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R Mentoring Program ( Join Us - Start on Your Schedule) 
My unique R-Guru mentoring program assumes no prior R knowledge. This comprehensive program includes: installing R and RStudio, familiarizing the basics of R, understanding R's data structures, learning the basics of data import and manipulation, and finally practicing data reporting and visualization. At the end of completing all weekly hands-on exercises, you will earn the R Clinical Trials Certificate! See Table of Tasks and Papers and Videos. E-mail me your R questions!
A. R-Guru Weekly Videos & Exercises (Install RStudio, Learn R, password required)
| Week / Video 1: What is R, Getting Data into R | Week / Video 5: DPLYR for SQL and %>% Piping |
| Week / Video 2: View, Create Vars, Data Frames and Manage Data Frames | Week / Video 6: Create SDTMs |
| Week / Video 3: Join, Summarize and Transpose Data Frames | Week / Video 7: Create ADaMs |
| Week / Video 4: Tidyverse Data Management Operations | Week / Video 8: Tables, Stats, Graphs |
B. R-Guru Class Weekly Structure
- For R-Guru Member & Unversity Students: Videos Show How to complete Exercises + Hands-on Exercises
- For Mentoring Class: Live Web Sessions + Student Journal + Videos + Hands-on Exercises
- R-Guru Class Introductions Presentation
- 30 Minute Live Lecture Intervals
- 5 Min. Introduction to Topics
- 20 Min. Demo of R Syntax and Examples
- 5 Min. Summary, Questions and Answers, Plans and Questions for Next Week's Topics
Mathura Ramanathan, Statistical Programming & CDISC Standards Consultant
R-Guru.com is a wealth of treasures and information for anyone who is motivated to get ready for R.
Amole Palande, Director, Statistical Programming at Chinook Therapeutics, Inc. A Novartis Company
I enjoyed learning R and believe I will be able to leverage/advance this knowledge as needed in my career.
Ekaterina Rudenko, Statistician
I liked that there was a lot of information packed in a precise manner. I feel like I learned a lot in just those 6 weeks.
Master Concepts: Getting Started with R, Data Structures, R Packages and Scripts, Descriptive Statistics, Statistical Graphs, Working with Messy Data, Conditionals, Controls and Functions, SQL and R
