Drop missing values using the drop_na() function
Replace missing values using the replace_na() function
Fill missing values using the fill() function
Missing data can be a “serious” headache for data analysts and scientists. This project-based course Handling Missing Values in R using tidyr is for people who are learning R and who seek useful ways for data cleaning and manipulation in R. In this project-based course, we will not only talk about missing values, but we will spend a great deal of our time here hands-on on how to handle missing value cases using the tidyr package. Be rest assured that you will learn a ton of good work here. By the end of this 2-hour-long project, you will calculate the proportion of missing values in the data and select columns that have missing values. Also, you will be able to use the drop_na(), replace_na(), and fill() function in the tidyr package to handle missing values. By extension, we will learn how to chain all the operations using the pipe function. This project-based course is an intermediate level course in R. Therefore, to complete this project, it is required that you have prior experience with using R. I recommend that you should complete the projects titled: “Getting Started with R” and “Data Manipulation with dplyr in R“ before you take this current project. These introductory projects in using R will provide every necessary foundation to complete this current project. However, if you are comfortable with using R, please join me on this wonderful ride! Let’s get our hands dirty!
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Import and Explore the data sets
Select Missing Variables
Drop Missing Values
Replace Missing Values
Fill Missing Values
Fill Missing Values - Exercises
Wrap up - Chain all operations
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
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