Slides from Sept 26 hands on walk through: Running GLM on large data using H2O + R.

1. 4/23/13
Hack Airline Data
With H2O in R
Awesome hands on workshop for running big data analysis through R.

3. Running H2O Through R
If you don’t already have h2o and R setup to talk:
1. Find an H2O flashdrive.
2. Download zip file and data. This has R package, jar and
data!
Make sure you know the file path to where it
downloaded or put it on your desktop.

4. Start An Instance of H2O
We’re going to run H2O from your computer.
To do this open your command line terminal (or wherever
you run java programming from)
CD to the directory with the h2o jar
</blah>/h2o-1.7.0.536

5. Java Call for H2O Instance
Enter the java command:
java –Xmx<memory> -jar h2o.jar -name <your name or
handle or whatev>
In the command above where you see memory specify the
amount of memory you want to allocate to h2o. We found
that we needed at least 4 gigs to run.

6. H2O and R
Ok. You have an instance of h2o running? Good. Now go to R.
In the R console either change your working directory or be
ready to give R an absolute path to the R package
>install.packages(install.packages("<unzipped h2o
directory>/R/h2oWrapper_1.0.tar.gz", repos = NULL, type =
"source")

7. Get Up To Speed
> h2oWrapper.installDepPkgs()
> localH2O = h2oWrapper.init(ip = "localhost", port = 54321,
startH2O = TRUE, silentUpgrade = FALSE, promptUpgrade
= TRUE)

8. Something Like This Should Happen
stuff
Stuff
Stuff
…
Successfully connected to http://localhost:54321

9. Import the Airline Data to R
We’re using the full data set.
You’re using a data set that will fit on your laptop.
>h2o.importFile(localH2O, "~/Desktop/Airlines.csv", key="",
parse = TRUE, sep = "")

10. GLM on Airlines Data in R
Watch the screen – We’ll do it together.
We’re not producing anything different in h2o R than you would
get with web GUI.
The primary difference is that you can now process data through
the familiar R interface that R without H2O chokes on.