Dealing With Missing Data in R

The Korshub Take

Missing data is an unavoidable challenge in real-world data science that can introduce subtle biases and skew results if improperly handled. This beginner R course equips analysts with specialized tools from the tidyverse ecosystem and the naniar package to visualize, diagnose, and impute missing values with scientific rigor.

Who It's For

R programmers, data analysts, and researchers who need systematic workflows to detect missingness patterns, eliminate data bias, and apply statistical imputation models.

Key Takeaways

• Visualization with naniar: Leverage tidyverse tools and the naniar package to map and visualize missing data patterns clearly.
• Bias & Pattern Analysis: Explore missingness mechanisms to identify hidden bias and structural anomalies in datasets.
• Data Imputation: Apply statistical imputation models to fill in missing values and assess the impact of imputed data on downstream analysis.