Plot/Chart - Bubble Plot from 3 Tables

This QScript creates a bubble plot from the data in three tables, where

Each of the tables contains a single column of numbers.
Labels can be matched exactly between the rows of the three tables.

The tables should be created prior to creating the Bubble plot.

The first table selected defines the data for the horizontal dimension (x-axis), the second table defines the data for the vertical dimension (y-axis), and the third table defines the data that is used to determine the size of the bubbles.

Example

Technical details

The script requests the user to select three tables. Each table must have a single column, consisting of numbers, and have common row labels. You are notified about row labels that only appear in one of the two tables. A scatterplot is created using the numbers extracted from the common rows.
The bubble plot will be created from static data copied from the selected tables. Any subsequent changes to the those tables (i.e. data updates, recoding, changing filters and weights) will not be reflected in the scatterplot. The script has to be rerun in order to obtain an updated chart.
This QScript requires Q 4.9 or later.

How to apply this QScript

Start typing the name of the QScript into the Search features and data box in the top right of the Q window.
Click on the QScript when it appears in the QScripts and Rules section of the search results.

OR

Select Automate > Browse Online Library.
Select this QScript from the list.

Customizing the QScript

This QScript is written in JavaScript and can be customized by copying and modifying the JavaScript.

Customizing QScripts in Q4.11 and more recent versions

Start typing the name of the QScript into the Search features and data box in the top right of the Q window.
Hover your mouse over the QScript when it appears in the QScripts and Rules section of the search results.
Press Edit a Copy (bottom-left corner of the preview).
Modify the JavaScript (see QScripts for more detail on this).
Either:
- Run the QScript, by pressing the blue triangle button.
- Save the QScript and run it at a later time, using Automate > Run QScript (Macro) from File.

Customizing QScripts in older versions

Copy the JavaScript shown on this page.
Create a new text file, giving it a file extension of .QScript. See here for more information about how to do this.
Modify the JavaScript (see QScripts for more detail on this).
Run the file using Automate > Run QScript (Macro) from File.

JavaScript

includeWeb('QScript Table Functions');
includeWeb('QScript Utility Functions');
includeWeb('QScript Selection Functions');
includeWeb('JavaScript Array Functions');
 
var version = fileFormatVersion();
if (version < 8.39) {
    log('This script is not able to run on this version of Q. Please upgrade to the latest version of Q.');
} else if (!main()) {
    log('QScript was cancelled.');
}
 
function main() {
    var group_item = project.report;
    var table_importance, output_importance;
    while (true) {
        table_importance = selectOneTableWithGroupNames('Select the table containing the data to show in the horizontal coordinates (i.e., the x-axis):', group_item);
        if (table_importance.length == 0) {
            log("There are no tables in your document to create a plot.");
            return false;
        } 
        output_importance = table_importance.calculateOutput();
        if (checkTableHasSingleColumnOfNumbers(output_importance, 'x-axis'))
            break;
    }
    var table_performance, output_performance;
    while (true) {
        table_performance = selectOneTableWithGroupNames('Select the table containing the data to show in the vertical coordinates (i.e., the y-axis):', group_item);
        output_performance = table_performance.calculateOutput();
        if (checkTableHasSingleColumnOfNumbers(output_performance, 'y-axis'))
            break;
    }
    var table_bubble, output_bubble;
    while (true) {
        table_bubble = selectOneTableWithGroupNames('Select the table containing the data to determine the bubble sizes:', group_item);
        output_bubble = table_bubble.calculateOutput();
        if (checkTableHasSingleColumnOfNumbers(output_bubble, 'bubble size'))
            break;
    }

    // Obtain row labels from tables
    var labels_importance = output_importance.rowLabels;
    var labels_performance = output_performance.rowLabels;
    var labels_bubble = output_bubble.rowLabels;
 
    // Remove labels corresponding to original NET rows
    if (output_importance.netRows) {
        function descendingSorter(a,b) { return b - a; }
        var importance_nets = output_importance.netRows.sort(descendingSorter);
        var performance_nets = output_performance.netRows.sort(descendingSorter);
        var bubble_nets = output_bubble.netRows.sort(descendingSorter);
        importance_nets.forEach(function (x) {
            labels_importance.splice(x, 1);
        });
        performance_nets.forEach(function (x) {
            labels_performance.splice(x, 1);
        });
        bubble_nets.forEach(function (x) {
            labels_bubble.splice(x, 1);
        });
    }
 
    // Figure out which labels are common to all
    var exclude_labels = ['R-Squared', 'SUM', 'NET'];
    labels_importance = difference(labels_importance, exclude_labels);
    labels_performance = difference(labels_performance, exclude_labels);
    labels_bubble = difference(labels_bubble, exclude_labels);
    var labels_common = intersection(intersection(labels_importance, labels_performance), labels_bubble);
 
    // Alert the user to those labels that are not common in the 3 tables
    if (labels_common.length > 0) {
        alertIfMeasurementsMissing(labels_importance, labels_common, 'x-axis');
        alertIfMeasurementsMissing(labels_performance, labels_common, 'y-axis');
        alertIfMeasurementsMissing(labels_bubble, labels_common, 'bubble size');
    } else {
        alert('There are no common rows between the selected tables.');
        return false;
    }
 
    // Extract values from common rows of all tables
    var importances_all = output_importance.get(output_importance.statistics[0]);
    var performances_all = output_performance.get(output_performance.statistics[0]);
    var bubbles_all = output_bubble.get(output_bubble.statistics[0]);
    var importances = [];
    var performances = [];
    var bubbles = [];
    labels_common.forEach(function (label, index) {
        importances.push(importances_all[output_importance.rowIndex(label)]);
        performances.push(performances_all[output_performance.rowIndex(label)]);
        bubbles.push(bubbles_all[output_bubble.rowIndex(label)]);
    });
 
    // Create blue question containing variable labels
    var data_file = project.dataFiles[0];
    var row_labels_variable_name = preventDuplicateVariableName(data_file, 'variable_labels');
    // We need the variable's results to contain all the unqiue categorical values.
    // Rely on Math.random() to mostly evenly distribute the values across all respondents.
    // We could also generate a sequence using the respondents' order in the data file,
    // but we cannot set "Access all data rows" in QScript.
    var row_labels_variable = data_file.newJavaScriptVariable('Math.floor(Math.random()*' + labels_common.length +')', false, row_labels_variable_name, 'Bubble plot (blue)', null);
    row_labels_variable.variableType = 'Categorical';
    for (var i = 0; i < labels_common.length; i++)
        row_labels_variable.valueAttributes.setLabel(i, labels_common[i]);
 
    // Create brown question containing the labels that correspond to the input tables'
    var xyz_variable_name = preventDuplicateVariableName(data_file, 'labels_scatter_plot');
    var xyz_variable = data_file.newJavaScriptVariable('Math.floor(Math.random()*3)', false, xyz_variable_name, 'Bubble plot (brown)', null);
    xyz_variable.variableType = 'Categorical';
    xyz_variable.valueAttributes.setLabel(0, table_importance.name);
    xyz_variable.valueAttributes.setLabel(1, table_performance.name);
    xyz_variable.valueAttributes.setLabel(2, table_bubble.name);
 
    // Create plot
    var plot = group_item.appendPlot('Bubble plot');
    plot.primary = row_labels_variable.question;
    plot.secondary = xyz_variable.question;
    plot.name = 'Bubble plot: ' + table_importance.name + ' vs ' + table_performance.name + ' vs ' + table_bubble.name + ' (bubble size)';
    var expression = "includeWeb('Table JavaScript Utility Functions');\r\n"
    + "var rule_name = '" + plot.name + "';\r\n"
    + "form.setSummary(rule_name);\r\n"
    + "requireNumericTableDimensionsStatistics(['Column %'], " + labels_common.length + ", 3, false, rule_name);\r\n"
    + "stats = table.get('Column %');\r\n";
    for (var i = 0; i < labels_common.length; i++) {
        expression += "stats[" + i + "][0] = " + importances[i] + ";\n";
        expression += "stats[" + i + "][1] = " + performances[i] + ";\n";
        expression += "stats[" + i + "][2] = " + bubbles[i] + ";\n";
    }
    expression += "table.set('Column %', stats)";
    addTableJavaScriptToTablesInArray([plot], expression);
 
    alert('The bubble plot will be created from static data copied from the two tables. ' + 
          'Any subsequent changes to the input tables (i.e. changing filters and weights) will not be reflected in the scatterplot.');
 
    log('A plot named "' + plot.name + '" has been added to the project.');
 
    return true;
}