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Mastering Data analysis with R

You're reading from   Mastering Data analysis with R Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization

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Product type Paperback
Published in Sep 2015
Publisher Packt
ISBN-13 9781783982028
Length 396 pages
Edition 1st Edition
Languages
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Author (1):
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Gergely Daróczi Gergely Daróczi
Author Profile Icon Gergely Daróczi
Gergely Daróczi
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Table of Contents (17) Chapters Close

Preface 1. Hello, Data! 2. Getting Data from the Web FREE CHAPTER 3. Filtering and Summarizing Data 4. Restructuring Data 5. Building Models (authored by Renata Nemeth and Gergely Toth) 6. Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth) 7. Unstructured Data 8. Polishing Data 9. From Big to Small Data 10. Classification and Clustering 11. Social Network Analysis of the R Ecosystem 12. Analyzing Time-series 13. Data Around Us 14. Analyzing the R Community A. References Index

The types and origins of missing data


First, we have to take a quick look at the possible different sources of missing data to identify why and how we usually get missing values. There are quite a few different reasons for data loss, which can be categorized into 3 different types.

For example, the main cause of missing data might be a malfunctioning device or the human factor of incorrectly entering data. Missing Completely at Random (MCAR) means that every value in the dataset has the same probability of being missed, so no systematic error or distortion is to be expected due to missing data, and nor can we explain the pattern of missing values. This is the best situation if we have NA (meaning: no answer, not applicable or not available) values in our data set.

But a much more frequent and unfortunate type of missing data is Missing at Random (MAR) compared to MCAR. In the case of MAR, the pattern of missing values is known or at least can be identified, although it has nothing to do with...

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