This is the landing page for Introduction to Data Analysis using R. This page is used for posting of regular announcements and information for students of the class.


Target Audience

Anyone who wants to do their own data analysis! This is a primer to get the complete novice up and running with the basic knowledge of how to use the statistical programming language R for data analysis. Topics include: R programming basics, importing data, properties of tidy data, visualizing data, reproducible research with Markdown and basic data wrangling. Designed to get you up and running with basic knowledge of R and Markdown ASAP. This course is designed as a pre-requisite for most upper division Statistics, and all Data Science courses which use R heavily.

See the syllabus for details about the current class offerings.


Announcements

This site is in the process of being updated. Many links will not work


Schedule (Fall 18)

Last Updated: 07/18/2018

Week 1: Getting started with R and R Studio

  • Day 1: Introduction to the class and R. [01_intro]
  • Assignments Due before Day 2
    • Complete Data Camp: Introduction to R - Chapter 1 (Intro to Basics)
    • Watch 1 minute video: “What is R Markdown?
  • Day 2: Reproducible Research using R Studio [02_rstudio]
  • Assignments: [Lab 1]
    • Directly based on Data Camp - Introduction to R.
    • You will submit the .RMD file for this assignment to Blackboard learn by the due date.

Week 2: Importing, exploring, managing data using functions.

Week 3: Factors, more data management and grouped summaries.

Week 4: Making pretty pictures to win friends and influence people.

Week 5: Installing R locally, Exploratory Project

  • Install R and R Studio on your personal computer.
    • Because cloud computers and lab computers are not always available.
    • Follow the Software Overview post to get your computer up and running with the R studio suite of awesomeness.
  • Exploratory Data Analysis Project
    • Now it’s time to put your new 1337 skillz to the test!
    • Instructions on the assignment and how to do the peer review.
    • Previous class projects can be seen [here].