I recently completed a set of data science for biostatistics training exercises for graduate students. I extensively used R for Data Science and Efficient R programming to develop a set of Adventure Time R-statistics slide decks. Whilst I recognize that they are very minimal in terms of text, I hope that the general visual flow can provide a sense of the big picture philosophy that R data science and R statistics offer contemporary scientists.
- WhyR? How tidy data, open science, and R align to promote open science practices.
- Become a data wrangleR. An introduction to the philosophy, tips, and associated use of dplyr.
- Contemporary data viz in R. Philosophy of grammar of graphics, ggplot2, and some simple rules for effective data viz.
- Exploratory data analysis and models in R. An explanation of the difference between EDA and model fitting in R. Then, a short preview of how to highlighting modelR.
- Efficient statistics in R. A visual summary of the ‘Efficient R Programming’ book ideas including chunk your work, efficient planning, efficient planning, and efficient coding suggestions in R.
I hope this collection of goodies can be helpful to others.