This document discusses various charting and visualization libraries for R and JavaScript. It outlines the evolution of charting libraries in R like base R, ggplot2, and plotly. It also lists popular JavaScript charting libraries like D3.js and Chart.js. The document demonstrates how charts like pie and bar charts can be created more efficiently in plotly compared to D3.js. It shows examples of dashboards created with Shiny and plotly that require less code than pure JavaScript equivalents. Finally, it discusses considerations for choosing R with plotly versus other options like D3.js and highlights benefits of using plotly in R.

1. PLOTCON 2016,NYC
TANYA CASHORALI
@TANYACASH21

2. BASE R (2000)
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3. GGPLOT2 (2007)
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4. D3.JS (2011)
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5. PLOTLY (2013)
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6. RCHARTS (2013)
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7. GGVIS (2014)
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8. JAVASCRIPT CHARTING LIBRARIES
 D3.js
 Highcharts
 Dygraphs
 FusionCharts
 Sigma.js
 Rickshaw
 Canvas.js
 Ember charts
 NVD3
 Chart.js
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9. PLOTLY TO THE RESCUE
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Pictured Above: Data Science Superhero

10. D3.JS PIE CHART
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67 lines of code!
https://bl.ocks.org/mbostock/3887235

11. PIE CHART IN PLOTLY
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12. BAR CHART VERSION IN PLOTLY
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13. D3.JS
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http://bl.ocks.org/mbostock/3884955
98 lines of code!

14. EQUIVALENT IN R WITH INTERACTIVITY
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15. CHART.JS DASHBOARD
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Online: http://coenraets.org/apps/olympic-dashboard/#dashboard/1
Source Code: https://github.com/ccoenraets/olympic-dashboard-chartjs/blob/master/www/js/dashboard1.js

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124 lines of just
javascript!!

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SHINYDASHBOARD + SHINY + PLOTLY
https://tcb-analytics.shinyapps.io/Plotcon2/

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58 TOTAL LINES OF CODE
ui.R
server.R

19. FLEXDASHBOARD + SHINY + PLOTLY
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• https://tcb-analytics.shinyapps.io/Plotcon/
• Data From: http://www.pro-football-reference.com/years/2016/passing.htm

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68 LINES OF R CODE

21. JAVASCRIPT EQUIVALENT
 script.js = 82 lines of code
 index.html = ~50 lines of handwritten,
uncompiled code
 Total of 132 lines of code
 Approximately double the dev time, no
scaling of variables
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Created by: www.brophyanalytics.com
Javascript: https://github.com/tcash21/tcash21.github.io/blob/master/uploads/script.js

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DEPLOY TO SHINYAPPS.IO IN 2 LINES
rsconnect::setAccountInfo(name='tcb-analytics', token=my_token', secret=my_secret')
deployApp('.', appName='Plotcon2')
• http://flowingdata.com/2016/10/07/learning-r-versus-d3-js-for-visualization/
“I guess it comes back to the original question of what
you ultimately want out of your visualization. For static
graphics, R is a good way to get quick results. For
interactive and web-native graphics, check out d3.js.”
- Nathan Yau, FlowingData

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USE CASES – PHYSICIAN CLINICAL INTEGRATION NETWORK
Shiny + Plotly (~100 total lines of R code)

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USE CASES – MARKET RESEARCH COMPANY
Shiny + Plotly
~340 lines of code

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MORE ADVANCED USE CASE – BIOTECH COMPANY
Required custom d3.js within Shiny in order to build b-
directional interactive tree

26. CONSIDERATIONS
 Some data visualization efforts require more
customization than Plotly charts via R can provide.
– In these cases we still leverage Plotly charts where possible
but will write custom d3.js or other Javascript to accomplish
what we need.
 Use the right tool for the job
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27. BENEFITS OF USING PLOTLY IN R
 Keep the analysis AND presentation layer in R as well
as publish results to web
 Efficiency (less coding = less time spent)
 Empowers data scientists to build beautiful interactive
dashboards
 Prototype potential solutions faster
 Confidence when pitching new prospects or bidding on
projects
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28. THANK YOU
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Brophy Analytics
www.brophyanalytics.com