Ggplot2 Essentials
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| Author | : Donato Teutonico |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 234 |
| Release | : 2015-06-25 |
| Genre | : Computers |
| ISBN | : 1785287559 |
ggplot2 is one of the most sophisticated and advanced packages of R and its use is constantly growing in the community of R programmers. This book is the perfect starting point for your journey in learning about one of the most refined and widely used plotting tools—ggplot2. You will start by taking a tour of the most relevant packages available in R to create plots, such as graphics, lattice, and ggplot2. Following this, you will take a deep dive into the concepts of ggplot2 by covering different kinds of plots that will help you gear up for your first hands-on project. After this first taste of the capabilities of ggplot2, you will explore in detail the visualization approach implemented in this package, the so-called grammar of graphics, providing you with the basics to understand the backstage concepts in the plotting functions. Finally, you will take a look at some specialized and sophisticated applications of ggplot2, such as how to realize a complex scatterplot matrix, heatmaps, and how to make spatial visualization graphs.
| Author | : Alboukadel Kassambara |
| Publisher | : Independently Published |
| Total Pages | : 94 |
| Release | : 2019-11-15 |
| Genre | : |
| ISBN | : 9781708503208 |
GGPlot2 is a powerful and a flexible R package for producing elegant graphics piece by piece. It has become a popular package for data visualization. This book presents the essentials of ggplot2 to easily create beautiful graphics in R.Key features: - Covers the most important graphic functions- Short, self-contained chapters with practical examples.
| Author | : Hadley Wickham |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 211 |
| Release | : 2009-10-03 |
| Genre | : Computers |
| ISBN | : 0387981411 |
Provides both rich theory and powerful applications Figures are accompanied by code required to produce them Full color figures
| Author | : Alboukadel Kassambara |
| Publisher | : STHDA |
| Total Pages | : 155 |
| Release | : 2017-11-14 |
| Genre | : Computers |
| ISBN | : 1979748101 |
Data visualization is one of the most important part of data science. Many books and courses present a catalogue of graphics but they don't teach you which charts to use according to the type of the data. In this book, we start by presenting the key graphic systems and packages available in R, including R base graphs, lattice and ggplot2 plotting systems. Next, we provide more than 200 practical examples to create great graphics for the right data using either the ggplot2 package and extensions or the traditional R graphics. With this book, you 'll learn: - How to quickly create beautiful graphics using ggplot2 packages - How to properly customize and annotate the plots - Type of graphics for visualizing categorical and continuous variables - How to add automatically p-values to box plots, bar plots and alternatives - How to add marginal density plots and correlation coefficients to scatter plots - Key methods for analyzing and visualizing multivariate data - R functions and packages for plotting time series data - How to combine multiple plots on one page to create production-quality figures.
| Author | : Leland Wilkinson |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 415 |
| Release | : 2013-03-09 |
| Genre | : Computers |
| ISBN | : 1475731000 |
Written for statisticians, computer scientists, geographers, research and applied scientists, and others interested in visualizing data, this book presents a unique foundation for producing almost every quantitative graphic found in scientific journals, newspapers, statistical packages, and data visualization systems. It was designed for a distributed computing environment, with special attention given to conserving computer code and system resources. While the tangible result of this work is a Java production graphics library, the text focuses on the deep structures involved in producing quantitative graphics from data. It investigates the rules that underlie pie charts, bar charts, scatterplots, function plots, maps, mosaics, and radar charts. These rules are abstracted from the work of Bertin, Cleveland, Kosslyn, MacEachren, Pinker, Tufte, Tukey, Tobler, and other theorists of quantitative graphics.
| Author | : Hadley Wickham |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 521 |
| Release | : 2016-12-12 |
| Genre | : Computers |
| ISBN | : 1491910364 |
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
| Author | : Keon-Woong Moon |
| Publisher | : Springer |
| Total Pages | : 356 |
| Release | : 2017-04-13 |
| Genre | : Computers |
| ISBN | : 3319530194 |
This book and app is for practitioners, professionals, researchers, and students who want to learn how to make a plot within the R environment using ggplot2, step-by-step without coding. In widespread use in the statistical communities, R is a free software language and environment for statistical programming and graphics. Many users find R to have a steep learning curve but to be extremely useful once overcome. ggplot2 is an extremely popular package tailored for producing graphics within R but which requires coding and has a steep learning curve itself, and Shiny is an open source R package that provides a web framework for building web applications using R without requiring HTML, CSS, or JavaScript. This manual—"integrating" R, ggplot2, and Shiny—introduces a new Shiny app, Learn ggplot2, that allows users to make plots easily without coding. With the Learn ggplot2 Shiny app, users can make plots using ggplot2 without having to code each step, reducing typos and error messages and allowing users to become familiar with ggplot2 code. The app makes it easy to apply themes, make multiplots (combining several plots into one plot), and download plots as PNG, PDF, or PowerPoint files with editable vector graphics. Users can also make plots on any computer or smart phone. Learn ggplot2 Using Shiny App allows users to Make publication-ready plots in minutes without coding Download plots with desired width, height, and resolution Plot and download plots in png, pdf, and PowerPoint formats, with or without R code and with editable vector graphics
| Author | : Kieran Healy |
| Publisher | : Princeton University Press |
| Total Pages | : 292 |
| Release | : 2018-12-18 |
| Genre | : Social Science |
| ISBN | : 0691181624 |
An accessible primer on how to create effective graphics from data This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way. Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible. Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings. Provides hands-on instruction using R and ggplot2 Shows how the “tidyverse” of data analysis tools makes working with R easier and more consistent Includes a library of data sets, code, and functions
| Author | : Graham J. Williams |
| Publisher | : CRC Press |
| Total Pages | : 295 |
| Release | : 2017-07-28 |
| Genre | : Business & Economics |
| ISBN | : 1351647490 |
The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years’ experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R’s capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.
| Author | : Eric Goh Ming Hui |
| Publisher | : Apress |
| Total Pages | : 254 |
| Release | : 2018-11-30 |
| Genre | : Computers |
| ISBN | : 1484242009 |
Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R’s syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions. Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statistics for data explorations. What You Will LearnDiscover R, statistics, data science, data mining, and big data Master the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functions Work with descriptive statistics Create data visualizations, including bar charts, line charts, scatter plots, boxplots, histograms, and scatterplots Use inferential statistics including t-tests, chi-square tests, ANOVA, non-parametric tests, linear regressions, and multiple linear regressions Who This Book Is For Those who are interested in data science, in particular data exploration using applied statistics, and the use of R programming for data visualizations.
