This chapter focused on some of the hardest challenges in data analysis in the means of cleansing data, and we covered the most important topics on missing and extreme values. Depending on your field of interest or industry you are working for, dirty data can be a rare or major issue (for example I've seen some projects in the past when regular expressions were applied to a JSON
file to make that valid), but I am sure you will find the next chapter interesting and useful despite your background – where we will learn about multivariate statistical techniques.
Mastering Data analysis with R
By :
Mastering Data analysis with R
By:
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