This is basically course notes that may eventually turn into a book. It's a collaborative effort with Brendan Cullen and Ouafaa Hmaddi and we'll see where it goes. The plan is to have multiple chapters of interactive exercises where you won't have to always leave the book to try things out. I'll update this README as we go and content is developed to provide a better overview of what we're covering (and link to any material we have created).
Please note that the sds-r project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Getting Started
Producing your first plot
Basic R Markdown
Basic data wrangling
Types of data
Tidy data
Joins
Collaborating with git and GitHub
A collaborative exploratory data analysis example
Collaborating with git and GitHub
A collaborative exploratory data analysis example
Introduction to visualizations
Visual perception
Color
Refining your plots
Geographic data
Visualizing uncertainty
Tables & fonts
Websites in R Markdown
Flex dashboards
Shiny
Data Types
Iteration
Batch load and processing dat
List columns
Parallel iterations
Writing functions
Package development
Inference vs. Prediction
Ethics in Machine Learning
Cross validation
Cloud computing
Extending lm: Ridge, Lasso, Elastic net
Feature engineering
K-nearest neighbor
Decision trees
Bagged trees & Random forests
Boosted Trees
Model Stacking