Computational Thinking for Social Scientists
Chapter 1 Hello World
##  "Hello, World!"
Make simple things simple, and complex things possible. - Alan Kay
This is the website for Computational Thinking for Social Scientists. This book intends to help social scientists think computationally and develop proficiency with computational tools and techniques to research computational social science. Mastering these tools and techniques not only enables social scientists to collect, wrangle, analyze, and interpret data with less pain and more fun, but it also let them work on research projects that would previously seem impossible.
The book is not intended to be a comprehensive guide for computational social science or any particular programming language, computational tool, or technique. For a general introduction to computational social science, I recommend Matthew Salganik ’s Bit By Bit (2017).
The book is currently divided into two main subjects (fundamentals and applications) and eight main sessions.
1.1 Part I Fundamentals
1.2 Part II Applications
Why R? R is free, easy to learn (thanks to
tidyverseand RStudio), fast (thanks to
Rcpp), runs everywhere, open (16,000+ packages; counting only ones available at CRAN), and has a growing massive and inclusive community (
Why R + Python + bash?
“For R and Python, Python is first and foremost a programming language. And that has a lot of good features, but it tends to mean, that if you are going to do data science in Python, you have to first learn how to program in Python. Whereas I think you are going to get up and running faster with R, than with Python because there’s just a bunch more stuff built in and you don’t have to learn as many programming concepts. You can focus on being a great political scientist or whatever you do and learning enough R that you don’t have to become an expert programmer as well to get stuff done.” - Hadley Wickham
- However, this feature of the R community also raises a challenge.
Compared to other programming languages, the R community tends to be more focused on results instead of processes. Knowledge of software engineering best practices is patchy: for instance, not enough R programmers use source code control or automated testing. Inconsistency is rife across contributed packages, even within base R. You are confronted with over 20 years of evolution every time you use R. R is not a particularly fast programming language, and poorly written R code can be terribly slow. R is also a profligate user of memory. - Hadley Wickham
RStudio, especially the tidyverse team, has made heroic efforts to amend the problems listed above. Readers will learn these recent advances in the R ecosystem and complement R with Python and Bash.
If you’re serious about programming, I strongly recommend learning Python. Learning Python also helps you fill gaps in the software engineering knowledge useful to be highly proficient in R.
1.3 Special thanks
This book is collected as much as it is authored. It is a remix version of PS239T, a graduate-level computational methods course at UC Berkeley, originally developed by Rochelle Terman then revised by Rachel Bernhard. I have taught PS239T three times (twice as an insturctor and once as a teaching assistant). Other teaching materials draw from the workshops I have created for D-Lab (Summer - Fall 2020) and Data Science Discovery Program (Spring 2020) at UC Berkeley. I also have cited all the other references whenever I am aware of related books, articles, slides, blog posts, or YouTube video clips.
1.4 Suggestions, questions, or comments
Please feel free to create issues if you find typos, errors, missing citations, etc via the GitHub repository associated with this book.
This work is licensed under a Creative Commons Attribution 4.0 International License.