For more details on how to use task views and how to contribute to them, see the repository of the CRAN Task Views Initiative.
The file format for CRAN task views leverages the R/Markdown format (see Xie, Allaire, Grolemund 2019) so that standard Markdown can be used for formatting and structuring the text and a handful of special R functions are provided to link to CRAN packages, other task views, GitHub projects, etc.
In
ctvversions prior to 0.9-0 (released in December 2021), task views used an XML-based format (introduced in Zeileis 2005, R News, 5(1), 39-40 [PDF]). See below for converting task views from the old XML-based format to the new R/Markdown format.
The format is mostly self-explanatory and is illustrated below using
an excerpt from the Econometrics task view which is hosted
on CRAN at https://CRAN.R-project.org/view=Econometrics and
maintained on GitHub at https://github.com/cran-task-views/Econometrics/.
---
name: Econometrics
topic: Econometrics
maintainer: Achim Zeileis, Grant McDermott, Kevin Tappe
email: Achim.Zeileis@R-project.org
version: 2022-09-13
source: https://github.com/cran-task-views/Econometrics/
---
Base R ships with a lot of functionality useful for (computational) econometrics,
in particular in the stats package. This functionality is complemented by many
packages on CRAN, a brief overview is given below. There is also a certain
overlap between the tools for econometrics in this view and those in the task
views on `r view("Finance")`, `r view("TimeSeries")`, and
`r view("CausalInference")`.
Further information can be formatted with standard Markdown syntax, e.g., for
_emphasizing text_ or showing something really important in **bold face**.
R/Markdown syntax with special functions can be used to link to a standard
package like `r pkg("mlogit")` or an important "core" package like
`r pkg("AER", priority = "core")`.
### Links
- Articles: [Special Volume on "Econometrics in R" in JSS (2008)](https://www.jstatsoft.org/v27/)
- [The Title of a Relevant Homepage](https://path/to/homepage/)The document structure consists of three main blocks: (1) Some
metainformation is given in the YAML header at the beginning (separated
by lines with ---), followed by (2) the information in the
main text, and (3) a concluding special section called
### Links.
The metainformation needs to provide the following elements:
name gives the name of the task view in
CamelCase. This is used as the identifier for installing the
view and as the name for the Markdown file, e.g.,
Econometrics.md, and the auto-generated HTML file, e.g.,
Econometrics.html. Hence, it should be not too long
(typically 1-3 words) and contain no special characters like spaces,
hyphens, etc.
topic is a plain text specification of the topic of
the task view. In the example above it is identical to the
name but is often a somewhat longer and more detailed title
(in title case).
maintainer gives the name(s) of the maintainer(s) in
a comma-separated list. The principal contact should be listed first,
followed by a couple of further co-maintainers that help keeping the
task view up to date.
email is the e-mail address of the principal contact
or possibly a dedicated mailing list shared by the
co-maintainers.
version is specified by a date in ISO 8601 format
(yyyy-mm-dd).
Additionally, there may be optional elements: source
can be used to link to the source repository (typically on GitHub) and
url for the URL of the published task view, respectively.
The latter is inserted automatically for the official task views on
CRAN.
The information in the main text should be a short description of the packages, explaining which packages are useful for which tasks. Standard Markdown format can be used to structure the document with sections, itemized and enumerated lists, bold face, italics, etc.
Additionally, short R code chunks with special functions are used for
linking to resources in the same repository: pkg() for
regular packages, pkg(..., priority = "core") for important
“core” packages, and view() for related task views. A
convenience function doi() creates links for DOIs (digital
object identifiers).
The distinction between “regular” and “core” packages is only important for the installation of CRAN task views because the user can specify whether all packages (default) or only the most important core packages should be installed (with all their dependencies).
If a core package is mentioned several times in the document, it
is sufficient to indicate priority = "core" for one of the
occurrences.
Rather than linking to another task view as a whole, e.g.,
view("Econometrics"), it is also possible to link to
specific sections of that view, e.g.,
view("Econometrics", "Instrumental variables").
Moreover, code projects in other repositories can be linked by using the functions:
bioc() for Bioconductor packages at https://www.Bioconductor.org/.github() for GitHub projects at https://github.com/.rforge() for R-Forge projects at https://R-Forge.R-project.org/.gcode() for projects in the Google Code archive at https://Code.Google.com/archive/.ohat() for Omegahat packages at https://www.Omegahat.net/.Note however that CRAN task views are intended mainly for packages on CRAN (as the name conveys). Thus, links to other repositories should be used for important packages/projects but not list all potentially relevant repositories. Also, it is not necessary to list the GitHub projects for all listed CRAN packages as these are typically provided on the package’s CRAN web page.
All CRAN packages included with the pkg() function will
be listed in a dedicated list below the information text when rendering
the HTML version of the task view. Also, the task views as well as
packages/projects in other repositories will be included automatically
in the list of links at the end of the HTML version.
Finally, additional links - e.g., to books, papers, blogs, interest
groups, mailing lists, etc. - can be included in the
### Links section at the end of the file in a standard
itemized list. As explained above this list of links will be
complemented automatically with links generated from the functions
view(), bioc(), github(),
etc.
To check whether a task view file has been formatted properly it can
be read into R and printed. This should display the metainformation and
the list of packages. Subsequently, it can be rendered to an HTML page
and displayed in a browser for checking whether the information text is
processed correctly. Finally, the function
check_ctv_packages() can be used to check whether some of
the listed packages are actually not available on CRAN or not currently
maintained (archived).
For illustration, the code below employs the
Econometrics.md file shipped within the ctv
package. Instead a local MyTopic.md with resulting
MyTopic.html could be used as well.
library("ctv")
file.copy(system.file("ctv", "Econometrics.md", package = "ctv"), "Econometrics.md")
ctv2html("Econometrics.md", cran = TRUE)
browseURL("Econometrics.html")
check_ctv_packages("Econometrics.md")Note that the code above is intended for authors of CRAN task views.
For end-users the functions ctv(),
available.views(), install.views(), and
update.views() are relevant. See https://github.com/cran-task-views/ctv/ for more
details.
The CRAN packages listed in task views should ideally be maintained actively, so that improved versions are released by the corresponding maintainers in case the daily CRAN checks discover any issues.
However, it is not straightforward to test for active maintenance fully automatically and even actively maintained packages may be temporarily archived on CRAN. Hence, the following strategy is adopted:
When a CRAN package from a task view is archived, it is still
listed in the task view like before. It is only flagged as archived in
the text and not installed automatically anymore by
install.views() and update.views().
If the package is still archived after (more than) 60 days, CRAN creates an issue in the GitHub repository of the task view (as in this example).
At this point the task view maintainers can decide to
If the package is still archived after (more than) 100 days, CRAN
follows up on the issue and requests removal of the package from the
task view. (For sufficiently relevant packages it may be sensible to
replace the pkg() link by a github() link in
the task view.)
The ctv package provides an (unexported) R function to
facilitate the transition from the legacy XML format for the task view
files to the new R/Markdown format described above. Simply using
ctv:::ctv_xml_to_rmd("MyTopic.ctv")will create a MyTopic.md file from the .ctv
file. This should do most necessary transformations automatically.
However, it is recommended to thoroughly check the resulting file and
improve it as appropriate. To check the resulting HTML output use
ctv2html("MyTopic.md")
browseURL("MyTopic.html")Note that one important difference between the XML and R/Markdown format is that the “package list” does not need to be listed separately anymore, it is auto-generated from the “info” text. Similarly, the “links” just need to provide those links that are not auto-generated from the “info” text.