4 ms·
"Leap" is quite the exaggeration IMO, especially talking about ML. This is improvement wrt to neural networks which had some support in R already (mxnet). In ML
by blahi 10y ago
"Leap" is quite the exaggeration IMO, especially talking about ML. This is improvement wrt to neural networks which had some support in R already (mxnet). In ML, in general, R is quite a bit ahead of the competition.
You have been able to use dplyr on spark dataframes for a while now (at least a year).
- thatcat 10y ago>In ML, in general, R is quite a bit ahead of the competition. Could you elaborate on this? I'm just starting out in R.
- baldfat 10y ago> You have been able to use dplyr on spark dataframes for a while now (at least a year) How? I didn't get it to work till sparklyr???? https://blog.rstudio.org/2016/09/27/sparklyr-r-interface-for-apache-spark/ https://blog.rstudio.org/2016/09/27/sparklyr-r-interface-for...
- blahi 10y agohttps://github.com/RevolutionAnalytics/dplyr-spark https://github.com/RevolutionAnalytics/dplyr-spark
- baldfat 10y ago> not yet endowed with a through test suite. Nonetheless we expect it to inherit much of its correctness, scalability and robustness from its main dependencies, dplyr and spark. > we don't recommend production use yet I was a bit disappointed by that package, though it is great to see it in the ecco-system of R. The RStudio sparklyr is 100% native and Production ready. You should check it out I was happily surprised.