The Big R-Book: From Data Science to Learning Machines and Big Data
by Philippe J.S. De Brouwer
– Published by John Wiley & Sons –
The book is a story about data, and how to use it successfully in a private company. The book aims at increasing your personal brand and value, as well as shareholder value.
You can learn about statistical models, muliti-criteria decision analysis, machine learning, artificial intelligence, big data, creating interactive websites, automated presentations, speed up code, program the GPU, etc. while learning to code in R. The book takes a pragmatic stance and gets you started!
The Parts of the Book
- Introduction: the importance of data science
- Starting with R and Elements of Statistics: the basics
- Data Import: SQL databases and importing data in R
- Data Wrangling: modifying data
- Modelling: building robust models and verify them, including popular machine learning techniques
- Introduction to Companies: what are companies, financial and management accounting, valuation of financial assets and multi criteria decision analysis
- Reporting: from automated slides to interactive websites
- Bigger and Faster R: using clusters, programming the GPU, big data, and code optimisation
- Appendices: extra code, notes, and levels of measurement
Some Statistics about the Book
- 803 R code examples or segments and 92 other code sections
- 145 illustrations
- 270 boxes with information, warnings, digressions, and further information
- 8 parts, plus front and back-matter
- 40 chapters plus 5 appendices, 129 sections, 223 subsections, 128 subsubsections
- 892 pages of fine print
- 258,450 words in 34,438 paragraphs
Sample Materials
Samples (note: these samples are generate from the submission to the editor – so, while the words are the same, it might look different in the book itself):Examples of dynamic websites that are explained in the book:
- An example of a dashboard (with flexdash)
- Simple simulator for the normal distribution (with shiny)
The same dashboard publised on: rpubs
Educational Materials: R-code, slides and apps
A series of videos based on the book: YouTube Playlist
| # | Title | R-Code | Slides | Videos | Other |
|---|---|---|---|---|---|
| 1 | Introduction | p01.R | Ch 1: Introduction: The past and the future of science | ||
| 2 | Starting with R and Elements of Statistics | p02.R |
Ch 4: How to install R on Windows / the big R-Book series / ch.4 Ch 4: Getting Started with R (console version) Ch 4: Getting Started with R (RStudio Version) Ch 7: The philosophy of the Tidyverse Ch 7: Getting Started the Tidyverse elements of Ch 4, 21, and 23: Introduction to Data Science with R |
||
| 3 | Data Import | p03.R | |||
| 4 | Data Wrangling | p04.R | |||
| 5 | Modelling | p05.R |
elements of Ch 4, 21, and 23: Introduction to Data Science with R |
||
| 6 | Introduction to Companies | p06.R | |||
| 7 | Reporting | p07.R | Ch 36: Interactive Dashboards (example with Covid19) |
Interactive Apps and Dashboards
R Markdown Examples Ch 36: a tutorial to build a {shiny} app |
|
| 8 | Bigger and Faster R | p08.R | |||
| 9 | Appendices | p09.R |
Get Your Copy
Available now on your favourite shop. For example:Citations
Automated citations for each chapter: onlinelibrary.wiley.com.
Bibtex code for the print-version:1 2 3 4 5 6 7 8 | @book{debrouwer2020,
title = {The Big R-Book: From Data Science to Learning Machines and Big Data},
author = {Philippe J.S. {De Brouwer}},
year = {2020},
publisher = {John Wiley & Sons, Ltd},
address = {New York},
isbn = {978-1-119-63272-6}
}
|
1 2 3 4 5 6 7 8 9 10 11 | @book{debrouwer2020online,
title = {The Big R-Book: From Data Science to Learning Machines and Big Data},
author = {Philippe J.S. {De Brouwer}},
year = {2020},
publisher = {John Wiley & Sons, Ltd},
address = {New York},
isbn = {978-1-119-63275-7},
doi = {https://doi.org/10.1002/9781119632757},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119632757},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119632757}
}
|