1  Introduction

Recently, I had to repeat myself while talking to a few students about Ewha GSIS Computational Social Science Workshop(s). Now, you and future students have this post instead!

This is what I wrote in my blog post about Ewha GSIS Computational Social Science Workshops that I have organized.

Following the same spirit in David Robinson’s tweet, I decided to write this book.

1.1 “Why Do I Need Computational Tools in Korean Studies?”

Simply put, there is so much more data out there that is useful for Korean Studies research, and we have faster computers, and handy tools to analyze such data.

Korean Studies curricula across the world are quite rich and interdisciplinary. Those courses often equip students with the history, culture, literature, and language of Korea to understand the country better. Yet, Korean Studies scholars and students are not exposed to computational methods/ tools that can handle big or complex data as much.

If you are already here, it probably means that you appreciate the increasing importance of the computational tools in your research. This book, and bootcamps based on this book, will teach you the basics of R, and give you sample codes based on Korean Studies-related examples.

In the age that we live in, I strongly believe that these computational methods/ tools will empower you in your research as well as in the job market given wide range of prospective jobs Korean Studies graduates seek and find (corporations, international organizations, think tanks, NGOs, media, academia etc.).

1.2 “Why R?”

R is free! There are so many packages that are rich with a wide range of functions that you would need in all kinds of research, analysis, and reporting. Many more are being built as you read this book! You can do from simple math to data pre-processing, from data visualization to regressions, from building your CV to building your website, from analyzing tweets to machine learning.

Python is probably getting more popular in the industry jobs in recent years. Yet, I think, for the time being, R is better suited for social science research. At least there are more books, tutorials, examples that you can learn from in terms of social sciences.

Once exposed to R, you may also consider learning Python as well if it seems more attractive for you.

1.3 “I don’t know anything about coding! Indeed, I am frustrated about coding!”

Then this book, and the bootcamps, are very much for you! I don’t expect the readers, and bootcamp participants, to have any prior knowledge of R, coding, or other statistical software.

This book is supposed to be a gentle introduction, so I do not go into the details of the R language. You can refer to the links that I provide in this book for more information. Furthermore, I also strongly encourage you to use Github's Copilot which is free for academic use, ChatGPT which is not necessarily a coding bot, but still helpful especially for simple tasks, Stackoverflow, and Google for help whenever you are stuck or come across an error.

Learning curve is steep in the beginning. So you may need a trigger to begin and NOT GIVE UP. This book plays this trigger role. So, there is no need to be intimidated by R, or your lack of background with coding. I got you covered!