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Mastering Scientific Computing with R

You're reading from   Mastering Scientific Computing with R Employ professional quantitative methods to answer scientific questions with a powerful open source data analysis environment

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781783555253
Length 432 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (12) Chapters Close

Preface 1. Programming with R 2. Statistical Methods with R FREE CHAPTER 3. Linear Models 4. Nonlinear Methods 5. Linear Algebra 6. Principal Component Analysis and the Common Factor Model 7. Structural Equation Modeling and Confirmatory Factor Analysis 8. Simulations 9. Optimization 10. Advanced Data Management Index

Summary

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.

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