ggplot2 elegant graphics for data analysis use r

Download Book Ggplot2 Elegant Graphics For Data Analysis Use R in PDF format. You can Read Online Ggplot2 Elegant Graphics For Data Analysis Use R here in PDF, EPUB, Mobi or Docx formats.

Ggplot2

Author : Hadley Wickham
ISBN : 9783319242774
Genre : Computers
File Size : 77. 72 MB
Format : PDF, Mobi
Download : 727
Read : 649

Download Now


This new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to: produce handsome, publication-quality plots with automatic legends created from the plot specification superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression save any ggplot2 plot (or part thereof) for later modification or reuse create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page.

Bioinformatics Data Skills

Author : Vince Buffalo
ISBN : 9781449367510
Genre : Computers
File Size : 37. 7 MB
Format : PDF, Mobi
Download : 704
Read : 527

Download Now


Learn the data skills necessary for turning large sequencing datasets into reproducible and robust biological findings. With this practical guide, you’ll learn how to use freely available open source tools to extract meaning from large complex biological data sets. At no other point in human history has our ability to understand life’s complexities been so dependent on our skills to work with and analyze data. This intermediate-level book teaches the general computational and data skills you need to analyze biological data. If you have experience with a scripting language like Python, you’re ready to get started. Go from handling small problems with messy scripts to tackling large problems with clever methods and tools Process bioinformatics data with powerful Unix pipelines and data tools Learn how to use exploratory data analysis techniques in the R language Use efficient methods to work with genomic range data and range operations Work with common genomics data file formats like FASTA, FASTQ, SAM, and BAM Manage your bioinformatics project with the Git version control system Tackle tedious data processing tasks with with Bash scripts and Makefiles

Guidebook To R Graphics Using Microsoft Windows

Author : Kunio Takezawa
ISBN : 9781118270158
Genre : Mathematics
File Size : 64. 25 MB
Format : PDF
Download : 638
Read : 450

Download Now


Introduces the graphical capabilities of R to readers new to the software Due to its flexibility and availability, R has become the computing software of choice for statistical computing and generating graphics across various fields of research. Guidebook to R Graphics Using Microsoft® Windows offers a unique presentation of R, guiding new users through its many benefits, including the creation of high-quality graphics. Beginning with getting the program up and running, this book takes readers step by step through the process of creating histograms, boxplots, strip charts, time series graphs, steam-and-leaf displays, scatterplot matrices, and map graphs. In addition, the book presents: Tips for establishing, saving, and printing graphs along with essential base-package plotting functions Interactive R programs for carrying out common tasks such as inputting values, moving data on a natural spline, adjusting three-dimensional graphs, and understanding simple and local linear regression Various external packages for R that help to create more complex graphics like rimage, gplots, ggplot2, tripack, rworldmap, and plotrix packages Throughout the book, concise explanations of key concepts of R graphics assist readers in carrying out the presented procedures, and any coverage of functions is clearly written out and displayed in the text as demos. The discussed techniques are accompanied by a wealth of screenshots and graphics with related R code available on the book's FTP site, and numerous exercises allow readers to test their understanding of the presented material. Guidebook to R Graphics Using Microsoft® Windows is a valuable resource for researchers in the fields of statistics, public health, business, and the life and social sciences who use or would like to learn how to use R to create visual representations of data. The book can also be used as a supplement for courses on statistical analysis at the upper-undergraduate level.

Using R For Data Analysis In Social Sciences

Author : Quan Li
ISBN : 9780190656232
Genre : Political Science
File Size : 62. 25 MB
Format : PDF, Mobi
Download : 341
Read : 1097

Download Now


Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate published findings. Using R for Data Analysis in Social Sciences adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. It also demonstrates how to replicate the findings in published journal articles and diagnose model assumption violations. Because the book integrates R programming, the logic and steps of statistical inference, and the process of empirical social scientific research in a highly accessible and structured fashion, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research.

A Primer In Biological Data Analysis And Visualization Using R

Author : Gregg Hartvigsen
ISBN : 9780231537049
Genre : Science
File Size : 85. 18 MB
Format : PDF, Kindle
Download : 479
Read : 1245

Download Now


R is a popular programming language that statisticians use to perform a variety of statistical computing tasks. Rooted in Gregg Hartvigsen's extensive experience teaching biology, this text is an engaging, practical, and lab-oriented introduction to R for students in the life sciences. Underscoring the importance of R and RStudio to the organization, computation, and visualization of biological statistics and data, Hartvigsen guides readers through the processes of entering data into R, working with data in R, and using R to express data in histograms, boxplots, barplots, scatterplots, before/after line plots, pie charts, and graphs. He covers data normality, outliers, and nonnormal data and examines frequently used statistical tests with one value and one sample; paired samples; more than two samples across a single factor; correlation; and linear regression. The volume also includes a section on advanced procedures and a final chapter on possible extensions into programming, featuring a discussion of algorithms, the art of looping, and combining programming and output.

Modeling Techniques In Predictive Analytics

Author : Thomas W. Miller
ISBN : 9780133886191
Genre : Computers
File Size : 70. 49 MB
Format : PDF, Kindle
Download : 358
Read : 598

Download Now


To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Learning R

Author : Richard Cotton
ISBN : 9781449357184
Genre : Computers
File Size : 76. 43 MB
Format : PDF, ePub
Download : 132
Read : 418

Download Now


Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you’ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts. The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what you’ve learned, and concludes with exercises, most of which involve writing R code. Write a simple R program, and discover what the language can do Use data types such as vectors, arrays, lists, data frames, and strings Execute code conditionally or repeatedly with branches and loops Apply R add-on packages, and package your own work for others Learn how to clean data you import from a variety of sources Understand data through visualization and summary statistics Use statistical models to pass quantitative judgments about data and make predictions Learn what to do when things go wrong while writing data analysis code

Modeling Techniques In Predictive Analytics With Python And R

Author : Thomas W. Miller
ISBN : 9780133892147
Genre : Computers
File Size : 33. 31 MB
Format : PDF, Mobi
Download : 195
Read : 724

Download Now


Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

An Introduction To Empirical Legal Research

Author : Lee Epstein
ISBN : 9780191646553
Genre : Social Science
File Size : 35. 2 MB
Format : PDF, ePub, Mobi
Download : 249
Read : 973

Download Now


Is the death penalty a more effective deterrent than lengthy prison sentences? Does a judge's gender influence their decisions? Do independent judiciaries promote economic freedom? Answering such questions requires empirical evidence, and arguments based on empirical research have become an everyday part of legal practice, scholarship, and teaching. In litigation judges are confronted with empirical evidence in cases ranging from bankruptcy and taxation to criminal law and environmental infringement. In academia researchers are increasingly turning to sophisticated empirical methods to assess and challenge fundamental assumptions about the law. As empirical methods impact on traditional legal scholarship and practice, new forms of education are needed for today's lawyers. All lawyers asked to present or assess empirical arguments need to understand the fundamental principles of social science methodology that underpin sound empirical research. An Introduction to Empirical Legal Research introduces that methodology in a legal context, explaining how empirical analysis can inform legal arguments; how lawyers can set about framing empirical questions, conducting empirical research, analysing data, and presenting or evaluating the results. The fundamentals of understanding quantitative and qualitative data, statistical models, and the structure of empirical arguments are explained in a way accessible to lawyers with or without formal training in statistics. Written by two of the world's leading experts in empirical legal analysis, drawing on years of experience in training lawyers in empirical methods, An Introduction to Empirical Legal Research will be an invaluable primer for all students, academics, or practising lawyers coming to empirical research - whether they are embarking themselves on an empirical research project, or engaging with empirical arguments in their field of study, research, or practice.

Ggplot2 The Elements For Elegant Data Visualization In R

Author : Alboukadel Kassambara
ISBN :
Genre :
File Size : 75. 66 MB
Format : PDF, ePub, Docs
Download : 822
Read : 393

Download Now


ggplot2 is a popular R package for data visualization. This book presents the most important functions available in the last version of ggplot2 (ver 1.0) to quickly and easily generate nice looking graphs. You will find many examples of R codes and graphics in this document. This book is organized in 3 parts. After giving a brief overview of ggplot2 (in the part 1, chapter 1), quick start guides are provided in chapters 2 - 14 for creating and customizing different types of graphs including box plots, violin plots, dot plots, stripcharts, density plots, histogram plots, scatter plots, bar plots, line plots, error bars, pie charts, qq plots and ECDF. The last chapter of the part 1 (chapter 15) describes how to save ggplots to a pdf or a png files for presentation. The part 2 of the book covers how to change graphical parameters including: - Main title, axis labels and legend titles (chapter 16) - Position and the appearance of plot legends (chapter 17) - Manual and automatic coloring (chapter 18) - Point shapes (chapter 19) - Line types (chapter 20) - Adding text annotations to a graph (chapter 21) - Adding straight lines to a plot: horizontal, vertical and regression lines (chapter 22) - Axis scales and transformations (chapter 23) - Axis ticks : customize tick marks and labels (chapter 24) - Themes and background colors (chapter 25) - Rotate a graph (chapter 26) - Facets: split a plot into a matrix of panels (chapter 27) The part 3 describes some extensions of ggplot2 including: - Mixing multiple graphs on the same page (chapter 28) - Plotting a correlation matrix heatmap (chapter 29) - Plotting survival curves (chapter 30) Each chapter is organized as an independent quick start guide. This means that, you don’t need to read the different chapters in sequence.

Top Download:

Best Books