Some statistics about the book

The release date for Zumel, Mount “Practical Data Science with R” is getting close. I thought I would share a few statistics about what goes into this kind of book.

“Practical Data Science with R” started formal work in October of 2012. We had always felt the Win-Vector blog represented practice and research for such an effort, but this is when we started outlining a concrete book proposal. Most of a book proposal is specifying and limiting scope down to something that has a coherent point of view.

By May 2013 we had three chapters written and were able to launch the MEAP (Manning Early Access Program, where chapters drafts are shared to subscribers). By December 2013 the book was “content complete” (everything had been written and was accepted by initial editors and technical reviewers). Even though a lot of work had gone into writing, editing and technical review (see On writing a technical book) the pace actually picked up at this point.

We continue working with additional formal technical reviewers, proof editors, copy editors, indexers, graphic artists, layout specialists, QA readers and many more to give the book what one editor called “the sparkle the book deserves.” The MEAP now has all chapters available to subscribers, though even subscribers will not see a great number of the fixes and improvements until the final book is released.

But let’s get down to some of the numbers produced in the process of writing the book.

• Final chapter count: 11 (one chapter got moved to the appendixes).
• Page count: 450 to 500 depending on some rendering options.
• Number of figures: 159.
• Number of words: about 130,000.
• Size of book text: 1.8MB.
• Number of git commits in book text repository: 742.
• Number of example code extracts: 274 (about 1.1MB).
• Size of example support site: 100MB.
• Number of git commits in example repository: 151.
• Number of book related emails in my email folder: 968.

We (Nina, myself and Manning Publications Co.) have put a lot into this book to make it easier for readers to get a lot out of it. We can’t wait to put it in your hands.

Just for the fun: the cover page of a book I very much respect that got me thinking about counting things.

Related posts:

1. On writing a technical book
2. Book Review: Ensemble Methods in Data Mining (Seni & Elder)
3. Data Science, Machine Learning, and Statistics: what is in a name?