Feature Engineering - Introduction to Dplyr Part 4

Conclude the dplyr tutorial series with advanced feature engineering techniques using both dplyr and base R. Learn to impute missing values, create new columns based on existing data, and explore four methods for combining datasets. Master 'mutate' and 'transmute' functions in dplyr, as well as 'ifelse' in base R for data manipulation. Apply these skills to tackle a wide range of data manipulation tasks, solidifying your proficiency in using dplyr for efficient data processing and analysis. Access accompanying code examples, related R programming resources, and the full video series for comprehensive learning.

Feature Engineering | Introduction to dplyr Part 4