15 Life Sciences
15.1 An Open Compendium of Soil Datasets
(Not R specific but looks really relevant)
This is a public compendium of global, regional, national and sub-national soil samples and/or soil profile datasets (points with Observations and Measurements of soil properties and characteristics). Datasets listed here, assuming compatible open license, are afterwards imported into the Global compilation of soil chemical and physical properties and soil classes and eventually used to create a better open soil information across countries. The specific objectives of this initiative are:
To enable data digitization, import and binding + harmonization, To accelerate research collaboration and networking, To enable development of more accurate / more usable global and regional soil property and class maps (typically published via https://OpenLandMap.org),
15.2 Assigning cell types with SingleR
This book covers the use of SingleR, one implementation of an automated annotation method for cell type annotation.
15.3 Data Science for the Biomedical Sciences
Daniel Chen, Anne Brown
We hope this book provides a gentle introduction to data science. The main goal is to understand how to work with spreadsheet data and how data can be manipulated for multiple purposes. If nothing else, the book hopes to help you plan how to structure your own datasets for your own analysis. Even if you never go on to program on your own, understanding the way data can be manipulated and having a plan for your own dataset in the processing pipeline, will go a long ways when leaning and doing the analysis on your own, and/or working with collegues and collaborators on a project.
15.4 Using R for Bayesian Spatial and Spatio-Temporal Health Modeling
By Andrew B. Lawson
Progressively more and more attention has been paid to how location affects health outcomes. The area of disease mapping focusses on these problems, and the Bayesian paradigm has a major role to play in the understanding of the complex interplay of context and individual predisposition in such studies of disease. Using R for Bayesian Spatial and Spatio-Temporal Health Modeling provides a major resource for those interested in applying Bayesian methodology in small area health data studies.
15.5 Computational Genomics with R
The aim of this book is to provide the fundamentals for data analysis for genomics. We developed this book based on the computational genomics courses we are giving every year.
15.6 Data Analysis for the Life Sciences
Rafael A Irizarry and Michael I Love
Data analysis is now part of practically every research project in the life sciences. In this book we use data and computer code to teach the necessary statistical concepts and programming skills to become a data analyst. Instead of showing theory first and then applying it to toy examples, we start with actual applications.
Pay what you want for the ebook, minimum $0.00
15.7 Data Analysis and Visualization in R for Ecologists
François Michonneau & Auriel Fournier
Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with ecology data in R.
This is an introduction to R designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about R syntax, the RStudio interface, and move through how to import CSV files, the structure of data frames, how to deal with factors, how to add/remove rows and columns, how to calculate summary statistics from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from R.
This lesson assumes no prior knowledge of R or RStudio and no programming experience.
15.8 R for applied epidemiology and public health
This handbook is produced by a collaboration of epidemiologists from around the world drawing upon experience with organizations including local, state, provincial, and national health agencies, the World Health Organization (WHO), Médecins Sans Frontières / Doctors without Borders (MSF), hospital systems, and academic institutions.
Written by epidemiologists, for epidemiologists.
15.9 Git and Github for Advanced Ecological Data Analysis
This material was prepared for a three-hour virtual session to teach Git and Github to a graduate-level course on Advanced Ecological Data Analysis taught at Rutgers University by Malin Pinsky and Rachael Winfree. (However, the only course-specific material is Section 4; the rest should be applicable to any reader.)
15.10 R for Health Data Science
In this age of information, the manipulation, analysis and interpretation of data have become a fundamental part of professional life. Nowhere more so than in the delivery of healthcare. From the understanding of disease and the development of new treatments, to the diagnosis and management of individual patients, the use of data and technology are now an integral part of the business of healthcare.
Those working in healthcare interact daily with data, often without realising it. The conversion of this avalanche of information to useful knowledge is essential for high-quality patient care. An important part of this information revolution is the opportunity for everybody to become involved in data analysis. This democratisation is driven in part by the open source software movement – no longer do we require expensive specialised software to do this.
The statistical programming language, R, is firmly at the heart of this.
This book will take an individual with little or no experience in data science all the way through to the execution of sophisticated analyses. We emphasise the importance of truly understanding the underlying data with liberal use of plotting, rather than relying on opaque and possibly poorly understood statistical tests. There are numerous examples included that can be adapted for your own data, together with our own R packages with easy-to-use functions.
15.11 Hydroinformatics at VT
This bookdown contains the notes and most exercises for a course on data analysis techniques in hydrology using the programming language R. The material will be updated each time the course is taught. If new topics are added, the topics they replace will be left, in case they are useful to others.
15.12 R for Conservation and Development Projects: A Primer for Practitioners
This book is aimed at conservation and development practitioners who need to learn and use R in a part-time professional context. It gives people with a non-technical background a set of skills to graph, map, and model in R. It also provides background on data integration in project management and covers fundamental statistical concepts. The book aims to demystify R and give practitioners the confidence to use it.
• Viewing data science as part of a greater knowledge and decision making system • Foundation sections on inference, evidence, and data integration • Plain English explanations of R functions • Relatable examples which are typical of activities undertaken by conservation and development organisations in the developing world • Worked examples showing how data analysis can be incorporated into project reports
15.13 R for Water Resources Data Science
Consists of 2 courses
Introductory: This course is most relevant and targeted at folks who work with data, from analysts and program staff to engineers and scientists. This course provides an introduction to the power and possibility of a reproducible programming language (R) by demonstrating how to import, explore, visualize, analyze, and communicate different types of data. Using water resources based examples, this course guides participants through basic data science skills and strategies for continued learning and use of R.
Intermediate: In this course, we will move more quickly, assume familiarity with basic R skills, and also assume that the participant has working experience with more complex workflows, operations, and code-bases. Each module in this course functions as a “stand-alone” lesson, and can be read linearly, or out of order according to your needs and interests. Each module doesn’t necessarily require familiarity with the previous module.
This course emphasizes intermediate scripting skills like iteration, functional programming, writing functions, and controlling project workflows for better reproducibility and efficiency. Approaches to working with more complex data structures like lists and timeseries data, the fundamentals of building Shiny Apps, pulling water resources data from APIs, intermediate mapmaking and spatial data processing, integrating version control in projects with git.
15.14 Reproducible Medical Research with R
Peter D.R. Higgins, MD, PhD, MSc
This is a book for anyone in the medical field interested in analyzing the data available to them to better understand health, disease, or the delivery of care. This could include nurses, dieticians, psychologists, and PhDs in related fields, as well as medical students, residents, fellows, or doctors in practice. I expect that most learners will be using this book in their spare time at night and on weekends, as the health training curricula are already packed full of information, and there is no room to add skills in reproducible research to the standard curriculum. This book is designed for self-teaching, and many hints and solutions will be provided to avoid roadblocks and frustration. Many learners find themselves wanting to develop reproducible research skills after they have finished their training, and after they have become comfortable with their clinical role. This is the time when they identify and want to address problems faced by patients in their practice with the data they have before them. This book is for you.
15.15 Modern Statistics for Modern Biology
Susan Holmes, Wolfgang Huber
The aim of this book is to enable scientists working in biological research to quickly learn many of the important ideas and methods that they need to make the best of their experiments and of other available data.
15.16 Orchestrating Single-Cell Analysis with Bioconductor
This is the website for “Orchestrating Single-Cell Analysis with Bioconductor”, a book that teaches users some common workflows for the analysis of single-cell RNA-seq data (scRNA-seq).
15.17 Statistics in R for Biodiversity Conservation Paperback
by Carl Smith , Antonio Uzal , Mark Warren
A practical handbook to introduce data analysis and model fitting using R to ecologists and conservation biologists. The book is aimed at undergraduate and post-graduate students and provides access to datasets and RScript.
Paid product ~$10
15.18 Numerical Ecology with R
by Daniel Borcard, François Gillet, Pierre Legendre
This new edition of Numerical Ecology with R guides readers through an applied exploration of the major methods of multivariate data analysis, as seen through the eyes of three ecologists. It provides a bridge between a textbook of numerical ecology and the implementation of this discipline in the R language. The book begins by examining some exploratory approaches.
15.19 Introduction to Data Analysis with R
by Jannik Buhr
This is a video lecture series with accompanying lecture script that is designed to read much like a book. The lecture is held in English for biochemists at Heidelberg University, Germany, but the examples covered are no specific to life sciences in order to enable a focus on learning the techniques with R.
15.20 WEHI Intro to Tidy R Course
A complete beginner’s introduction to tidy R for data transformation, visualization and analysis automation — with applications in experimental biology.
This book is based on a short course developed for biomedical scientists at the WEHI Medical Research Institute. The content is designed to make learners comfortable with using R for exploratory analysis of large data sets, but does not cover statistics. The material and teaching examples draw on popular (non-biological) data sets, as well as gene expression and drug screening data types.
15.21 Experimental Design for Laboratory Biologists: Maximising Information and Improving Reproducibility
This practical guide shows biologists how to design reproducible experiments that have low bias, high precision, and results that are widely applicable. With specific examples using both cell cultures and model organisms, it shows how to plan a successful experiment. It demonstrates how to control biological and technical factors that can introduce bias or add noise, and covers rarely discussed topics such as graphical data exploration, choosing outcome variables, data quality control checks, and data pre-processing. It also shows how to use R for analysis, and is designed for those with no prior experience. This is an ideal guide for anyone conducting lab-based biological research.