# Data engineering and data shaping in Practical Data Science with R 2nd Edition

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A kind reader recently shared the following comment on the *Practical Data Science with R 2nd Edition* live-site.

Thanks for the chapter on data frames and data.tables. It has helped me overcome an obstacle freeing me from a lot of warnings telling me my data table was not a real . It reduced the calculation time for a scenario in modelStudio from 30 minutes to 7 minutes. Following the advice in your book is helping me a lot with understanding R and the models you can create with R: Thanks

This is exactly what we were hoping for when we added *Chapter 5 Data engineering and data shaping* to the 2nd edition of the book. The chapter is organized by data manipulation task (what you are trying to do, or your sub-goal) and then teaches the mere methodology in base-`R`

, `data.table`

, and `dplyr`

. The hope was: a Rosetta Stone of data manipulation solutions, that would help many readers- and not lock them into any one notation.

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