A key property of a variable set or question is the structure. The Variable Set or Question Structure determines:
 How Tables are created and manipulated, see Reference Table below.
 How variables appear when used in R code.
The Variable Set/Question Structure is automatically inferred when the data is imported. It can be modified in Displayr by selecting a Variable Set in the Data Sets tree and either:
 Combining or splitting variables into/from a Variable Set (Split or Combine).
 Modifying it in the object inspector under Properties > GENERAL > Structure.
It can be modified in Q on the Variables and Questions tab by:
 Combining or splitting variables into/from a Question using Set Question.
 Modifying the Variable Type or Question Type dropdowns.
Variable Set/Question Structures vary, nonexhaustively, on the following dimensions:
 The properties of the variables: Text, Binary, Nominal, Ordinal, Numeric, Date.
 The number of variables in the set/question: one or more than one.
 Whether the variables within a set/question are organized in a twodimensional structure (i.e., a grid) or not.
 Whether the variables contain structural dependencies (i.e., where the meaning of the values in one of the variables is structurally related to the meanings of another of the variables).
Examples of the appropriate structure given a type of survey question are below. Where names in Displayr and Q differ, Displayr's name is listed first, Q's second:
An overview of how each structure appears in a Displayr table is under Reference Table below.
Single Variable
Text
A single variable containing text (or, numeric data that is interpreted as text). For example, data obtained from a question like:
Please enter the name of the last soft drink you bought.
_____________
Nominal / Pick One
A single variable that contains unordered, mutually exclusive, and exhaustive categories (i.e., has a nominal measurement scale). For example, data generated by the following question:
Are you...
o Male
o Female
Whereas a Text Variable Set stores the data as text, a Nominal Variable Set has both Value Attributes and Data Reductions.
Ordinal / Pick One
A single variable that contains ordered, mutually exclusive, and exhaustive categories (i.e., has a ordinal measurement scale). For example, data generated by the following question:
How old are you?
o Under 30
o 30 to 50
o 50 or more
For most purposes, an Ordinal Variable Set is identical to a Nominal Variable Set. The only difference is that some statistical tests will take the ordering into account. In Q, both question types are Pick One questions with different Variable Type settings.
Numeric / Number
A numeric variable (i.e., it has an interval or ratio measurement scale). For example, data that represents the temperature at a given point in time.
Date/Time
A numeric variable where the values represent times and/or dates. It contains the number of milliseconds since 1/1/1970.
JavaScript variables have special inbuilt functions for manipulating date questions (e.g., use Q.Year/Month/Day/Hour/Second() to extract bits of a date or time, and Q.YearDif/MonthDif/WeekDif/DayDif/HourDif/MinuteDif/SecondDif() to compare two of them).
Date/Time variables can be converted to different time scales (e.g., months, weeks, minutes) by clicking on the variable and pressing Date/Time in the Object Inspector in Displayr or clicking the Values button on the Variables and Questions tab in Q.
Multiple variables
Text  Multi
Multiple related variables that contain text, e.g. generated from a question like:
Please type in the names of your three favorite soft drinks
1.____________
2.____________
3.____________
Binary  Multi / Pick Any
There are only two nonmissing values in each variable. Where the variable originally contains more than two categories, they are combined (see Value Attributes). This is the main way that nonmutually exclusive categories are represented in a Data Set (see also Binary  Multi (Compact) below). Common examples of Binary  Multi Variable Sets / Pick Any Questions are lists of products purchased by people in a customer database, and responses to multiple response questions in surveys, such as:
Which of the following have you bought in the past week? Tick all that apply.
[] Coke
[] Pepsi
[] Fanta
[] None of these
Note that a row in a Data Set can have three possible values in a variable in a Binary  Multiple Variable Set / Pick Any Question: the value that corresponds to a category being applicable or being selected (1), the value that corresponds to it not being selected (0), and a missing value category, which is represented as a NaN in the data.
Nominal  Multi / Pick One  Multi
A set of categorical variables sharing the same scale points, where the scale points are mutually exclusive and unordered.
Which meal did you eat most recently at each of these restaurants?
Breakfast  Lunch  Dinner  

McDonald's  o  o  o 
Burger King  o  o  o 
Wendy's  o  o  o 
Ordinal  Multi / Pick One  Multi
A set of categorical variables sharing the same scale points, where the scale points are mutually exclusive and ordered.
In the vast majority of instances, Ordinal  Multi data is analyzed in the same way as Nominal  Multi data. In Q, both question types are Pick One  Multi questions with different Variable Type settings.
How would you rate your satisfaction with your most recent meal at each of these restaurants?
Low  Medium  High  

McDonald's  o  o  o 
Burger King  o  o  o 
Wendy's  o  o  o 
Numeric  Multi / Number  Multi
A series of numeric variables measured on the same scale. For example:
Next to the brands below, please indicate how many times you have purchased them in the past week.
Coke ___
Pepsi ___
Fanta ___
Grid
Binary  Grid / Pick Any  Grid
This is a generalization of a Binary  Multi Variable Set / Pick Any Question where the variables can be thought of as being ordered in two dimensions. For example, the data generated from a series of related questions such as:
Which of these brands are fun?
[] Coke  [] Pepsi  [] Fanta 
Which of these brands are sexy?
[] Coke  [] Pepsi  [] Fanta 
Which of these brands are masculine?
[] Coke  [] Pepsi  [] Fanta 
Displayr and Q infer the structure of the grid by inspecting the variables' labels at the time of importing the data. Where Displayr or Q cannot discern the structure of the data, this can be set when changing the Variable Set structure / Question type.
Numeric  Grid / Number  Grid
This is a generalization of a Numeric  Multi Variable Set / Number  Multi Question, where the variables can be ordered in two dimensions. For example, the data generated by:
In the past month, how many economy flights did you take on...
Qantas ___ United ___ SAS ___
In the past month, how many business class flights did you take on...
Qantas ___ United ___ SAS ___
Displayr and Q infer the structure of the grid by inspecting the variables' labels at the time of importing the data. Where Displayr or Q cannot discern the structure of the data, this can be set by changing the Variable Set structure.
Structural dependencies
Binary  Multi (Compact) / Pick Any  Compact
The same underlying data as a Binary  Multi Variable Set / Pick Any Question, except that is stored in a maxmulti format. That is, the first variable contains the first response, the second variable contains the second response, etc. This format should only be used to represent multiple response data when there are truly huge code frames (e.g., thousands of options). It is generally inferior to a Nominal structure as it is unwieldy for data manipulation (e.g., for use in formulas) and it cannot accommodate the notion of missing data.
Ranking
Multiple numeric variables that represent a ranking, where the highest number is most preferred and ties are permitted. For example:
Rank the following brands according to how much you like them... Place a 3 next to the brand you like most, a 2 in your next preferred brand and a 1 next to your least preferred brand.
Coke ____
Pepsi ____
Fanta ____
Note that if your question uses lowest numbers as indicating alternatives being more preferred you will need to reverse the values assigned to each rank.
Experiment
This question type is used to represent the various different types of experiments, from randomized experiments (Fully randomized experiments through to Conjoint Analysis and Choice Modeling) (see Experiments in Q).
Which of these would you buy?
Coke  Pepsi  Fanta 
$2.00  $2.10  $1.80 
o  o  o 
Reference Table
Structure  Shown in Displayr Data Sets tree  What is shown in a Table  Example 
Nominal / Pick One  Category proportions  
Ordinal / Pick One  Ordered category proportions 

Numeric / Number  Average  
Text  Raw text  
Date/Time (stored in a YYYY/MM/DD or similar format) 
Proportion in each aggregated date  
BinaryMulti / Pick Any (commonly used for multiselect questions and Top 2 boxes) 
Proportion selected a particular response(s) for a variable (such as Aware) 

BinaryMulti (Compact) / Pick Any  Compact (multiselect data in maxmulti format where each variable is a selection number) 
Proportion selected a response  
NominalMulti / Pick One  Multi (commonly used to group brands to show in the same table) 
Proportion of category selected for each variable  
OrdinalMulti / Pick One  Multi (commonly used for ratings across brands) 
Proportion of category selected for each variable  
Numeric  Multi / Number  Multi (commonly used for numeric answers across brands) 
Average of each variable  
Binary  Grid / Pick Any  Grid (commonly used to group multiselects across brands) 
Proportion selected each pair of attributes  
Numeric  Grid / Number  Grid 
Average of each pair of attributes  
Ranking 
Probability % of item being chosen as first (based on coefficient from logit model)  
Experiment

Coefficient from Experiment 