# 11 Field specific

## 11.1 Analyzing Financial and Economic Data with R

Marcelo S. Perlin

Not surprisingly, fields with abundant access to data and practical applications, such as economics and finance, it is expected that a graduate student or a data analyst has learned at least one programming language that allows him/her to do his work efficiently. Learning how to program is becoming a requisite for the job market.

## 11.2 Computer-age Calculus with R

Daniel Kaplan

R is closely associated with statistics, but not with calculus. It turns out that R is an excellent language for doing calculus.

This book shows how to do common calculus calculations using R.

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

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.

## 11.4 Data Science in Education Using R

Ryan A. Estrellado, Emily A. Bovee, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez

Dear Data Scientists, Educators, and Data Scientists who are Educators:

This book is a warm welcome and an invitation. If you’re a data scientist in education or an educator in data science, your role isn’t exactly straightforward. This book is our contribution to a growing movement to merge the paths of data analysis and education. We wrote this book to make your first step on that path a little clearer and a little less scary.

## 11.5 Data Skills for Reproducible Science

PsyTeachR team, University of Glasgow

This course provides an overview of skills needed for reproducible research and open science using the statistical programming language R. Students will learn about data visualisation, data tidying and wrangling, archiving, iteration and functions, probability and data simulations, general linear models, and reproducible workflows. Learning is reinforced through weekly assignments that involve working with different types of data.

## 11.6 Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data

Michael Friendly, David Meyer

Presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data.

It explains how to use graphical methods for exploring data, spotting unusual features, visualizing fitted models, and presenting results.

Paid ~$80 http://ddar.datavis.ca/

## 11.7 Learning Microeconometrics with R

Christopher P. Adams

This book provides an introduction to the field of microeconometrics through the use of R. The focus is on applying current learning from the field to real world problems. It uses R to both teach the concepts of the field and show the reader how the techniques can be used. It is aimed at the general reader with the equivalent of a bachelor’s degree in economics, statistics or some more technical field. It covers the standard tools of microeconometrics, OLS, instrumental variables, Heckman selection and difference in difference. In addition, it introduces bounds, factor models, mixture models and empirical Bayesian analysis.

Paid ~$100 https://www.routledge.com/Learning-Microeconometrics-with-R/Adams/p/book/9780367255381

## 11.8 Public Policy Analytics: Code & Context for Data Science in Government

Ken Steif, Ph.D

The goal of this book is to make data science accessible to social scientists and City Planners, in particular. I hope to convince readers that one with strong domain expertise plus intermediate data skills can have a greater impact in government than the sharpest computer scientist who has never studied economics, sociology, public health, political science, criminology etc.

## 11.9 Handbook of Regression Modeling in People Analytics

It is the author’s firm belief that all people analytics professionals should have a strong understanding of regression models and how to implement and interpret them in practice, and the aim with this book is to provide those who need it with help in getting there.

http://peopleanalytics-regression-book.org/index.html

For accompanying solutions to some of the questions

https://keithmcnulty.github.io/peopleanalytics-regression-book/solutions/

## 11.10 R for Excel users

Julie Lowndes & Allison Horst

This course is for Excel users who want to add or integrate R and RStudio into their existing data analysis toolkit. It is a friendly intro to becoming a modern R user, full of tidyverse, RMarkdown, GitHub, collaboration & reproducibility.

## 11.11 R Programming with Minecraft

Brooke Anderson, Karl Broman, Gergely Daróczi, Mario Inchiosa, David Smith, and Ali Zaidi

Minecraft is awesome fun, especially in creative mode, where you can build all sorts of crazy stuff. But ambitious building projects can be really tedious to create by hand. With the miner R package, you can write R code to manipulate your Minecraft world and create even more awesome stuff.

Here’s an introduction Rstats NYC conference talk on it: https://www.youtube.com/watch?v=r_JgPF8MJpY

## 11.12 R for SEO

Even though R’ is a terrific option for SEO, there are simply not enough resources out there. This guide is not here to deliver a course about R, there are plenty already. This guide is meant to be as practical as possible. How things should be done in an “R-ish way” is not the purpose of this guide. Grab what you want to grab and feel free to submit your own solution.

## 11.13 R for Water Resources Data Science

Ryan Peek and Rich Pauloo

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.

## 11.14 Technical Foundations of Informatics

Michael Freeman and Joel Ross

This book covers the foundation skills necessary to start writing computer programs to work with data using modern and reproducible techniques. It requires no technical background. These materials were developed for the INFO 201: Technical Foundations of Informatics course taught at the University of Washington Information School; however they have been structured to be an online resource for anyone hoping to learn to work with information using programmatic approaches.

## 11.15 An introduction to quantitative analysis of political data in R

Erik Gahner Larsen & Zoltán Fazekas

In this book, we aim to provide an easily accessible introduction to R for the collection, study and presentation of different types of political data. Specifically, the book will teach you how to get different types of political data into R and manipulate, analyze and visualize the output. In doing this, we will not only teach you how to get existing data into R, but also how to collect your own data.

## 11.16 Machine Learning for Factor Investing

Guillaume Coqueret and Tony Guida

This book is intended to cover some advanced modelling techniques applied to equity investment strategies that are built on firm characteristics.

## 11.17 Introduction to Econometrics with R

Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer

Instead of confronting students with pure coding exercises and complementary classic literature like the book by Venables & Smith (2010), we figured it would be better to provide interactive learning material that blends R code with the contents of the well-received textbook Introduction to Econometrics by Stock & Watson (2015) which serves as a basis for the lecture.

## 11.18 How to be a modern scientist

A book about how to be a scientist the modern, open-source way. The face of academia is changing. It is no longer sufficient to just publish or perish. We are now in an era where Twitter, Github, Figshare, and Alt Metrics are regular parts of the scientific workflow. Here I give high level advice about which tools to use, how to use them, and what to look out for. This book is appropriate for scientists at all levels who want to stay on top of the current technological developments affecting modern scientific careers.

Pay what you want for the ebook, minimum $0.00

## 11.19 Cryptocurrency Research: Open Source R Tutorial

Riccardo (Ricky) Esclapon – LinkedIn, Personal Website

John Chandler Johnson – LinkedIn

Kai R. Larsen – LinkedIn, ResearchGate

**What you will learn**:

R: The tutorial is in R. For those without experience programming in R we have a high-level version to help you learn before attempting the full version. Scroll down for a breakdown of the individual sections for an overview of what you will learn throughout.

Tidyverse: You will get more familiar with tools from the tidyverse, including dplyr, ggplot2, tibble, and purrr. These tools provide an excellent complete ecosystem to do data science in R.

Machine Learning: You will learn to create machine learning models and how to fairly assess their performance.

Cryptocurrency Data: You will learn these tools analyzing the latest cryptocurrency data. The tutorial automatically refreshes every 12 hours and the data is publicly available and refreshed hourly.

This tutorial is free and you can access it via https://cryptocurrencyresearch.org/.