Introduction to R and Basic Data Analysis

Rabia Malik is a Lecturer/Assistant Professor in the Department of Government at the University of Essex, which she joined in July 2020. Before this, she received her Ph.D. in Political Science from the University of Rochester in 2016, was a Post-Doctoral Associate at New York University Abu Dhabi (2016-2019) and spent a year at the Lahore University of Management Sciences (LUMS). Her research uses both observational and experimental data to study questions related to distributive politics and development, political accountability, clientelism, and gender, particularly in South Asia. She has also taught classes on quantitative methods, authoritarianism, and South Asian politics. Rabia’s research has appeared in The Journal of Politics, The British Journal of Political Science, Comparative Political Studies and Legislative Studies Quarterly.

Course Description
Master the fundamentals of R – from cleaning messy data to running statistical analyses – in an accessible, hands-on course.

Module 1: Introduction to the R Environment (5, 6, 7, 8 May, 10:00 – 13:00)
Get started with R and RStudio through hands-on practice with core syntax, data structures, basic programming concepts, and simple visualizations.

Module 2: Data Manipulation with R (12, 13, 14, 15 May, 10:00 – 13:00)
Learn how to import, clean, and transform real-world datasets using base R and tidyverse tools, including techniques for validating data to prepare for analysis.

Module 3: Basic Statistical Analysis in R (19, 20, 21, 22 May, 10:00 – 13:00)
Use R to summarize data, create effective visualizations, run essential statistical tests, explore relationships, and make reproducible reports with R Markdown.

Course Outline

Module 1: Introduction to the R Environment
This module introduces participants to the foundations of working in R. It focuses on understanding the RStudio interface, core R syntax, and essential data structures. Participants gain early experience with simple programming concepts that support later data analysis.
• Navigating R and RStudio
• R syntax, data types, and basic operations
• Working with vectors, matrices, and data frames
• Introduction to control structures and simple functions
• Practicing writing and running R scripts

Module 2: Data Manipulation with R
This module develops practical data wrangling skills using base R and tidyverse. Participants learn how to bring data into R, diagnose and address common data-quality issues, and apply systematic cleaning workflows. Emphasis is placed on handling real-world messy data, including text, dates, and missing values.
• Reading and writing data in various formats
• Data cleaning and preprocessing
• Data validation and quality checks
• Introduction to data manipulation using base R and tidyverse tools
• Working with dates and times in R

Module 3: Basic Statistical Analysis in R
This module introduces fundamental statistical concepts and analytical techniques using R. Participants learn how to summarise data, explore relationships, and conduct core inferential procedures. The module concludes with an introduction to R Markdown for creating reproducible reports.
• Descriptive statistics in R
• Probability distributions and random number generation
• Hypothesis testing
• Correlation analysis
• Simple linear regression
• Introduction to reporting outputs with R Markdown