.. _Tableau_8.1: Tableau 8.1 =========== Have H\ :sub:`2`\ O Installed """"""""""""""""""""""""""""" Perquisites : - 64-bit Oracle Java 1.6+ - Tableau 8.1 installed - H\ :sub:`2`\ O installed in R (follow download guide :ref:`R_Installation`) Metadata """""""" Tableau requires a data input of some sort in order to run but when dealing with immensely big data as a H2O user would typically do, it is impractical and unnecessarily time consuming to feed all the data into Tableau. So the solution is to create a small metadata file that can be easily loaded from disk. Note: in particular, geo attributes like cities and latitude/longitude coordinates need to be imported into Tableau. Aggregates and calculated fields cannot be converted so even if you have a vector of cities (New York City, San Francisco, Houston, Austin, Seattle) as a output from R into Tableau as a new measure the user cannot convert the measure into geographical points. The metadata is just a file with factor levels of the data frame that’ll give a quick overview of the unique input values and cardinality of each column. This format will also allow the user to calculate across the table making it easier actually visualize some of the data coming from H2O. Example: if a column has a 352 categorical values and a binomial GLM is ran with 352 coefficients in the output, Tableau will allow the plotting of the coefficients against the column. The following data is a truncated metadata file for 116 million rows airline `data set `_. This metadata file in particular is only about 352 rows big. A extra column was prepend to the data file to grab the column names of the actual data set, this column will help populate certain parameters in the workbook. :: Colnames Year Month DayofMonth DayOfWeek UniqueCarrier Origin OCity Year 1988 1 1 1 PI SYR Syracuse Month 1989 2 2 2 PS LGA New York DayofMonth 1990 3 3 3 TW JFK New York DayOfWeek 1991 4 4 4 UA UCA Utica DepTime 1992 5 5 5 WN CHO Charlottesville CRSDepTime 1993 6 6 6 EA CLT Charlotte ArrTime 1994 7 7 7 HP EWR Newark CRSArrTime 1996 8 8 NW LYH Lynchburg UniqueCarrier 1998 9 9 CO ROA Roanoke FlightNum 2000 10 10 DL BWI Baltimore TailNum 2001 11 11 AA MYR Myrtle Beach ActualElapsedTime 2003 12 12 US GSO Greensboro CRSElapsedTime 2004 13 AS ISP Islip AirTime 2005 14 AQ CHS Charleston ArrDelay 2006 15 MQ ORF Norfolk DepDelay 2007 16 OO BGM Binghamton Origin 2008 17 XE LAX Los Angeles Dest 18 EV SEA Seattle Distance 19 DH SJC San Jose Create R, Tableau, and Data Connections """"""""""""""""""""""""""""""""""""""" **Step 1** Install Rserve which is the package built to create R servers, and once installed load up the Rserve library and run Rserve or run.Rserve with your desired port. :: > install.packages("Rserve") > library(Rserve) > run.Rserve(port=6311) **Step 2** Open Demo_Template_8.1.twb which should have all the calculated fields containing R script already in the sidebar. Navigate to "Help > Settings and Performance > Manage R Connection" to establish a connection to the R serve. .. image:: tableau_r_connection1.png :width: 50% Input the server ip address and port: .. image:: tableau_r_connection2.png :width: 50% **Step 3** Set the workbook's connection to the airlines_meta.csv data by navigating to the data section on the left sidebar, right clicking on the airlines_meta and choosing to "Edit Connection." .. image:: tableau_data_connection1.png :width: 40% Simply provide the location of the airlines_meta.csv file and submit the new path. .. image:: tableau_data_connection2.png :width: 40% **Step 4** Configure the IP Address and Port that H\ :sub:`2`\ O will launch at as well as the path to the full airlines data file. Fill in the parameters to creating a new GLM model, for documentation on what the parameters represent visit our GLM documentation page :ref:`GLMmath`. .. image:: tableau_h2o_parameters.png :width: 40% Execute Scripts """"""""""""""" .. image:: tableau_execute.png :width: 40% Double click on the calculated fields under Dimensions in the order they are listed: #. Execute "00 Load H2O and Tableau functions" to run library(h2o) and define other R functions to be used in Tableau. #. Execute "01 Init H2O & Parse Data" will run h2o.init() and h2o.importFile(). #. Execute "02 Compute Aggregation with H2O's ddply" will run h2o.ddply() and create measures for use in "Flights By Month" and "Flights By Airport" worksheets. #. Execute "03 Run GLM" to build a GLM model in H2O and grab back coefficient values that will be plotted in multiple worksheets. Navigate through the worksheets and dashboard """"""""""""""""""""""""""""""""""""""""""""" .. image:: tableau_dashboard.png :width: 100%