AttributePivoter

Group Rows By and (optionally) Group Columns By define a logical nesting of row groups.

For each such logically nested group, the AttributePivoter computes summary statistics, which it emits as table rows through the Summary port. The value for the most specific row grouping attribute for the summary row is given a value of “<attrName> <description>”, where <attrName> is the name of the row grouping attribute, and <description> is the value given to the “Row Group Summary Line Description” parameter.

For example, if grouping by an attribute called “Region_RSS”, the summary row over all regions would have a value of “Region_RSS Total” for the “Region_RSS” attribute, assuming the row group summary line descriptor is left with the default value of “Total”.

Input features are grouped by “grouping attributes”, and statistics are computed on the specified analyzed attribute in each group. There are two kinds of grouping attributes which work together to define these groups:

• Row Grouping Attributes: The user specifies an ordered set of attributes that divide the statistics into rows. There is a single row of result data for each unique set of values for the specified set of row grouping attributes.
• Column Grouping Attribute: The user can optionally specify a single attribute to define columns in the resulting rows. If specified, each unique value of the column grouping attribute contributes a column of statistical data to the result, for each statistic being computed. Additionally, if there is more than one unique value for the column, a summary column will be generated for each statistic.
  If no column grouping attribute is selected, each row will contain a single computed result for each selected output statistic.

Because the row grouping attributes are ordered, they effect a logical nesting of groups. At the lowest level, a complete set of unique values is represented as a single row of the result. One level up is the logical grouping consisting of the set of rows where all row grouping attributes are unique except for the last one specified. This logical nesting carries up to the first specified row grouping attributes.

A row resulting from a complete set of unique data values is known as a “data row”. There is an additional “summary row” generated for each logically nested grouping, which summarizes the data for the data rows contained in the grouping.

The sequence of resulting rows form a table with the following attributes:

1. All of the row grouping attributes, whose combined values specify the actual group
2. For each pivot summary type, an attribute with the corresponding statistic, computed over all features in the row group.
3. If there was more than a single column group defined, each of the attributes in (2.) will be repeated for all column groups, along with a summary value (that is, a “grand total”) computed over the attribute values over all column groups. The method for computing the summary value depends on the statistic it is representing:
   • Count and Sum statistics are summarized with the sum of the computed statistics for the row group.
   • Average statistics are summarized with the average of all values in the row group.
   • Min values are summarized by the minimum of all occurrences of the analyzed attribute for the group.
   • Max values are summarized by the maximum of all occurrences of the analyzed attribute for the group.
     

The first data feature and first summary feature emitted will contain additional attributes containing the schema information needed to write the data out by a feature type configured for dynamic writing.

In this example, we start with a simple CSV table of statistics to be pivoted and analyzed. The CSV features are routed into an AttributePivoter.

In the parameters dialog, we choose to Group Rows By region, then potential. The order is important.

The Attribute to Analyze is observed - the column containing our statistics. We have requested Count, Sum, and Average calculations.

The output now contains the added statistics.

Features with attributes for analysis.

A single new feature will be output containing all computed statistics for each completely defined row group. The attributes on these features are described in the “Result Grouping and Tabular Structure” section.

The first row emitted from the Data port will contain information to define the schema for a dynamically configured feature type.

For each logically nested grouping as described in the “Result Grouping and Tabular Structure” section, a summary row will be emitted prior to the first data row for the group. It provides summary information for all rows contained in the group. An overview summary of all rows will always be generated.

The first row emitted from the Summary port will contain information to define the schema for a dynamically configured feature type.

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.