R Graphics Cookbook
There are many ways of making graphs in R, each with its advantages and disadvantages. The focus here is on the ggplot2 package, which is based on the Grammar of Graphics (by Leland Wilkinson) to describe data graphics.
R Graphics Cookbook
This cookbook provides more than 150 recipes to help scientists, engineers, programmers, and data analysts 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.
The goal of this cookbook is to provide solutions to common tasks and problems in analyzing data. 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.
R is a powerful tool for statistics, graphics, and statisticalprogramming. It is used by tens of thousands of people daily to performserious statistical analyses. It is a free, open source system whoseimplementation is the collective accomplishment of many intelligent,hard-working people. There are more than 10,000 available add-on packages, and Ris a serious rival to all commercial statistical packages.
The range of recipes is broad. It starts with basic tasks before movingon to input and output, general statistics, graphics, and linearregression. Any significant work with R will involve most or all ofthese areas.
Each recipe presents one way to solve a particular problem. Of course,there are likely several reasonable solutions to each problem. When weknew of multiple solutions, we generally selected the simplest one. Forany given task, you can probably discover several alternative solutionsyourself. This is a cookbook, not a bible.
This book helps you create the most popular visualizations - from quickand dirty plots to publication-ready graphs. The text relies heavily onthe ggplot2 package for graphics, but other approaches are covered aswell.
ggplot2 is an R package for producing statistical, or data, graphics.Unlike most other graphics packages, ggplot2 has an underlying grammar,based on the Grammar of Graphics (Wilkinson 2005), that allows you tocompose graphs by combining independent components. This makes ggplot2powerful. Rather than being limited to sets of pre-defined graphics, youcan create novel graphics that are tailored to your specific problem.
The main aim of the book is to show, using real datasets, whatinformation graphical displays can reveal in data. The target readershipincludes anyone carrying out data analyses who wants to understand theirdata using graphics.
ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
This chapter presents the basic principles for data description and visualization. After a brief introduction, we consider the description and visualization of structural properties of the business process in Sect. 4.2. The description and visualization for collections of process instances is treated in Sect. 4.3, which later outlines the essentials of interactive and dynamic graphics. Section 4.4 introduces frequently used visualization techniques together with applications to the use cases. Finally, Sect. 4.5 discusses certain aspects of infographics and reporting.
Module 2: Visualization of Biomedical Big Data Instructors: Hadley Wickham and Dianne CookModule description: In this module, we will present general-purpose techniques for visualizing any sort of large data sets, as well as specific techniques for visualizing common types of biological data sets. Often the challenge of visualizing Big Data is to aggregate it down to a suitable level. Understanding Big Data involves an iterative cycle of visualization and modeling. We will illustrate this with several case studies during the workshop. The first segment of this module will focus on structured development of graphics using static graphics. This will use the ggplot2 package in R. It enables building plots using grammatically defined elements, and producing templates for use with multiple data sets. We will show how to extend these principles for genomic data using the ggplot2-based ggbio package. The second segment will focus on interactive graphics for rapid exploration of Big Data. We will also demonstrate interactive techniques for high-performance local display using cranvas, and for easily creating interactive web graphics with ggvis. In addition we will explain how to create simple web GUIs for managing complex summaries of biological data using the shiny package. We will use a hands-on teaching methodology that combines short lectures with longer practice sessions. As students learn about new techniques, they will also be able to put them into practice and receive feedback from experts. We will teach using R and Rstudio. We will assume some familiarity with R. Recommended Reading: Cookbook for R, by Winston Chang, available at www.cookbook-r.com. 041b061a72