Category: data management
Dynamic documents: a smarter way to share results
Learn how to use R Markdown to create a dynamic document for reproducible reports, automate updates, and seamlessly integrate code, text, and results in R. Read more
How to create reproducible workflows for longitudinal data analysis
Learn best practices for longitudinal data analysis, including Git, renv, and reproducible workflows to improve efficiency, accuracy, and collaboration. Read more
How to include control variables in a cross-lagged panel model
Learn how to include control variables in a Cross-Lagged Panel Model (CLPM) in R, using time-constant and time-varying predictors to refine causal analysis. Read more
How to include time-varying predictors in Latent Growth Models (LGM) in R
This post explores how to incorporate time-varying predictors into Latent Growth Models (LGM) to analyse changes in variables over time. It discusses data preparation, using R code for modeling, and differentiating between concurrent and lagged effects. Read more
Understanding the longitudinal data workflow: a comprehensive guide
Unlock insights from longitudinal data with our step-by-step guide, from import to analysis. Master the workflow to elevate your research. Read more
Complete guide to visualizing longitudinal data in R
Unlock the secrets of longitudinal data in R with this complete guide. Master the art of insightful visualizations for impactful analysis. Read more
Exploring longitudinal data in R: tables and summaries
Unlock the power of R for longitudinal data analysis! Learn to navigate tables and summaries with ease in our latest guide. Read more
Cleaning longitudinal data in R: a step-by-step guide
Explore key R commands for cleaning longitudinal data, including recoding variables and handling missing data effectively. Read more
Preparing longitudinal data for analysis in R: a step-by-step guide
Explore the crucial steps of data preparation for longitudinal analysis in R, from importing to reshaping data, in this step-by-step guide. Read more
Data structures for longitudinal analysis: wide versus long
Learn how to create, store and reshape longitudinal data. Reshaping data from wide to long data format and back using R. Hands on examples Read more