Another 9 R books added to BigBookofR

The collection of R books at BigBookofR continues to grow!

Thanks to @erictleaungu , @StanLazic, @rohmanoabdur, @preposterior, @lago67522285 and @lfvasconcelos_ for their contributions.

Computing for the social sciences

By Dr. Benjamin Soltoff

The goal of this course is to teach you basic computational skills and provide
you with the means to learn what you need to know for your own research. I
start from the perspective that you want to analyze data, and programming is a
means to that end. You will not become an expert programmer – that is a given.
But you will learn the basic skills and techniques necessary to conduct
computational social science, and gain the confidence necessary to learn new
techniques as you encounter them in your research.

We will cover many different topics in this course, including:

• Elementary programming techniques (e.g. loops, conditional statements,
  functions)
• Writing reusable, interpretable code
• Problem-solving – debugging programs for errors
• Obtaining, importing, and munging data from a variety of sources
• Performing statistical analysis
• Visualizing information
• Creating interactive reports
• Generating reproducible research

https://www.bigbookofr.com/social-science.html#computing-for-the-social-sciences

Experimental Design for Laboratory Biologists: Maximising Information and Improving Reproducibility

Stanley E. Lazic

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.

https://www.bigbookofr.com/life-sciences.html#experimental-design-for-laboratory-biologists-maximising-information-and-improving-reproducibility

Doing meta-analysis with R: A hands-on guide

Mathias Harrer, Pim Cuijpers, Toshi A. Furukawa, David D. Ebert

This book serves as an accessible introduction into how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools.

Advanced, but highly relevant topics such as network meta-analysis, multi-/three-level meta-analyses, Bayesian meta-analysis approaches, SEM meta-analysis are also covered.

https://www.bigbookofr.com/statistics.html#doing-meta-analysis-with-r-a-hands-on-guide

Crime by the Numbers: A Criminologist’s Guide to R

Jacob Kaplan

This book introduces the programming language R and is meant for undergrads or graduate students studying criminology. R is a programming language that is well-suited to the type of work frequently done in criminology – taking messy data and turning it into useful information. While R is a useful tool for many fields of study, this book focuses on the skills criminologists should know and uses crime data for the example data sets.

https://www.bigbookofr.com/social-science.html#crime-by-the-numbers-a-criminologists-guide-to-r-1

Analyzing US Census Data: Methods, Maps, and Models in R

Kyle Walker

Census data are widely used in the United States across numerous research and applied fields, including education, business, journalism, and many others. Until recently, the process of working with US Census data has required the use of a wide array of web interfaces and software platforms to prepare, map, and present data products. The goal of this book is to illustrate the utility of the R programming language for handling these tasks, allowing Census data users to manage their projects in a single computing environment.

https://www.bigbookofr.com/social-science.html#analyzing-us-census-data-methods-maps-and-models-in-r

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.

https://www.bigbookofr.com/life-sciences.html#data-science-for-the-biomedical-sciences

A Business Analyst’s Introduction to Business Analytics

Adam Fleischhacker

This textbook goes farther than just teaching you to make computational models using software or mathematical models using statistics. It teaches you how to align computational and mathematical models with real-world scenarios; empowering you to communicate with and leverage the expertise of business stakeholders while using modern software stacks and statistical workflows. In this book, you do not learn business analytics to make models; you learn business analytics to add tangible value in the real-world.

https://www.bigbookofr.com/data-science.html#a-business-analysts-introduction-to-business-analytics

Hydroinformatics at VT

JP Gannon

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.

https://www.bigbookofr.com/life-sciences.html#hydroinformatics-at-vt

An Introduction to Statistical Learning

Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani

As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. Each chapter includes an R lab. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

https://www.bigbookofr.com/statistics.html#an-introduction-to-statistical-learning

That’s it for this round of additions!

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