When conducting quantitative research, I work with R. R is a great programming language for social scientists, and what's even more awesome, it's free! I love R and the supportive R community, from which I have benefited a lot. Though I can only be considered as a beginner among all R users, I'd like to share some of my thoughts and tips from working with R with other beginners in quantitative social research.
I have written some codes and shared my thoughts on the following issues, with real examples in my own research: data cleaning, reshaping and manipulation; regression analysis; general linear models and advanced models; network analysis; agent-based modelling; web-scraping; text mining (esp. Chinese) and sentiment analysis; GIS and temporal-spatial analysis; interactive data visualization. Hopefully they can contribute to your journey with R. The R-related posts can be found here.
I also publish data and programming codes to share reproducible research projects. You can find the quantitative and qualitative datasets in the zipped file below. The files are for academic purposes only. Researchers who download the dataset agrees to not edit or distribute the files without the author's permission. Works based on the dataset below should cite the author's work in proper citation format.
Some reproducible codes, data files and slides for selected publications can be found via the link to my GitHub page: https://github.com/huiquanR/