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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

Floating point operations and numerical data types


From the standpoint of mathematical operations, we usually think of numbers as just that. However, a computer takes a more broken down approach to numbers. Most of the time this doesn't matter, but when we have to deal with large datasets and are concerned about either memory or speed, it can make a big difference. R essentially has two numeric data types; integer and double precision (also called numeric). The integer data type handles exact values denoted as integers. As per the IEEE floating point standard, the double precision type handles values as rounded decimals. R has no single data type as is used in languages like C.

Here, we will create two vectors of integers, x and y, and divide one by the other. Mathematically (but not computationally), we get whole number results. Let's look at the following code:

> x <- as.integer(seq(1, 10, by = 1))
> y <- as.integer(seq(2, 20, by = 2))
> x
 [1]  1  2  3  4  5  6  7  8  9 10...
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