Rdutils {tools}

R Documentation

Rd Utilities

Description

Utilities for computing on the information in Rd objects.

Usage

Rd_db(package, dir, lib.loc = NULL)
Rd_parse(file, text = NULL)

Arguments

package	a character string naming an installed package.
dir	a character string specifying the path to a package's root source directory. This should contain the subdirectory ‘man’ with R documentation sources (in Rd format). Only used if package is not given.
lib.loc	a character vector of directory names of R libraries, or NULL. The default value of NULL corresponds to all libraries currently known. The specified library trees are used to search for package.
file	a connection, or a character string giving the name of a file or a URL to read documentation in Rd format from.
text	character vector with documentation in Rd format. Elements are treated as if they were lines of a file.

Details

Rd_db builds a simple database of all Rd sources in a package, as a list of character vectors with the lines of the Rd files in the package. This is particularly useful for working on installed packages, where the individual Rd files in the sources are no longer available.

Rd_parse is a simple top-level Rd parser/analyzer. It returns a list with components

meta

a list containing the Rd metadata (aliases, concepts, keywords, and documentation type);

data

a data frame with the names (tags) and corresponding text (vals) of the top-level sections in the R documentation object;

rest

top-level text not accounted for (currently, silently discarded by Rdconv, and hence usually the indication of a problem).

Note that at least for the time being, only the top-level structure is analyzed.

Warning

These functions are still experimental. Names, interfaces and values might change in future versions.

See Also

parse_Rd, an experimental parser.

Examples

## Build the Rd db for the (installed) base package.
db <- Rd_db("base")
## Run Rd_parse on all entries in the Rd db.
db <- lapply(db, function(txt) Rd_parse(text = txt))
## Extract the metadata.
meta <- lapply(db, "[[", "meta")

## Keyword metadata per Rd file.
keywords <- lapply(meta, "[[", "keywords")
## Tabulate the keyword entries.
kw_table <- sort(table(unlist(keywords)))
## The 5 most frequent ones:
rev(kw_table)[1 : 5]
## The "most informative" ones:
kw_table[kw_table == 1]

## Concept metadata per Rd file.
concepts <- lapply(meta, "[[", "concepts")
## How many files already have \concept metadata?
sum(sapply(concepts, length) > 0)
## How many concept entries altogether?
length(unlist(concepts))

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