diff --git a/getting-started.md b/getting-started.md deleted file mode 100644 index c08794f..0000000 --- a/getting-started.md +++ /dev/null @@ -1,80 +0,0 @@ ---- -layout: page -title: "Resources for Getting Started with R" -permalink: /getting-started.html ---- - -
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- -### Some tips on getting started: - - - Download R [here](http://cran.r-project.org/) - - After you install R, I recommend you install [RStudio](http://www.rstudio.org/). RStudio is a program that makes R easier to use, and is being widely adopted by both beginning and advanced R users. Importantly, it works across most computer platforms, and has features that make it easy to [share your work](http://rpubs.com/), collaborate, and do [proper version control](http://rstudio.org/docs/version_control/overview). - - [Google](http://www.google.com) is your first stop for most questions. You'll most likely reach most of the resources below while searching for your specific question. - - Our regular R work sessions and [listserv](https://groups.google.com/d/forum/davis-rug) are great places to ask questions, especially if you are not sure what you are looking for. - -### Web Resources - -***Everyone learns R differently, for different purposes. Thus, you'll probably -need to learn specialized packages for your field or application. Resources -and books here are general-purpose, with some bent towards ecological statistics -applications.*** - -There's no need to reinvent the wheel. Here are lists of beginner's resources which others have compiled. R tools evolve rapidly, though, so be sure to -check that your guide is up-to-date! - - - [Rseek](http://rseek.org/) is a search engine for R resources. - - RStudio's [Getting Help with R](http://www.rstudio.org/docs/help_with_r) page or [online learning](http://www.rstudio.com/resources/training/online-learning/#R) page. - - - [Beginner tips from Revolution Analytics](http://blog.revolutionanalytics.com/beginner-tips/) - - A [free online course](https://www.coursera.org/course/rprog) from Coursera - - [List of Free Online R Tutorials](http://pairach.com/2012/06/17/r_tutorials_non-uni/) - - - For more advanced usage, Hadley Wickham's [Advanced R](http://adv-r.had.co.nz/) is a free e-book and he is also writing a book on [creating R packages](http://r-pkgs.had.co.nz/) - - - Online materials from an [R-based ecological statistics course](http://www.unc.edu/courses/2010fall/ecol/563/001/index.html) from UNC. - - - Jenny Bryan's excellent Data wrangling course [STAT545](http://stat545.com/) (all materials and code openly available) - - - Ethan White's semester course [Data Carpentry Course for Biologists](http://www.datacarpentry.org/semester-biology/) - - - [RStudio cheatsheets](https://www.rstudio.com/resources/cheatsheets/) for anything & everything - -### Books - - - *R in a nutshell* by Joseph Adler is a good book that's available in the Davis library and [online](http://proquest.safaribooksonline.com/9781449377502) for Davis users. - - [Ecological Models and Data with R](http://ms.mcmaster.ca/~bolker/emdbook/) by Ben Bolker. - - The freely available [Modern Diver: An intro to statistical and data sciences in R](https://ismayc.github.io/moderndiver-book/) by Chester Ismay and Albert Y. Kim - - A great and in depth book by Hadley Wickham & Garrett Grolemund: [R for Data Science](http://r4ds.had.co.nz/) - - [The Art of R Programming](http://www.nostarch.com/artofr.htm) - - [Introductory R](http://www.introductoryr.co.uk/) - - *A Primer of Ecology with R* by M. Henry H. Stevens is also available [online](http://www.springerlink.com/content/l48073/?p=6e7edb19e2964135bb5b67aa016171de&pi=15#section=64711&page=5&locus=0) - -### Other Mailing Lists, Discussion Boards and Resources - -These mailing list are very useful not just as a place to ask questions. They are probably where you will find your answers when you search on Google. - - - The [R-Help mailing list](http://www.r-project.org/mail.html) and it's many subgroups, including an [Ecology-specific group](https://stat.ethz.ch/mailman/listinfo/r-sig-ecology) - - [Stack Overflow](http://stackoverflow.com/) is a popular Q&A site for computer programming that a lot of [discussions about R](http://stackoverflow.com/questions/tagged/r). - - [The Davis Scientific and Statistical Computing List](https://lists.ucdavis.edu/sympa/info/statscicomp) is mostly used by advanced users and includes some of the developers of R. - -### Miscellaneous - - - [R-bloggers](http://www.r-bloggers.com/) - - The [One R Tip A Day Twitter Account](https://twitter.com/RLangTip) - - [#rstats](https://twitter.com/#!/search/%23rstats?q=%23rstats) is a common hashtag for discussing R on Twitter - - -
- -[Getting Started](#some-tips-on-getting-started) - -[Web Resources](#web-resources) - -[Books](#books) - -[Mailing Lists](#other-mailing-lists-discussion-boards-and-resources) - -[Miscellaneous](#miscellaneous) -
diff --git a/r-at-davis.md b/r-at-davis.md deleted file mode 100644 index f3307fd..0000000 --- a/r-at-davis.md +++ /dev/null @@ -1,31 +0,0 @@ ---- -layout: page -title: "R Courses at Davis" -permalink: /r-at-davis.html ---- - -
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-There are a few courses at UC Davis that use R. - - - [Duncan Temple Lang](http://www.stat.ucdavis.edu/~duncan/) (one of the developers of R) teaches [*Statistical Computing* (STA141)](http://eeyore.ucdavis.edu/stat141/), a course mostly about R but also more general topics in computer science for statistics. He also organizes an informal seminar series on statistical computing. [STA 242](http://eeyore.ucdavis.edu/stat242/) is a more advanced version of the course. He also will teach STA 135 - *Multivariate Data Analysis* this year, which is not about R but uses R for data exploration, data mining, and regression. - - [Richard McElreath](http://xcelab.net/rm/)'s *Statistical Rethinking* (ANT298) is a course in Bayesian statistical methods which doesn't focus on R but teaches enough for the applications in the course. No longer taught but materials all available online. - - [Data Wrangling for Ecologists](https://gge-ucd.github.io/wRangling-Ecology/) was a ECL298 taught by [Ryan Peek](https://ryanpeek.github.io/): *Using data science tools (R and Git) for exploration, analysis and research* Winter Quarter 2017. The course may be taught by someone else in the future, but is still up in the air. All materials available on website or [here](https://github.com/gge-ucd/wRangling_Seminar). - - [Marissa Baskett](http://www.des.ucdavis.edu/faculty/baskett/) and [Sebastian Schreiber](http://www-eve.ucdavis.edu/sschreiber/) teach *Computational methods in population biology* (ECL298) in alternate years. This course also isn't explicitly about R but teaches enough basics so as to be able to use it for the applications in the course. They also created this handy [cheat sheet](http://www.des.ucdavis.edu/faculty/baskett/downloads/Rcommands.pdf) - - *Design, Analysis, and Interpretation of Experiments* (PLS 205) has in the past provided an optional extra section to learn techniques in R in addition to SAS - - Carole Hom teaches *Introduction to Dynamic Models in Modern Biology* (BIS 132) where R is used for differential and difference equation modeling. - - There are occasional [paid workshops](http://www.hafnerconsulting.com/ucd2012/) offered on campus - - At least one section of STA100 uses R - - [Robert Hijmans](http://www.des.ucdavis.edu/FacultyInfo.aspx?ID_Number=83) teaches *Quantitative Geography* (GEO200CN). It is a survey course about spatial data analysis and modeling using R, including subjects such as point pattern analysis, kriging, inference, cellular automata and Markov chains. It has lectures, disussions, and a intensive lab. - - [Jim Fadel](http://animalscience.ucdavis.edu/faculty/fadel/) teaches ABG250: *Mathematical Modeling in Biological Systems*, which uses R and teaches enough for the applications in class - - The political science methods sequence (POL211, POL212, POL213) uses R. - - [Andrew Latimer](http://www.plantsciences.ucdavis.edu/faculty/latimer/index.htm)'s *Applied Statistical Modeling for Environmental Science* (PLS298) uses R in addition to JAGS and OPENBugs. It assumes familiarity with R. - -### General Statistics Resources at UC Davis - - - Here is [a page compiling stats and modeling courses](stats.html) that Ecology students can take at UC Davis - - The UC Davis Department of Statistics has a [consulting service](http://anson.ucdavis.edu/stats-lab/services) that is free for disseration-related statistical advice. You get a 1-hour meeting with a statistician to discuss your research and they will send you a write-up of their recommendations. - - -
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