Category:QScript Online Library
The QScript Online Library contains a set of QScripts that can be used directly from Q. These scripts have been written to accomplish tasks that users commonly face in Q, including data checking, data cleaning, generating new variables, generating new tables, and more. These scripts don't need to be modified or saved to your computer.
The scripts are run by selecting Automate > Online Library and selecting a script from the list. QScripts from the QScript Online Library will appear with this icon: 
. In older versions of Q these scripts may be run by selecting Tools > QScripts > Online Library and selecting the script in the menu. This feature is available in Q 4.7 and higher.
The scripts in the Online Library will prompt the user for any information that they require to complete the specified task. For example, if the script makes changes to questions in your project then the user will be shown a list of applicable questions when the script runs.
See also
- QScript Snippets for other examples of QScripts.
- Rule Online Library for a library of rules (which are applied directly to tables and charts).
- JavaScript
- QScript for an overview of the documentation involving JavaScript.
Pages in category ‘QScript Online Library’
The following 194 pages are in this category, out of 194 total.
C
- Calculation - First
- Calculation - Last
- Choice Modeling - Analyze with Experiment Question (Legacy) - Experiment Question from JMP Files
- Choice Modeling - Analyze with Experiment Question (Legacy) - Experiment Question from Sawtooth CHO File
- Choice Modeling - Analyze with Experiment Question (Legacy) - Experiment Question from Sawtooth Dual Format Files
- Choice Modeling - Analyze with Experiment Question (Legacy) - Save Variable(s) - Attribute Importance
- Choice Modeling - Analyze with Experiment Question (Legacy) - Save Variable(s) - Utilities
- Choice Modeling - Analyze with Experiment Question (Legacy) - Save Variable(s) - Utilities (Min 0, Max 100)
- Choice Modeling - Analyze with Experiment Question (Legacy) - Save Variable(s) - Within-Attribute Preference Shares
- Choice Modeling - Analyze with Experiment Question (Legacy) - Save Variable(s) - Zero-Centered Diffs (Utilities with Mean 0, Mean Range 100)
- Choice Modeling - Convert Alchemer (Survey Gizmo) Conjoint Data for Analysis
- Choice Modeling - Diagnostic - Class Parameters Table
- Choice Modeling - Diagnostic - Experimental Design - Balances and Overlaps of Design
- Choice Modeling - Diagnostic - Experimental Design - Numeric Design
- Choice Modeling - Diagnostic - Experimental Design - Parameter Standard Errors of Design
- Choice Modeling - Diagnostic - Parameter Statistics Table
- Choice Modeling - Diagnostic - Posterior Intervals Plot
- Choice Modeling - Diagnostic - Trace Plots
- Choice Modeling - Save Variable(s) - Class Membership
- Choice Modeling - Save Variable(s) - Class Membership Probabilities
- Choice Modeling - Save Variable(s) - Individual-Level Coefficients
- Choice Modeling - Save Variable(s) - Proportion of Correct Predictions
- Choice Modeling - Save Variable(s) - RLH (Root Likelihood)
- Choice Modeling - Save Variable(s) - Utilities (Mean 0)
- Choice Modeling - Save Variable(s) - Utilities (Mean 0, Max Range 100)
- Choice Modeling - Save Variable(s) - Utilities (Mean 0, Mean Range 100)
- Choice Modeling - Save Variable(s) - Utilities (Min 0)
- Choice Modeling - Save Variable(s) - Utilities (Min 0, Max Range 100)
- Choice Modeling - Save Variable(s) - Utilities (Min 0, Mean Range 100)
- Combo Box (Drop-Down) Filters on an Output
- Create New Variables - Binary Variable(s)
- Create New Variables - Bottom 2 Category Variable(s) (Bottom 2 Boxes)
- Create New Variables - Bottom 3 Category Variable(s) (Bottom 3 Boxes)
- Create New Variables - Bottom K Category Variable(s) (Bottom K Boxes)
- Create New Variables - Case-Level Shares
- Create New Variables - Combine Questions as a Grid
- Create New Variables - Flatten Question(s)
- Create New Variables - Log Transform Variable(s)
- Create New Variables - Numeric Variable(s) from Code/Category Midpoints
- Create New Variables - Rebase Multiple Response Data in Variable(s) to NET
- Create New Variables - Recode Net Promoter Score (NPS) Variable(s)
- Create New Variables - Scale Variable(s) - Center Within Case
- Create New Variables - Scale Variable(s) - Center Within Variable
- Create New Variables - Scale Variable(s) - Ranks Within Case
- Create New Variables - Scale Variable(s) - Ranks Within Variable
- Create New Variables - Scale Variable(s) - Standardize Within Case
- Create New Variables - Scale Variable(s) - Standardize Within Variable
- Create New Variables - Scale Variable(s) - Unit Interval Within Case
- Create New Variables - Scale Variable(s) - Unit Interval Within Variable
- Create New Variables - Square-Root Variable(s)
- Create New Variables - Top 2 Category Variable(s) (Top 2 Boxes)
- Create New Variables - Top 3 Category Variable(s) (Top 3 Boxes)
- Create New Variables - Top K Category Variable(s) (Top K Boxes)
- Create New Variables - Translate Text
- Create New Variables - Variable(s) with Outliers Removed
- Create New Variables - Variables from Merged Categories
D
- Data - Countries from IP Address(es) (Geocoding)
- Delete Tables and Plots - Delete If Broken
- Delete Tables and Plots - Delete If Not Significant at Specified Level
- Delete Tables and Plots - Delete If Not Significant at the 0.001 Level (99.9%)
- Delete Tables and Plots - Delete If Not Significant at the 0.01 Level (99%)
- Delete Tables and Plots - Delete If Not Significant at the 0.05 Level (95%)
- Delete Tables and Plots - Delete If Sample Size Lower Than Specified Value
- Dimension Reduction - Diagnostic - Quality Table
- Dimension Reduction - Save Variable(s) - Components/Dimensions
F
H
M
- Machine Learning - Diagnostic - Analysis Report
- Machine Learning - Diagnostic - Prediction-Accuracy Table
- Machine Learning - Diagnostic - Table of Discriminant Function Coefficients
- Machine Learning - Save Variable(s) - Discriminant Variables
- Machine Learning - Save Variable(s) - Predicted Values
- Machine Learning - Save Variable(s) - Probabilities of Each Response
- Marketing - Legacy TURF Analysis
- Marketing - MaxDiff - Analyze with Ranking Question (Legacy) - MaxDiff Ranking Question from Design and Choice Data
- Marketing - MaxDiff - Analyze with Ranking Question (Legacy) - Preference Share Variables from Individual-Level Parameter Means (All Alternatives)
- Marketing - MaxDiff - Analyze with Ranking Question (Legacy) - Sawtooth-Style Preference Share Variables from Individual-Level Parameter Means (K Alternatives)
- Marketing - MaxDiff - Analyze with Ranking Question (Legacy) - Zero-Centered Utilities from Individual-Level Parameter Means (All Alternatives)
- Marketing - MaxDiff - Convert Alchemer (Survey Gizmo) MaxDiff Data for Analysis
- Marketing - MaxDiff - Diagnostic - Class Parameters Table
- Marketing - MaxDiff - Diagnostic - Class Preference Shares Table
- Marketing - MaxDiff - Diagnostic - Parameter Statistics Table
- Marketing - MaxDiff - Diagnostic - Posterior Intervals Plot
- Marketing - MaxDiff - Diagnostic - Trace Plots
- Marketing - MaxDiff - Save Variable(s) - Class Membership
- Marketing - MaxDiff - Save Variable(s) - Class Membership Probabilities
- Marketing - MaxDiff - Save Variable(s) - Individual-Level Coefficients
- Marketing - MaxDiff - Save Variable(s) - Preference Shares
- Marketing - MaxDiff - Save Variable(s) - Proportion of Correct Predictions
- Marketing - MaxDiff - Save Variable(s) - RLH (Root Likelihood)
- Marketing - MaxDiff - Save Variable(s) - Sawtooth-Style Preference Shares (K Alternatives)
- Marketing - MaxDiff - Save Variable(s) - Zero-Centered Utilities
- Missing Data - Save Variable(s) - Filter for Complete Cases
- Modify Data - Add Bottom K Category NETs (Bottom K Boxes)
- Modify Data - Add Top K Category NETs (Top K Boxes)
- Modify Data - Merge 10 Point Scales Into 3 Categories
- Modify Data - Merge 7 Point Scales Into 3 Categories
- Modify Data - Merge and Flatten Scales
- Modify Data - Merge Categories of Size 0.5% or Less
- Modify Data - Merge Categories of Size 1% or Less
- Modify Data - Merge Categories of Size 2% or Less
- Modify Data - Merge Categories of Size 3% or Less
- Modify Data - Merge Categories of Size 4% or Less
- Modify Data - Merge Categories of Size 5% or Less
- Modify Data - Merge Scales (Custom)
- Modify Data - Move NET to the Top for All Questions
- Modify Data - Remove Don't Know Categories
- Modify Data - Use Question as Template for Modifying Other Questions
- Modify Footers - Add Descriptions of Selected Data (e.g., Question Name, Skips, Filtering) from File
- Modify Labels - Add Variable Names and Values to Labels
- Modify Labels - Relabel Other/Specify Categories as Other
- Modify Labels - Remove Variable Names and Values from Labels
- Modify Tables or Plots - Number All Tables in Project
- Modify Tables or Plots - Remove JavaScript from Selected Tables and Plots
- Modify Tables or Plots - Rename Table Page Names and Titles with Table Name
- Modify Tables or Plots - Replace Question in All Selected Tables and Plots
- Move Data - Move All Filters to the Top
- Move Data - Move All Hidden Questions to the Bottom
- Move Data - Move All Weights to the Top
- Move Data - Move Variables to New Position
- Multivariate - Table of All Combinations Of Pick Any Categories
P
- Plot/Chart - Bubble Plot from 3 Tables
- Plot/Chart - Scatterplot from 2 Tables
- Preliminary Project Setup - Check for Errors in Data File Construction
- Preliminary Project Setup - Hide Uninteresting Data
- Preliminary Project Setup - Identify Questions with Straight-Lining/Flat-Lining
- Preliminary Project Setup - Remove Truncated Text from Variable Labels
- Preliminary Project Setup - Reverse Scales in Question(s)
- Preliminary Project Setup - Suggest Better Question Names from Source Labels
- Preliminary Project Setup - Summary Plots
- Preliminary Project Setup - Summary Tables
- Preliminary Project Setup - Tables for Data Checking
R
- Recode - Recode High Values (Capping) in Numeric Variable(s)
- Recode - Recode Low Values (Capping) in Numeric Variable(s)
- Recode - Recode Variable(s) Using Code/Category Midpoints
- Recode - Set High Values to Missing in Numeric Variable(s)
- Recode - Set Low Values to Missing in Numeric Variable(s)
- Recode - Set Value of Don't Knows to NaN
- Recode - Turn Off Missing Data Selection for Specific Values
- Regression - Diagnostic - Multicollinearity Table (VIF)
- Regression - Diagnostic - Plot - Cook's Distance
- Regression - Diagnostic - Plot - Cook's Distance vs Leverage
- Regression - Diagnostic - Plot - Influence Index
- Regression - Diagnostic - Plot - Normal Q-Q
- Regression - Diagnostic - Plot - Residuals vs Fitted
- Regression - Diagnostic - Plot - Residuals vs Leverage
- Regression - Diagnostic - Plot - Scale-Location
- Regression - Diagnostic - Prediction-Accuracy Table
- Regression - Diagnostic - Test Residual Heteroscedasticity
- Regression - Diagnostic - Test Residual Normality (Shapiro-Wilk)
- Regression - Diagnostic - Test Residual Serial Correlation (Durbin-Watson)
- Regression - Legacy Driver Analysis - Table of Beta Scores
- Regression - Legacy Driver Analysis - Table of Contribution Scores
- Regression - Legacy Driver Analysis - Table of Elasticity Scores
- Regression - Legacy Driver Analysis - Table of Kruskal Importance Scores
- Regression - Legacy Driver Analysis - Table of Linear Regression Coefficients
- Regression - Legacy Driver Analysis - Table of Relative Weights (Relative Importance Analysis)
- Regression - Legacy Driver Analysis - Table of Shapley Importance Scores
- Regression - Save Variable(s) - Fitted Values
- Regression - Save Variable(s) - Predicted Values
- Regression - Save Variable(s) - Probabilities of Each Response
- Regression - Save Variable(s) - Residuals
S
T
- Tables - Banner Tables
- Tables - Combine Tables
- Tables - Crosstabs
- Tables - Raw Data - Question
- Tables - Raw Data - Variable(s)
- Tables - Table from Data Set
- Tables - Tables Showing Don't Knows
- Text Analysis - Advanced - Save Variable(s) - Tidied Text
- Text Analysis - Save Variable(s) - Categories
- Text Analysis - Save Variable(s) - First Category
- Text Analysis - Sentiment
- Text Box Filters on an Output
