Applying R Scripts in Rulex Processes

The R Bridge task allows you to perform statistical calculations via R script on Rulex data, and either overwrite the original dataset with the output results, or create a new dataset (such as clusters or advanced association structures).

This type of task may be useful when you already have statistical algorithms in R, and want to use them in Rulex without having to rewrite any of the logic.

The R script can either be entered directly in the task, or a reference can be provided to an external script file.

Prerequisites

a Rulex process has been created
R software is installed where Rulex is running.

Additional tabs

The following additional tabs are provided:

Documentation tab where you can document your task,
Parametric options tab where you can configure process variables instead of fixed values. Parametric equivalents are expressed in italics in this page (PO).

Procedure

Drag and drop the R Bridge task onto the stage.
Connect the task that contains the dataset on which you want to perform the R script to the R Bridge task.
Double click the R Bridge task.
Configure the script options as described in the table below.
Save and compute the task.

R bridge options

R bridge options
Name	PO	Description
General options
Get input from	setref	Select the type of data you want to use as input for the R script. Possible options are: Dataset (i.e. the main data table) Clusters Advanced association structure (association rules, bundle hypotheses or replacement rules table).
Name of the Rulex input table in R script	rinputname	Enter the name you want to use within the R script to reference the data received from Rulex. For example, if you selected Dataset in the Get input from option, and you enter Input as the name here in this option, the main Rulex data table will be referred to as a data frame called "Input" within the R script.
Get R script from file	scriptfromfile	Select this option if you want to reference an external script file. Alternatively you can enter the script code directly in the R Code edit box below. This edit box will be disabled if you decide to reference an external file to avoid confusion on which script will be applied.
Select R script	filename	Click here and browse to the external file, which contains the R script you want to apply. This option will be enabled only if you have selected the Get R script from file option.
Store output in	outref	Select the type of table that will be populated by the data.frame selected as output in the R script. Possible options are: Dataset (i.e. the main data table) Clusters Advanced association structure (association rules, bundle hypotheses or replacement rules table).
Name of the output data frame in R script	routputname	Enter the name you want to use in the R script to reference the data Rulex will receive from R. This data will populate the table according to the option selected in Store output in.
Select file to store R console output	debugfilename	Browse to the text file where the R script console output will be saved after execution of the R Bridge task.
R Code	rcode	Enter the R script code you want to execute. This text box is enabled only if you have not selected the Get R script from file option.
Connection options
Select path to Rterm command	rcommand	Browse to the location of the Rterm command. The default path on a Windows system, corresponding to the default value of this option, is “C:/Program Files/R/R-3.3.2/bin/x64/Rterm.exe”. If the path is not correctly specified, the R Bridge task will not work correctly and a warning message will be displayed. The RBridge task cannot compute if another Rterm process is pending. You can monitor this on Windows via the Task Manager (Processes).
R host	rhostname	Enter the address of the host where R is installed. If Rulex is running on the same desktop where it is installed you can enter localhost.
R port	rport	Specify the port used for data transfer between Rulex and R (i.e. input table transfer from Rulex to R, output data frame transfer from R to Rulex). System firewall exception Make sure you define an exception in the system firewall on this port, allowing the data exchange between the two applications. Otherwise, the task will not be executed and a warning message will be displayed signaling a communication problem between Rulex and R.
R port for signals	rportaux	Specify the port used for signal transfer between Rulex and R (i.e. progress bar updates). System firewall exception Make sure you define an exception in the system firewall on this port, allowing the data exchange between the two applications. Otherwise, the task will not be executed and a warning message will be displayed signaling a communication problem between Rulex and R.

Example

The following examples are based on the Walmart Recruiting - Store Sales Forecasting dataset.

The scenario groups and sums the weekly sales of all departments and stores in Rulex, using R to generate an arima model for the corresponding time series and execute the Shapiro-Wilk and kpss statistical tests on it and then return the results of these tests to Rulex.

The following steps were performed:

We import the train.csv dataset.
We then group and sum the data in a Data Manager task.
We then add a R Bridge task and enter the R script code directly in the task options.
We finally use the Take a look function to check the results of the R script.

Procedure	Screenshot

Procedure	Screenshot
Importing the train.csv dataset via an Import from Text File task, and select comma as the data separator. Then right-click the task and select Take a look to display the imported data. The original dataset contains 421570 patterns.
In the Data Manager task, group the dataset by the Date attribute, and apply the SUM function to the Weekly_Sales attribute. Then save and compute the task. 143 patterns are now displayed in the task.
Connect an R Bridge task to the process, and configure it as follows: Get input from: Dataset Name of the Rulex input table in R script: x Store output in: Dataset Name of the output data frame in R script: output
Enter the script to the right in the R Code text box. The script computes our reference time series, as the log-transform of the Weekly_Sales column deltas over time. Then we build an ARMA model on this time series and, via the lmtest and the tseries R libraries (see library(“IMTest”), library(“tseries”) in the R script code) we execute: the Shapiro-Wilk test, to test the hypothesis that our time series data come from a normal distribution, and the kpss test to check if our time series is stationary around a deterministic trend.	y=x$Weekly_Sales y=as.vector(y) vend=ts(y) diff.vend=diff(vend) l.vend=log(vend) arma11=arima(l.vend, order=c(1,0,1), include.mean=FALSE) arma11.df=data.frame(arma11$coef, arma11$sigma2, arma11$loglik,arma11$aic, arma11$model$Pn) library("IMTest") library("tseries") sh=shapiro.test(arma11$residuals) kp=kpss.test(l.vend) shapiro=c(sh$statistic,0,sh$p.value,sh$method,sh$data.name) kpss=c(kp$statistic,kp$parameter,kp$p.value,kp$method,kp$data.name) output=data.frame(namescol=c("statistic","parameter","p.value","method","data.name"), shapiro, kpss)
Save and compute the task, then right-click it and select Take a look to explore the results of the Shapiro-Wilk and kpss tests.