Posted on Categories Practical Data Science, Pragmatic Data Science, StatisticsTags , , ,

Practical Data Science with R examples

One of the big points of Practical Data Science with R is to supply a large number of fully worked examples. Our intent has always been for readers to read the book, and if they wanted to follow up on a data set or technique to find the matching worked examples in the project directory of our book support materials git repository.

Some readers want to work much closer to the sequence in the book. To make working along with book easier we extracted all book examples and shared them with our readers (in a Github directory, and a downloadable zip file, press “Raw” to download). The direct extraction from the book guarantees the files are in sync with our revised book. However there are trade-offs, sometimes (for legibility) the book mixed input and output without using R’s comment conventions. So you can’t always just paste everything. Also for a snippet to run you may need some libraries, data and results of previous snippets to be present in your R environment.

To help these readers we have added a new section to the book support materials: knitr markdown sheets that work all the book extracts from each chapter. Each chapter and appendix now has a matching markdown file that sets up the correct context to run each and every snippet extracted from the book. In principle you can now clone the entire zmPDSwR repository to your local machine and run all the from the CodeExamples directory by using the RStudio project in RunExamples. Correct execution also depens on having the right packages installed so we have also added a worksheet showing everything we expect to see installed in one place: InstallAll.Rmd (note some of the packages require external dependencies to work such as a C compiler, curl libraries, and a Java framework to run).