9 new R books added ๐Ÿ“š๐Ÿ“š๐Ÿ“š

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This is the first time Iโ€™m writing up a blog post to summarise new books that have been added to Big Book Of R. Iโ€™ll keep doing so in future!

A quick thank you to @LeganaFingerfut for adding R for Health Data Science, and to @RCoderWeb for adding the lidR package guide.

R for Health Data Science

by Ewan Harrison and Riinu Pius

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 โ€“ 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.

We have a lot of fun teaching this course and focus on making the material as accessible as possible. We limit equations to a minimum in favour of code, and use examples rather than lengthy explanations. We are grateful to the many individuals and students who have helped refine this book and welcome suggestions and bug reports.


Telling Stories With Data

Rohan Alexander

This aim of this book is to help you learn how to tell stories with data. It establishes a foundation on which you can build and share knowledge, based on data, about an aspect of the world of interest to you.

In this book we explore, prod, push, manipulate, knead, and ultimately, try to understand the implications of, data. The motto of the university from which I took my PhD is โ€˜Naturam primum cognoscere rerumโ€™ or roughly โ€˜first to learn the nature of things,โ€™ and we will indeed attempt to do that. But the original quote continues โ€˜temporis aeterni quoniam,โ€™ or roughly โ€˜for eternal time,โ€™ and it is tools, approaches, and workflows that enable you to establish lasting knowledge that I focus on in this book.


Data Analysis and Visualization in R for Ecologists

Franรงois Michonneau & Auriel Fournier

Data Carpentryโ€™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.


The Effect: An Introduction to Research Design and Causality

Nick Huntington-Klein

The Effect is a book intended to introduce students (and non-students) to the concepts of research design and causality in the context of observational data. The book is written in an intuitive and approachable way and doesnโ€™t overload on technical detail. Why teach regression and research design at the same time when they are fundamentally different things? First learn why you want to structure a design in a certain way, and what it is you want to do to the data, and then afterwards learn the technical details of how to run the appropriate model.


ISLR tidymodels Labs

Emil Hvitfeldt

This book aims to be a complement to the 1st version An Introduction to Statistical Learning book with translations of the labs into using the tidymodels set of packages.

The labs will be mirrored quite closely to stay true to the original material.


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.

Paid ~$100


R for applied epidemiology and public health

EpiR authors

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), Mรฉdecins Sans Frontiรจres / Doctors without Borders (MSF), hospital systems, and academic institutions.

Written by epidemiologists, for epidemiologists.


The lidR package

Jean-Romain Roussel, Tristan R.H. Goodbody, Piotr Tompalski

lidR is an R package for manipulating and visualizating airborne laser scanning (ALS) data with an emphasis on forestry applications. The package is entirely open source and is integrated within the geospatial R ecosytem (i.e. raster, sp, sf, rgdal etc.). This guide has been written to help both the ALS novice, as well as seasoned point cloud processing veterans.


Project Management Fundamentals for Data Analysts

Oscar Baruffa

In Project Management Fundamentals for Data Analysts, Iโ€™ve boiled the concepts down to the bare essentials which can be read in under 15 minutes โ€“ you can certainly fit that into your crazy schedule (and it will help your future schedule not be so chaotic!).

These concepts can be used to great effect on their own if you wish to never read another word on the topic. Itโ€™ll also provide a solid foundation if you want to dive deeper into more formal courses or sophisticated theory.

Paid $TBC


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