Mastering Scientific Computing with R: Employ professional quantitative methods to answer scientific questions with a powerful open source data analysis environment

Paul Gerrard

$54.99

3.6 (7 Ratings)

Paperback Jan 2015 432 pages 1st Edition

Paul Gerrard

$54.99

3.6 (7 Ratings)

Paperback Jan 2015 432 pages 1st Edition

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Mastering Scientific Computing with R

Chapter 2. Statistical Methods with R

This chapter will present an overview of how to summarize your data and get useful statistical information for downstream analysis. We will also show you how to plot and get statistical information from probability distributions and how to test the fit of your sample distribution to well-defined probability distributions. We will also go over some of the functions used to perform hypothesis testing including the Student's t-test, Wilcoxon rank-sum test, z-test, chi-squared test, Fisher's exact test, and F-test.

Before we begin, we will load the gene expression profiling data from the E-GEOD-19577 study entitled "MLL partner genes confer distinct biological and clinical signatures of pediatric AML, an AIEOP study" from the ArrayExpress website to use as a sample dataset for some of our examples. For simplicity, we will not go into the details of how the data was generated, except to mention that the study evaluates the expression...

Probability distributions

R makes it very easy to plot and get statistical information on many probability distributions. For those who are not familiar with probability distributions, they are defined as a table or an equation that links each outcome of a statistical experiment with its probability of occurrence. A summary of many common probability distributions available in R is available in the following table:

Probability distribution	R name
Beta	beta
Binomial	binom
Cauchy	cauchy
Chi square	chisq
Exponential	exp
F	f
Gamma	gamma
Geometric	geom
Hypergeometric	hyper
Logistic	logis
Lognormal	lnorm
Negative Binomial	nbinom
Normal	norm
Poisson	pois
Student t	t
Uniform	unif
Tukey	tukey
Weibull	weib
Wilcoxon	wilcox

You can also get this summary in R by entering help("distributions"). For additional probability distributions, and the packages needed to load them, you can consult the CRAN distributions page at http://cran.r-project.org/web/views...

Description

If you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.

Who is this book for?

What you will learn

Master data management in R
Perform hypothesis tests using both parametric and nonparametric methods
Understand how to perform statistical modeling using linear methods
Model nonlinear relationships in data with kernel density methods
Use matrix operations to improve coding productivity
Utilize the observed data to model unobserved variables
Deal with missing data using multiple imputations
Simplify highdimensional data using principal components, singular value decomposition, and factor analysis

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Arnold Salvacion Mar 01, 2015

High recommended!My review of the book can be found in http://r-nold.blogspot.com/2015/03/book-review-mastering-scientific.html

Amazon Verified review

Visier68 Feb 28, 2015

“Mastering Scientific Computing with R” is a comprehensive book that combines an entry-level approach to data analysis with content targeted to experienced users, who are aiming to streamline their workflow.The author starts with an overview of R, explaining data types and other fundamental concepts, which should be sufficient for novice readers to understand how R differs in its coding style from traditional programming languages. On the other hand, the book is intentionally not written as a cookbook for data analysis. Hence, readers will benefit from previous experience with tools like RStudio and IPython, supporting experiments with functions and code snippets from the book for interactive learning.The first part is covering basic stuff like distributions, hypothesis testing, linear plus nonlinear models and PCA. For advanced users, things get more interesting with Structural Equation Modeling (SEM) in chapter 7, where the author provides a brief but good illustration of practical applications with OpenMx. The book includes a well-written chapter on simulation, explaining the powerful features of Monte Carlo method. It is followed by a chapter on optimization, which is equally useful in solving analytical problems.A strength of this book is that it provides in-depth information regarding some more advanced analytical procedures without being limited to specific domains like bioinformatics or econometrics, making it a recommended addition to the analyst’s bookshelf.What I am missing is a part covering practical deployment of the methodology described, especially in terms of reproducible research and scientific publishing. Reproducibility is essential, and R excels here by encouraging users to focus on scripting and automation rather than point-and-click. This is mentioned several times, but deserves more attention given its importance in science.Tools like Markdown and Pandoc support the creation of fully dynamic documents with minimal effort, which has vastly improved streamlining of workflows in scientific publishing. It would be useful to include some examples and references to further reading.While “Mastering Scientific Computing with R” is quite readable for novice users, I would recommend the book to people who are already familiar with basic tools and building blocks of data analysis, aiming to improve their knowledge about algorithms and standard procedures that typically make up a scientific workflow. But the book is also useful for non-scientific applications and the information provided may be easily transferred for exploitation in any specific domain.

Kongi Feb 27, 2015

The book is helpful to people who are just learning how to use R for modelling and simulation. It could be considered as an introduction to Scientific Computing using R not Mastering. I recommend topics such as high performance, parallel computation to be added in further editions of the book.Still glad i bought the book because i picked few points from it.

TCM Feb 28, 2015

The “mastering series” provides advanced tutorials and this was exactly what I was looking for. This book goes beyond most other books on R and gives insights into topics such as generalized linear models (GLM), principal component analysis (PCA) and Monte Carlo simulations. The introduction of this book is too advanced for R beginners but provides a good repetition of R’s capabilities and strength. For the rest of this book the reader needs a good grasp of R, statistics and mathematics. As such it distinguishes itself from most other book on R I have seen so far and meets my level of expectation. In any case, this book cannot be recommended as an entry level introduction neither to R nor statistics but is very useful for scientist interested in exploring possibilities for sound data analysis.

Al Feb 18, 2015

This book looks more like an overview of a variety of topics than a Mastering book. I wouldn't recommeded for an in depth coverage of the topics it exposes. Although it could be used as a quick guide to implement a variety of scientific methods in R. I purchase it as part of Packt Publishing $5 Christmas bonanza. For that price, you can't go wrong.One thing missing that I would've liked to see covered is reproducible research. I would think any new scientific programming book in R will cover this topic or at least gives examples of reproducible research.

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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Mastering Scientific Computing with R

Chapter 2. Statistical Methods with R

Descriptive statistics

Probability distributions

Fitting distributions

Hypothesis testing

Summary

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