Difference between revisions of "Statistical Algorithms Importer: R-blackbox Project"

Latest revision as of 17:09, 18 October 2018

This page explains how to create a R-blackbox project using the Statistical Algorithms Importer (SAI) portlet.

R-blackbox Project, SAI

Project Configuration

Define project's metadata

R-blackbox Info, SAI

Add input and output parameters and click on "Set Code" to indicate the main file to execute (i.e. the .r file)

R-blackbox I/O, SAI

Add information about the running environment (e.g. R version etc.)

R-blackbox Interpreter, SAI

After the software creation phase a Main.R file and a Taget folder are created

R-blackbox Create, SAI

Example Code

R code in sample:

#
# author Giancarlo Panichi
#
test<-"checkinput"
write.csv(test,file="program.txt")

Example Download

File:RBlackBox.zip

Inheritance of Global and Infrastructure Variables

at each run of the process the globalvariables.csv file is created locally to the process (i.e. it can be read as ./globalvariables.csv), which contains the following global variables that are meant to allow the process to properly contact the e-Infrastructure services:

• gcube_username (the user who run the computation, e.g. gianpaolo.coro)

• gcube_context (the VRE the process was run in, e.g. d4science.research-infrastructures.eu/gCubeApps/RPrototypingLab)

• gcube_token (the token of the user for the VRE, e.g. 1234-567-890)

The format of the CSV file is like the one of the following example:

globalvariable,globalvalue
gcube_username,gianpaolo.coro
gcube_context,/d4science.research-infrastructures.eu/gCubeApps/RPrototypingLab
gcube_token,1234-567-890