Intro to R and Reproducible Research

EFB 645: Spring 2026

Course Materials

This page contains lectures,assignments and other resources, and will expand as the semester progresses. For Homework Submissions and Discussion Boards see the Blackboard Course Page

Instructors: Dr. Hyatt Green (Illick 201) and Dr. Elie Gurarie (Illick 206)

Office hours: TBD

Locations and Times: Baker 309; MW 10:35-11:30

Overview

This is a broad, practical, introduction to R Programming. Here is a detailed Syllabus.

Perhaps the most important immediate need is to download and install R and Rstudio:

• To download R visit: http://www.r-project.org/
  
• To download R Studio visit: https://posit.co/products/open-source/rstudio/?sid=1

Lectures and Labs

Module 1 - Introduction

Operators | Data Types | Data Structures

• Lecture 0: Intro to R
• Lecture 1: Operators
• Lecture 2: Data Classes
  • Lecture for Jan 21 and 26 (remote).
  • Link for remote access
  • quant_prog.R function
• Lecture 3: Data Structures (Jan 28)
  • Birds.R function

Module 2 - Working with data

Indexing | Importing/Exporting | Plotting | Summarizing

• Lecture 4: Indexing Vectors (Feb 2)
  • Top300MF_names.Rdata
• Lecture 5: Indexing Dataframes (Feb 4)
• Lecture 6: Importing and Exporting Data (Feb 9)
  • Puromycin_data (csv)
  • qPCR data (txt)
• Lecture 7: Plotting Basics (Feb 11)
• Lecture 8: Plotting Part II (Feb 18):
  • slides
  • document style
  • World Development Index data (csv)
• Lecture 9: Plotting Part III (Feb 23):
  • slides
  • document style
  • Rmarkdown
• Lecture 10: Data Summaries and Aggregation (Feb 25)
• Lecture 11: Merging Data (Mar 2)

Module 3 - Effective Programming

Functions | Loops | Lists | Apply

• Lecture 12: Writing Functions (Mar 4)
• Lecture 13: Mainly Loops!
• Lecture 14: HW Feedback, ifelse(), and ddply() (March 18):
  • HW feedback slides
  • if, else, and ifelse() slides
  • ddply() slides
• Lecture 15: apply mentality and becoming a list ninja
• Lecture 16: Working with Dates and ddply()
  • slides
  • ACW-met data (csv)

Module 4 - Advanced tools

Shiny | LLM’s | Building Packages

• Lecture 17: Creating R-Shiny Apps
  • slides
  • document style
• Lecture 18: Smart use of LLM’s
  • slides
  • WDI data
  • app5.R
• Lecture 19: Regular Expressions
  • slides
  • NCBI 16S Headers (txt)
    
  • USGS Gauges (txt)
• Lecture 20: Building R Packages
  • slides
  • document-style
  • content for combinatoR package:
    • single.csv,
    • mixture.csv
    • fittingfunctions.R

Tools and Tricks in Reproducible Research

• Markdown Basics
• YAML and chunks

Homeworks

Submit all assignments to the Blackboard page

• Homework 01 (due Jan 21)
  • (Note: this is an Rmd file which you will download, modify, knit, and upload.)
    
• Homework 02 (due Jan 28)
  • (Note: this is an html file. You will start your own Rmd file from scratch to complete the questions. Then you will knit and upload.)
• Homework 03 (due Feb 4)
• Homework 04 (due Feb 11)
  • Elightenment Thinkers Data
• Homework 05 (due Feb 25)
  • World Development Index Data
• Homework 06 (due Mar 04)
  • (Note: this is an Rmd file which you will download, modify, knit, and upload.)
• Homework 07 (due Mar 25)
  • (Note: this is an Rmd file which you will download, modify, knit, and upload.)
  • Here’s the nicer-looking html
• Homework 08 (due Apr 17)