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  • Book Overview & Buying Mastering RStudio: Develop, Communicate, and Collaborate with R
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Mastering RStudio: Develop, Communicate, and Collaborate with R

Mastering RStudio: Develop, Communicate, and Collaborate with R

By : Julian Hillebrand, Nierhoff
4.6 (5)
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Mastering RStudio: Develop, Communicate, and Collaborate with R

Mastering RStudio: Develop, Communicate, and Collaborate with R

4.6 (5)
By: Julian Hillebrand, Nierhoff

Overview of this book

RStudio helps you to manage small to large projects by giving you a multi-functional integrated development environment, combined with the power and flexibility of the R programming language, which is becoming the bridge language of data science for developers and analyst worldwide. Mastering the use of RStudio will help you to solve real-world data problems. This book begins by guiding you through the installation of RStudio and explaining the user interface step by step. From there, the next logical step is to use this knowledge to improve your data analysis workflow. We will do this by building up our toolbox to create interactive reports and graphs or even web applications with Shiny. To collaborate with others, we will explore how to use Git and GitHub and how to build your own packages to ensure top quality results. Finally, we put it all together in an interactive dashboard written with R.
Table of Contents (12 chapters)
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11
Index

The concept of reproducible research


The possibility of replicating studies and researches is essential for strengthening the scientific evidence of the related outcomes. Especially when it comes to public trials of the influence on important social decision-making processes; it is particularly relevant to disclose all data sets, analytical methods, and related findings. By enabling such transparency, it is possible for every interested person to reproduce the results of any scientific research. With this approach, all findings can be checked for accuracy and are thus, possibly, corrected or confirmed by third-parties, even though an analysis can be reproducible and still not valid. A basic reproducible workflow could look like the following:

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