This lesson was commisioned by the EPSRC Digital Health Hub for AMR, for delivery to the UK HSA w/c 16th September 2024.
An introduction to R for non-programmers using the Gapminder data. In addition, this lesson makes use of open data from the Centre for Consumer Research Data and bespoke synthetic data provided by UKHSA.
Please see https://ucl-arc.github.io/r-amr-epidemiology for a rendered version of this material,
the lesson template documentation
for instructions on formatting, building, and submitting material,
or run make in this directory for a list of helpful commands.
The goal of this lesson 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 lesson has been expanded to incorporate additional Software Carpentry content on the use of Git for Version Control, navigating files and directories in a terminal, SQL and new content on the creation and validation of regression models.
A variety of third party packages are used throughout this workshop. These are not necessarily the best, nor are they comprehensive, but they are packages we find useful, and have been chosen primarily for their usability.
Content developed and modified by:
Current Maintainers:
Previous Maintainers:
- David Mawdsley
- Jeff Oliver
- Tom Wright
- SWC The Unix Shell: Navigating Files and Directories
- SWC Version Control with Git, modified for context.
- LC SQL
- https://libguides.princeton.edu/R-linear_regression
- https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/assumptions-of-linear-regression/
- https://cran.r-project.org/web/packages/broom/vignettes/broom.html
- https://jmsallan.netlify.app/blog/linear-regression-with-broom/
- https://stats.oarc.ucla.edu/r/dae/logit-regression/
- https://github.com/jenineharris/logistic-regression-tutorial/blob/main/20211210-logistic-regression-tutorial-code.R
- https://dept.stat.lsa.umich.edu/~jerrick/courses/stat506_f23/08-sql.html
- https://www.quackit.com/sqlite/tutorial/about_sqlite.cfm