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Small Team, Big Value: Using R to Design Visualizations

Ian Lyttle
January 30, 2020

Small Team, Big Value: Using R to Design Visualizations

Ian Lyttle

January 30, 2020
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  1. Small Team, Big Value 
 Using R to Design Visualizations

    Ian Lyttle, Schneider Electric @ijlyttle
  2. Who are Schneider Electric? • We are more than 150,000

    employees worldwide. • We help our customers use electricity safely and efficiently.
  3. How do we use data? Our products collect and transmit

    data on things like: - power consumption - number of operations - room temperature Our customers have questions like: - “When will I need to replace the power-supply battery?” - “Did my facility use too much energy last month?” - “Do I have enough cooling in my data-center?”
  4. SE Customer-Facing Tools

  5. SE businesses use different visualization toolsets, including: Each runs in

    the browser and uses JavaScript. None of these is ggplot2. SE Customer-Facing Tools
  6. SE businesses each have data-science teams. SE also has a

    central data-science team. This is who I work with. Import Tidy Transform Visualize Model Communicate Wickham & Grolemund,
 R for Data Science How are we organized?
  7. How are we organized? Import Tidy Transform Visualize Model Communicate

    Wickham & Grolemund,
 R for Data Science Import, Tidy, Transform, Model -Larger team, based in France -Uses Python
 -Deploy models and data as web services
 https://exchange.se.com/develop Visualize, Communicate -Smaller team, based in US -Uses R -Deploy practices
  8. How are we organized? Import Tidy Transform Visualize Model Communicate

    Wickham & Grolemund,
 R for Data Science Visualize, Communicate -Smaller team, based in US -Uses R -Deploy practices Ian Lyttle Haley Jeppson PhD Student, Iowa State University
  9. By Mike Murphy, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=295635 Data-Visualization Guidelines for

    Customer-Facing Tools Challenge from SE UI Design Team:
  10. https://www.pinterest.com/pin/392587292493950305/

  11. • Solid advice • Uses ggplot2 • No code in

    book Claus Wilke Technique https://serialmentor.com/dataviz/
  12. Jenny Bryan Tooling https://twitter.com/JennyBryan/status/878016566826225664 • Focus on what’s important. •

    Work towards good-enough. • Workflow to support this: • {usethis} package • https://happygitwithr.com • https://rstats.wtf
  13. Communicate David Robinson https://resources.rstudio.com/rstudio-conf-2019/the-unreasonable-effectiveness-of-public-work

  14. By Libby VanderPloeg, https://giphy.com/gifs/cute-feminist-girlpower-3o7abBphHJngINCHio Community Gabriela de Queiroz • Founder,

    R-Ladies • Began in San Francisco • Now hundreds of chapters • Diversity of input leads to better outcomes.
  15. SE Categorical Palette SE brand colors optimized for marketing: Not

    optimized for data-visualization. • Propose a related categorical-palette • Take into account color-vision deficiency • Remain “Schneidery” Challenge
  16. SE Categorical Palette SE brand colors optimized for marketing: Not

    optimized for data-visualization. • Propose a related categorical-palette • Take into account color-vision deficiency • Remain “Schneidery” Challenge Technique • Book offers useful advice • {colorspace} package • Achim Zeileis @ UseR! 2019 • Think in terms of hue, chroma, luminance
  17. Hue Chroma Luminance Color-vision deficiency Perceptual distance Visible Gamut RGB

    Color space Just-noticeable difference CIEDE-2000 “Crayola bright”
  18. https://tenor.com/view/jake-gyllenhaal-mr-music-jake-gyllenhaal-the-sack-lunch-bunch-gif-15872324

  19. SE Categorical Palette SE brand colors optimized for marketing: Not

    optimized for color-vision deficiency. • Propose a new palette • Take into account color-vision deficiency • Remain “Schneidery” Challenge Technique • Book offers some advice • {colorspace} package • Achim Zeileis @ UseR! 2019 • Think in terms of hue, chroma, luminance
  20. SE Categorical Palette Technique • Book offers some advice •

    {colorspace} package • Achim Zeileis @ UseR! 2019 • Think in terms of hue, chroma, luminance • Need numbers to show
 perceptual-differences • {farver} package • {paleval} ijlyttle/paleval: • Built on {colorspace} and {farver} • Good enough for now, improve later Tooling
  21. SE Categorical Palette • Need numbers to show
 perceptual-differences •

    {farver} package • {paleval} ijlyttle/paleval: • Built on {colorspace} and {farver} • Good enough for now, improve later Tooling • Created internal blog • {distill} via GitHub Enterprise Communication
  22. SE Categorical Palette • Seek more opinions,
 new perspectives •

    Community@Work, Data Visualization: • By practitioners for practitioners • Point-of-contact with SE UI Design Team Community • Created internal blog • {distill} via GitHub Enterprise Communication
  23. SE Categorical Palette Proposed categorical palette: • Takes into account

    color-vision deficiency. • Potentially “Schneidery”. • Useful feedback: may remove reds. Designed using R, delivered as hex-codes. Result
  24. More Broadly Grammar-of-graphics as description and tool (ggplot2) Interactivity: Vega-Lite

    vegawidget/ggvega Move things off your computer into the world! Get to know a local university
  25. Small Team, Big Value 
 Using R to Design Visualizations

    Ian Lyttle, Schneider Electric @ijlyttle @vegawidget, @r-box https://exchange.se.com/develop