02 September 2022

Today we have another huge addition of books to the library, now consisting at 350 R programming books! Thanks to Gary and Abraham for the additions!

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Using Spark from R for performance with arbitrary code

by Jozef Hajnala

This book provides practical insights into using the sparklyr interface to gain the benefits of Apache Spark while still retaining the ability to use R code organized in custom-built functions and packages.

https://www.bigbookofr.com/big-data.html#using-spark-from-r-for-performance-with-arbitrary-code

Big Data with R – Exercise book

by James Blair

This 2-day workshop covers how to analyze large amounts of data in R. We will focus on scaling up our analyses using the same dplyr verbs that we use in our everyday work. We will use dplyr with data.table, databases, and Spark. We will also cover best practices on visualizing, modeling, and sharing against these data sources. Where applicable, we will review recommended connection settings, security best practices, and deployment options.

https://www.bigbookofr.com/big-data.html#big-data-with-r—exercise-book

Fundamentals of Wrangling Healthcare Data with R

by J. Kyle Armstrong

“In this course we will review some of the tools of the trade, namely, R’s tidyverse (Wickham and Grolemund 2017; Winter 2019) – a collection of R packages designed with a common framework to aide in common data wrangling and data management tasks.

Data Wrangling is one subset set of skills within the Data Science Process. We will carefully investigate how decisions made while collecting and preparing the data have down-stream effects on model performance.”

https://www.bigbookofr.com/getting-cleaning-and-wrangling-data.html#fundamentals-of-wrangling-healthcare-data-with-r

Data Science for Economists and Other Animals

by Grant McDermott

Introduce Economics graduate students to the modern data science toolkit

https://www.bigbookofr.com/finance.html#data-science-for-economists-and-other-animals

Applied longitudinal data analysis in brms and the tidyverse

by A Solomon Kurz

A translation of the examples and figures from Singer and Willett’s classic Applied longitudinal data analysis: Modeling change and event occurrence. 

https://www.bigbookofr.com/statistics.html#applied-longitudinal-data-analysis-in-brms-and-the-tidyverse

Recoding Introduction to Mediation, Moderation, and Conditional Process Analysis

by A Solomon Kurz

A translation of the code from the second edition of Andrew F. Hayes’s Introduction to Mediation, Moderation, and Conditional Process Analysis.

https://www.bigbookofr.com/statistics.html#recoding-introduction-to-mediation-moderation-and-conditional-process-analysis

An R Exercise in Data Collection, Cleaning, and Merging U.S. Census Data

by Sean Conner

A step-by-step walkthrough exercise using U.S. Census data.

https://www.bigbookofr.com/social-science.html#an-r-exercise-in-data-collection-cleaning-and-merging-u.s.-census-data

Bayesian Hierarchical Models in Ecology

by Steve Midway

Hierarchical Models in Ecology Using Bayesian Inference

https://www.bigbookofr.com/life-sciences.html#bayesian-hierarchical-models-in-ecology

Advanced Regression Methods – Companion to BER642

by Cheng HUA

Different multiple regression methods are presented including an overview of ordinary least squares regression, ordinal regression, logistic and probit regression, loglinear, mixed, and regression discontinuity. Interpretation of results diagnostics, and appications are covered for the several glm models.

https://www.bigbookofr.com/statistics.html#advanced-regression-methods—companion-to-ber642

Little Book of R for Biomedical Statistics

by Avril Coghlan

This is a simple introduction to biomedical statistics using the R statistics software.

https://www.bigbookofr.com/life-sciences.html#little-book-of-r-for-biomedical-statistics

A Little Book of R for Time Series

by Avril Coghlan

This is a simple introduction to time series analysis using the R statistics software.

https://www.bigbookofr.com/statistics.html#a-little-book-of-r-for-time-series

A Little Book of R for Multivariate Analysis

by Avril Coghlan

This is a simple introduction to multivariate analysis using the R statistics software.

https://www.bigbookofr.com/statistics.html#a-little-book-of-r-for-multivariate-analysis

A Little Book of R for Bioinformatics

by Avril Coghlan

This is a simple introduction to bioinformatics, with a focus on genome analysis, using the R statistics software.

https://www.bigbookofr.com/life-sciences.html#a-little-book-of-r-for-bioinformatics

Circular Visualization in R

by Zuguang GU

This is the documentation of the circlize R package.

https://www.bigbookofr.com/packages.html#circular-visualization-in-r

The R Manuals

by R Development Core team

This is a restyled version of the R manuals, originally provided by the R Development Core team.

https://www.bigbookofr.com/r-programming.html#the-r-manuals

Reproducible Analytical Pipelines (RAP) Companion

by Matthew Gregory

Reproducible Analytical Pipelines require a range of tools and techniques to implement that can be a challenge to overcome, and this book address some of the common knowledge gaps and hard-to-Google problems that upcoming RAP-pers face.

https://www.bigbookofr.com/data-databases-and-engineering.html#reproducible-analytical-pipelines-rap-companion

The Turing Way

by The Turing Way Community

The Turing Way is a handbook to reproducible, ethical and collaborative data science. We involve and support a diverse community of contributors to make data science accessible, comprehensible and effective for everyone. Our goal is to provide all the information that researchers and data scientists in academia, industry and the public sector need at the start of their projects to ensure that they are easy to reproduce at the end.

https://www.bigbookofr.com/career-and-community.html#the-turing-way

Applied Microeconometrics with R

by Achim Zeileis

“This project will gradually turn the course materials for the “Econometrics and Statistics: Microeconometrics” course at Universität Innsbruck into an online book.

The topics covered roughly follow the book Analysis of Microdata by Winkelmann & Boes (2009, Springer-Verlag) and encompass: models for categorical responses (binary, multinomial, ordered), count data, limited dependent variables, and duration models.”

https://www.bigbookofr.com/finance.html#applied-microeconometrics-with-r

Flexible Regression Models

by Nikolaus Umlauf

This script aims to cover the core knowledge of flexible regression models, frequentist and Bayesian estimation, computational details and software implementations. The script assumes a certain basic knowledge of the linear regression model and the generalized linear model (GLM).

https://www.bigbookofr.com/statistics.html#flexible-regression-models

Data Analytics

by Achim Zeileis

This collection of R tutorials accompanies the new course Data Analytics organized jointly in the bachelor curriculum “Wirtschaftswissenschaften” and the complementary subject area “Digital Science” at Universität Innsbruck and its Digital Science Center (DiSC).

https://www.bigbookofr.com/statistics.html#data-analytics

Larger-Than-Memory Data Workflows with Apache Arrow

by Danielle Navarro

In this tutorial you will learn how to use the arrow R package to create seamless engineering-to-analysis data pipelines. You’ll learn how to use interoperable data file formats like Parquet or Feather for efficient storage and data access. You’ll learn how to exercise fine control over data types to avoid common data pipeline problems. During the tutorial you’ll be processing larger-than-memory files and multi-file datasets with familiar dplyr syntax, and working with data in cloud storage.

https://www.bigbookofr.com/big-data.html#larger-than-memory-data-workflows-with-apache-arrow

Web Scraping with R

by Steve Pittard

Web Scraping with R.  A rich source of examples and instruction.

https://www.bigbookofr.com/getting-cleaning-and-wrangling-data.html#web-scraping-with-r

Analysing Data using Linear Models

by Stéphanie M. van den Berg

“This book is for bachelor students in social, behavioural and management sciences that want to learn how to analyse their data, with the specific aim to answer research questions. The book has a practical take on data analysis: how to do it, how to interpret the results, and how to report the results. All techniques are presented within the framework of linear models: this includes simple and multiple regression models, linear mixed models and generalised linear models. This approach is illustrated using R.”

https://www.bigbookofr.com/statistics.html#analysing-data-using-linear-models

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