06 December 2021
Every time I update Big Book of R I’m blown away by how much good stuff is out there! In this release there’s 9 new books which covers the widest range of topics of any release to date :). Thanks to Sivuyile Nzimeni for one of the adds!
Thinking Outside The Grid – A “bare bones” intro to Rtistry concepts in R using ggplot.
Recently I’ve discovered the courage to dive into creative coding and generative aRt in R. Something that the R community calls “Rtistry.” My Rtistry journey so far has been an amazing and tranquil expedition into a world that seemed intimidating and scary on the outside but is honestly just a bottomless pit of fun and creativity on the inside.
I’m going to talk about some very basic concepts and perspectives you can think about while starting your own Rtistry journey in ggplot. This includes basics on geoms, aesthetics, layering, etc. But then I’m also going to walk you through two of my Rtistry examples and code to get you started.
This article is intended for those who have some experience with ggplot building in R but may not have realized how to transition from making “regular” visuals to Rtistry. This article goes over basic concepts that more seasoned users may already know.
Data Analytics with R: A Recipe book
The structure and design of this book is based on iterative learning, starting with the most basic and build by adding one new element concept. the book has been structured to be small easily consumable chunks similar to that of a recipe card. The concept for a recipe card is that they are self contained, providing all the ingredients, preparation, and instructions required to create a meal. While a cookbook may consist of many recipes, there is no expectation to read, understand, and master all the recipes in order to prepare a meal. Following this as the central theme the book, it has been designed as a number of data analytics recipes focusing on the R language.
Handbook of Graphs and Networks in People Analytics: With Examples in R and Python
The technology of graphs is all around us, and enables so many of the ways in which we live our lives today. That same technology is also available to us at no cost as an analytic tool to allow us to better understand network structures and dynamics in the fields of science, technology, economics, sociology and psychology to name just a few. It is available to academics and practitioners alike, and can be used on problems ranging from a very small network analysis which takes a few minutes on a laptop, to massive scale network mining requiring days or weeks of processing time.
But here’s the problem: few people really know how to do network analysis. It is still considered by many as a deep specialism or even a ‘dark art.’ It shouldn’t be.
This book aims to make the field of graph and network analysis more approachable to students and professionals by explaining the most important elements of theory and sharing common methodologies using open source programming languages like R and Python. It does so by explaining theory in as much detail as is necessary to support analytical curiosity and interpretation, and by using a wide array of example data sets and code snippets to demonstrate the specific implementation and interpretation of methodologies.
R Development Guide
R Contribution Working Group
This guide is heavily influenced by the Python Developer Guide, and is a comprehensive resource for contributing to R Core – for both new and experienced contributors. It is maintained by the R Contribution Working Group. We welcome your contributions to R Core!
Hiring Data Scientists and Machine Learning Engineers
It’s quite possible that the only thing more confusing than defining data science is actually hiring data scientists. Hiring Data Scientists and Machine Learning Engineers is a concise, practical guide to cut through the confusion. Whether you’re the founder of a brand new startup, the senior vice president in charge of “digital transformation” at a global industrial company, the leader of a new analytics effort at a non-profit, or a junior manager of a machine learning team at a tech giant, this book will help walk you through the important questions you need to answer to determine what role and which skills you should hire for, how to source applicants, how to assess those applicants’ skills, and how to set your new hires up for success. Special emphasis is placed on in-office vs remote hiring situations.
Paid, varies ~$34
Ace The Data Science Interview
Kevin Huo, Nick Singh
Authored by two Ex-Facebook employees, Ace the Data Science Interview is the best way to prepare for Data Science, Data Analyst, and Machine Learning interviews, so that you can land your dream job at FAANG, tech startups, or Wall Street.
Paid ~ $30
Model Estimation by Example: Demonstrations with R
This document provides ‘by-hand’ demonstrations of various models and algorithms. The goal is to take away some of the mystery of them by providing clean code examples that are easy to run and compare with other tools.
The code was collected over several years, so is not exactly consistent in style, but now has been cleaned up to make it more so. Within each demo, you will generally find some imported/simulated data, a primary estimating function, a comparison of results with some R package, and a link to the old code that was the initial demonstration.
DevOps for Data Science
At some point, most data scientists reach the point where they want to show their work to others. But the skills and tools to deploy data science are completely different from the skills and tools needed to do data science.
If you’re a data scientist who wants to get your work in front of the right people, this book aims to equip you with all the technical things you need to know that aren’t data science.
Hopefully, once you’ve read this book, you’ll understand how to deploy your data science, whether you’re building a DIY deployment system or trying to work with your organization’s IT/DevOps/SysAdmin/SRE group to make that happen.
Solutions to ggplot2: Elegant Graphics for Data Analysis
This is the website for “Solutions to ggplot2: Elegant Graphics for Data Analysis,” a solution manual to the exercises in the 3rd edition of ggplot2: Elegant Graphics for Data Analysis, written by Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen. While there are bookdown solution manuals to Hadley Wickham’s Advanced R and Mastering Shiny, there is no such thing for the ggplot2 book. This website is an attempt to fill this missing void.