Faceting Column Data
Faceting regroups your data according to your configurations. When working on a column, you can apply various faceting methods.
TIBCO Clarity provides the following faceting methods specific to a column:
- Text facet: Use the text faceting to see the different values a column contains, and the number of occurrences of each value.
- Custom text facet: Use a regular expression to see the different values a column contains, and the number of occurrences of each value.
- Numeric facet: Use the numeric faceting to get an overview of the number distribution for the cell values in a column.
- Custom numeric facet: Use a regular expression to get an overview of the number distribution for the cell values in a column.
- Text pattern facet: Use the text pattern faceting to find out the patterns used in a column and filter out inconsistent data formats.
- Keyword facet: Use keyword to facet values in a column and you can see the number of occurrences of each value.
- Timeline facet: Use the timeline faceting to check data formats. Before you apply the timeline facet, you have to define the validation rules.
- Scatterplot facet: Use the scatterplot faceting to get a graphical representation of numerical values.
- Customized facets: Use the following supported customized facets to analyze your data:
- Word facet lists all the different words used in a column. A word is a text string defined between two whitespace characters.
- Duplicates facet detects duplicate values in a column.
- Numeric log facet displays the logarithm of numbers instead of actual values.
- 1-bounded numeric log facet displays the logarithm of numbers.
- Text length facet organizes the objects according to the number of characters contained in a string.
- Log of text length facet displays a chart to analyze text length.
- Unicode char-code facet lists the characters used in a string.
- Facet by error lists invalid values in a column.
- Facet by blank lists the blank values in a column.
The following figure is text faceting result of the Gender column in the Sample-students_records dataset.
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