# Datasets¶

## General Datasets¶

Here, the choice set of interest \(X=\{x_1,\ldots,x_m\}\) consists of finitely many general/unstructured alternatives, and
the available data is a collection of **menus** of such alternatives and the **observed choices** at these menus.
Formally, a menu is a set \(A\subseteq X\), and the observed choice(s) at this menu is (are) captured by the set \(C(A)\), where \(\emptyset\subseteq C(A)\subseteq A\).

A **general dataset**

is a collection of \(k\) observations, with each of them a pair of a menu and the alternative(s) chosen from it (if any).
In particular, if \(C(A)\) contains more than one alternative for some menu \(A\) in \(\mathcal{D}\),
it is understood that the decision maker has chosen (or may be thought of as having chosen)
any or all these alternatives at \(A\), possibly over different instances where \(A\) was presented in \(\mathcal{D}\) (see also merging).
If \(C(A)=\emptyset\), then it is understood that the decision maker has chosen the **no-choice/outside option**, hence
to **avoid** or **defer** choice at menu \(A\).

It is also possible that the data available to the analyst features a **default/status quo option**, reflecting situations where the decision
maker was initially endowed with some alternative \(s\in A\) before asked to choose from menu \(A\).

A **general dataset with default/status quo alternatives**

is a collection of \(k\) observations, with each of them a pair comprising a **decision problem** and the alternative(s) that was/were observed to be chosen at this decision problem.
In each decision problem \((A_i,s_i)\), \(A_i\) is a menu and \(s_i\in A_i\) the default/status quo alternative at that menu,
while \(\emptyset\neq C(A_i,s_i)\subseteq A_i\) is required to hold for all \(i\leq k\) in such datasets.

Tip

To be analyzable by Prest v1.0.2, a general dataset must be a .csv file.

An example general dataset with default/status quo alternatives.

An example hybrid general dataset containing both types of observations.

To import such a dataset into Prest, select *“Workspace -> Import general dataset”* and select the target file from the relevant directory.

The new pop-up window features four column headers under *“Columns”*: **Subject**, **Menu**, **Default** and **Choice**.
Select the appropriate column name in your .csv file from the drop-down menu to match the corresponding column header.
If your dataset does not feature default alternatives, select *“None”* for the **Default** header.

To view the imported dataset in Prest, double-click on it in the workspace area.

## Budgetary Datasets¶

Here, consumer behavior with respect to \(n\) commodities is analyzed when data is available on
the **prices** of these commodities, captured by a vector \(p\in\mathbb{R}^n_{+}\), and also on consumer **demand** at these prices,
captured by a vector/**consumption bundle** \(x\in\mathbb{R}^n_+\).

A **budgetary dataset**

is a collection of \(k\) observations, with each of them a pair \((p^i,x^i)\) comprising the consumption bundle \(x^i\) that was observed to be chosen when prices were \(p^i\).

Tip

To be analyzable by Prest v1.0.2, a budgetary dataset must be a .csv file.

To import such a dataset, go to *“Workspace -> Import budgetary dataset”* and select the target file from the relevant directory.

**Budgetary datasets with** \(n\) **goods must have the following structure:**

Column 1: subject ID

Column 2: price of good 1

Column \(n+1\): price of good \(n\)

Column \(n+2\): demand of good 1

Column \(2n+1\): demand of good \(n\)

To view the imported dataset, double-click on it in the workspace area. **An extra column with the total expenditure associated with each observation is automatically added.**