Handling Missing Values in R using tidyr

von
Coursera Project Network
In diesem angeleitetes Projekt werden Sie:

Drop missing values using the drop_na() function

Replace missing values using the replace_na() function

Fill missing values using the fill() function

Clock2 hours
IntermediateMittel
CloudKein Download erforderlich
VideoVideo auf geteiltem Bildschirm
Comment DotsEnglisch
LaptopNur Desktop

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!

Kompetenzen, die Sie erwerben werden

  • Missing Data
  • Data Manipulation
  • tidyr
  • R Programming
  • dplyr

Schritt für Schritt lernen

In einem Video, das auf einer Hälfte Ihres Arbeitsbereichs abgespielt wird, führt Sie Ihr Dozent durch diese Schritte:

  1. Getting Started

  2. Import and Explore the data sets

  3. Select Missing Variables

  4. Drop Missing Values

  5. Replace Missing Values

  6. Fill Missing Values

  7. Fill Missing Values - Exercises

  8. Wrap up - Chain all operations

Ablauf angeleiteter Projekte

Ihr Arbeitsbereich ist ein Cloud-Desktop direkt in Ihrem Browser, kein Download erforderlich

Ihr Dozent leitet Sie in einem Video mit geteiltem Bildschirm Schritt für Schritt an.

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