Resources

Here are some online resources I find useful for empirical work. Nowadays, it is quite easy to spot a myriad of similar things out there. But to me, many are of poor quality, some even misleading and may result in bad habits. Indistinguishable names also makes Googling hard. This motivates me to constantly maintain this list so that I can consult them anywhere anytime without taking up memory.

Maths & Stats

Tripos papers sorted by course.
Research in the CMS for undergraduate students.
You and Your Research by Richard Hamming.

Computing

R Packages: Organize, Test, Document, and Share Your Code by Hadley Wickham.
A data.table and dplyr tour by Atrebas.
Org-Mode Reference Card.
Biblatex Cheat Sheet.

Philosophy

Code and Data for the Social Sciences by Matthew Gentzkow and Jesse Shapiro
The Plain Person’s Guide to Plain Text Social Science by Kieran Healy

Program

R for Data Science by Hadley Wickham and Garrett Grolemund
- Hadley Wickham’s sermon for the tidyverse revolution
- Core concepts: Transformation / Tidy Data / Functions / Vectors & Lists / Visualisation
R Programming for Data Science by Roger Peng
- Some useful parts: The apply Family / Regular Expressions / Parallel Computing
Exploratory Data Analysis with R by Roger Peng
- Some useful parts: The ggplot2 Plotting System / Color Palettes
Advanced R by Hadley Wickham
- Discusses advanced topics and reveals the spirit of R: Functional Programming
Efficient R Programming by Colin Gillespie and Robin Lovelace
Automate the Boring Stuff with Python by Al Sweigart
A Byte of Python by Swaroop CH
The Hitchhiker’s Guide to Python by Kenneth Reitz
Python Data Science Handbook by Jake VanderPlas
Stata: Masayuki Kudamatsu’s useful links

Wrangle

dplyr: The black magic that transcends R from its base
- Useful: filter()/select()/group_by()/summarise()/mutate()/nest()/…
data.table: Convenient for working with large datasets
- Useful: rbindlist()/fread()/fwrite()/…
magrittr: Useful pipe operator %>% enhances readability
Teach the tidyverse to Beginners by David Robinson

Scrape

Automated Collection of Web and Social Data taught by Pablo Barberá and Dan Cervone at ECPR Summer School
rvest / RSelenium / tabulizer / urllib / BeautifulSoup / Selenium
- Common scenarios: Web pages / Tables / Web forms / RSS feeds / PDF files

Visualize

Introduction to R graphics with ggplot2 taught by Ista Zahn at Harvard IQSS Workshop
A Layered Grammar of Graphics by Hadley Wickham
Data Visualization for Social Science by Kieran Healy
- Explains every useful aspects of ggplot2 along with some useful packages such as ggrepel for adding labels and maps for creating maps
- Some best parts: How ggplot Works / Geoms / Scales, Guides, and Themes / Labels / Maps / Colors / Models

Automate

Learn Enough Command Line to Be Dangerous by Michael Hartl
Working with CSVs on the Command Line by Brian Connelly
Pandoc User’s Guide
Why Use Make by Mike Bostock
Minimal Make Tutorial by Karl Broman
The Unix Workbench by Sean Kross

Collaborate

Git vs. Dropbox from a Researcher’s Perspective by Michael Stepner
An Introduction to Version Control Using GitHub Desktop by Daniel van Strien
Git/Github Guide: A Minimal Tutorial by Karl Broman
Happy Git and GitHub for the useR by Jenny Bryan and TAs
Pro Git by Scott Chacon and Ben Straub

Communicate

R Markdown Quick Tour
Basics of Jupyter Notebook
Literate Programming and knitr by Roger Peng
Bookdown by Yihui Xie
Blogdown by Yihui Xie, Amber Thomas, and Alison Presmanes Hill

Typeset

Text Analysis

Text as Data by Justin Grimmer and Brandon Stewart
Text as Data by Matthew Gentzkow, Bryan Kelly, and Matt Taddy
Texts as Data taught by Justin Grimmer at Stanford
Text Mining with R by Julia Silge and David Robinson
- Useful: tf-idf / N-grams / Sentiment Analysis / Topic Modeling
Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper

Statistical Learning

An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
Advanced Data Analysis from an Elementary Point of View by Cosma Shalizi
Computer Age Statistical Inference by Bradley Efron and Trevor Hastie
Big Data taught by Matt Taddy at Chicago Booth
Statistical Machine Learning taught by Larry Wasserman at CMU

Style Guides

R Style Guide / lintr
Python Style Guide / pycodestyle
SQL Style Guide / sqlint
Towards LaTeX Coding Standards by Didier Verna
Markdown Style Guide by Google

Hybrid Materials

Data Science and Social Science taught by Pablo Barberá and Dan Cervone at NYU
Computing for the Social Sciences taught by Benjamin Soltoff at Chicago
Computational Tools for Social Science taught by Rochelle Terman at Chicago
Data Wrangling, Exploration, and Analysis with R taught by Jenny Bryan at UBC