Download or read book R pour la statistique et la science des donn es written by and published by . This book was released on 2018-10-25 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Le logiciel R est un outil incontournable de statistique, de visualisation de données et de science des données tant dans le monde universitaire que dans celui de l'entreprise. Ceci s'explique par ses trois principales qualités : il est gratuit, très complet et en essor permanent. Récemment, il a su s'adapter pour entrer dans l'ère du big-data et permettre de recueillir et traiter des données hétérogènes et de très grandes dimensions (issues du Web, données textuelles, etc.). Ce livre s'articule en deux grandes parties : la première est centrée sur le fonctionnement du logiciel R tandis que la seconde met en oeuvre une trentaine de méthodes statistiques au travers de fiches. Ces fiches sont chacune basées sur un exemple concret et balayent un large spectre de techniques pour traiter des données. Ce livre s'adresse aux débutants comme aux utilisateurs réguliers de R. Il leur permettra de réaliser rapidement des graphiques et des traitements statistiques simples ou élaborés.
Download or read book Modern Data Science with R written by Benjamin S. Baumer and published by CRC Press. This book was released on 2021-03-31 with total page 830 pages. Available in PDF, EPUB and Kindle. Book excerpt: From a review of the first edition: "Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.
Download or read book R for Data Science written by Hadley Wickham and published by O'Reilly Media. This book was released on 2023-10-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn data into insight, knowledge, and understanding. Ideal for current and aspiring data scientists, this book introduces you to doing data science with R and RStudio, as well as the tidyverse--a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly. You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Each section in this edition includes exercises to help you practice what you've learned along the way. Updated for the latest tidyverse best practices, new chapters dive deeper into visualization and data wrangling, show you how to get data from spreadsheets, databases, and websites, and help you make the most of new programming tools. You'll learn how to: Visualize-create plots for data exploration and communication of results Transform-discover types of variables and the tools you can use to work with them Import-get data into R and in a form convenient for analysis Program-learn R tools for solving data problems with greater clarity and ease Communicate-integrate prose, code, and results with Quarto
Download or read book R for Data Science written by Hadley Wickham. Garrett Grolemund and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book R for Political Data Science written by Francisco Urdinez and published by CRC Press. This book was released on 2020-11-17 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists, thinking about the many real-world problems faced in their work. The book has 16 chapters and is organized in three sections. The first, on the use of R, is for those users who are learning R or are migrating from another software. The second section, on econometric models, covers OLS, binary and survival models, panel data, and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation, fuzzy merge of large datasets, web mining, quantitative text analysis, network analysis, mapping, spatial cluster analysis, and principal component analysis. Key features: Each chapter has the most up-to-date and simple option available for each task, assuming minimal prerequisites and no previous experience in R Makes extensive use of the Tidyverse, the group of packages that has revolutionized the use of R Provides a step-by-step guide that you can replicate using your own data Includes exercises in every chapter for course use or self-study Focuses on practical-based approaches to statistical inference rather than mathematical formulae Supplemented by an R package, including all data As the title suggests, this book is highly applied in nature, and is designed as a toolbox for the reader. It can be used in methods and data science courses, at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD, political consultants, or a public official, all of whom need to transform their datasets into substantive and easily interpretable conclusions.
Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2023-06-08 with total page 579 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverse—a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly. You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way. You'll understand how to: Visualize: Create plots for data exploration and communication of results Transform: Discover variable types and the tools to work with them Import: Get data into R and in a form convenient for analysis Program: Learn R tools for solving data problems with greater clarity and ease Communicate: Integrate prose, code, and results with Quarto
Download or read book Statistiques en sciences humaines avec R 2e dition written by Jean-Herman Guay and published by Presses de l'Université Laval. This book was released on 2014-10-17T00:00:00-04:00 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aujourd'hui, avec la formidable disponibilité des données quantitatives et l'accès aux outils d'analyse, il n'y a plus de raison d'ignorer les méthodes quantitatives. Elles doivent devenir, plus qu'avant, une composante essentielle de l'analyse en sciences sociales. Cet ouvrage présente le logiciel R, car il est offert gratuitement, appuyé par une communauté scientifique, accessible sur les principales plateformes et il génère rapidement des graphiques de très haute qualité sous des formats multiples. Pour guider l'apprentissage de ce logiciel, nous adoptons différentes stratégies : • Une approche directe et vulgarisée des notions de statistiques... presque sans formule ; • Des premiers pas jusqu'à l'analyse multivariée ; • Le traitement des données, la production de graphiques ; • Des exemples réels : corruption, mortalité routière, marché immobilier, votes au Congrès américain, élections françaises ; • Une centaine de codes disponibles sur Internet et exécutables en quelques secondes ; • Une réflexion méthodologique et épistémologique pour mieux comprendre les données statistiques. Au fil des pages, nous tenterons de montrer que les précieuses possibilités du logiciel R exigent de leurs utilisateurs qu'ils sortent du périmètre des " chiffres " pour entrer dans des considérations théoriques et qualitatives afin de redonner ensuite un sens aux chiffres.
Download or read book Introduction aux statistiques en sciences du langage written by Clara Solier and published by . This book was released on 2023-10-18 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Doing Data Science in R written by Mark Andrews and published by Sage Publications Limited. This book was released on 2021-04-10 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: This approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry out data analysis. Introducing the fundamentals of data science and R before moving into more advanced topics like Multilevel Models and Probabilistic Modelling with Stan, it builds knowledge and skills gradually. This book: Focuses on providing practical guidance for all aspects, helping readers get to grips with the tools, software, and statistical methods needed to provide the right type and level of analysis their data requires Explores the foundations of data science and breaks down the processes involved, focusing on the link between data science and practical social science skills Introduces R at the outset and includes extensive worked examples and R code every step of the way, ensuring students see the value of R and its connection to methods while providing hands-on practice in the software Provides examples and datasets from different disciplines and locations demonstrate the widespread relevance, possible applications, and impact of data science across the social sciences
Download or read book A Guide to R for Social and Behavioral Science Statistics written by Brian Joseph Gillespie and published by SAGE Publications. This book was released on 2020-02-07 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Guide to R for Social and Behavioral Science Statistics is a short, accessible book for learning R. This handy guide contains basic information on statistics for undergraduates and graduate students, shown in the R statistical language using RStudio®. The book is geared toward social and behavioral science statistics students, especially those with no background in computer science. Written as a companion book to be used alongside a larger introductory statistics text, the text follows the most common progression of statistics for social scientists. The guide also serves as a companion for conducting data analysis in a research methods course or as a stand-alone R and statistics text. This guide can teach anyone how to use R to analyze data, and uses frequent reminders of basic statistical concepts to accompany instructions in R to help walk students through the basics of learning how to use R for statistics.
Download or read book Introduction to Data Science written by Rafael A. Irizarry and published by CRC Press. This book was released on 2019-11-20 with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.
Download or read book Statistics With R written by Jenine K. Harris and published by SAGE Publications. This book was released on 2019-12-19 with total page 785 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing on examples from across the social and behavioral sciences, Statistics With R: Solving Problems Using Real-World Data introduces foundational statistics concepts with beginner-friendly R programming in an exploration of the world’s tricky problems faced by the "R Team" characters.
Download or read book Textbook English written by Elen Le Foll and published by John Benjamins Publishing Company. This book was released on 2024-07-15 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a systematic, empirical account of the language typically presented in English as a Foreign Language (EFL) textbooks, based on a large corpus of EFL textbooks used in secondary schools. A modified version of the Multi-Dimensional Analysis (MDA) framework serves to examine linguistic variation both within textbooks and compared to corpora representing ‘real-life’ English as used outside the EFL classroom. The results highlight the characteristics of Textbook English that define it as a distinct variety of English. In light of the study's pedagogical implications, this book proposes a range of corpus-based approaches to improve the naturalness of textbook texts. It also contributes to advancing quantitative corpus linguistics methodology: its detailed online supplements aim for methodological transparency and reproducibility in line with the principles of Open Science. This book will be of interest to linguistics and language education students and researchers, as well as EFL teachers, textbook authors and editors, and those involved in curriculum development and teacher training.
Download or read book Statistics and Data with R written by Yosef Cohen and published by John Wiley & Sons. This book was released on 2008-11-20 with total page 618 pages. Available in PDF, EPUB and Kindle. Book excerpt: R, an Open Source software, has become the de facto statistical computing environment. It has an excellent collection of data manipulation and graphics capabilities. It is extensible and comes with a large number of packages that allow statistical analysis at all levels – from simple to advanced – and in numerous fields including Medicine, Genetics, Biology, Environmental Sciences, Geology, Social Sciences and much more. The software is maintained and developed by academicians and professionals and as such, is continuously evolving and up to date. Statistics and Data with R presents an accessible guide to data manipulations, statistical analysis and graphics using R. Assuming no previous knowledge of statistics or R, the book includes: A comprehensive introduction to the R language. An integrated approach to importing and preparing data for analysis, exploring and analyzing the data, and presenting results. Over 300 examples, including detailed explanations of the R scripts used throughout. Over 100 moderately large data sets from disciplines ranging from Biology, Ecology and Environmental Science to Medicine, Law, Military and Social Sciences. A parallel discussion of analyses with the normal density, proportions (binomial), counts (Poisson) and bootstrap methods. Two extensive indexes that include references to every R function (and its arguments and packages used in the book) and to every introduced concept.
Download or read book Beginning Data Science with R written by Manas A. Pathak and published by Springer. This book was released on 2014-12-08 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: “We live in the age of data. In the last few years, the methodology of extracting insights from data or "data science" has emerged as a discipline in its own right. The R programming language has become one-stop solution for all types of data analysis. The growing popularity of R is due its statistical roots and a vast open source package library. The goal of “Beginning Data Science with R” is to introduce the readers to some of the useful data science techniques and their implementation with the R programming language. The book attempts to strike a balance between the how: specific processes and methodologies, and understanding the why: going over the intuition behind how a particular technique works, so that the reader can apply it to the problem at hand. This book will be useful for readers who are not familiar with statistics and the R programming language.
Download or read book Data Analytics Using R written by Matthias Dehmer and published by Walter de Gruyter. This book was released on 2020-07 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order best exploit the incredible quantities of data being generated in most diverse disciplines data sciences increasingly gain worldwide importance. The book gives the mathematical foundations to handle data properly. It introduces basics and functionalities of the R programming language which has become the indispensable tool for data sciences. Thus it delivers the reader the skills needed to build own tool kits of a modern data scientist.
Download or read book Learning R written by Richard Cotton and published by "O'Reilly Media, Inc.". This book was released on 2013-09-09 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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, youâ??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 youâ??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
