Master Longitudinal Data Wrangling using R

Longitudinal datasets are one of the most powerful sources of evidence in the social sciences. They allow researchers to study how individuals, organisations, and societies change over time. However, working with longitudinal data can quickly become overwhelming. Multiple waves, inconsistent variable names, missing data, and complex data structures make longitudinal data wrangling one of the biggest challenges researchers face.

This course shows you how to efficiently clean, reshape, and prepare longitudinal datasets using R so you can move from messy raw data to analysis-ready datasets with confidence.

Instead of spending weeks struggling with merging waves, restructuring panel data, or fixing variable coding, you will learn a clear and reproducible workflow for longitudinal data wrangling in R. By the end of the course, you will know how to prepare complex panel datasets for statistical analysis while writing clean, reusable code.

The course focuses on practical data wrangling techniques used by researchers working with longitudinal and panel data, including selecting variables, recoding data, merging waves, reshaping datasets, handling missing values, and automating repetitive data preparation tasks.

All examples use realistic longitudinal survey data, so the skills you learn transfer directly to your own research projects.

What You Will Learn

Understanding longitudinal data structures

Before cleaning data, it is essential to understand how longitudinal datasets are organised.

In this section, you will learn the key concepts behind longitudinal and panel data, including:

• The difference between longitudinal and cross-sectional datasets
• Common longitudinal study designs
• Wide vs long data structures
• Time-varying and time-constant variables
• The Age–Period–Cohort challenge in longitudinal research

Understanding these concepts will help you design better workflows for longitudinal data preparation and analysis.

Core data wrangling tools in R

Cleaning longitudinal datasets often requires a combination of several data manipulation steps.

In this section, you will learn how to perform essential data wrangling tasks in R, including:

• Selecting variables and cases from large datasets
• Recoding variables and handling missing values
• Working with categorical variables and factors
• Creating new variables for analysis
• Writing clear and reproducible data wrangling pipelines

These tools form the foundation of efficient data cleaning workflows for panel and longitudinal data.

Merging and reshaping longitudinal datasets

One of the most common challenges in longitudinal research is combining data across waves and restructuring datasets into formats suitable for analysis.

In this section, you will learn how to:

• Merge datasets across waves using unique identifiers
• Identify and explore attrition and non-response patterns
• Reshape data between wide and long formats
• Prepare datasets for panel models and longitudinal analysis

These are core skills for anyone working with panel data, repeated measures datasets, or cohort studies.

Automating longitudinal data preparation

Longitudinal projects often involve repeating the same data preparation steps across many waves.

Instead of copying and modifying code manually, you will learn how to automate data wrangling tasks in R, including:

• Iteration with functional programming
• Writing reusable R functions
• Processing multiple datasets efficiently
• Building scalable workflows for large longitudinal studies

These techniques allow you to handle complex datasets while keeping your code clean, efficient, and reproducible.

Learn by doing with real longitudinal data

The course combines videos, written explanations, quizzes, and hands-on practical exercises so you learn by applying the techniques directly.

The practical exercises use a synthetic version of Understanding Society (UKHLS), one of the largest longitudinal household surveys in the world. You will prepare multiple waves of the dataset, clean variables, reshape the data, and explore patterns across time.

Each practical includes:

• Step-by-step exercises
• Full solution code
• Detailed walkthrough videos

By the end of the course, you will have built a complete workflow for preparing longitudinal data in R that you can adapt to your own datasets.

Who this course is for

This course is designed for researchers who want to become confident working with longitudinal and panel data in R, including:

• PhD students working with longitudinal datasets
• Social scientists using panel studies or cohort data
• Data analysts working with repeated-measures data
• Researchers transitioning from cross-sectional to longitudinal analysis

Prerequisites

You should already be comfortable with:

• Basic R syntax
• Working with data frames
• Basic data manipulation

If you are new to R, consider first taking the course Introduction to R for Social Researchers, which provides the foundations needed to follow the workflows taught here.

What you will be able to do after this course

After completing the course, you will be able to:

• Prepare complex longitudinal datasets for analysis in R
• Merge and reshape multi-wave panel data efficiently
• Clean and recode variables across survey waves
• Handle missing data and attrition patterns
• Automate repetitive data preparation tasks

Most importantly, you will gain a clear workflow for longitudinal data wrangling, allowing you to focus on answering research questions rather than fighting with messy data.