The course is for researchers, public health professionals, epidemiologists, and clinicians who want to improve their R coding skills, who want to learn modern tools in the R ecosystem, like the tidyverse and Shiny, or who want to get started writing software in R.

Date: 02.09. - 06.09.2019

Lecturer: Malcolm Barrett, MPH

Malcolm Barrett is a PhD candidate in epidemiology at the University of Southern California. He studies vision loss and eye diseases that affect vision, like diabetic retinopathy. Specifically, he works on methods to improve the study of how vision impacts quality of life, including tools from psychometrics and causal inference, to make vision-specific quality of life analyses more accurate and more interpretable. In addition to applied research, Malcolm also develops R packages for epidemiologic and biostatistical methods, teaches R, and organizes the Los Angeles R Users Group. He regularly contributes to open source software, including favorite community projects like ggplot2 and R Markdown.

Learning objectives:

At the end of the week, participants will have:

• Mastered the tidyverse, a set of principled tools for data science. The tidyverse is a friendly, readable, and fast set of packages intended to work well together, to improve code readability, and to make analyses more reproducible.

• Written dynamic documents using best practices for reproducible research, including using R Markdown. R Markdown intertwines code and text so that reports and articles are fully reproducible and exportable to PDF, HTML, Word, and more. R Markdown also has excellent support for citation management and formatting for journals.

• Modeled simple statistical and causal problems using both classical regression (linear, logistic) and G-methods.

• Written robust functions, programmed with functions, and created a basic R package.

• Built ready-to-share web applications entirely in R using the Shiny framework.

Pre-requisits:

Basic epidemiology and statistics. Some experience with R programming will be helpful, but those new to R can take a free introductory course on DataCamp: https://www.datacamp.com/courses/free-introduction-to-r  

Materials:

R for Data Science (https://r4ds.had.co.nz/) and Advanced R, ed. 2 (https://adv-r.hadley.nz/), both free online.

Course Information:

• Course language: English

• ECTS points: 3

• Course Fee: 510€ for students, 750€ for other participants

Contact: tanja.tegude@charite.de