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Hands-On Data Science with R

You're reading from   Hands-On Data Science with R Techniques to perform data manipulation and mining to build smart analytical models using R

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789139402
Length 420 pages
Edition 1st Edition
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Authors (4):
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Nataraj Dasgupta Nataraj Dasgupta
Author Profile Icon Nataraj Dasgupta
Nataraj Dasgupta
Vitor Bianchi Lanzetta Vitor Bianchi Lanzetta
Author Profile Icon Vitor Bianchi Lanzetta
Vitor Bianchi Lanzetta
Doug Ortiz Doug Ortiz
Author Profile Icon Doug Ortiz
Doug Ortiz
Ricardo Anjoleto Farias Ricardo Anjoleto Farias
Author Profile Icon Ricardo Anjoleto Farias
Ricardo Anjoleto Farias
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Toc

Table of Contents (16) Chapters Close

Preface 1. Getting Started with Data Science and R FREE CHAPTER 2. Descriptive and Inferential Statistics 3. Data Wrangling with R 4. KDD, Data Mining, and Text Mining 5. Data Analysis with R 6. Machine Learning with R 7. Forecasting and ML App with R 8. Neural Networks and Deep Learning 9. Markovian in R 10. Visualizing Data 11. Going to Production with R 12. Large Scale Data Analytics with Hadoop 13. R on Cloud 14. The Road Ahead 15. Other Books You May Enjoy

Saving analysis for future work

R packages, especially the popular ones, are frequently updated. Although the packages go through a rigorous testing process before being accepted on CRAN, it is sometimes necessary to be able to save your work at a point in time. This comes up often during production development using R packages. Reproducibility of results are also oftentimes needed in regulated environments by regulators such as the FDA.

R provides some unique and mature capabilities to store and persist data at a point in time. To retrieve the results, all the user needs to do is simply revert to a pre-saved version of the work.

The three most popular methods of saving R work in a reproducible framework are:

  • Packrat
  • Checkpoint
  • Rocker

Packrat

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