Data analysis is one of the important topics because the process of harvesting raw data into usable data and then analyzing them to produce scientific conclusions are crucial in most economic and technical decisions. Data analysis techniques are becoming more and more important in the digital age with the explosion of data. According to the development of science and technology, there are now a lot of supporting tools for data analysis, including commercial and open source software.
R software makes a big difference because R is open source software and completely free. Importantly, R has a lot of analytical features from statistics to finance, time series forecasting; especially, R has always been updated from researchers around the world, even readers can contribute to the development of R.
However, readers need to know how to use the source codes (packages), functions and write the correct syntax in R to serve a particular data analysis, which makes R less friendly. Although there are many R language manuals, the content of this book is selected and focused to provide a wide range of data mining techniques at work, from accountants to manufacturing engineers. In addition, the structure of the book is designed so that readers can quickly refer to the full range of functions and syntax to minimize the time to search for commands in R.
Instead of emphasizing pure theory, the authors presented the book “DATA MINING WITH R” (“Khai thác dữ liệu với R”) in such a way that readers can quickly refer to execution commands as well as illustrative examples for typical analytical techniques represented by the following ten chapters:
- Chapter 1: About R Software
- Chapter 2: Objects and functions
- Chapter 3: Basic Statistics
- Chapter 4: Random variables and probability distribution
- Chapter 5: Chart
- Chapter 6: Testing statistical assumptions
- Chapter 7: Analysis of variances
- Chapter 8: Regression Analysis
- Chapter 9: The Six Sigma Method
- Chapter 10: Text Mining
This book could not have been completed without the rich source of references from the researchers, the experts who created the packages in particular as well as R in general. In addition, the authors also thank colleagues for their input and help in completing this book. Unexpected mistakes are inevitable; the authors are very appreciated and grateful for the valuable comments from readers.
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