In this final tutorial of the dplyr series, we will cover ways to do feature engineering both with dplyr (“mutate” and “transmute”) and base R (“ifelse”). You’ll learn how to impute missing values as well as create new values based on existing columns. In addition, we’ll go over four different ways to combine datasets. If you’ve followed all the videos in the series, you should be ready to get up and running with dplyr and use it to tackle a range of data manipulation tasks.

Github:
https://github.com/datasciencedojo/tutorials

Be sure to also check our accompanying blog post here:
https://blog.datasciencedojo.com/explorations-with-dplyr/

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Introduction to dplyr: Feature Engineering

About The Author
- Data Science Dojo is a paradigm shift in data science learning. We enable all professionals (and students) to extract actionable insights from data.

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