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
Release on 2019-06-03 | by G. Sudhamathy, C. Jothi Venkateswaran
Author: G. Sudhamathy, C. Jothi Venkateswaran
Pubpsher: MJP Publisher
Chapter 1 - Basics of R, Chapter 2 - Data Types in R , Chapter 3 - Data Preparation. Chapter 4 - Graphics using R, Chapter 5 - Statistical Analysis Using R, Chapter 6 - Data Mining Using R, Chapter 7 - Case Studies. Huge volumes of data are being generated by many sources like commercial enterprises, scientific domains and general public daily. According to a recent research, data production will be 44 times greater in 2020 than it was in 2010. Data being a vital resource for business organizations and other domains like education, health, manufacturing etc., its management and analysis is becoming increasingly important. This data, due to its volume, variety and velocity, often referred to as Big Data, also includes highly unstructured data in the form of textual documents, web pages, graphical information and social media comments. Since Big Data is characterised by massive sample sizes, high dimensionality and intrinsic heterogeneity, traditional approaches to data management, visualisation and analytics are no longer satisfactorily applicable. There is therefore an urgent need for newer tools, better frameworks and workable methodologies for such data to be appropriately categorised, logically segmented, efficiently analysed and securely managed. This requirement has resulted in an emerging new discipline of Data Science that is now gaining much attention with researchers and practitioners in the field of Data Analytics.
Carol S. Parke's Essential First Steps to Data Analysis: Scenario-Based Examples Using SPSS provides instruction and guidance on preparing quantitative data sets prior to answering a study's research questions. Such preparation may involve data management and manipulation tasks, data organization, structural changes to the data files, or conducting preliminary analysis. Twelve research-based scenarios are used to present the content. Each scenario tells the "story" of a researcher who thoroughly examined their data and the decisions they made along the way. The scenario begins with a description of the researcher's study and his/her data file(s), then describes the issues the researcher must address, explains why they are important, shows how SPSS was used to address the issues and prepare data, and shares the researcher's reflections and any additional decision-making. Finally, each scenario ends with the researcher's written summary of the procedures and outcomes from the initial data preparation or analysis.
Release on 2020-05-22 | by Tomas Kalina,Mirjam van der Burg,Roshini Sarah Abraham,Marta Rizzi
Author: Tomas Kalina,Mirjam van der Burg,Roshini Sarah Abraham,Marta Rizzi
Pubpsher: Frontiers Media SA
We acknowledge the initiation and support of this Research Topic by the International Union of Immunological Societies (IUIS). We hereby state publicly that the IUIS has had no editorial input in articles included in this Research Topic, thus ensuring that all aspects of this Research Topic are evaluated objectively, unbiased by any specific policy or opinion of the IUIS.
Gillen's Stroke Rehabilitation: A Function-Based Approach, 3rd Edition is the only comprehensive, evidence-based stroke rehabilitation resource for occupational therapists. Extensively updated with the latest research in assessment and intervention, this essential text presents a holistic, application-based approach that integrates background medical information, samples of functionally based evaluations, and current treatment techniques and intervention strategies to help you confidently manage the growing number of stroke rehabilitation clients. UNIQUE! Case studies challenge you to apply rehabilitation concepts to realistic scenarios. Evidence-based clinical trials and outcome studies clearly outline the basis for stroke interventions. UNIQUE! Survivor's Perspectives help you understand the stroke rehabilitation process from the client's point-of-view. UNIQUE! A multidisciplinary approach highlights discipline-specific distinctions in stroke rehabilitation among occupation and physical therapists, physicians, and speech-language pathologists. Review questions in each chapter help you assess your understanding of rehabilitation concepts. Key terms and chapter objectives at the beginning of each chapter help you study more efficiently. Three new chapters broaden your understanding of stroke intervention in the areas of Using Technology to Improve Limb Function, Managing Speech and Language Deficits after Stroke, and Parenting after Stroke. Learning activities and interactive references on a companion Evolve Resources website help you review textbook content and locate additional information.