Introduction to Modern R is a 3-day course that turns data analysts into skilled R programmers. The course is run as a series of short lectures followed by group exercises. I regularly update the course to reflect current best practices, the latest things I’ve learned, and feedback from previous students. At the end of class participants receive a PDF version of my slides along with a workbook that summarizes key points from the course.
While I prefer to teach on-site and in person, I can also provide live training via Zoom for remote and distributed teams.
Day 1: RStudio and the Tidyverse. Navigating the RStudio GUI. Base R, packages and the Tidyverse. Importing Excel and CSV files. Data visualization with ggplot2: data, aesthetics, geoms and layers. Data manipulation and summary statistics with dplyr. Piping with %>%.
Day 2: Base R and Package Development. Base R: Atomic data types, vectors, vectorized operations, $, conditional logic, writing your own functions, environments. When to write a function. Creating your own package using devtools and roxygen2. Build and Check. Required files (e.g. Description), exporting functions and creating documentation. When to create a package.
Day 3: RMarkdown and Shiny. Reproducible research with RMarkdown. The Markdown language. Embedding R code in Markdown. Knitting files. YAML, Chunks and Chunk Options. Creating web applications with Shiny. Client vs. Server programming. Adding UI elements and connecting them to the server. Reactive programming.
Introduction to Modern R can be customized in a variety of ways.
The most common customization is to add examples that use your company's data. The standard course uses simple datasets because they allow students focus on key concepts. In all likelihood your company's data will look different than the classroom data and require some processing before it can be analyzed. By adding examples that use your company's data we can help minimize friction as your analysts start applying what they learn in class oo their job.
Topics can also be added and removed from the course based on your specific needs. For example, string manipulation is not covered in the standard course. But if this topic is important for your analysts, we can add to the syllabus.
Analysts normally have questions as they apply the course content to the their own data and analyses. We can create a post-training support plan to help them with this transition.
Ari is both a gifted teacher and a very insightful programmer -- attributes you don't always find strongly within the same person! He developed a clear plan to advance the R programming skills of our group and boosted our overall competency to the degree he projected in our SOI. In short, his vision matches execution, and he has the skills, insight and experience needed to succeed as a statistical consultant. Thank you Ari!
Ari’s Training Blueprint was immensely helpful in building a solid foundation for a transition to an R-based work environment and moved us efficiently along a productive trajectory.
We have an in-house training program, but these trainings tend to be generic. One of the great ideas that Ari brought was training specific to our needs and to the type of work we do. He spent time up front to understand our team and what we are trying to develop. His deep knowledge of R helped him understand our mission and come up to speed on the packages developed by the experienced R user in our group.
What I liked most about Ari’s Training Blueprint was that Ari has a great sense for how to introduce new information without overloading people. His training was effective because he made the experience a positive one, with early wins for group members less familiar with R.
I would definitely recommend Ari’s Training Blueprint to anyone who works with teams to make effective use of R. Ari helped our team develop skills and greatly enhance our level of comfort using R.