- Notes:
- Tentative calendar (weekly topics), subject to changes depending on the pace of the course.
- Labs: For the covered topics in a given week, the associated lab takes place on Th/Fr of the current week.
- Dates: Aug 23-25
- Topics: Introduction, policies/logistics, and course in a nutshell.
- Lecture material
- About the Course (slides)
- Introduction: Big Picture (slides)
- Lab: No lab
- Reading:
- Course policies, and FAQs
- To Do:
- Dates: Aug 28-Sep 01:
- Topics: Getting started with R, and comprehensive review of the RStudio workspace.
- Lecture material
- About R (slides)
- First contact with R (tutorial)
- Intro to Rmd files (tutorial)
- Data Types and Vectors (slides)
- Lab material:
- Reading:
- Markdown tutorial by CommonMark
- www.markdowntutorial.com/
- Introduction to R Markdown by RStudio
- Cheat sheet:
- WARM-UP: due Sep-09
- Dates: Sep 04-08 (Holiday Sep-04)
- Topics: Getting to know R vectors and concepts like atomicity, vectorization, recycling, and subsetting.
- Lecture material
- Intro to vectors (tutorial)
- Arrays and Factors (slides)
- Lists (slides)
- Lab material:
- Reading:
- chapter 20: Vectors from R for Data Science by Grolemund and Wickham.
- WARM-UP: due Sep-16
- Dates: Sep 11-15
- Topics: Fundamental low-level stuff for the rest of the course.
- Lecture material
- Filesystem (slides)
- Shell Basics (slides)
- Git Basics (slides)
- Lab material:
- Reading:
- Installing Git and Github from Happy Git and GitHub for the useR by Jenny Bryan et al.
- Cheat sheet:
- Dates: Sep 18-22
- Topics: Data Tables, typical storage formats, and relation with data frames.
- Lecture material
- Data Tables (slides)
- Data Frames (slides)
- Importing Tables in R (slides)
- Basic manipulation of data frames (slides)
- Lab material:
- Reading:
- Organizing data in spreadsheets by Karl Broman
- Introduction to dplyr introductory vignette by Hadley Wickham
- Cheat sheet:
- Dates: Sep 25-29
- Topics: Data wrangling (reshaping, aggregating) with
"dplyr", and graphs with"ggplot2". - Lecture Material
- Introduction to the R package
dplyr - Introduction to the R package
ggplot2 - Introduction to shiny apps (slides)
- Introduction to the R package
- Lab material:
- Reading:
- Tidy Data by Hadley Wickham
- Cheat sheet:
- HOMEWORK: Problem set 2
- Dates: Oct 02-06
- Topics: Basics of Functions, R expressions, and conditionals.
- Lecture Material
- Lab material:
- Reading:
- chapter 19: Functions from R for Data Science by Grolemund and Wickham.
- Cheat sheet:
- Dates: Oct 09-13
- Topics: Basics of loops, and more about functions.
- Lecture Material
- Lab material:
- Reading:
- chapter 21: Iteration from R for Data Science by Grolemund and Wickham.
- Cheat sheet:
- HOMEWORK: Problem set 3
- Dates: Oct 16-20
- Topics: Random numbers, sampling, and monte carlo simulation.
- Lecture Material
- Lab material:
- Reading:
- TBA.
- Cheat sheet:
- HOMEWORK: Problem set 3
- Dates: Oct 23-27
- Topics: Character strings.
- Lecture Material
- Lab material:
- Reading:
- Strings in R by Gaston Sanchez.
- chapter 14: Strings from R for Data Science by Grolemund and Wickham.
- Cheat sheet:
- Dates: Oct 30-Nov 03
- Topics: Introduction to Regular Expressions.
- Lecture Material
- Lab material:
- Reading:
- Strings in R by Gaston Sanchez.
- Cheat sheet:
- HOMEWORK: Problem set 4
- Dates: Nov 06-10
- Topics: Extensible Markup Language (XML) and related formats.
- Dates: Nov 13-17
- Topics: Visual Perception, Colors, Effective Statistical Graphics
- Dates: Nov 27-Dec 01
- Topics: TBA