The Statistical Computing Series

The Statistical Computing Series is a monthly event for learning various aspects of modern statistical computing from practitioners in the Department of Biostatistics. We focus on topics related to the R language, Python, and related tools, but we include the broadest possible range of content related to effective statistical computation. The format varies, depending on the speaker and the topic, from lectures to demonstrations to hands-on workshops.

If you have a particular topic you would like to see covered, please send a request.

There have been several requests for coverage of various topics. Here is a short list, if you are interested in contributing but are seeking inspiration:

  • writing R functions with formula arguments
  • writing R functions with methods
  • using makefiles
  • other graphics packages (base graphics)
  • lme4/nlme
  • reshape (package not function)/plyr
  • R data structures
  • bootstrapping / random number generating
  • imputation (using various packages and functions)
  • bibtex
  • software for slide presentations

Time & Location

Fourth Friday of each month at 1:30 pm in the Biostatistics Conference Room (11105, 2525 West End Avenue).

Email Notification

We send out email notifications the week of a particular presentation. If you would like to be added to the list, please let us know.

Fall 2017 Schedule

Introduction to Jupyter Notebooks for Interactive and Reproducible Research!

29 September, 2017 Chris Fonnesbeck

Jupyter (formerly IPython) notebooks are a flexible and powerful tool for data science in both local and cloud-based environments. The notebooks allow data analyses to be integrated with markdown text, html, math, multimedia and other supporting materials and technologies to make scientific programming more literate and the generation of reports, web pages and even presentation slides seamless. While originally designed as a Python front-end, Jupyter works with R, Julia, Spark, and dozens of other languages via custom-built kernels. This presentation will introduce Jupyter notebooks and demonstrate how they can provide a powerful platform for reproducible quantitative research.

GitHub repository with notebook


Intermediate Version Control and Collaboration Workflows using Git and GitHub

27 October, 2017 Chris Fonnesbeck

Git has become a standard tool for version control of code for scientific computing and software development. Its effectiveness as a collaborative system is enhanced by commercial repository management services such as GitHub, BitBucket and GitLab, which provide remote repositories for working with teams on larger projects, as well as services for managing issues and code contributions from users. This tutorial will cover intermediate Git functionality required to use remote repositories effectively, including branching, cloning, merging and rebasing. I will also demonstrate best practices for participating in collaborative GitHub projects, such as creating issues and pull requests. This tutorial will assume participants are familiar with elementary Git usage.


Click to view previous presentations

Topic revision: r142 - 24 Oct 2017, ChrisFonnesbeck
 

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