As part of our NSF-funded passion-driven statistics project, we have just started to share more widely our “translation code” aimed at supporting folks in learning code-based software and in moving more easily between them.  The pdf includes all of the basic syntax for managing, displaying and analyzing data, translated across SAS, R, Python, Stata and SPSS.

These notes contain many sample calculations. It is important to do these yourself—type them in at your keyboard and see what happens on your screen—to get the feel of working in R.

Exercises in the middle of a section should be done immediately when you get to them, and make sure you have them right before moving on.

Many other similar introductions are scattered around the web; a partial list is in the “contributed documentation” section on the R web site (http://cran.r-project.org/other-docs.html). This particular version is limited (it has similar coverage to the standard Introduction to R manual and targets biologists who are neither programmers nor statisticians (yet).

Reproducibility is unquestionably at the heart of science. Scientists face numerous challenges in this context, not least the lack of concepts, tools, and workflows for reproducible research in today's curricula.This short course introduces established and powerful tools that enable reproducibility of computational geoscientific research, statistical analyses, and visualisation of results using R (http://www.r-project.org/) in two lessons:1. Reproducible Research with R MarkdownOpen Data, Open Source, Open Reviews and Open Science are important aspects of science today. In the first lesson, basic motivations and concepts for reproducible research touching on these topics are briefly introduced. During a hands-on session the course participants write R Markdown (http://rmarkdown.rstudio.com/) documents, which include text and code and can be compiled to static documents (e.g. HTML, PDF).R Markdown is equally well suited for day-to-day digital notebooks as it is for scientific publications when using publisher templates.2. GitLab and DockerIn the second lesson, the R Markdown files are published and enriched on an online collaboration platform. Participants learn how to save and version documents using GitLab (http://gitlab.com/) and compile them using Docker containers (https://docker.com/). These containers capture the full computational environment and can be transported, executed, examined, shared and archived. Furthermore, GitLab's collaboration features are explored as an environment for Open Science.Prerequisites: Participants should install required software (R, RStudio, a current browser) and register on GitLab (https://gitlab.com) before the course.This short course is especially relevant for early career scientists (ECS).Participants are welcome to bring their own data and R scripts to work with during the course.All material by the conveners will be shared publicly via OSF (https://osf.io/qd9nf/).