The packages you need to download to install PySpark.Fire up your terminal one more time, throw the IPython command and keep your fingers crossed! ipython notebook In your terminal type: ipython kernelspec install -replace -name ir -user /Library/Frameworks/R.framework/Versions/3.1/Resources/library/IRkernel/kernelspecĪfter you run that in terminal, go back into R and run: library(IRdisplay)Īt this point you should be set to go. In that case, there is a simple work-around. If that is the case, then you’ve quickly found the problem that took me hours of detective work to track down.
print(system.file("kernelspec", package = "IRkernel"))Ĭhances are the package is sending the R kernel to somewhere like “/Library/Frameworks/R.framework/Versions/3.1/Resources/library/IRkernel/kernelspec”. Run the following command in R to find the path IRkernel is hitting. In my case, installspec() wouldn’t fire up, so I did a little detective work. Then in R: library(RCurl)Īt this point the R kernel should work (in theory) by executing the installspec() function from your new IRkernel package but… Make sure to place the file in your R working directory. Note, since the rzmq package includes dependencies, we’ll be cloning the GitHub repo and installing it locally. Note, it may be a good idea to install them one at a time.
If you use Homebrew: brew install libzmq3Īssuming that those libraries brewed without any errors, start R in your terminal by typing “R” or fire up R-Studio. My original method: If the above method doesn’t work, you may have more luck here. If not, the instructions below show you how to clone the IRkernel GitHub repo and install from source on your local machine. Repos = c('', getOption('repos')), type = 'source') install.packages(c('rzmq','repr','IRkernel','IRdisplay'),
Next, fire up R, install from source and start your kernel. Or, if you use MacPorts sudo port install zmq If you use Homebrew: xcode-select -install Note: Make sure you’ve got Xcode installed. Update: This install method is less involved The ability to add an R kernel to the IPython environment gives one the ability to run Python and R side-by-side in the same programming environment. IPython is a great tool for developers, particularly for R programmers who are accustomed to the luxury of running blocks of code during development.
0 kommentar(er)
