Part 3 of 3: 300+ milestone for Big Book of R

This post is the final installment of a 3-part series highlighting 35 new entries to Big Book of R.

Read part 1 and part 2.

The site now has well over 300 free R programming titles.

Onto the third batch of new books!

Financial Econometrics – R Tutorial Guidance

by Yizhi Wang, Samuel Vigne

This is an R tutorial book for Financial Econometrics in PDF format.—r-tutorial-guidance

R Guide to Accompany Introductory Econometrics for Finance

by Robert Wichmann, Chris Brooks

This free software guide for R with freely downloadable datasets brings the econometric techniques to life, showing readers how to implement the approaches presented in Introductory Econometrics for Finance using this highly popular software package. Designed to be used alongside the main textbook, the guide will give readers the confidence and skills to estimate and interpret their own models while the textbook will ensure that they have a thorough understanding of the conceptual underpinnings.

R Companion to Real Econometrics

by Tony Carilli

The intended audience for this book is anyone making using of Real Econometrics: The Right Tools to Answer Important Questions 2nd ed. by Michael Bailey who would like to learn to use R, RStudio, and the tidyverse to complete empirical examples from the text. This book will be useful to anyone wishing to integrate R and the Tidyverse into an econometrics course.

Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R

by Joseph F. Hair Jr., G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt, Nicholas P. Danks, Soumya Ray

An open access (free and unlimited) book with concise guidelines on how to apply and interpret Partial Least Squares Structural Equation Modeling (PLS-SEM). It includes an illustrative, step-by-step application of PLS-SEM using the highly user-friendly SEMinR package. It adopts a case-study approach that focuses on the illustration of relevant analysis steps.

pipeR Tutorial 

by Kun Ren

pipeR is an R package that helps you better organize your code in pipeline built with %>>%, Pipe() or pipeline(), which is much easier to read, write, and maintain.

An(other) introduction to R

by Felix Lennert

In the following, you will receive a gentle introduction to R and how you can use it to work with data. This tutorial was heavily inspired by Richard Cotton’s “Learning R” (Cotton 2013) and Hadley Wickham’s and Garrett Grolemund’s “R for Data Science” (abbreviated with R4DS).

Text Mining for Social Scientists

by Felix Lennert

This script will cover the pre-processing of text, the implementation of supervised and unsupervised approaches to text, and in the end, I will briefly touch upon word embeddings and how social science can use them for inquiry.

An Introduction to Text Processing and Analysis with R

by Michael Clark

Dealing with text is typically not even considered in the applied statistical training of most disciplines. This is in direct contrast with how often it has to be dealt with prior to more common analysis, or how interesting it might be to have text be the focus of analysis. This document and corresponding workshop will aim to provide a sense of the things one can do with text, and the sorts of analyses that might be useful.

R Shiny Applications in Finance, Medicine, Pharma and Education Industry

by Loan Robinson

The book is a guide to help you understand the codes of five applications you will receive after you purchase the book. If you can go through all of the codes, you can easily create a complex and brilliant R Shiny application.

Instead of spending hours and hours trying to understand, have the ideas, write the codes, apply application features, you can use the codes to quickly apply and learn the codes. There are many advanced features, it takes years to learn them, now you have it, hand on and work through it.

Biostatistics for Biomedical Research

by Frank E Harrell Jr

The book is aimed at exposing biomedical researchers to modern biostatistical methods and statistical graphics, highlighting those methods that make fewer assumptions, including nonparametric statistics and robust statistical measures. In addition to covering traditional estimation and inferential techniques, the course contrasts those with the Bayesian approach, and also includes several components that have been increasingly important in the past few years, such as challenges of high-dimensional data analysis, modeling for observational treatment comparisons, analysis of differential treatment effect (heterogeneity of treatment effect), statistical methods for biomarker research, medical diagnostic research, and methods for reproducible research.

R Workflow for Reproducible Data Analysis and Reporting

by Frank E Harrell Jr

This work is intended to foster best practices in reproducible data documentation and manipulation, statistical analysis, graphics, and reporting. It will enable the reader to efficiently produce attractive, readable, and reproducible research reports while keeping code concise and clear. Readers are also guided in choosing statistically efficient descriptive analyses that are consonant with the type of data being analyzed.

HR Analytics in R

by Chester Ismay, Albert Y. Kim, Hendrik Feddersen

The intention of this book is to encourage more ‘data driven’ decisions in HR. HR Analytics is not anymore a nice-to-have addon but rather the way HR practitioners should conduct HR decision making in the future. Where applicable, human judgement is ‘added’ onto a rigorous analysis of the data done in the first place.

To achieve this ideal world, I need to equip you with some fundamental knowledge of R and RStudio, which are open-source tools for data scientists. I am well aware that on one side you want to do something for your career in HR, however you are most likely completely new to coding.

Reproducible statistics for psychologists with R: Lab Tutorials

by Matthew J. C. Crump

This is a series of labs/tutorials for a two-semester graduate-level statistics sequence in Psychology @ Brooklyn College of CUNY. The goal of these tutorials is to 1) develop a deeper conceptual understanding of the principles of statistical analysis and inference; and 2) develop practical skills for data-analysis, such as using the increasingly popular statistical software environment R to code reproducible analyses.

Computational Social Science: Theory & Application

by Paul C. Bauer

The goals for this course are twofold. First, I hope you will gain a solid understanding of how access to big data (digital traces) is changing the social sciences in terms of a) new substantial and theoretical insights, and in terms of b) new methodologies. Second, I hope you will learn which and how big data could be used to answer further pressing questions you might encounter in the future.

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