This is the landing page for Introduction to R (MATH 130). 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 has been updated for Fall 18. Please notify me of any broken or missing links.


Schedule

Last Updated: 09/19/2018

Prepare: Get connected and install programs

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)
    • Learn about the R Studio IDE by completing the Data Camp assignment: Working with the R Studio IDE (Part 1)
    • Watch 1 minute video: “What is R Markdown?
  • Day 2: Reproducible Research using R Studio [02_rstudio]
  • Assignments:
    • Finish the Introduction to R entire course on Data Camp.
    • [Lab 1] This is 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: Putting it all together: Exploratory 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].


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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.