The book provides readers with the conceptual foundation to use applied statistical methods in everyday research. Each statistical method is developed within the context of practical, real-world examples and is supported by carefully developed pedagogy and jargon-free definitions. Theory is introduced as an accessible and adaptable tool and is always contextualized within the pragmatic context of real research projects and definable research questions.
Author Robert Stinerock has also created a wide range of online resources, including: R scripts, complete solutions for all exercises, data files for each chapter, video and screen casts, and interactive multiple-choice quizzes.
How to present charts and tables that viewers will grasp immediately: visual information anyone can use!
In an information-overloaded world, you simply must present information effectively. Using charts and tables, you can present categorical and numerical data far more clearly and efficiently. In this Element, we’ll show you exactly how to select and develop easy-to-understand charts and tables for the types of data you’re most likely to work with.
The production of clean data is a complex and time-consuming process that requires both technical know-how and statistical expertise. Statistical Data Cleaning brings together a wide range of techniques for cleaning textual, numeric or categorical data. This book examines technical data cleaning methods relating to data representation and data structure. A prominent role is given to statistical data validation, data cleaning based on predefined restrictions, and data cleaning strategy.
Key features:Focuses on the automation of data cleaning methods, including both theory and applications written in R. Enables the reader to design data cleaning processes for either one-off analytical purposes or for setting up production systems that clean data on a regular basis. Explores statistical techniques for solving issues such as incompleteness, contradictions and outliers, integration of data cleaning components and quality monitoring. Supported by an accompanying website featuring data and R code.
This book enables data scientists and statistical analysts working with data to deepen their understanding of data cleaning as well as to upgrade their practical data cleaning skills. It can also be used as material for a course in data cleaning and analyses.
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 doUse data types such as vectors, arrays, lists, data frames, and stringsExecute code conditionally or repeatedly with branches and loopsApply R add-on packages, and package your own work for othersLearn how to clean data you import from a variety of sourcesUnderstand data through visualization and summary statisticsUse statistical models to pass quantitative judgments about data and make predictionsLearn what to do when things go wrong while writing data analysis code
RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You’ll gain valuable programming skills and support your work as a data scientist at the same time.Work hands-on with three practical data analysis projects based on casino gamesStore, retrieve, and change data values in your computer’s memoryWrite programs and simulations that outperform those written by typical R usersUse R programming tools such as if else statements, for loops, and S3 classesLearn how to write lightning-fast vectorized R codeTake advantage of R’s package system and debugging toolsPractice and apply R programming concepts as you learn them
Coming to grips with R can be tough, even for seasoned statisticians and data analysts. Enter R For Dummies, the quick, easy way to master all the R you'll ever need. Requiring no prior programming experience and packed with practical examples, easy, step-by-step exercises, and sample code, this extremely accessible guide is the ideal introduction to R for complete beginners. It also covers many concepts that intermediate-level programmers will find extremely useful.Master your R ABCs ? get up to speed in no time with the basics, from installing and configuring R to writing simple scripts and performing simultaneous calculations on many variables Put data in its place ? get to know your way around lists, data frames, and other R data structures while learning to interact with other programs, such as Microsoft Excel Make data dance to your tune ? learn how to reshape and manipulate data, merge data sets, split and combine data, perform calculations on vectors and arrays, and much more Visualize it ? learn to use R's powerful data visualization features to create beautiful and informative graphical presentations of your data Get statistical ? find out how to do simple statistical analysis, summarize your variables, and conduct classic statistical tests, such as t-tests Expand and customize R ? get the lowdown on how to find, install, and make the most of add-on packages created by the global R community for a wide variety of purposes Open the book and find: Help downloading, installing, and configuring R Tips for getting data in and out of R Ways to use data frames and lists to organize data How to manipulate and process data Advice on fitting regression models and ANOVA Helpful hints for working with graphics How to code in R What R mailing lists and forums can do for you
“Its biggest advantage is that it aims only to teach R...It organizes R commands very efficiently, with much teaching guidance included. I would describe this book as being handy--it’s the kind of book that you want to keep in your jacket pocket or backpack all the time, ready for use, like a Swiss Army knife.” (Loveday Conquest, University of Washington)
“Whilst several books focus on learning statistics in R..., the authors of this book fill a gap in the market by focusing on learning R whilst almost completely avoiding any statistical jargon...The fact that the authors have very extensive experience of teaching R to absolute beginners shines throughout.” (Mark Mainwaring, Lancaster University)
“Exactly what is needed...This is great, nice work. I love the ecological/biological examples; they will be an enormous help.” (Andrew J. Tyne, University of Nebraska-Lincoln)