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).

R port for signals

rportaux

Specify the port used for signal transfer between Rulex and R (i.e. progress bar updates).

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.