Keywords:
R (Computer program language).
;
Electronic books.
Description / Table of Contents:
A popular entry-level guide into the use of R as a statistical programming and data management language for students, post-docs, and seasoned researchers now in a new revised edition, incorporating the updates in the R environment, and also adding guidance on the use of more complex statistical analyses and tools.
Type of Medium:
Online Resource
Pages:
1 online resource (251 pages)
Edition:
2nd ed.
ISBN:
9780191091926
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=4811930
DDC:
570.2855133
Language:
English
Note:
Cover -- Contents -- Preface -- Introduction to the second edition -- What this book is about -- How the book is organized -- Why R? -- Updates -- Acknowledgements -- 1 Getting and Getting Acquainted with R -- 1.1 Getting started -- 1.2 Getting R -- 1.3 Getting RStudio -- 1.4 Let's play -- 1.5 Using R as a giant calculator (the size of your computer) -- 1.6 Your first script -- 1.7 Intermezzo remarks -- 1.8 Important functionality: packages -- 1.9 Getting help -- 1.10 A mini-practical-some in-depth play -- 1.11 Some more top tips and hints for a successful first (and more) R experience -- Appendix 1a Mini-tutorial solutions -- Appendix 1b File extensions and operating systems -- 2 Getting Your Data into R -- 2.1 Getting data ready for R -- 2.2 Getting your data into R -- 2.3 Checking that your data are your data -- 2.4 Basic troubleshooting while importing data -- 2.5 Summing up -- Appendix Advanced activity: dealing with untidy data -- 3 Data Management, Manipulation, and Exploration with dplyr -- 3.1 Summary statistics for each variable -- 3.2 dplyr verbs -- 3.3 Subsetting -- 3.4 Transforming -- 3.5 Sorting -- 3.6 Mini-summary and two top tips -- 3.7 Calculating summary statistics about groups of your data -- 3.8 What have you learned …lots -- Appendix 3a Comparing classic methods and dplyr -- Appendix 3b Advanced dplyr -- 4 Visualizing Your Data -- 4.1 The first step in every data analysis-making a picture -- 4.2 ggplot2: a grammar for graphics -- 4.3 Box-and-whisker plots -- 4.4 Distributions: making histograms of numeric variables -- 4.5 Saving your graphs for presentation, documents, etc. -- 4.6 Closing remarks -- 5 Introducing Statistics in R -- 5.1 Getting started doing statistics in R -- 5.2 χ2 contingency table analysis -- 5.3 Two-sample t-test -- 5.4 Introducing... linear models -- 5.5 Simple linear regression.
,
5.6 Analysis of variance: the one-way ANOVA -- 5.7 Wrapping up -- Appendix Getting packages not on CRAN -- 6 Advancing Your Statistics in R -- 6.1 Getting started with more advanced statistics -- 6.2 The two-way ANOVA -- 6.3 Analysis of covariance (ANCOVA) -- 6.4 Overview: an analysis workflow -- 7 Getting Started with Generalized Linear Models -- 7.1 Introduction -- 7.2 Counts and rates-Poisson GLMs -- 7.3 Doing it wrong -- 7.4 Doing it right-the Poisson GLM -- 7.5 When a Poisson GLM isn't good for counts -- 7.6 Summary, and beyond simple Poisson regression -- 8 Pimping Your Plots: Scales and Themes in ggplot2 -- 8.1 What you already know about graphs -- 8.2 Preparation -- 8.3 What you may want to customize -- 8.4 Axis labels, axis limits, and annotation -- 8.5 Scales -- 8.6 The theme -- 8.7 Summing up -- 9 Closing Remarks: Final Comments andEncouragement -- General Appendices -- Appendix 1 Data Sources -- Appendix 2 Further Reading -- Appendix 3 R Markdown -- Index.
Permalink
