Normal view MARC view ISBD view

Getting Started with R : An introduction for biologists

By: Beckerman, Andrew P.
Contributor(s): Petchey, Owen L.
Material type: TextTextSeries: eBooks on Demand.Publisher: Oxford : Oxford University Press, 2012Description: 1 online resource (124 p.).ISBN: 9780191624148.Subject(s): Bioinformatics | Programming languages (Electronic computers) | R (Computer program language)Genre/Form: Electronic books.Additional physical formats: Print version:: Getting Started with R : An introduction for biologistsDDC classification: 572.80285 Online resources: Click here to view this ebook.
Contents:
Cover; Table of Contents; Preface; What this book is about; What you need to know to make this book work for you; How the book is organized; Chapter 1: Why R?; Chapter 2: Import, Explore, Graph I-Getting Started; 2.1 Where to put your data; 2.2 Make a folder for your instructions (code, script); 2.3 How to get your data into R and where it is stored in R's brain; 2.4 Working with R-hints for a successful first (and more) interaction; 2.5 Make your first script file; 2.6 Starting to control R; 2.7 Making R work for you-developing a workflow; 2.8 And finally . . .
Chapter 3: Import, Explore, Graph II-Importing and Exploring3.1 Getting your data into R; 3.2 Checking that your data is your data; 3.3 Summarizing your data-quick version; 3.4 How to isolate, find, and grab parts of your data-I; 3.5 How to isolate, find, and grab parts of your data-II; 3.6 Aggregation and how to use a help file; 3.7 What your first script might look like (what you should now know); Chapter 4: Import, Explore, Graph III-Graphs; 4.1 The first step in data analysis-making a picture; 4.2 Making a picture-bar graphs; 4.2.1 Pimp my barplot; 4.3 Making a picture-scatterplots
4.3.1 Pimp my scatterplot: axis labels4.3.2 Pimp my scatterplot: points; 4.3.3 Pimp my scatterplot: colours (and groups); 4.3.4 Pimp my scatterplot: legend; 4.4 Plotting extras: pdfs, layout, and the lattice package; Chapter 5: Doing your Statistics in R-Getting Started; 5.1 Chi-square; 5.2 Two sample t-test; 5.2.1 The first step: plot your data; 5.2.2 The two sample t-test analysis; 5.3 General linear models; 5.3.1 Always start with a picture; 5.3.2 Potential statistical and biological hypotheses-it's all about lines; 5.3.3 Specifying the model; 5.3.4 Plot, model, then assumptions
5.3.5 Interpretation5.3.6 Treatment contrasts and coefficients; 5.3.7 Interpretation; 5.4 Making a publication quality figure; 5.4.1 Coefficients, lines, and lines(); 5.4.2 Expanded grids, prediction, and a more generic model plotting method; 5.4.3 The final picture; 5.4.4 An analysis workflow; Chapter 6: Final Comments and Encouragement; Appendix: References and Datasets; Index; A; B; C; D; E; F; G; H; I; L; M; N; O; P; R; S; T; U; V; W; X; Y
Summary: Learning how to get answers from data is an integral part of modern training in the natural, physical, social, and engineering sciences. One of the most exciting changes in data management and analysis during the last decade has been the growth of open source software. The open source statistics and programming language R has emerged as a critical component of any researcher's toolbox. Indeed, R is rapidly becoming the standard software for analyses, graphical presentations, andprogramming in the biological sciences.This book provides a functional introduction for biologists new to R. While te
Tags from this library: No tags from this library for this title. Log in to add tags.
Item type Current location Call number URL Status Date due Barcode
Electronic Book UT Tyler Online
Online
QH324.2 .B889 2012 (Browse shelf) http://uttyler.eblib.com/patron/FullRecord.aspx?p=886478 Available EBL886478

Cover; Table of Contents; Preface; What this book is about; What you need to know to make this book work for you; How the book is organized; Chapter 1: Why R?; Chapter 2: Import, Explore, Graph I-Getting Started; 2.1 Where to put your data; 2.2 Make a folder for your instructions (code, script); 2.3 How to get your data into R and where it is stored in R's brain; 2.4 Working with R-hints for a successful first (and more) interaction; 2.5 Make your first script file; 2.6 Starting to control R; 2.7 Making R work for you-developing a workflow; 2.8 And finally . . .

Chapter 3: Import, Explore, Graph II-Importing and Exploring3.1 Getting your data into R; 3.2 Checking that your data is your data; 3.3 Summarizing your data-quick version; 3.4 How to isolate, find, and grab parts of your data-I; 3.5 How to isolate, find, and grab parts of your data-II; 3.6 Aggregation and how to use a help file; 3.7 What your first script might look like (what you should now know); Chapter 4: Import, Explore, Graph III-Graphs; 4.1 The first step in data analysis-making a picture; 4.2 Making a picture-bar graphs; 4.2.1 Pimp my barplot; 4.3 Making a picture-scatterplots

4.3.1 Pimp my scatterplot: axis labels4.3.2 Pimp my scatterplot: points; 4.3.3 Pimp my scatterplot: colours (and groups); 4.3.4 Pimp my scatterplot: legend; 4.4 Plotting extras: pdfs, layout, and the lattice package; Chapter 5: Doing your Statistics in R-Getting Started; 5.1 Chi-square; 5.2 Two sample t-test; 5.2.1 The first step: plot your data; 5.2.2 The two sample t-test analysis; 5.3 General linear models; 5.3.1 Always start with a picture; 5.3.2 Potential statistical and biological hypotheses-it's all about lines; 5.3.3 Specifying the model; 5.3.4 Plot, model, then assumptions

5.3.5 Interpretation5.3.6 Treatment contrasts and coefficients; 5.3.7 Interpretation; 5.4 Making a publication quality figure; 5.4.1 Coefficients, lines, and lines(); 5.4.2 Expanded grids, prediction, and a more generic model plotting method; 5.4.3 The final picture; 5.4.4 An analysis workflow; Chapter 6: Final Comments and Encouragement; Appendix: References and Datasets; Index; A; B; C; D; E; F; G; H; I; L; M; N; O; P; R; S; T; U; V; W; X; Y

Learning how to get answers from data is an integral part of modern training in the natural, physical, social, and engineering sciences. One of the most exciting changes in data management and analysis during the last decade has been the growth of open source software. The open source statistics and programming language R has emerged as a critical component of any researcher's toolbox. Indeed, R is rapidly becoming the standard software for analyses, graphical presentations, andprogramming in the biological sciences.This book provides a functional introduction for biologists new to R. While te

Description based upon print version of record.

There are no comments for this item.

Log in to your account to post a comment.