https://github.com/datalorax/sds-r

Repo for a draft book on social data science methods with R
https://github.com/datalorax/sds-r

data-science r rstats social-data-science

Last synced: 2 months ago
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Repo for a draft book on social data science methods with R

• Host: GitHub
• URL: https://github.com/datalorax/sds-r
• Owner: datalorax
• License: cc-by-4.0
• Created: 2020-10-14T22:34:58.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2022-07-01T00:09:28.000Z (over 2 years ago)
• Last Synced: 2023-03-01T23:28:05.171Z (almost 2 years ago)
• Topics: data-science, r, rstats, social-data-science
• Language: R
• Homepage: https://sds.pub
• Size: 61.8 MB
• Stars: 9
• Watchers: 4
• Forks: 4
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md
  • Code of conduct: CODE_OF_CONDUCT.md

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README

        
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# Social Data Science with R

This is basically course notes that may eventually turn into a book. It's a collaborative effort with [Brendan Cullen](https://github.com/brendanhcullen) and [Ouafaa Hmaddi](https://github.com/ohmaddi) 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).

## Code of Conduct

Please note that the sds-r project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

## Book outline 

### Section 1: Foundations

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

### Section 2: Data visualization & Communication

Introduction to visualizations    

Visual perception     

Color    

Refining your plots     

Geographic data    

Visualizing uncertainty    

Tables & fonts    

Websites in R Markdown    

Flex dashboards    

Shiny   

### Section 3: Functional Programming

Data Types    

Iteration    

Batch load and processing dat    

List columns    

Parallel iterations    

Writing functions    

Package development    

### Section 4: Machine Learning

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