Handling Missing Data in R

Learn how to identify, analyze, and fix missing values to ensure cleaner, more reliable datasets

What you'll learn
- Identify and Diagnose Missing Data
- Visualize Patterns of Missingness
- Apply Appropriate Imputation Techniques
- Evaluate the Impact of Missing Data Treatment

Requirements
- Basic knowledge of R

Description
Missing data is one of the most common and often underestimated challenges in data analysis. When not handled properly, missing values can bias results, distort relationships, and undermine the validity of your findings.Handling Missing Data in Ris a practical, hands-on course designed to equip you with the knowledge and tools to address this issue confidently and effectively.

You’ll begin by understanding what missing data is, why it occurs, and how to classify it into key types—MCAR (Missing Completely at Random), MAR (Missing at Random), and MNAR (Missing Not at Random). Using R, you’ll learn how to diagnose and explore missingness with built-in functions. You’ll then move into visualization techniques using packages such asnaniar,VIM, andtidyr, which help uncover hidden patterns in incomplete datasets.

The course will guide you through a range of strategies for handling missing data, from deletion methods to imputation approaches such as mean, median, and group-wise filling, as well as more advanced multiple imputation with themicepackage. Through guided labs and real-world case studies, you’ll apply these methods in practice, compare outcomes, and learn to document your workflow transparently.

By the end of this course, you’ll be able to make informed, data-driven decisions about missing data, implement robust imputation techniques, and ensure your analyses are accurate, reproducible, and trustworthy—skills essential for any data analyst, researcher, or data scientist working in R.

Who this course is for:
- Data analysts, statisticians, and researchers who work with real-world datasets and need to address incomplete data issues.
- R users at an intermediate level who are comfortable with data manipulation and visualization (e.g., using dplyr and ggplot2) but want to deepen their understanding of missing data techniques.
- Graduate students and academics conducting quantitative research who want to improve the validity and reliability of their analyses.
- Data science professionals seeking practical, reproducible methods to diagnose, visualize, and impute missing values in R.
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