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Bezig met laden... R Graphics Cookbook: Practical Recipes for Visualizing Datadoor Winston Chang
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Meld je aan bij LibraryThing om erachter te komen of je dit boek goed zult vinden. Op dit moment geen Discussie gesprekken over dit boek. This book is about leveraging R for all your graphical needs. It starts with the graphing functions in base R using the plot function. Starting chapter 3, ggplot2 takes over. The provides great amount of detail on how to use the ggplot and geom_* functions for most types of graphs. I don't think the author expects us to learn the syntax in one go, so for me, it'll be a go-to reference book. I would've preferred the appendix to be one of the early chapters. I think that would help understand the basics better. If you're ever fumbling around with data in R, you're probably familiar with the built-in, unattractive graphics. ggplot2's been increasingly recognized as a necessity for getting the most out of your imagery. It offers nearly complete control over your graphics output, building them layer by layer. [N.B. This review includes images, and was formatted for my site, dendrobibliography -- located here.] I spent a solid year learning and exploring R as a graduate student before I cracked open Winston Chang's R Graphics Cookbook and started learning ggplot2's little oddities. ggplot2 is itself almost like another language within R, but it's thankfully a very simple language -- far more simple and far more flexible, I feel, than the built-in graphics options. Since you'll be printing your graphics step by step -- your boundaries before your lines; your lines separately from points; etc. -- it's easy to keep track of where every impact on the output image is occurring, allowing you to easily tweak the code and get immediate results. E.g., if annotations are not lining up where you want, or font size needs to be reduced. Chang's cookbook is separated by what feature you need to either edit or create, making it easy to jump to what the reader needs. Full sections are devoted to bar graphs, line graphs, scatter plots, data distribution graphics, customizing annotations, axes, legends, color options, and cetera. Nearly 400 pages of text and images showing different ways of customizing and displaying every piece of your graphics. It's not a book you read cover to cover -- just the resource that 'cookbook' implies, meeting the reader's specific needs. If you want to just jump into the code and see what you can do with your own data, there's no better place to start. Almost no time is devoted to unnecessary exercises or teaching you the fundamentals of the R programming language. Exploring the far reaches of the Internet is a free alternative that's likely just as helpful, but Chang's book serves as a great reference, and contains almost everything you need all in one. I've uploaded examples of ggplot2 imagery produced for my thesis and an in-progress journal publication, including time series insect outbreak records (link), an arranged grid of superposed epoch analyses showing deviations in climate data around outbreak event years for our entire study region (link), a similar grid showing customized all the superposed epoch analyses for our individual sites (link), and -- just for fun -- a customized way to show outbreak dates as point data rather than time series (link). geen besprekingen | voeg een bespreking toe
This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R ́s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works. Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you ́re ready to get started. Use R ́s default graphics for quick exploration of data Create a variety of bar graphs, line graphs, and scatter plots Summarize data distributions with histograms, density curves, box plots, and other examples Provide annotations to help viewers interpret data Control the overall appearance of graphics Render data groups alongside each other for easy comparison Use colors in plots Create network graphs, heat maps, and 3D scatter plots Structure data for graphing Geen bibliotheekbeschrijvingen gevonden. |
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Google Books — Bezig met laden... GenresDewey Decimale Classificatie (DDC)519.502855133Natural sciences and mathematics Mathematics Applied Mathematics, Probabilities Statistical MathematicsLC-classificatieWaarderingGemiddelde:
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