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Datasets are stored in data/, not as regular R objects in the package. This means you need to document them in a slightly different way: instead of documenting the data directly, you quote the dataset’s name.

#' Prices of over 50,000 round cut diamonds
#' A dataset containing the prices and other attributes of almost 54,000
#'  diamonds. The variables are as follows:
#' @format A data frame with 53940 rows and 10 variables:
#' \describe{
#'   \item{price}{price in US dollars ($326--$18,823)}
#'   \item{carat}{weight of the diamond (0.2--5.01)}
#'   \item{cut}{quality of the cut (Fair, Good, Very Good, Premium, Ideal)}
#'   \item{color}{diamond colour, from D (best) to J (worst)}
#'   \item{clarity}{a measurement of how clear the diamond is (I1 (worst), SI2,
#'     SI1, VS2, VS1, VVS2, VVS1, IF (best))}
#'   \item{x}{length in mm (0--10.74)}
#'   \item{y}{width in mm (0--58.9)}
#'   \item{z}{depth in mm (0--31.8)}
#'   \item{depth}{total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43--79)}
#'   \item{table}{width of top of diamond relative to widest point (43--95)}
#' }

Note the use of two additional tags that are particularly useful for documenting data:

  • @format, which gives an overview of the structure of the dataset. This should include a definition list that describes each variable. There’s currently no way to generate this with Markdown, so this is one of the few places you’ll need to Rd markup directly.

  • @source where you got the data form, often a URL.


As well as documenting every object inside the package, you can also document the package itself by documenting the special sentinel "_PACKAGE". This automatically include information parsed from the DESCRIPTION, including title, description, list of authors, and useful URLs.

We recommend placing package documentation in {pkgname}-package.R, and have @keywords internal. Use usethis::use_package_doc() to set up automatically.

Here’s an example:

#' @keywords internal 

Package documentation is a good place to put # Package options that documents options used by the package.

Some notes:

  • By default, aliases will be added so that both ?pkgname and package?pkgname will find the package help. If there’s an existing function called pkgname, use @aliases {pkgname}-package NULL to override the default.

  • Use @references to point to published material about the package that users might find helpful.


  • S3 generics are regular functions, so document them as such. If necessary, include a section that provides additional details for developers implementing methods.

  • S3 classes have no formal definition, so document the constructor.

  • It is your choice whether or not to document S3 methods. Generally, it’s not necessary to document straightforward methods for common generics like print(). (You should, however, always @export S3 methods).

    If your method is more complicated, you should document it by setting @rdname or @describeIn. For complicated methods, you might document in their own file (i.e. @rdname generic.class; for simpler methods you might document with the generic (i.e. @describeIn generic). Learn more about these tags in vignette("reuse").

  • Generally, roxygen2 will automatically figure out the generic that the method belongs to, and you should only need to use @method if there is ambiguity. For example, is the method for all(), or the data.frame method for all.equal()?. If this happens to you, disambiguate with (e.g.) @method all.equal data.frame.


S4 generics are also functions, so document them as such.

Document S4 classes by adding a roxygen block before setClass(). Use @slot to document the slots of the class. Here’s a simple example:

#' An S4 class to represent a bank account
#' @slot balance A length-one numeric vector
Account <- setClass("Account",
  slots = list(balance = "numeric")

S4 methods are a little more complicated. Unlike S3 methods, all S4 methods must be documented. You can document them in three places:

  • In the class. Most appropriate if the corresponding generic uses single dispatch and you created the class.

  • In the generic. Most appropriate if the generic uses multiple dispatches and you control it.

  • In its own file. Most appropriate if the method is complex. or the either two options don’t apply.

Use either @rdname or @describeIn to control where method documentation goes. See the next section for more details.


  • R6 methods can be documented in-line, i.e. the method’s documentation comments come right before the definition of the method.

  • Method documentation can use the @description, @details, @param, @return and @examples tags. These are used to create a subsection for the method, within a separate ‘Methods’ section. All roxygen comment lines of a method documentation must appear after a tag.

  • @param tags that appear before the class definition are automatically inherited by all methods, if needed.

  • R6 fields and active bindings can make use of the @field tag. Their documentation should also be in-line.

  • roxygen2 checks that all public methods, public fields, active bindings and all method arguments are documented, and issues warnings otherwise.

  • To turn off the special handling of R6 classes and go back to the roxygen2 6.x.x behavior, use the r6 = FALSE option in DESCRIPTION, in the Roxygen entry: Roxygen: list(r6 = FALSE).

roxygen2 automatically generates additional sections for an R6 class:

  • A section with information about the superclass(es) of the class, with links. In HTML this includes a list of all inherited methods, with links.

  • An ‘Examples’ section that contains all class and method examples. This section is run by R CMD check, so method examples must work without errors.

An example from the R6 tutorial:

#' R6 Class Representing a Person
#' @description
#' A person has a name and a hair color.
#' @details
#' A person can also greet you.

Person <- R6::R6Class("Person",
public = list(

    #' @field name First or full name of the person.
    name = NULL,

    #' @field hair Hair color of the person.
    hair = NULL,

    #' @description
    #' Create a new person object.
    #' @param name Name.
    #' @param hair Hair color.
    #' @return A new `Person` object.
    initialize = function(name = NA, hair = NA) {
      self$name <- name
      self$hair <- hair

    #' @description
    #' Change hair color.
    #' @param val New hair color.
    #' @examples
    #' P <- Person("Ann", "black")
    #' P$hair
    #' P$set_hair("red")
    #' P$hair
    set_hair = function(val) {
      self$hair <- val

    #' @description
    #' Say hi.
    greet = function() {
      cat(paste0("Hello, my name is ", self$name, ".\n"))