Data Analytics in Julia [🔗 Book]

By Rongxin Ouyang, PhD student in Computational Communication, NUS

Scope

This short book provides a practical guide for data analysis in social science using Julia. It replicates common analytical steps in the field.

Because of its speed.

Outline

• ✅ Chapter 1. Installation

  • ✅ How to install Julia
  • ✅ How to install Julia as a Jupyter kernal for notebooks
  • ✅ The basics of operations, data structures, packages, methods, and define functions

• ✅ Chapter 2. Data Loading and Selection

  • ✅ Load a dataframe from a local file, an online link, and a common datasets; or create it from scratch
  • ✅ Select by rows, columns, and conditions.

• ✅ Chapter 3. Transformation and calculation

  • ✅ Split and combine
  • ✅ Grouping
  • ✅ Sorting
  • ✅ Transforming between long / wide tables
  • ✅ Find / fill / drop missing values

• ✅ Chapter 4. Pipeline and Useful Packages

  • ✅ Data pipeline
  • ✅ Manipulate strings
  • ✅ Network

• ✅ Chapter 5.1 Models and Tests

  1. ✅ Common parametric tests (t-tests and ANOVA)
  2. ✅ Regression (multi-variate regression and fixed effects models)
  3. ✅ Path Analysis
     1. ✅ Mediation
     2. ✅ Moderation
     3. ✅ Conditional Path Analysis

• ✅ Chapter 5.2 Models and Tests (continued)

  1. 🚧 / ✅ Counterfactual Framework
     1. 🚧 / ✅ Instrumental Variables
     2. 🚧 / ✅ Regression Discontinuity Design
     3. 🚧 / ✅ Difference-in-Difference
     4. 🚧 / ✅ Synthetic Control
     5. 🚧 / ✅ Synthetic Difference-in-Difference

• ✅ Chapter 6. Visualization (ggplot2-like)

  • ✅ Scatterplot, barplot, lineplot, and histogram
  • ✅ Styles and themes
  • ✅ Multiple-plots in facets

• ✅ Chapter 7. Using R and Python in Julia

  • ✅ Using R functions and R code blocks in Julia
  • ✅ Using Python functions and Python code blocks in Julia

License

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