SICSS Boot Camp (Beta)

SICSS Boot camp is an online training program designed to provide you with beginner level skills in coding so that you can follow the more advanced curriculum we teach at the partner locations of the Summer Institutes in Computational Social Science. The videos and materials linked below are designed for complete beginners and are best viewed as a sequence since each video builds upon content introduced in previous tutorials. To learn more– or get started–click on the “Welcome to Boot Camp” video below.

Welcome to Boot Camp
Installing R Studio
R Basics
Data “Wrangling”
Visualization
Basic Programming
Modeling
Communicating and Collaborating

Welcome to Boot Camp

This video introduces you to SICSS boot camp, explains our philosophy, and tells you how to get started (2:44).


Installing RStudio

An introduction to open-source software that teaches you how to install both R and Rstudio and customize your interface (14:11).


Additional Resources: Installing Rstudio on Windows/Mac

R Basics

Create and name different types of objects (e.g. vectors and dataframes) and learn about different classes of objects (e.g. numeric vs. string) as well as special features that make RStudio easier to use (17:36).


Materials from Video: Coronavirus Tweets Dataset

Additional Resources: R for Data Science (Chapter 4)

Exercises: COMING SOON!

Data “Wrangling”

Learn how to clean, reshape, and restructure data with the tidyverse (22:45).


Materials from Video: Apple Mobility Dataset

Additional Resources: R for Data Science (Chapters 9-13)

Exercises: COMING SOON!

Visualization

Learn how to visualize your data using ggplot (30:54)


Materials from Video: Apple Mobility .csv file

Additional Resources: R for Data Science (Chapters 3 & 28)

Exercises: COMING SOON!

Basic Programming

Write functions, loops, and learn how to debug your code (28:32)


Materials from Video: Senators Dataset

Additional Resources: R for Data Science (Chapters 17-21)

Exercises: COMING SOON!

Modelling

Bivariate plots, multivariate regression, and more (14:32)!


Materials from Video: Opioid Dataset

Additional Resources: R for Data Science (Chapters 22-25)

Exercises: COMING SOON!

Communicating and Collaborating

RMarkdown, Rpres, Shiny and Github (23:16)


Additional Resources: R for Data Science (Chapters 26 & 27)

Exercises: COMING SOON!