Exploring Data Science With R And The Tidyverse A Concise Introduction
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Exploring Data Science with R and the Tidyverse
This book introduces the reader to data science using R and the tidyverse. No prerequisite knowledge is needed in college-level programming or mathematics (e.g., calculus or statistics). The book is self-contained so readers can immediately begin building data science workflows without needing to reference extensive amounts of external resources for onboarding. The contents are targeted for undergraduate students but are equally applicable to students at the graduate level and beyond. The book develops concepts using many real-world examples to motivate the reader. Upon completion of the text, the reader will be able to: Gain proficiency in R programming Load and manipulate data frames, and "tidy" them using tidyverse tools Conduct statistical analyses and draw meaningful inferences from them Perform modeling from numerical and textual data Generate data visualizations (numerical and spatial) using ggplot2 and understand what is being represented An accompanying R package "edsdata" contains synthetic and real datasets used by the textbook and is meant to be used for further practice. An exercise set is made available and designed for compatibility with automated grading tools for instructor use.
Exploring Data Science with R and the Tidyverse
This book introduces the reader to data science using R and the tidyverse. No prerequisite knowledge is needed in college-level programming or mathematics (e.g., calculus or statistics). The book is self-contained so readers can immediately begin building data science workflows without needing to reference extensive amounts of external resources for onboarding. The contents are targeted for undergraduate students but are equally applicable to students at the graduate level and beyond. The book develops concepts using many real-world examples to motivate the reader. Upon completion of the text, the reader will be able to: Gain proficiency in R programming Load and manipulate data frames, and "tidy" them using tidyverse tools Conduct statistical analyses and draw meaningful inferences from them Perform modeling from numerical and textual data Generate data visualizations (numerical and spatial) using ggplot2 and understand what is being represented An accompanying R package "edsdata" contains synthetic and real datasets used by the textbook and is meant to be used for further practice. An exercise set is made available and designed for compatibility with automated grading tools for instructor use.
Data Science with R for Psychologists and Healthcare Professionals
This introduction to R for students of psychology and health sciences aims to fast-track the reader through some of the most difficult aspects of learning to do data analysis and statistics. It demonstrates the benefits for reproducibility and reliability of using a programming language over commercial software packages such as SPSS. The early chapters build at a gentle pace, to give the reader confidence in moving from a point-and-click software environment, to the more robust and reliable world of statistical coding. This is a thoroughly modern and up-to-date approach using RStudio and the tidyverse. A range of R packages relevant to psychological research are discussed in detail. A great deal of research in the health sciences concerns questionnaire data, which may require recoding, aggregation and transformation before quantitative techniques and statistical analysis can be applied. R offers many useful and transparent functions to process data and check psychometric properties. These are illustrated in detail, along with a wide range of tools R affords for data visualisation. Many introductory statistics books for the health sciences rely on toy examples - in contrast, this book benefits from utilising open datasets from published psychological studies, to both motivate and demonstrate the transition from data manipulation and analysis to published report. R Markdown is becoming the preferred method for communicating in the open science community. This book also covers the detail of how to integrate the use of R Markdown documents into the research workflow and how to use these in preparing manuscripts for publication, adhering to the latest APA style guidelines.