes as if all suggested packages are available. But if you choose to use suggested packages conditionally in your vignettes, the knitr chunk options purl and eval may be useful for achieving this. See Chapter 17 for more discussion of vignettes.

11.2.1.1 Whether and how to guard in a test

As with vignettes, the tidyverse team does not usually guard the use of a suggested package in a test. In general, for vignettes and tests, we assume all suggested packages are available. The motivation for this posture is self-consistency and pragmatism. The key packages needed to run tests or build vignettes (e.g. testthat or knitr) appear in Suggests, not in Imports or Depends. Therefore, if the tests are actually executing or the vignettes are being built, that implies that an expansive notion of package dependencies has been applied. Also, empirically, in every important scenario of running R CMD check, the suggested packages are installed. This is generally true for CRAN and we ensure that it’s true in our own automated checks. However, it’s important to note that other package maintainers take a different stance and choose to protect all usage of suggested packages in their tests and vignettes.

Sometimes even the tidyverse team makes an exception and guards the use of a suggested package in a test. Here’s a test from ggplot2, which uses testthat::skip_if_not_installed() to skip execution if the suggested sf package is not available.

test_that("basic plot builds without error", {
  skip_if_not_installed("sf")

  nc_tiny_coords <- matrix(
    c(-81.473, -81.741, -81.67, -81.345, -81.266, -81.24, -81.473,
      36.234, 36.392, 36.59, 36.573, 36.437, 36.365, 36.234),
    ncol = 2
  )

  nc <- sf::st_as_sf(
    data_frame(
      NAME = "ashe",
      geometry = sf::st_sfc(sf::st_polygon(list(nc_tiny_coords)), crs = 4326)
    )
  )

  expect_doppelganger("sf-polygons", ggplot(nc) + geom_sf() + coord_sf())
})

What might justify the use of skip_if_not_installed()? In this case, the sf package can be nontrivial to install and it is conceivable that a contributor would want to run the remaining tests, even if sf is not available.

Finally, note that testthat::skip_if_not_installed(pkg, minimum_version = "x.y.z") can be used to conditionally skip a test based on the version of the other package.

11.2.2 Minimum versions

If you need a specific version of a package, specify it in parentheses after the package name:

Imports:
    dplyr (>= 1.0.0),
    tidyr (>= 1.1.0)

You always want to specify a minimum version (dplyr (>= 1.0.0)) rather than an exact version (dplyr (== 1.0.0)). Since R can’t have multiple versions of the same package loaded at the same time, specifying an exact dependency dramatically increases the chance of conflicting versions2.

Versioning is most important if you will release your package for use by others. Usually people don’t have exactly the same versions of packages installed that you do. If someone has an older package that doesn’t have a function your package needs, they’ll get an unhelpful error message if your package does not advertise the minimum version it needs. However, if you state a minimum version, they’ll learn about this problem clearly, probably at the time of installing your package.

Think carefully if you declare a minimum version for a dependency. In some sense, the safest thing to do is to require a version greater than or equal to the package’s current version. For public work, this is most naturally defined as the current CRAN version of a package; private or personal projects may adopt some other convention. But it’s important to appreciate the implications for people who try to install your package: if their local installation doesn’t fulfill all of your requirements around versions, installation will either fail or force upgrades of these dependencies. This is desirable if your minimum version requirements are genuine, i.e. your package would be broken otherwise. But if your stated requirements have a less solid rationale, this may be unnecessarily conservative and inconvenient.

In the absence of clear, hard requirements, you should set minimum versions (or not) based on your expected user base, the package versions they are likely to have, and a cost-benefit analysis of being too lax versus too conservative. The de facto policy of the tidyverse team is to specify a minimum version when using a known new feature or when someone encounters a version problem in authentic use. This isn’t perfect, but we don’t currently have the tooling to do better, and it seems to work fairly well in practice.

11.2.3 Other dependencies

There are three other fields that allow you to express more specialised dependencies:

  • Depends: Prior to the roll-out of namespaces in R 2.14.0 in 2011, Depends was the only way to “depend” on another package. Now, despite the name, you should almost always use Imports, not Depends. You’ll learn why, and when you should still use Depends, in Chapter 11.

    You can also use Depends to state a minimum version for R itself, e.g. Depends: R (>= 4.0.0). Again, think carefully if you do this. This raises the same issues as setting a minimum version for a package you depend on, except the stakes are much higher when it comes to R itself. Users can’t simply consent to the necessary upgrade, so, if other packages depend on yours, your minimum version requirement for R can cause a cascade of package installation failures.

    • The backports package is useful if you want to use a function like tools::R_user_dir(), which was introduced in 4.0.0 in 2020, while still supporting older R versions.
    • The tidyverse packages officially support the current R version, the devel version, and four previous versions. We proactively test this support in the standard build matrix we use for continuous integration.
    • Packages with a lower level of use may not need this level of rigour. The main takeaway is: if you state a minimum of R, you should have a reason and you should take reasonable measures to test your claim regularly.
  • LinkingTo: if your package uses C or C++ code from another package, you need to list it here.

  • Enhances: packages listed here are “enhanced” by your package. Typically, this means you provide methods for classes defined in another package (a sort of reverse Suggests). But it’s hard to define what that means, so we don’t recommend using Enhances.

You can also list things that your package needs outside of R in the SystemRequirements field. But this is just a plain text field and is not automatically checked. Think of it as a quick reference; you’ll also need to include detailed system requirements (and how to install them) in your README.

11.2.3.1 An R version gotcha

Before we leave this topic, we give a concrete example of how easily an R version dependency can creep in and have a broader impact than you might expect. The saveRDS() function writes a single R object as an .rds file, an R-specific format. For almost 20 years, .rds files used the “version 2” serialization format. “Version 3” became the new default in R 3.6.0 (released April 2019) and cannot be read by R versions prior to 3.5.0 (released April 2018).

Many R packages have at least one .rds file lurking within and, if that gets re-generated with a modern R version, by default, the new .rds file will have the “version 3” format. When that R package is next built, such as for a CRAN submission, the required R version is automatically bumped to 3.5.0, signaled by this message:

NB: this package now depends on R (>= 3.5.0)
  WARNING: Added dependency on R >= 3.5.0 because serialized objects in
  serialize/load version 3 cannot be read in older versions of R.
  File(s) containing such objects:
    'path/to/some_file.rds'

Literally, the DESCRIPTION file in the bundled package says Depends: R (>= 3.5.0), even if DESCRIPTION in the source package says differently3.

When such a package is released on CRAN, the new minimum R version is viral, in the sense that all packages listing the original package in Imports or even Suggests have, to varying degrees, inherited the new dependency on R >= 3.5.0.

The immediate take-away is to be very deliberate about the version of .rds files until R versions prior to 3.5.0 have fallen off the edge of what you intend to support. This particular .rds issue won’t be with us forever, but similar issues crop up elsewhere, such as in the standards implicit in compiled C or C++ source code. The broader message is that the more reverse dependencies your package has, the more thought you need to give to your package’s stated minimum versions, especially for R itself.

11.2.4 Nonstandard dependencies

In packages developed with devtools, you may see DESCRIPTION files that use a couple other nonstandard fields for package dependencies specific to development tasks.

The Remotes field can be used when you need to install a dependency from a nonstandard place, i.e. from somewhere besides CRAN or Bioconductor. One common example of this is when you’re developing against a development version of one of your dependencies. During this time, you’ll want to install the dependency from its development repository, which is often GitHub. The way to specify various remote sources is described in a devtools vignette.

The dependency and any minimum version requirement still need to be declared in the normal way in, e.g., Imports. usethis::use_dev_package() helps to make the necessary changes in DESCRIPTION. If your package temporarily relies on a development version of usethis, the affected DESCRIPTION fields might evolve like this:

Stable -->               Dev -->                       Stable again
----------------------   ---------------------------   ----------------------
Package: yourpkg         Package: yourpkg              Package: yourpkg
Version: 1.0.0           Version: 1.0.0.9000           Version: 1.1.0
Imports:                 Imports:                      Imports: 
    usethis (>= 2.1.3)       usethis (>= 2.1.3.9000)       usethis (>= 2.2.0)
                         Remotes:   
                             r-lib/usethis 

It’s important to note that you should not submit your package to CRAN in the intermediate state, meaning with a Remotes field and with a dependency required at a version that’s not available from CRAN or Bioconductor. For CRAN packages, this can only be a temporary development state, eventually resolved when the dependency updates on CRAN and you can bump your minimum version accordingly.

You may also see devtools-developed packages with packages listed in DESCRIPTION fields in the form of Config/Needs/*. This pattern takes advantage of the fact that fields prefixed with Config/ are ignored by CRAN and also do not trigger a NOTE about “Unknown, possibly mis-spelled, fields in DESCRIPTION”.

The use of Config/Needs/* is not directly related to devtools. It’s more accurate to say that it’s associated with continuous integration workflows made available to the community at https://github.com/r-lib/actions/ and exposed via functions such as usethis::use_github_actions(). A Config/Needs/* field tells the setup-r-dependencies GitHub Action about extra packages that need to be installed.

Config/Needs/website is the most common and it provides a place to specify packages that aren’t a formal dependency, but that must be present in order to build the package’s website. On the left is an example of what might appear in DESCRIPTION for a package that uses various tidyverse packages in the non-vignette articles on its website, which is also formatted with styling that lives in the tidyverse/template GitHub repo. On the right is the corresponding excerpt from the configuration of the workflow that builds and deploys the website.

in DESCRIPTION                  in .github/workflows/pkgdown.yaml
--------------------------      ---------------------------------
Config/Needs/website:           - uses: r-lib/actions/setup-r-dependencies@v1
    tidyverse,                    with:
    tidyverse/tidytemplate          extra-packages: pkgdown
                                    needs: website

Continuous integration and package websites are discussed more in ?? and ??, respectively. These chapters are a yet-to-be-(re)written task for the 2nd edition.

The Config/Needs/* convention is handy because it allows a developer to use DESCRIPTION as their definitive record of package dependencies, while maintaining a clean distinction between true runtime dependencies versus those that are only needed for specialized development tasks.

11.3 Exports

For a function to be usable outside of your package, you must export it. When you create a new package with usethis::create_package(), it starts by exporting nothing. You can still experiment interactively with load_all() (since that loads all functions, not just exported), but if install the package and reload RStudio you’ll notice that no functions are available.

To export an object, put @export in its roxygen block. For example:

#' @export
foo <- function(x, y, z) {
  ...
}

This will then generate export(), exportMethods(), exportClass() or S3method() depending on the type of the object.

You export functions that you want other people to use. Exported functions must be documented, and you must be cautious when changing their interface — other people are using them! Generally, it’s better to export too little than too much. It’s easy to export things that you didn’t before; it’s hard to stop exporting a function because it might break existing code. Always err on the side of caution, and simplicity. It’s easier to give people more functionality than it is to take away stuff they’re used to.

I believe that packages that have a wide audience should strive to do one thing and do it well. All functions in a package should be related to a single problem (or a set of closely related problems). Any functions not related to that purpose should not be exported. For example, most of my packages have a utils.R file that contains many small functions that are useful for me, but aren’t part of the core purpose of those packages. I never export these functions.

# Defaults for NULL values
`%||%` <- function(a, b) if (is.null(a)) b else a

# Remove NULLs from a list
compact <- function(x) {
  x[!vapply(x, is.null, logical(1))]
}

That said, if you’re creating a package for yourself, it’s far less important to be so disciplined. Because you know what’s in your package, it’s fine to have a local “misc” package that contains a passel of functions that you find useful. But I don’t think you should release such a package.

The following sections describe what you should export if you’re using S3, S4 or RC.

11.4 When should you take a dependency?

https://www.tidyverse.org/blog/2019/05/itdepends/

11.4.1 Tidyverse dependencies

Here’s our policies about the role of different packages. use_tidy_dependencies(). “Free” dependencies.

11.5 Namespace

11.5.1 Motivation

As the name suggests, namespaces provide “spaces” for “names”. They provide a context for looking up the value of an object associated with a name.

Without knowing it, you’ve probably already used namespaces. For example, have you ever used the :: operator? It disambiguates functions with the same name. For example, both plyr and Hmisc provide a summarize() function. If you load plyr, then Hmisc, summarize() will refer to the Hmisc version. But if you load the packages in the opposite order, summarize() will refer to the plyr version. This can be confusing. Instead, you can explicitly refer to specific functions: Hmisc::summarize() and plyr::summarize(). Then the order in which the packages are loaded won’t matter.

Namespaces make your packages self-contained in two ways: the imports and the exports. The imports defines how a function in one package finds a function in another. To illustrate, consider what happens when someone changes the definition of a function that you rely on: for example, the simple nrow() function in base R:

nrow
#> function (x) 
#> dim(x)[1L]
#> <bytecode: 0x557efa75f610>
#> <environment: namespace:base>

It’s defined in terms of dim(). So what will happen if we override dim() with our own definition? Does nrow() break?

dim <- function(x) c(1, 1)
dim(mtcars)
#> [1] 1 1
nrow(mtcars)
#> [1] 32

Surprisingly, it does not! That’s because when nrow() looks for an object called dim(), it uses the package namespace, so it finds dim() in the base environment, not the dim() we created in the global environment.

The exports helps you avoid conflicts with other packages by specifying which functions are available outside of your package (internal functions are available only within your package and can’t easily be used by another package). Generally, you want to export a minimal set of functions; the fewer you export, the smaller the chance of a conflict. While conflicts aren’t the end of the world because you can always use :: to disambiguate, they’re best avoided where possible because it makes the lives of your users easier.

11.5.2 Search path

To understand why namespaces are important, you need a solid understanding of search paths. To call a function, R first has to find it. R does this by first looking in the global environment. If R doesn’t find it there, it looks in the search path, the list of all the packages you have attached. You can see this list by running search(). For example, here’s the search path for the code in this book:

search()
#>  [1] ".GlobalEnv"        "tools:quarto"      "package:stats"    
#>  [4] "package:graphics"  "package:grDevices" "package:utils"    
#>  [7] "package:datasets"  "package:methods"   "Autoloads"        
#> [10] "package:base"

There’s an important difference between loading and attaching a package. Normally when you talk about loading a package you think of library(), but that actually attaches the package.

If a package is installed,

  • Loading will load code, data and any DLLs; register S3 and S4 methods; and run the .onLoad() function. After loading, the package is available in memory, but because it’s not in the search path, you won’t be able to access its components without using ::. Confusingly, :: will also load a package automatically if it isn’t already loaded. It’s rare to load a package explicitly, but you can do so with requireNamespace() or loadNamespace().

  • Attaching puts the package in the search path. You can’t attach a package without first loading it, so both library() or require() load then attach the package. You can see the currently attached packages with search().

If a package isn’t installed, loading (and hence attaching) will fail with an error.

To see the differences more clearly, consider two ways of running expect_that() from the testthat package. If we use library(), testthat is attached to the search path. If we use ::, it’s not.

old <- search()
testthat::expect_equal(1, 1)
setdiff(search(), old)
#> character(0)
expect_true(TRUE)
#> Error in expect_true(TRUE): could not find function "expect_true"
    
library(testthat)
expect_equal(1, 1)
setdiff(search(), old)
#> [1] "package:testthat"
expect_true(TRUE)

There are four functions that make a package available. They differ based on whether they load or attach, and what happens if the package is not found (i.e., throws an error or returns FALSE).

Throws error Returns FALSE
Load loadNamespace("x") requireNamespace("x", quietly = TRUE)
Attach library(x) require(x, quietly = TRUE)

Of the four, you should only ever use two:

  • Use library(x) in data analysis scripts. It will throw an error if the package is not installed, and will terminate the script. You want to attach the package to save typing. Never use library() in a package.

  • Use requireNamespace("x", quietly = TRUE) inside a package if you want a specific action (e.g. throw an error) depending on whether or not a suggested package is installed.

You never need to use require() (requireNamespace() is almost always better), or loadNamespace() (which is only needed for internal R code). You should never use require() or library() in a package: instead, use the Depends or Imports fields in the DESCRIPTION.

Now’s a good time to come back to an important issue which we glossed over earlier. What’s the difference between Depends and Imports in the DESCRIPTION? When should you use one or the other?

Listing a package in either Depends or Imports ensures that it’s installed when needed. The main difference is that where Imports just loads the package, Depends attaches it. There are no other differences. The rest of the advice in this chapter applies whether or not the package is in Depends or Imports.

Unless there is a good reason otherwise, you should always list packages in Imports not Depends. That’s because a good package is self-contained, and minimises changes to the global environment (including the search path). The only exception is if your package is designed to be used in conjunction with another package. For example, the analogue package builds on top of vegan. It’s not useful without vegan, so it has vegan in Depends instead of Imports. Similarly, ggplot2 should really Depend on scales, rather than Importing it.

Now that you understand the importance of the namespace, let’s dive into the nitty gritty details. The two sides of the package namespace, imports and exports, are both described by the NAMESPACE. You’ll learn what this file looks like in the next section. In the section after that, you’ll learn the details of exporting and importing functions and other objects.

11.5.3 Workflow

Generating the namespace with roxygen2 is just like generating function documentation with roxygen2. You use roxygen2 blocks (starting with #') and tags (starting with @). The workflow is the same:

  1. Add roxygen comments to your .R files.

  2. Run devtools::document() (or press Ctrl/Cmd + Shift + D in RStudio) to convert roxygen comments to .Rd files.

  3. Look at NAMESPACE and run tests to check that the specification is correct.

  4. Rinse and repeat until the correct functions are exported.

11.5.4 Imports

NAMESPACE also controls which external functions can be used by your package without having to use ::.

It’s confusing that both DESCRIPTION (through the Imports field) and NAMESPACE (through import directives) seem to be involved in imports. This is just an unfortunate choice of names. The Imports field really has nothing to do with functions imported into the namespace: it just makes sure the package is installed when your package is. It doesn’t make functions available. You need to import functions in exactly the same way regardless of whether or not the package is attached.

Depends is just a convenience for the user: if your package is attached, it also attaches all packages listed in Depends. If your package is loaded, packages in Depends are loaded, but not attached, so you need to qualify function names with :: or specifically import them.

It’s common for packages to be listed in Imports in DESCRIPTION, but not in NAMESPACE. The converse is not true. Every package mentioned in NAMESPACE must also be present in the Imports or Depends fields.

11.5.5 R functions

If you are using just a few functions from another package, my recommendation is to note the package name in the Imports: field of the DESCRIPTION file and call the function(s) explicitly using ::, e.g., pkg::fun().

If you are using functions repeatedly, you can avoid :: by importing the function with @importFrom pkg fun. Operators can also be imported in a similar manner, e.g., @importFrom magrittr %>%.

Alternatively, if you are repeatedly using many functions from another package, you can import all of them using @import package. This is the least recommended solution because it makes your code harder to read (you can’t tell where a function is coming from), and if you @import many packages, it increases the chance of conflicting function names.


  1. The difference between loading and attaching a package is covered in more detail in Section 11.5.2.↩︎

  2. The need to specify the exact versions of packages, rather than minimum versions, comes up more often in the development of non-package projects. The renv package provides a way to do this, by implementing project-specific environments (package libraries). renv is a reboot of an earlier package called packrat. If you want to freeze the dependencies of a project at exact versions, use renv instead of (or possibly in addition to) a DESCRIPTION file.↩︎

  3. The different package states, such as source vs. bundled, are explained in Section 4.2.↩︎