roxygen2 is extensible, and this vignette will show you how. It
starts with an introduction to the basic workflow of
roxygenize() and the key data structures that power it.
Then we’ll show you how you can use its two extension points:
Add a new tag to generate a new top-level section in an
.Rdfile. This allows you to repeat yourself less when documenting your package. (Seevignette("reuse")for other techniques.)Add a new roclet. This lets you take full advantage of the computational machinery behind
roxygenize()to compute anything you want or produce any artefact you can imagine.
How roxygenize() works
You’ve probably used roxygenize() (or
devtools::document()) a bunch without ever really thinking
about what’s going on behind the scenes. But if you’re going to extend
roxygen2, you’ll need to know exactly what’s happening:
Loads the package under roxygenizing, as well as any further packages (“packages” in
load_options()).Parses all R files in the package, using available tags.
Finds the roclets (see
roclet()) from itsrocletsargument or the “roclets” option (load_options()). It defaults to using the Collate “roclet”, the Rd roclet, and NAMESPACE roclet, but you can also add your own.Runs the different methods of all those roclets, in order and independently: clean (
roclet_clean), preprocess (roclet_preprocess()), process (roclet_process()), and output (roclet_output()). Only process and output are routinely used. For example, if you think of the Rd roclet, its process method digests information from the tag parsing, combines inherits, etc. to create the content of each documentation topic, and its output method writes those topics to disk.
Key data structures
Before we dive into extending roxygen2, we need to first discuss two important data structures that power roxygen: tags and blocks.
Tags
A tag (a list with S3 class roxy_tag) represents a
single tag. It has the following fields:
tag: the name of the tag.raw: the raw contents of the tag (i.e. everything from the end of this tag to the beginning of the next).val: the parsed value, which we’ll come back to shortly.fileandline: the location of the tag in the package. Used withroxy_tag_warning()to produce informative error messages.
You can construct tag objects by hand with
roxy_tag():
roxy_tag("name", "Hadley")
#> [????:???] @name 'Hadley' {unparsed}
str(roxy_tag("name", "Hadley"))
#> List of 5
#> $ file: chr NA
#> $ line: chr NA
#> $ raw : chr "Hadley"
#> $ tag : chr "name"
#> $ val : NULL
#> - attr(*, "class")= chr [1:2] "roxy_tag_name" "roxy_tag"However, you should rarely need to do so (except in tests), because you’ll typically have them given to you in a block object, as you’ll see shortly.
Blocks
A block (a list with S3 class roxy_block) represents a
single roxygen block. It has the following fields:
-
tags: a list ofroxy_tags. -
call: the R code associated with the block (usually a function call). -
fileandline: the location of the R code. -
object: the evaluated R object associated with the code.
The easiest way to see the basic structure of a
roxy_block() is to generate one by parsing a roxygen block
with parse_text():
text <- "
#' This is a title
#'
#' This is the description.
#'
#' @param x,y A number
#' @export
f <- function(x, y) x + y
"
# parse_text() returns a list of blocks, so I extract the first
block <- parse_text(text)[[1]]
block
#> <roxy_block> [<text>:8]
#> $tag
#> [line: 2] @title 'This is a title' {parsed}
#> [line: 4] @description 'This is the description.' {parsed}
#> [line: 6] @param 'x,y A number' {parsed}
#> [line: 7] @export '' {parsed}
#> [line: 8] @usage '<generated>' {parsed}
#> [line: 8] @.formals '<generated>' {parsed}
#> [line: 8] @backref '<generated>' {parsed}
#> $call f <- function(x, y) x + y
#> $object <function>
#> $topic f
#> $alias fYou’ll notice that some of the tags didn’t exist in the original block:
-
@titleand@descriptionare extracted from the text that appears before the first explicit tag. -
@usageis generated automatically from the function formals. -
@.formalsis an “internal” tag that doesn’t generate any output but is used to pass some important data around. -
@backrefstores the source location of the block so we can later record which.Rfiles contributed to each.Rdfile.
Adding a new .Rd tag
The most common way to extend roxygen2 is to create a new tag that
adds output to .Rd files. This requires defining a few
methods:
Define a
roxy_tag_parse()method that describes how to parse our new tag.Define a
roxy_tag_rd()method that describes how to convert the tag into.Rdcommands.If the tag’s content is meant to appear in a custom section (as opposed to, say, the examples section), define a
format()method that describes how to create the.Rdstring.
To illustrate the basic idea, we’ll create a new @tip
tag that will create a bulleted list of tips about how to use a
function. The idea is to take something like this:
#' @tip The mean of a logical vector is the proportion of `TRUE` values.
#' @tip You can compute means of dates and date-times!That generates Rd like this:
\section{Tips and tricks}{
\itemize{
\item The mean of a logical vector is the proportion of \code{TRUE} values.
\item You can compute means of dates and date-times!
}
}The first step is to define a method for
roxy_tag_parse() that describes how to parse the tag text.
The name of the class will be roxy_tag_{tag}, which in this
case is roxy_tag_tip. This function takes a
roxy_tag as input, and its job is to set x$val
to a convenient parsed value that will be used later by the roclet. Here
we want to process the text using Markdown so we can just use
tag_markdown():
roxy_tag_parse.roxy_tag_tip <- function(x) {
tag_markdown(x)
}(There are lots of other built in options that you can read about in
?tag_markdown.)
We can check this works by using parse_text():
text <- "
#' Title
#'
#' @tip The mean of a logical vector is the proportion of `TRUE` values.
#' @tip You can compute means of dates and date-times!
#' @md
f <- function(x, y) {
# ...
}
"
block <- parse_text(text)[[1]]
block
#> <roxy_block> [<text>:7]
#> $tag
#> [line: 2] @title 'Title' {parsed}
#> [line: 4] @tip 'The mean of a logical vector is the proportion ...' {parsed}
#> [line: 5] @tip 'You can compute means of dates and date-times!' {parsed}
#> [line: 6] @md '' {parsed}
#> [line: 7] @usage '<generated>' {parsed}
#> [line: 7] @.formals '<generated>' {parsed}
#> [line: 7] @backref '<generated>' {parsed}
#> $call f <- function(x, y) { ...
#> $object <function>
#> $topic f
#> $alias f
str(block$tags[[2]])
#> List of 5
#> $ file: chr "<text>"
#> $ line: int 4
#> $ tag : chr "tip"
#> $ raw : chr "The mean of a logical vector is the proportion of `TRUE` values."
#> $ val : chr "The mean of a logical vector is the proportion of \\code{TRUE} values."
#> - attr(*, "class")= chr [1:2] "roxy_tag_tip" "roxy_tag"Here I explicitly turn Markdown parsing on using @md;
it’s usually turned on for a package using roxygen2’s
load_options().
Next, we define a method for roxy_tag_rd(), which must
create an rd_section(). We’re going to create a new custom
section called tip. It will contain a character vector of
tips:
roxy_tag_rd.roxy_tag_tip <- function(x, base_path, env) {
rd_section("tip", x$val)
}This additional layer is needed because there can be multiple tags of
the same type in a single block, and multiple blocks can contribute to
the same .Rd file. The job of rd_section() is
to combine all the tags into a single top-level Rd section. Each tag
generates an rd_section which is then combined with any
previous section using merge(). The default
merge.rd_section() just concatenates the values together
(rd_section(x$type, c(x$value, y$value))); you can override
this method if you need more sophisticated behaviour.
We called the custom section “tip” just like our tag, but we needn’t have done so: it really depends on how you want to map the input tags to output Rd code.
We then need to define a format() method to convert this
object into text for the .Rd file:
format.rd_section_tip <- function(x, ...) {
paste0(
"\\section{Tips and tricks}{\n",
"\\itemize{\n",
paste0(" \\item ", x$value, "\n", collapse = ""),
"}\n",
"}\n"
)
}We can now try this out with roclet_text():
topic <- roc_proc_text(rd_roclet(), text)[[1]]
topic$get_section("tip")
#> \section{Tips and tricks}{
#> \itemize{
#> \item The mean of a logical vector is the proportion of \code{TRUE} values.
#> \item You can compute means of dates and date-times!
#> }
#> }
#> Note that there is no namespacing so if you’re defining multiple new tags I recommend using your package name as the common prefix.
Using your new tag
Now that the three methods are created, we still need to make them
available to roxygenize(). First, you need to export the
method:
- If the package defining the tag imports roxygen2, use
@export. - If the package defining the tag only suggests roxygen2, use
@exportMethod.
Next, you’ll need to load the tag-defining package in the package
where you want to use it. For example, if you created some new tags in
{packageFoo}, and would like to use these tags in your documentation for
{packageBar}, append this line to the DESCRIPTION of
{packageBar}:
Roxygen: list(packages = "packageFoo")
See load_options() for more details.
Creating a new roclet
Creating a new roclet is usually a two part process. First, you
define new tags that your roclet will work with, unless your roclet only
needs information from existing tags, or only needs the path to the
package source1. Second, you define a roclet that tells
roxygenize() what to compute and produce based on this
information.
Custom tags
In this example we will make a new @memo tag which helps
you to remember what you’re planning to work on in the future by
displaying notes when you document your package. We choose this syntax
for @memo:
#' @memo [Headline] DescriptionFor example:
#' @memo [EFFICIENCY] Currently brute-force; find better algorithm.As above, we first define a parse method. This time we use custom format based on a regular expression:
roxy_tag_parse.roxy_tag_memo <- function(x) {
if (!grepl("^\\[.*\\].*$", x$raw)) {
roxy_tag_warning(x, "Invalid memo format")
return()
}
parsed <- stringi::stri_match(str = x$raw, regex = "\\[(.*)\\](.*)")[1, ]
x$val <- list(
header = parsed[[2]],
message = parsed[[3]]
)
x
}Then we check it works with parse_text():
text <- "
#' @memo [TBI] Remember to implement this!
#' @memo [API] Check best API
f <- function(x, y) {
# ...
}
"
block <- parse_text(text)[[1]]
block
#> <roxy_block> [<text>:4]
#> $tag
#> [line: 2] @memo '[TBI] Remember to implement this!' {parsed}
#> [line: 3] @memo '[API] Check best API' {parsed}
#> [line: 4] @usage '<generated>' {parsed}
#> [line: 4] @.formals '<generated>' {parsed}
#> [line: 4] @backref '<generated>' {parsed}
#> $call f <- function(x, y) { ...
#> $object <function>
#> $topic f
#> $alias f
str(block$tags[[1]])
#> List of 5
#> $ file: chr "<text>"
#> $ line: int 2
#> $ tag : chr "memo"
#> $ raw : chr "[TBI] Remember to implement this!"
#> $ val :List of 2
#> ..$ header : chr "TBI"
#> ..$ message: chr " Remember to implement this!"
#> - attr(*, "class")= chr [1:2] "roxy_tag_memo" "roxy_tag"We don’t need a format method because our tag won’t be used to produce Rd sections2.
The roclet
Next, we create a constructor for the roclet, which uses
roclet(). Our memo roclet doesn’t have any
options so this is very simple:
memo_roclet <- function() {
roclet("memo")
}To give the roclet behaviour, you need to define methods. There are two methods that almost every roclet will use:
roclet_process()is called with a list of blocks, and returns an object of your choosing.roclet_output()produces side-effects (usually writing to disk) using the result fromroclet_process().
For this roclet, we’ll have roclet_process() collect all
the memo tags into a named list:
roclet_process.roclet_memo <- function(x, blocks, env, base_path) {
results <- list()
for (block in blocks) {
tags <- block_get_tags(block, "memo")
for (tag in tags) {
msg <- paste0("[", tag$file, ":", tag$line, "] ", tag$val$message)
results[[tag$val$header]] <- c(results[[tag$val$header]], msg)
}
}
results
}And then have roclet_output() print them to the
screen:
roclet_output.roclet_memo <- function(x, results, base_path, ...) {
for (header in names(results)) {
messages <- results[[header]]
cat(paste0(header, ": ", "\n"))
cat(paste0(" * ", messages, "\n", collapse = ""))
}
invisible(NULL)
}Then you can test if it works by using
roc_proc_text():
results <- roc_proc_text(
memo_roclet(),
"
#' @memo [TBI] Remember to implement this!
#' @memo [API] Check best API
f <- function(x, y) {
# ...
}
#' @memo [API] Consider passing z option
g <- function(x, y) {
# ...
}
"
)
roclet_output(memo_roclet(), results)
#> TBI:
#> * [<text>:2] Remember to implement this!
#> API:
#> * [<text>:3] Check best API
#> * [<text>:8] Consider passing z optionAdding a roclet to your workflow
To use a roclet when developing a package, call
roxygen2::roxygenize(roclets = "yourPackage::roclet")where yourPackage::roclet is the function which creates
the roclet, e.g. memo_roclet above.
You can also add the roclet to the target package’s DESCRIPTION file, like this:
See load_options() for more details.
Your package only needs to be installed when the user documents the
package, which doesn’t correspond precisely to any field in the
DESCRIPTION. However, you can think of it as a development
dependency and hence put it in the Suggests: field:
usethis::use_package("yourPackage", type = "Suggests")You don’t have to do this, but it will help other developers working on the target package.
Conclusion: further extension ideas
This vignette is quite rough, so you might want to also read some roxygen2 source code to understand all the extension points.
Do not hesitate to also look for examples of roxygen2 extensions. For
instance, the roxygenlabs
package (former incubator of roxygen2 features) used a third extension
point: it extended the Rd roclet with further methods, thus
creating a supercharged Rd roclet rather than a brand-new roclet. Or,
the plumber2 package
only uses the parsing features from roxygen2, and does not use
roxygenize() at all.
