The First R

This study looks into how children learn about the 'first R'—race—and challenges the current assumptions with case-study examples from three child-care centers. Parents and teachers will find this remarkable study reveals that the answer to how children learn about race might be more startling than could be imagined.

Writers since Piaget have questioned when and how children assimilate racist attitudes - or simply become aware of racial differences. This book offers stirring evidence that the answers may be more surprising than we ever imagined. The rich accounts of children's behaviour around race are drawn from Van Ausdale's ethnographies, conducted in several multi-ethnic day-care centres. When she persistently divested herself of any authoritative role, children as young as three years gradually revealed to her a surprising array of racial attitudes, assumptions and behaviours - most of which they normally withhold from parents and adults. The careful ethnographic analysis, conducted over many months, lead the authors to question many long-held assumptions about the nature of race and racial learning in society. The stories of the children are compelling, often endearing and unforgettable. They will change the way parents, teachers and other educators understand the world as seen by children.

"The First R. Austin Freeman Megapack" collects 27 mystery tales featuring the forensic sleuth Dr. Thorndyke (and others). Included in this volume are: THE RED THUMB MARK (1907) THE MAN WITH THE NAILED SHOES (1909) THE STRANGER'S LATCHKEY (1909) THE ANTHROPOLOGIST AT LARGE (1909) THE BLUE SEQUIN (1909) THE MOABITE CIPHER (1909) THE MANDARIN'S PEARL (1909) THE ALUMINIUM DAGGER (1909) A MESSAGE FROM THE DEEP SEA (1909) THE EYE OF OSIRIS (1911) THE MYSTERY OF 31 NEW INN (1912) THE CASE OF OSCAR BRODSKI (1912) A CASE OF PREMEDITATION (1912) THE ECHO OF A MUTINY (1912) A WASTREL'S ROMANCE (1912) THE OLD LAG (1912) THE UTTERMOST FARTHING (1914) A SILENT WITNESS (1914) THE GREAT PORTRAIT MYSTERY (1918) THE BRONZE PARROT (1918) POWDER BLUE AND HAWTHORN (1918) THE ATTORNEY'S CONSCIENCE (1918) THE LUCK OF BARNABAS MUDGE (1918) THE MISSING MORTGAGEE (1918) PERCIVAL BLAND'S PROXY (1918) THE CASE OF THE WHITE FOOTPRINTS (1920) HELEN VARDON'S CONFESSION (1922) If you enjoy this volume, please search this ebook store for "Wildside Megapack" to see more entries in the series, collecting great tales of adventure, mystery, science fiction, westerns, ghost stories, and much more. (Sort by publication date to see the most recent additions.)

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

The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: –The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops –Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R –How to access R’s thousands of functions, libraries, and data sets –How to draw valid and useful conclusions from your data –How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.

Probability theory is one branch of mathematics that is simultaneously deep and immediately applicable in diverse areas of human endeavor. It is as fundamental as calculus. Calculus explains the external world, and probability theory helps predict a lot of it. In addition, problems in probability theory have an innate appeal, and the answers are often structured and strikingly beautiful. A solid background in probability theory and probability models will become increasingly more useful in the twenty-?rst century, as dif?cult new problems emerge, that will require more sophisticated models and analysis. Thisisa text onthe fundamentalsof thetheoryofprobabilityat anundergraduate or ?rst-year graduate level for students in science, engineering,and economics. The only mathematical background required is knowledge of univariate and multiva- ate calculus and basic linear algebra. The book covers all of the standard topics in basic probability, such as combinatorial probability, discrete and continuous distributions, moment generating functions, fundamental probability inequalities, the central limit theorem, and joint and conditional distributions of discrete and continuous random variables. But it also has some unique features and a forwa- looking feel.

A guide to managing your time by learning how to balance your life.

Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), "bridges" to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/. Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds, where he has taught R for geographic research over many years, with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan, where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena, where he develops and teaches a range of geographic methods, with a focus on ecological modeling, statistical geocomputing, and predictive mapping. All three are active developers and work on a number of R packages, including stplanr, sabre, and RQGIS.