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Report Writing for Data Science in R

Report Writing for Data Science in R
This book teaches the fundamental concepts and tools behind reporting modern data analyses in a reproducible manner. As data analyses become increasingly complex, the need for clear and reproducible report writing is greater than ever. The material for this book was developed as part of the industry-leading Johns Hopkins Data Science Specialization. Printed versions are available through Lulu (see link below).

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This book teaches the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducibility is the idea that data analyses should be published or made available with their data and software code so that others may verify the findings and build upon them.  The need for reproducible report writing is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This book will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.

If you are interetsed in a printed version of this book, you can purchase one through Lulu.

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    The Book + Lecture Videos (HD)

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    This package includes the book and high definition lecture video files (720p). The collection also contains live demonstrations of how to use various aspects of R that could not be included in the book. The videos are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license.

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    作者

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    Roger D. Peng

    Roger D. Peng is a Professor of Statistics and Data Sciences at the University of Texas at Austin. Previously, he was Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and the Co-Director of the Johns Hopkins Data Science Lab. He is the author of the popular book R Programming for Data Science and 10 other books on data science and statistics. Roger is a Fellow of the American Statistical Association and is the recipient of the Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to public health. Roger received a PhD in Statistics from the University of California, Los Angeles. His current research focuses on building analytic design theory for improving the quality of data analyses and on the development of statistical methods for addressing environmental health problems.

    The Leanpub Podcast

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    1.Getting Started with R

    2.What is Reproducible Reporting?

    3.The Data Science Pipeline

    4.Literate Statistical Programming

    5.Organizing a Data Analysis

    6.Structure of a Data Analysis: Part 1

    7.Structure of a Data Analysis: Part 2

    8.Markdown

    9.Using knitr for Reproducible Reports

    10.Communicating Results Over E-mail

    11.Reproducibility Check List

    12.Evidence-based Data Analysis

    13.Public Reproducibility Resources

    About the Author

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