Software Carpentry: R for Reproducible Scientific Analysis

About this Class

Link for ALL class sessions including help sessions. Register Meeting number: 172 866 2623 Password: NYy4m3V3i3* Dial in: 650-479-3207 The goal of this workshop is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. This is very important! Please follow this link and install R and Rstudio onto your computer before class. If you have trouble doing this please send email to ncibtep@nih.gov and we will help you. This class is held weekly for 6 weeks. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to this training. Nov 3, 11 AM - 1 PM, Week 1, Introduction to R and RStudio, Project Management with RStudio and Seeking Help (link to recording) Nov 10, 11 AM - 1 PM, Week 2, Data Structures, Exploring Data Frames and Subsetting Data Nov 17, 11 AM - 1 PM, Week 3, Creating Publication-Quality Graphics with ggplot2 Nov 24 NO CLASS Thanksgiving Holiday Dec 1, 11 AM - 1 PM, Week 4, Control Flow, Vectorization and Functions Explained Dec 8, 11 AM - 1 PM, Week 5, Writing Data, Dataframe Manipulation with dplyr and Dataframe Manipulation with tidyr Dec 15,11 AM - 1 PM, Week 6, Producing Reports with knitr and Writing Good Software For more information and class updates, please see the GitHub page for the class at https://abcsfrederick.github.io/Intro-to-R-Fall2020/Schedule Help Sessions Nov 5, 12 - 1 PM, Week 1, Setting up access to the Course Materials with Git, Questions from Week 1 Nov 12, 12 - 1 PM, Week 2 Nov 19, 12 - 1 PM, Week 3 Dec 3, 12 - 1 PM, Week 4 Dec 10, 12 - 1 PM, Week 5 Dec 17, 12 - 1 PM, Week 6