Название: Statistics With R: Solving Problems Using Real-World Data
Автор: Jenine K. Harris
Издательство: SAGE Publications, Inc.
Год: 2020
Страниц: 784
Язык: английский
Формат: pdf
Размер: 15.1 MB

Drawing on examples from across the social and behavioral sciences, Statistics with R: Solving Problems Using Real-World Data by Jenine K. Harris introduces foundational statistics concepts with beginner-friendly R programming in an exploration of the world’s tricky problems faced by the "R Team" characters. Inspired by the programming group "R Ladies," the R Team works together to master the skills of statistical analysis and data visualization to untangle real-world, messy data using R.

R is a coding language used to conduct statistical analyses and to create visual displays of data. The options available in R for analysis and graphing are competitive with, and in some cases surpass, the Statistical Package for the Social Sciences (SPSS) and Statistical Analysis System (SAS) software packages. However, R statistical software is free and open source. Anyone can contribute a “package” to R and, if it is accepted to the Comprehensive R Archive Network (CRAN), it becomes available for all R users worldwide to access and use.

The use of R has been growing and, by some metrics, has caught up to or surpassed SPSS and SAS when it comes to data-related jobs. Because R is free, it allows people and organizations with fewer resources to access and use a high-quality data science tool. Consequently, the
software is more egalitarian and inclusive, creating opportunities for collaborations and contributions to emerge from less privileged nations and individuals.

Each chapter uses one or more publicly available data sets, and most chapters include instructions for importing the data directly into R from the original online location to encourage the use of reproducible research practices.

The goals of this book:
The main goal of this book is to prepare students and other readers for the messy and exciting reality of working with data. As data on many aspects of life accumulate at unprecedented rates, there is an increasing need for people who can clean, manage, and analyze information. Secondarily, the book aims to encourage women and other underrepresented groups to consider data science careers. Finally, this book aims to improve the quality of social science through the promotion of reproducible research practices. Science has been increasingly under scrutiny for use of questionable research practices, some of which can be overcome through well-formatted and documented code for data importing, cleaning, and analysis.

To reach all three of these goals, I employed several strategies, such as a narrative writing style, diversity in the main and supporting characters, a focus on social problems, use of publicly available data and open-source R statistical software, and demonstrations of reproducible research practices.

The audience for the book:
This book was written with first-year statistics courses in the social sciences in mind. Often, these courses start with descriptive statistics and probability theory and continue through general and generalized linear modeling. Others who may find this book useful include people working with data who are interested in learning R for the first time, learning more R, or reinforcing and improving their statistical skills. General readers interested in data science might find this book to be an accessible introduction to one of the primary software packages and many of the foundational concepts used in data science.