- Release notes
- Getting started
- Installation
- Configuration
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- Authentication
- Working with Apps and Discovery Accelerators
- AppOne menus and dashboards
- AppOne setup
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- TemplateOne 2021.4.0 setup
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- Purchase to Pay Discovery Accelerator Setup
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- Order to Cash Discovery Accelerator Setup
- Basic Connector for AppOne
- SAP Connectors
- Introduction to SAP Connector
- SAP input
- Checking the data in the SAP Connector
- Adding process specific tags to the SAP Connector for AppOne
- Adding process specific Due dates to the SAP Connector for AppOne
- Adding automation estimates to the SAP Connector for AppOne
- Adding attributes to the SAP Connector for AppOne
- Adding activities to the SAP Connector for AppOne
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- Superadmin
- Dashboards and charts
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- How to ....
- Working with SQL connectors
- Introduction to SQL connectors
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- CData Sync extractions
- Running a SQL connector
- Editing transformations
- Releasing a SQL Connector
- Scheduling data extraction
- Structure of transformations
- Using SQL connectors for released apps
- Generating a cache with scripts
- Setting up a local test environment
- Separate development and production environments
- Useful resources

Process Mining
This example explains how to interface the UiPath Process Mining platform with external R scripts to implement external data processing.
Follow these steps to be able to use R-script in the platform.
Step |
Action |
---|---|
1 |
Download the latest version of the R package from https://cran.r-project.org/bin/windows/base/. |
2 |
Install R on the server. Note: this must be the server on which UiPath Process Mining is installed.
|
3 |
Locate the installation directory and find path of Rscript.exe. For example: C:/Apps/Rscript.exe |
R is installed on the server, and developers can connect to it with a connection string.
The installation path is needed to create connection strings for an R script.
Start with some dummy data, to test your workspace setup. For example, use the “Hello World” example as described in Example: Creating a Python Script.
The dummy R script will than contain:
write("Hello world!", stderr()); quit("default", 1)
High-level Overview
In this example an R script is created which clusters cases based on their traces.
Steps
- Setting up the Server Settings;
- Writing the script.
- Setting up the data source;
- Setting up a script data source;
The generic script datasource requires handlers for all external processes that you want to run.
Follow these steps to add the script handler for R script.
Step |
Action |
---|---|
1 |
Go to the Superadmin Settings tab. |
2 |
Add a field
GenericScriptHandlers with as value an object with one key, “r”, which has as value the path to your python executable. For example:
|
3 |
Click on SAVE. |
In your text editor, start a blank text file and enter the following code.
## get command line arguments
args <- commandArgs(trailingOnly=TRUE)
inputfile <- args[1]
## read csv file
input <- file(inputfile, 'r')
df <- read.table(input, header=TRUE, sep=";")
## pre-processing
df <- table(df)
df <- as.data.frame.matrix(df)
df <- df[, sapply(data.frame(df), function(df) c(length(unique(df)))) > 1] #remove columns with unique value
## cluster
df <- scale(df)
kc <- kmeans(df, centers = 5)
cluster <- kc$cluster
## output
resultdata <- cbind(rownames(df), cluster)
colnames(resultdata)[1] <- 'Case ID'
write.table(resultdata, row.names = FALSE, sep=";", qmethod = "double")
## get command line arguments
args <- commandArgs(trailingOnly=TRUE)
inputfile <- args[1]
## read csv file
input <- file(inputfile, 'r')
df <- read.table(input, header=TRUE, sep=";")
## pre-processing
df <- table(df)
df <- as.data.frame.matrix(df)
df <- df[, sapply(data.frame(df), function(df) c(length(unique(df)))) > 1] #remove columns with unique value
## cluster
df <- scale(df)
kc <- kmeans(df, centers = 5)
cluster <- kc$cluster
## output
resultdata <- cbind(rownames(df), cluster)
colnames(resultdata)[1] <- 'Case ID'
write.table(resultdata, row.names = FALSE, sep=";", qmethod = "double")
Follow the steps below.
Step |
Action |
---|---|
1 |
Save the text file as
script.r .
|
2 |
Upload the
script.r file to your workspace.
|
.CSV
like string. It should be placed in the Globals table since it will serve as input in a table definition.
csvtable
function to define input data.
For this example, we have an application with the an Events table. See illustration below.
R_input_data
from the Globals table to Events.
Step |
Action |
---|---|
1 |
Open the app in your development environment, and go to the Data tab. |
2 |
Select the Globals table. Right-click on the Globals table in the table item list and select New expression…. |
3 |
Set the type to Lookup. |
4 |
Select Events as input table. |
5 |
Enter the following expression:
|
6 |
Enter R_input_data in the name field. |
7 |
Click on OK to save the expression attribute in the Globals table. |
The expression attribute is created in the Globals table. See illustration below.
Next, set up a datasource table in the application which will call the script.
Follow these steps to set up the script data source.
Step |
Action |
---|---|
1 |
In the Data tab, create a new Connection string table. |
2 |
Rename the
New_table to RscriptExample .
|
3 |
Right click on the
RscriptExample table and click Advanced > Options….
|
4 |
In the Table Options dialog, set the Table scope to Workspace. |
5 |
Double click on the
RscriptExample table to open the Edit Connection String Table window.
|
6 |
Enter the following as Connection string: ``'driver={mvscript |
7 |
Enter the following as Query:
See illustration below. |
8 |
Click on OK, and click on YES to reload the data. |
When loading the data, new attributes are detected. Click on YES(2x) and click on OK.
Rscript_example
table now has two datasource attributes, Case_ID and cluster.
See illustration below.