The preceding examples and quick theoretical background introduced text mining algorithms to structure plain English texts into numbers for further analysis. In the next chapter, we will concentrate on some similarly important methods in the process of data analysis, such as how to polish this kind of data in the means of identifying outliers, extreme values, and how to handle missing data.
Mastering Data analysis with R
By :
Mastering Data analysis with R
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Overview of this book
Table of Contents (19 chapters)
Mastering Data Analysis with R
Credits
www.PacktPub.com
Preface
Free Chapter
Hello, Data!
Getting Data from the Web
Filtering and Summarizing Data
Restructuring Data
Building Models (authored by Renata Nemeth and Gergely Toth)
Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth)
Unstructured Data
Polishing Data
From Big to Small Data
Classification and Clustering
Social Network Analysis of the R Ecosystem
Analyzing Time-series
Data Around Us
Analyzing the R Community
References
Customer Reviews