RCaller

list<-c(1,2,2,2,2,3,3,3)

fmeOutput<-data.frame(mean=mean(list), median=median(list))

Note in the above example you can assign list to the value of a numeric input table column (for example, list<-myInput$testColumn)

The output of this script will be a single feature with outputs mean and median

Imagine that you have source tree data from a nearby park, including a tree trunk diameter attribute, Diameter.

If the dataset name is Trees, when you connect the dataset to the RCaller, an input port Trees will be created.

Ensure in the Columns section of the Inputs that Diameter is a numeric datatype.

In the R Script section, specifying fmeOutput<-data.frame(MeanDiameter=mean(Trees$Diameter)) will calculate the mean diameter of the trees.

Specifying MeanDiameter in the Attributes to Expose parameter will expose the mean diameter attribute, making it possible to use it later on in the workflow.

Download R Installers from:

https://www.r-project.org/

Windows:

Install R following the usual Windows installation steps.

For more information, please see https://cran.r-project.org/bin/windows/base/.

macOS:

For installing R on macOS, either you can either download the latest R image from:

https://www.r-project.org/

Or, alternatively use the homebrew, package manager command:

brew install R

This requires the homebrew package manager to be installed on the system. Homebrew can be downloaded from:

https://brew.sh/

Linux:

Run /opt/<FME version>/utilities/rcaller_setup from the command line (for example, /opt/fme-desktop-2020/utilities/rcaller_setup)

The Linux setup script in the Installing the R Interpreter section will install commonly used R packages. If more are required, first try to install them via system r-cran-* packages (often available on Debian and Ubuntu). Only if a system package does not exist should they be installed via the R Command Prompt instructions below.

1. Open an R command prompt.
   • Windows: Run the R GUI as an administrator by right-clicking it in the Start menu and selecting “Run as administrator…” .
   • Linux/macOS: Launch the R Console (as root unless R has already been configured to find per-user packages).
2. Run the following command at the R command prompt:
   
   install.packages("<package name>")
   
   For example:
   
   install.packages("sqldf")
3. This will launch a window prompting you to select a download mirror. Once a mirror is selected, the package will be installed to the system-wide R library. It is important that this is done with administrative privileges, otherwise the package will be installed to a user library and FME will not be able to use it.
4. To verify that the package was installed correctly, check the location listed when you run
   
   .libPaths()
   
   at the command line. There should be a folder called “<package name>”.

Optionally, you can place your R libraries in a shared resources folder. The root location of this folder is set in FME Workbench under Tools > FME Options > Default Paths > Shared FME Folders. Within the Shared FME Folder structure, R packages must be placed in the R directory, located under a Plugins directory. You may need to manually create these directories:

<Shared_FME_Folder>\Plugins\R

For more information, see Default Paths.

The RCaller requires definition of one or more Tables, which will become input ports to the transformer. The Import… button provides a quick way to populate the input table definitions from the source feature types in the workspace.

Note that certain FME attribute and table names may not be valid in R as data frame or column names (notably attribute names starting with an underscore "_"). To avoid issues, these names will be converted to valid R names. The adjusted names will be shown in the Data frames section on the left of the script editor.

The RCaller has a single output port. Attributes created in the script need to be entered by the user in the Attributes to Expose parameter in order to have these appear in subsequent transformers or the FME Data Inspector table view. The attributes set on the feature are determined by the columns set on the fmeOutput data frame at runtime. A helpful editor is used to construct the R Script, and provides convenient drag and drop access to data frames, columns, and published and private parameters which can be used within the script.

The number of features output will depend on the length of the largest column in the fmeOutput data frame. In this way the RCaller can be used to output a single value, or a list, or matrix of values.

When the R raster package is loaded, rasters are supported on input and output. On input, raster geometry in FME will be converted to a RasterBrick object in R under the raster column for that table. That is, the column InputTableName$raster will contain a RasterBrick object for each feature that had a raster geometry, and NA for each feature that didn’t. On output, raster objects in the fmeOutput$raster column in R will be converted to raster geometry in FME.

Defining Values

There are several ways to define a value for use in a Transformer. The simplest is to simply type in a value or string, which can include functions of various types such as attribute references, math and string functions, and workspace parameters.

Using the Text Editor

The Text Editor provides a convenient way to construct text strings (including regular expressions) from various data sources, such as attributes, parameters, and constants, where the result is used directly inside a parameter.

Text Editor

Using the Arithmetic Editor

The Arithmetic Editor provides a convenient way to construct math expressions from various data sources, such as attributes, parameters, and feature functions, where the result is used directly inside a parameter.

Arithmetic Editor

Conditional Values

Set values depending on one or more test conditions that either pass or fail.

Parameter Condition Definition Dialog

Content

Expressions and strings can include a number of functions, characters, parameters, and more.

When setting values - whether entered directly in a parameter or constructed using one of the editors - strings and expressions containing String, Math, Date/Time or FME Feature Functions will have those functions evaluated. Therefore, the names of these functions (in the form @<function_name>) should not be used as literal string values.

Content Types

String Functions	These functions manipulate and format strings.
Special Characters	A set of control characters is available in the Text Editor.
Math Functions	Math functions are available in both editors.
Date/Time Functions	Date and time functions are available in the Text Editor.
Math Operators	These operators are available in the Arithmetic Editor.
FME Feature Functions	These return primarily feature-specific values.
FME Parameters	FME and workspace-specific parameters may be used.
Creating and Modifying User Parameters	Create your own editable parameters.

Table Tools

Transformers with table-style parameters have additional tools for populating and manipulating values.

Note: Not all tools are available in all transformers.