Objectives

Upon completion of this session, you will be able to do the following:

  1. Develop strategies for addressing common raw data quality issues and note potential data wrangling pitfalls.
  2. Expand your data cleaning toolkit by exploring the tidyverse, a curated suite of packages for data science.

Lectures

Lecture recording links will be emailed directly to students.

Material

Day Module Slides Code Resource Cheatsheets/Guides
Day 5 Data Cleaning HTML,PDF Rmd Lab, Key, Key HTML Day 5 Cheatsheet
Project Guidelines HTML, Rmd Example RMD, Example HTML Instructions

Homework

🚨 SURVEY: Day 5 check-in

📝 HOMEWORK 1: Questions (HTML)

📝 HOMEWORK 2: Questions (Rmd), Questions (HTML)

Drop Boxes

Assignment Drop Boxes

Note: only people taking the course for credit must turn in the assignments. However, we will evaluate all submitted assignments in case others would like feedback on their work.

Library

RStudio cheatsheets

RStudio IDE cheatsheet (PDF)