(This article was first published on

**R – christopher lortie**, and kindly contributed to R-bloggers)Purpose

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.

Slide decks

**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.

Here is the knitted RMarkdown html notes from the course too **https://cjlortie.github.io/r.stats/**, and all the materials can be downloaded from the associated **GitHub repo**.

I hope this collection of goodies can be helpful to others.

To

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