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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

In this chapter, we saw how data is stored and accessed in R. We also discussed how to write functions. You should now be able to write and access data in vectors, arrays, and data frames, and load your data into R. We also learned how to make basic plots using built-in R functions and the ggplot2 package. You should also know how to use flow-control statements in your code and write your own functions and use built-in tools to troubleshoot your code.

Now that you have a foundation in R data structures, we will move on to statistical methods in the next chapter, where you will find out how to obtain useful statistical information from your dataset and fit your data to known probability distributions.

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