This chapter has gone through techniques for data handling. Prior to data analysis, it is important to get the data into a consistently structured format, which string processing and pattern matching are helpful in doing. We have also gone through some basic memory management considerations. Finally, we discussed how to handle missing data using deletion, and two imputation approaches. The techniques of this chapter yield no new scientific insights or information about relationships in the data, yet they are often crucial preliminary steps prior to descriptive or inferential analyses. For large-scale projects, additional technologies designed just for data management, such as database platforms, will be important adjuncts to R by itself.
Mastering Scientific Computing with R
Mastering Scientific Computing with R
Overview of this book
Table of Contents (17 chapters)
Mastering Scientific Computing with R
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Programming with R
Statistical Methods with R
Linear Models
Nonlinear Methods
Linear Algebra
Principal Component Analysis and the Common Factor Model
Structural Equation Modeling and Confirmatory Factor Analysis
Simulations
Optimization
Advanced Data Management
Index
Customer Reviews