Add a Data Filter

The Data Filter provides a variety of ways to identify subsets of data. Using Data Filter commands and options, you can select complex subsets of data, hide these subsets in plots, or exclude them from analyses.

The basic syntax appears as follows:

dt << Data Filter( <local>, <invisible>, <Add Filter (...)>, <Mode>, <Show Window( 0 | 1 ), <No Outline Box( 0 | 1 )> );

You can send an empty Data Filter message to a data table, and the initial Data Filter window appears, showing the Add Filter Columns panel that lists all the variables in the data table.

Mode takes three arguments, all of which are optional: Select(bool), Show(bool), Include(bool). These arguments turn on or off the corresponding options. The default value for Select is true (1). The default value for Show and Include is false (0).

Add Filter() adds rows and builds the WHERE clauses that describe a subset of the data table. The basic syntax appears as follows:

Add Filter( Columns( col, ... ), Where( ... ), ... )

To add columns to the data filter, list the columns names separated by commas. Note that this is not a list data structure.

You can define one or more WHERE clauses to specify the filtered columns, as follows:

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );

df = dt << Data Filter(

Mode( Show( 1 ) ),

Add Filter(

Columns( :age, :sex, :height ),

Where( :age == {13, 14, 15} ),

Where( :sex == "M" ),

Where( :height >= 52 & :height <= 65 )

)

);

This script selects 13, 14, and 15-year-old males with a height greater than or equal to 52 and less than or equal to 65. In this filtered data, you can instead select the excluded ages by adding the Invert Selection message to the preceding script.

df << ( Filter Column( :age ) << Invert Selection );

In this example, ages other than 13, 14, and 15 in the filtered data are selected.

You can also use Add Filter() to select matching strings from columns with the Multiple Response property or Multiple Response modeling type.

dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );

df = dt << Data Filter(

Location( {437, 194} ),

Add Filter(

Columns( :Brush Delimited ),

Match None( Where( :Brush Delimited == {"Before Sleep", "Wake"} ) ),

Display( :Brush Delimited, Size( 121, 70 ), Check Box Display )

// defines the display options. "Brush Delimited" appears above

// the list of values. Size is the size of the filter list. The

// values appear next to check boxes.

)

);

This script selects rows with values in the Brush Delimited column that do not match either of the specified values ("Before Sleep", "Wake"). Other available scripting options include Match Any, Match All, Match Exactly, and Match Only. See “多重响应” in the Using JMP book for details about the Multiple Response property and the Multiple Response modeling type.

You can also send messages to an existing Data Filter object:

Clear(), Display( ... ), Animate(), Mode(), ...

Clear takes no arguments and clears the data filter.

To prevent the data filter from appearing when the script is run, set Show Window to 0 as follows:

obj = ( dt << Data Filter( Show Window( 0 ) ) );

To specify which values to exclude, use the != operator. The following example excludes ages 16 and 17 from the filtered values.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );

df = dt << Data Filter(

Add Filter( Columns( :age, :sex ) ),

Match( Columns( :age, :sex ),

Where( :sex = "M" ), Where( :age != {16, 17} )

)