Data Science in Infectious Disease Modeling using R

Online Lab Meeting Times:

• Monday, July 7, 1:00 - 2:30 PM ET and 3:00 - 4:30 PM ET
• Tuesday July 8, 1:00 - 2:30 PM ET and 3:00 - 4:30 PM ET

Classroom: Virtual

Module Summary:

This course will foster a problem solving mindset while exploring more advanced concepts in R. There are multiple approaches to coding an analysis; contents covered in this course will serve as a guide to help you decide which approach is best for your particular situation.  

We will draw concepts from R for Data Science, Advanced R, and R for Epidemiology books along with the instructors’ experiences with infectious disease modeling.  We will work in both base R and the tidyverse to wrangle messy data and build an analytic workflow.  The course will consist of three themes: Foundations in data science not often covered in Intro R courses, Advanced data wrangling tailored to infectious disease research, and Special considerations for SISMID and public health data.

Prerequisites:

Familiarity with R base and Tidyverse.

Module Content:

• Foundation Theme 

  • Structuring (file organization and object naming)
  • Building “good” functions and environment scoping
  • Programming for speed (writing efficient functions, parallelize processes, and when to use another language)
  
  

  Problem solving using advanced data wrangling 

  • Pivot tables, joins and look up tables
  • Regular Expressions
  • Advanced troubleshooting using conditions (TryCatch, log, etc.) 
  • Implementing parallelized processes 
  
  

  Special considerations 

  • Working with encrypted files
  • Web scraping? 
  • Common implementation/coding pain points in most popular SISMID courses