Welcome to the companion site for The Essentials of Data Science: Knowledge Discovery Using R by Graham Williams, published by CRC Press in 2017. The book is a journey to knowledge discovery using R, and introduces the idea of using templates to rapidly get started with data science.
Here you will find the templates from the book, extended OnePageR guides that go beyond the book, and the datasets used throughout. Data science changes rapidly, and the Data Science Desktop Survival Guide carries this material forward with regular updates.
Templates
The templates are LaTeX documents using KnitR to
include dynamic R code and its output. Each .Rnw file is processed to produce the
.R script and the .pdf document.
End-to-End Samples
| Template | Task | Files |
|---|---|---|
| ports | Data visualisation | RnwRPDFData (xlsx) |
| audit | Binary classification | RnwRPDF |
| weatherAUS | Binary classification | RnwRPDF |
Data Preparation
A starting point for any data science task focused on ingesting, wrangling, cleaning, and transforming data in preparation for analytic modelling.
| Template | Task | Files |
|---|---|---|
| data | Data preparation | RnwRPDF |
Modelling and Reporting
| Template | Task | Files |
|---|---|---|
| model | Predictive modelling (in development) | RnwR |
| report | Project report outline | Rnw |
OnePageR Guides
These extended guides go beyond the material in the book.
| Guide | Topic | Files |
|---|---|---|
| IntroRO | Introducing R | RnwRPDF |
| DataO | Data | RnwRPDF |
| DateTimeO | Dates and times | RPDF |
| StringsO | Strings | RPDF |
| StyleO | Coding style | RPDF |
| TemplateO | Templates | RnwRPDF |
| VisualiseO | Visualising data | RJuliaPDF |
Data
Public datasets for exploring data science with the templates and guides.
| Dataset | Size | Download |
|---|---|---|
| audit | 185 KB | audit.csv |
| banking | 4.9 MB | banking.csv |
| Unbalanced credit card transactions | 56 MB | creditcard.csv |
| ferry | 27 MB | ferry.csv |
| ports | 9 KB | ports.xlsx |
| telco | 977 KB | telco.csv |
| weatherAUS | 16 MB | weatherAUS.csv |
The credit card dataset is described in A. Dal Pozzolo, O. Caelen, R. A. Johnson and G. Bontempi, Calibrating Probability with Undersampling for Unbalanced Classification, IEEE Symposium Series on Computational Intelligence (SSCI), Cape Town, South Africa, 2015. It is also available from Kaggle, and is provided here with permission.
MLHub
MLHub provides a selection of pre-built machine learning models that support the material from the book, run from the command line, including the ports visualisation demonstration used in the templates above.