How to Find Help Using R Mailing Lists - dummies

How to Find Help Using R Mailing Lists

The R Development Core Team actively supports four different mailing lists. At the R mailing list website, you can find up-to-date information about these lists, as well as find links to subscribe or unsubscribe from the lists. When you subscribe to a mailing list, you can choose to receive either individual e-mail messages or a daily digest.

The four important mailing lists are

  • R-help: This is the main R Help mailing list. Anyone can register and post messages on this list, and people discuss a wide variety of topics (for example, how to install packages, how to interpret R’s output of statistical results, or what to do in response to warnings and error messages).

  • R-announce: This list is for announcements about significant developments in the R code base.

  • R-packages: This list is where package authors can announce news about their packages.

  • R-devel: This is a specialist mailing list aimed at developers of functions or new R packages — in other words, serious R developers! It’s more about programming than about general topics.

Before posting a message to any of the R mailing lists, make sure that you read the posting guidelines. In particular, make sure you include a good, small, reproducible example.

In addition to these general mailing lists, you also can participate in about 20 special interest group mailing lists.

The R mailing list website also contains links to more than 20 mailing lists for special interest groups. We list them here by topic:

Operating systems:

  • R-SIG-Mac: Mac ports of R

  • R-SIG-Debian: Debian ports of R

  • R-SIG-Fedora: Fedora and Redhat ports of R

Advanced modeling:

  • R-sig-dynamic-models: Dynamic simulation models in R

  • R-sig-Epi: Epidemiological data analysis

  • R-sig-ecology: Ecological data analysis

  • R-sig-gR: Graphical models

  • R-sig-networks: Network- or graph-related software within R

  • R-sig-phylo: Phylogenetic and comparative methods and analyses

  • R-sig-Robust: Robust statistics

  • R-sig-mixed-models: Mixed effect models, notably lmer()-related

Fields of application for R:

  • R-SIG-Finance: Finance

  • R-sig-Geo: Geographical data and mapping

Specialist development:

  • R-sig-DB: Database Interfaces

  • R-SIG-GUI: GUI development

  • R-SIG-HPC: High-performance computing


  • R-sig-Jobs: Announcements of jobs where R is used

  • R-sig-mediawiki: The R extension for MediaWiki

  • R-sig-QA: Quality assurance and validation

  • R-sig-teaching: Teaching statistics (and more) using R

  • R-sig-Wiki: The development of an “R wiki”