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R is a very powerful tool, but I fail to see how it's adding value in this case. It seems like the author is advocating just using R to clean up the data and p
by couchand 12y ago
R is a very powerful tool, but I fail to see how it's adding value in this case. It seems like the author is advocating just using R to clean up the data and put it in JSON. The same can be done (without overhead for context switching and server-client architecture) in JS itself. Tools like Crossfilter [0] can be seamlessly integrated with D3.
[0]: https://square.github.io/crossfilter/ https://square.github.io/crossfilter/
- maxerickson 12y agoPerhaps the intent is the other way around, encouraging people familiar with R to take a look at using D3.
- baldfat 12y agoThis is the way I would want my work flow: 1) Use R clean the data 2) Use R to turn the raw data into analytically data 3) Use R to do exploratory charting etc [Most project stop here] 4) Final Product: RMarkdown (For static reports), Shiny (Interactive variable charts), or now D3 if I want to have interactive charts. My interactive charts would be very few. I wouldn't want to do the first three steps with javascript.
- ayy88 12y agoI think the strongest use case for interfacing R with JS (be it through plot.ly, Shiny, rCharts etc) is when there is more serious statistical modelling going on (i.e. plotting things like credible intervals or model coefficients). But often you find that you add a 'statistical layer' after you already have an existing plot (i.e. adding a confidence band to a line plot). Maybe it makes sense to have one tool that handles all these cases.