基本觀念

• 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

程式語言

• 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

揉資料

• 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

抓資料

• 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

視覺化

• 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

自動化

• 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

協作

• 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

交流

• 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

排版

• LaTeX Color
• Color Hexa
• Color Blender
• ColorBrewer 2.0

文字分析

• 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

統計學習

• 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
• Statistical Machine Learning taught by Larry Wasserman at CMU

程式風格

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

綜合材料

• Big Data taught by Matt Taddy at Chicago Booth
• 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
• Data Wrangling, Exploration, and Analysis with R taught by Jenny Bryan at UBC