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"Are you a researcher or instructor who has been wanting to learn R and RStudio®, but you don't know where to begin? Do you want to be able to perform all the same functions you use in IBM® SPSS® in R? Is your license to IBM® SPSS® expiring, or are you looking to provide your students guidance to a freely-available statistical software program? Moving from IBM® SPSS® to R and RStudio ®: A Statistics Companion is a concise and easy-to-read guide for users who want to know learn how to perform statistical calculations in R. Brief chapters start with a step-by-step introduction to R and RStudio, offering basic installation information and a summary of the differences. Subsequent chapters walk through differences between SPSS and R, in terms of data files, concepts, and structure. Detailed examples provide walk-throughs for different types of data conversions and transformations and their equivalent in R. Helpful and comprehensive appendices provide tables of each statistical transformation in R with its equivalent in SPSS and show what, if any, differences in assumptions factor to into each function. Statistical tests from t-tests to ANOVA through three-factor ANOVA and multiple regression and chi-square are covered in detail, showing each step in the process for both programs. By focusing just on R and eschewing detailed conversations about statistics, this brief guide gives adept SPSS users just the information they need to transition their data analyses from SPSS to R"--

ABSTRACTUsing a Conversational Modelling Language (CML), a special computer language has been implemented to allow the specification of mathematical programming models and their parameterization. The language has been designed to be "natural" in the mathematical programming context and to include interfaces with computer routines, such as MPSX, for optimization. Features implemented include equation writing, preparation of computer input, automatic checking and removal of redundancies, and the ability to display solutions given sufficient specification of decision variables. Examples of the use of the language in building interactive decision support systems are given.

Hospitals monitoring is becoming more complex and is increasing both because staff want their data analysed and because of increasing mandated surveillance. This book provides a suite of functions in R, enabling scientists and data analysts working in infection management and quality improvement departments in hospitals, to analyse their often non-independent data which is frequently in the form of trended, over-dispersed and sometimes auto-correlated time series; this is often difficult to analyse using standard office software. This book provides much-needed guidance on data analy

"This book introduces the programming language R and covers the necessary skills to conduct quantitative research in criminology. While it can apply to other social science research, it does focus on skills specifically for criminology such as spatial joins, mapping, and scraping data from PDFs. The goal of this book is that by the end, a person without any prior programming experience can take raw crime data (e.g. from a .rds file, an Excel file, a PDF, from a website), be able to clean it, visualize the data, present it using R Markdown, and change it to a format ready for analysis (e.g. subset and aggregate the data)"--

Based on their extensive experience with teaching R and statistics to applied scientists, the authors provide a beginner's guide to R. The text covers how to download and install R, import and manage data, elementary plotting, an introduction to functions, advanced plotting, and common beginner mistakes.--[book cover]

"Provides a step-by-step guide on how to use various publicly-available, remotely-sensed time series data sources for environmental monitoring and assessment. Readers will learn how extract valuable information on global changes from a 20-year collection of ready-to-use remotely-sensed data through the free, open GIS and statistical software R. The case studies are from the Mediterranean region- a designated hot spot regarding climate change effects. Each chapter is dedicated to specific remote sensing products chosen for their spatial resolution. The methods used are adapted from large-scale to smaller-scale problems for different land cover areas"--