There are two ways to use R scripts in custom expressions in Spotfire: registering R scripts as data functions and using the TERR_string() and related functions. Registering R scripts as data functions allows you to create your own functions using R syntax and call them from the custom expression and calculated column dialog boxes. You can also edit the saved expression functions from the Spotfire user interface. Using the TERR_string() and related functions allows you to execute R expressions that have been written as text strings within the custom expression. You can also use other functions such as TERR_integer(), TERR_date(), etc. to specify the data type of the output. The other options are not ways to use R scripts in custom expressions, but rather ways to use property controls or action controls, which are devices that can change the content of a property or trigger a script, respectively.Reference:Registering the TERR Script as an Expression Function,Add your own functions using R,Custom Expressions Introduction

Data functions are scripts that can perform any type of calculation and return the results to an analysis. They can be created using R, Python, or other languages, and they can take input from data tables, columns, or properties, and output data tables, columns, or properties. Data functions can be executed on demand, automatically, or triggered by markings or filters. Data functions can be added to an analysis using the Tools > Register Data Functions menu option in the Spotfire Analyst client, or using the Add Data Function option in the Spotfire Server web interface.Reference:What are data functions?,Spotfire Data Function Library: Python and R Scripts for Data Analysis,Linear Regression in R | A Step-by-Step Guide & Examples

A data function is a reusable calculation that can be applied to data in a Spotfire analysis. A data function can have input and output parameters that define how the data function interacts with the data and the analysis. The input parameters specify what data or values are required for the data function to run, and the output parameters specify what data or values are returned by the data function.The types of input or output parameters for a data function are1:

Column: A column parameter represents a single column of data from a data table. A column parameter can be used as input to provide a vector of values to the data function, or as output to create a new column in the data table or overwrite an existing column. A column parameter can have a data type such as real, integer, string, or boolean.

Document property: A document property parameter represents a value that is stored in the analysis document and can be used to control various aspects of the analysis, such as filters, markings, colors, etc. A document property parameter can be used as input to provide a scalar value to the data function, or as output to create a new document property or overwrite an existing document property. A document property parameter can have a data type such as real, integer, string, boolean, date, or time.

Value: A value parameter represents a scalar value that is entered by the user or calculated by the data function. A value parameter can be used as input to provide a constant or variable value to the data function, or as output to return a single value from the data function. A value parameter can have a data type such as real, integer, string, boolean, date, or time.

The other options, column property, table, and visualization, are not valid types of input or output parameters for a data function.Reference:

Details on Data Functions -- Parameters

Configuring Data Function Parameters

How to select/deselect inputs/outputs/parameters in Statistica Data Function for Spotfire

How to pass input from Spotfire to Statistica Data Function?

The <code>mod-manifest.json</code> file is a JSON file that defines the metadata and configuration of a visualization mod.It contains information such as the name, ID, version, description, and author of the mod, as well as the files used by the mod, such as HTML, CSS, JavaScript, and images1.The <code>mod-manifest.json</code> file also specifies the axis definitions and placement for the mod, which determine how the data columns are mapped to the visualization and how the axis selectors are displayed in the user interface2. Therefore, the options A (All files included in the mod) and D (Axis definitions and placement) are correct. The other options are not listed in the <code>mod-manifest.json</code> file.CORS configuration data is not relevant for visualization mods, as they are not web services that need to handle cross-origin requests3.Visualizations affected by the mod is not a property of the mod itself, but rather a result of how the mod is used in an analysis.Reference:1: Mod manifest - TIBCO Software1,2: Axis definitions and placement - TIBCO Software2,3: Cross-Origin Resource Sharing (CORS) - MDN Web Docs3

To make a visualization mod accessible to other users, you can either save the mod to the Spotfire Library or embed the mod in the analysis. Saving the mod to the Spotfire Library allows other users to browse and use the mod from the Library Browser, as long as they have the appropriate permissions. Embedding the mod in the analysis allows other users to open the analysis and see the mod without having to install it separately.However, embedding the mod in the analysis will increase the size of the analysis file and may affect the performance12.Reference:Saving a Visualization Mod to the Library,Embedding a Visualization Mod in an Analysis