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Handling Missing Values in R using tidyr
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!
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Rating | Not enough ratings |
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Length | 2 weeks |
Effort | 2 hours |
Starts | Jun 26 (44 weeks ago) |
Cost | $9 |
From | Coursera Project Network via Coursera |
Instructor | Arimoro Olayinka Imisioluwa |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science IT & Networking |
Tags | Data Science Data Analysis Information Technology Data Management |
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Rating | Not enough ratings |
---|---|
Length | 2 weeks |
Effort | 2 hours |
Starts | Jun 26 (44 weeks ago) |
Cost | $9 |
From | Coursera Project Network via Coursera |
Instructor | Arimoro Olayinka Imisioluwa |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science IT & Networking |
Tags | Data Science Data Analysis Information Technology Data Management |
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