Software Carpentry: Unix, Git, and R for Reproducible Research
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Software Carpentry: Unix, Git, and R for Reproducible Research
Software Carpentry aims to help researchers get their work done in less time and with less pain by teaching them basic research computing skills. This hands-on workshop will cover basic concepts and tools for conducting reproducible research with data using the Bash Unix shell, version control with Git, and R/RStudio. Learners are expected to attend the full two days.
By the end of the workshop, learners will:
- Manipulate files and data using a Unix shell, Git and R
- Automate operations on multiple files using a Unix shell and R
- Navigate and manipulate data, R scripts, and output in the RStudio environment
- Familiarize themselves with common data types, programming concepts and procedures in R
- Practice project management and collaboration in Git
- Identify and resolve errors independently
- Identify options for self-directed and guided learning related to Unix, Git and R
- Practice creating functions, loops and conditionals to streamline and automate programs
- Recognize key practices for annotating and documenting code
The workshop is open to anyone. You do not need any programming experience to participate but you will need access to a Windows, Mac or Linux computer. The workshop will be hosted online using Zoom; instructions for accessing course material and installing software will be sent in advance. For questions, contact Data Education Coordinator Nathaniel Porter (ndporter@vt.edu).
For more information on the workshop, visit https://ndporter.github.io/2022-08-04-vt-swc-r-online/.
If you are an individual with a disability and desire an accommodation, please email library-event-accessibility-g@vt.edu at least 10 business days prior to the event.
- Dates & Times:
- 9:00am - 5:00pm, Thursday, August 4, 2022
9:00am - 5:00pm, Friday, August 5, 2022
- Location:
- Online
- Audience:
- Alumni Beginners Faculty/Staff Graduate Students Public Undergraduates
- Categories:
- Workshop > Data Science Workshop