71–80 of 90 posts
R has a nice web development framework called Shiny. While it is not comparable to say Django or Flask, Shiny does make it incredibly easy to share data analysis. If one wants to share statistical analysis or create a data oriented dashboard, then there is definitely a reason to consider R and Shiny. Note that Python has Dash, which is comparable to Shiny, but it is less mature as far as I know. While previously Shin…
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In my experience, R is really fast since I t was designed to store data in columnar format which we now all know is best for data analysis. So, in most cases, scaling up computation is quite easy. To scale out, you can use Apache Spark with R, the interface I’ve worked on, sparklyr is quite easy to use and allows you to scale out computation. Just to give you an example of what’s possible, I was playing around yester…
You can plug any compute kernel you want into spark, that's not a pro or con of R. Column stores are standard in any analytics pipeline today. They make up Python's Pandas, R's dplyr, and Java's DataFrame. How or why does R stand out for 'massive amounts of data'? R does not have have meaningful out of core compute offerings that compare with something like Dask. R does not at all have cluster compute offerings that…
I don't see how the "GPU DataFrames" provided in cuDF would enhance a raytracer in any way.
How come there's no source in the git repo? You shouldn't just throw up the PDF and call it a day, github isn't just a trendy file host.
Good point. Just added the code. Thanks!
R has a nice web development framework called Shiny. While it is not comparable to say Django or Flask, Shiny does make it incredibly easy to share data analysis. If one wants to share statistical analysis or create a data oriented dashboard, then there is definitely a reason to consider R and Shiny. Note that Python has Dash, which is comparable to Shiny, but it is less mature as far as I know. While previously Shin…
Dash by plotly is also amazing its like shiny but for python! - we were able to whip together an app that would let you drag and drop xyyy data and get a scatter plot instantly - you can try it here (first load takes 1-2s): https://dash-app-dx9g2r0la6-8000.cloud.kyso.io It was also really easy to make it, maybe 250 lines of python in total (guide to making this app is here: https://kyso.io/KyleOS/creating-an-interact…
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You can plug any compute kernel you want into spark, that's not a pro or con of R. Column stores are standard in any analytics pipeline today. They make up Python's Pandas, R's dplyr, and Java's DataFrame. How or why does R stand out for 'massive amounts of data'? R does not have have meaningful out of core compute offerings that compare with something like Dask. R does not at all have cluster compute offerings that…
> That raytracing example you linked would run at interactive rates with cudf, I really don't see any basis for perf arguments in R's favour, and 'massive data' arguments are laughable here. I don't see how the "GPU DataFrames" provided in cuDF would enhance a raytracer in any way.
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I don't think there's any reason to learn R for anyone who is already proficient at programming. Despite being proficient with R, the only times I used it in the last two years were for ggplot. And even for data vis, I'm increasingly using Python and JS. There's a bunch of comments below which can be summed up with 'use R because doesn't have a direct python equivalent' but they're all missing the point that the Pyth…
Tidyverse is not just some " " -- it's an entire workflow, centered around functional programming and tidy data ( https://vita.had.co.nz/papers/tidy-data.pdf ), and nothing in Python comes close. R has many warts, but its lisp roots and metaprogramming strengths have allowed the tidyverse devs, and other excellent programmers working with R, to dramatically improve the language, and spawn a whole new style of statist…
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> That raytracing example you linked would run at interactive rates with cudf, I really don't see any basis for perf arguments in R's favour, and 'massive data' arguments are laughable here. I don't see how the "GPU DataFrames" provided in cuDF would enhance a raytracer in any way.
You don’t see how a gpu accelerated numeric array would speed up ray tracing?
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You don’t see how a gpu accelerated numeric array would speed up ray tracing?
The bottlenecks for raytracing are primarily in scene traversal/intersection testing--which does not benefit from a GPU-accelerated array structure.
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The bottlenecks for raytracing are primarily in scene traversal/intersection testing--which does not benefit from a GPU-accelerated array structure.
You are literally as wrong as it is possible to be wrong. http://hoxxep.github.io/webgl-ray-tracing-demo/ https://devblogs.nvidia.com/accelerated-ray-tracing-cuda/
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In my experience, R is really fast since I t was designed to store data in columnar format which we now all know is best for data analysis. So, in most cases, scaling up computation is quite easy. To scale out, you can use Apache Spark with R, the interface I’ve worked on, sparklyr is quite easy to use and allows you to scale out computation. Just to give you an example of what’s possible, I was playing around yester…
You can plug any compute kernel you want into spark, that's not a pro or con of R. Column stores are standard in any analytics pipeline today. They make up Python's Pandas, R's dplyr, and Java's DataFrame. How or why does R stand out for 'massive amounts of data'? R does not have have meaningful out of core compute offerings that compare with something like Dask. R does not at all have cluster compute offerings that…
Bonus: modern Fortran is a joy to develop in, far more fun than Python. And you get to compile to machine code, either for a processor or a GPU.