Data Analysis for Social Science with RStudio

This repository is a sample code of the data import, data visualization, data transformation and data export in R programming. It also used for a sharing session at the Young Research Fellow Seminar.

This course is primarily designed for the Young Research Fellow at Future Forum for the fundamental data analysis by using RStudio application in empirical research. The goal of this seminar is to provide an overview of R commands in data import, data visualization, data transformation, data analysis and data export, with an emphasis on practical implementation. I will provide a set of slides, codes and notes. There are additional background papers that are largely optional.

Seminar videos are available on Youtube

Course Materials

2022 Young Research Fellow Seminar

To do the exercises, you should download the datasets from this reponsitory.

• Course Instruction
• Pre-Exercise with RStudio

2021 Young Research Fellow Seminar

• Main Data Visualization — with the Rmd file, you can use these codes to replicate it or download it with a PDF export file here.
• Findata Example, is a real particle with 2017 financial inclusion microdata covering nearly 150,000 people in 144 economies, representing more than 97% of the world’s population which produced by the World Bank.
• Maliecon Example, is a code for a research project on economic development in Mali for the master thesiss which takes from Phay Thounimith. I would like to point out that he used French in that code.
• Slide, a brief description of the purpose of the sharing section and something should be avoided when plotting.
• Problem Sets, is a exercise file for vectors, matrices and data transformations. More exercises in this file come from the book of R for Data Science by Garrett Grolemund and Hadley Wickham, come from Programming Exercises for R by Nastasiya F. Grinberg and Robin J. Reed, and from Data Analysis and Graphics Using R – an Example-Based Approach by John Maindonald and W. John Braun.
• Solutions: All problem sets solutions can be found in this repository.

Main References

This is a partial list of various interesting and useful books that will be touched during the course.

• Grolemund, G. and Wickham, H. (2016). R for Data Science:Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media, Inc.
• Healy, K. (2018). Data Visualization: A Practical Introduction. Princeton University Press.
• Maindonald, J. and Braun, W.J. (2010). Data Analysis and Graphics Using R – an Example-Based Approach. 3rd ed, Cambridge University Press.
• Thulin, M. (2021). Modern Statistics with R: From Wrangling and Exploring Data to Inference and Predictive Modelling. Northern Hemisphere.