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Agile Analytics: Visualisation in Shiny and R

Agile Analytics: Visualisation in Shiny and R. Links for downloads. R direct link Windows: https://cran.ma.imperial.ac.uk/bin/windows/base/R-3.5.0-win.exe MAC: https://cran.ma.imperial.ac.uk/bin/macosx/R-3.5.0.pkg R studio: Windows: https://download1.rstudio.org/RStudio-1.1.453.exe

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Agile Analytics: Visualisation in Shiny and R

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  1. Agile Analytics:Visualisation in Shiny and R

  2. Links for downloads • R direct link • Windows: https://cran.ma.imperial.ac.uk/bin/windows/base/R-3.5.0-win.exe • MAC: https://cran.ma.imperial.ac.uk/bin/macosx/R-3.5.0.pkg • R studio: • Windows: https://download1.rstudio.org/RStudio-1.1.453.exe • MAC: https://download1.rstudio.org/RStudio-1.1.453.dmg • This presentation and Codes:http://bit.ly/RShinyCourse • Our Linkedin profiles: • Eduardo Contreras https://www.linkedin.com/in/eduardocontrerasc/ • Michael Mortenson https://www.linkedin.com/in/michael-mortenson-44321832/

  3. Motivation https://apiumtech.com/blog/agile-project-management-benefits/

  4. Motivation

  5. Motivation • SELECTED ITEMS FROM THE AGILE MANIFESTIO (http://agilemanifesto.org/principles.html): • Our highest priority is to satisfy the customer through early and continuous delivery of valuable software • Welcome changing requirements, even late in development. Agile processes harness change for the customer's competitive advantage • Working software is the primary measure of progress • Simplicity--the art of maximizing the amount of work not done--is essential

  6. Motivation Visual, interactive, accessible solutions Flexible ‘analyst-friendly’ technology Simple first, scalable later: “simple models for a complex world”

  7. Motivation

  8. What will be covered? • Load data from CSV • Output data in a Table • Plot data and filter source • Calculate a logistic regression • Testing the model • Alternative models • Upload to Google Cloud (and server settings)

  9. What do you need to use R Shiny? • R Studio • Shiny libraries • Other useful libraries • Plotly: Interactive Graphics • DT: Data tables outputs • Dplyr: Data Transformations • Rpart: Decision Trees • e1071: Support Vector Machines

  10. How does it works? • It has two main components • The User Interface (UI) • The Server

  11. The basics • We will use Shinydashboard, makes it easy to use Shiny • Lets Run the App by opening the R file and click in Run APP

  12. The basics • The dashboard has three parts: header, sidebar and body

  13. Basic Dashboard • Adding content to the Sidebar

  14. Basic Dashboard • Adding content to the Body

  15. And the last step • Adding the Header

  16. Time to explore the App • Install libraries • Load the Excel File • Explore the dataset • Plot only “Married” customers • Calculate the logistic regression • Download the Results

  17. All events triggered are in the Server Section

  18. And you can link to other files as a way to organise your code better

  19. Testing the data

  20. Going live https://cloud.google.com https://filezilla-project.org

  21. Where to learn more… • R programminghttps://www.datacamp.com/courses/free-introduction-to-r • Free Shiny Interactive Tutorialhttps://www.datacamp.com/courses/building-web-applications-in-r-with-shiny • More tricks of Shinydashboard and actionshttps://rstudio.github.io/shinydashboard/structure.htmlhttp://shiny.rstudio.com/gallery/widget-gallery.html • Gallery of plots with codehttps://plot.ly/r/ • Useful data transformations with Dplyr and Tidyrhttps://dplyr.tidyverse.org/https://blog.rstudio.com/2014/07/22/introducing-tidyr/

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