d.pizza: Data pizza

Description

An artificial dataset inspired by a similar dataset pizza.sav in Arbeitsbuch zur deskriptiven und induktiven Statistik by Toutenburg et.al.
The dataset contains data of a pizza delivery service in London, delivering pizzas to three areas. Every record defines one order/delivery and the according properties. A pizza is supposed to taste good, if its temperature is high enough, say 45 Celsius. So it might be interesting for the pizza delivery service to minimize the delivery time.
The dataset is designed to be as evil as possible. As far as the description is concerned, it should pose the same difficulties that we have to deal with in everyday life. It contains the most used datatypes as numerics, factors, ordered factors, integers, logicals and a date. NAs are scattered everywhere partly systematically, partly randomly (except in the index).

Usage

data(d.pizza)

Arguments

Format

A data frame with 1209 observations on the following 17 variables.

index: a numeric vector, indexing the records (no missings here).
date: Date, the delivery date
week: integer, the weeknumber
weekday: integer, the weekday
area: factor, the three London districts: Brent, Camden, Westminster
count: integer, the number of pizzas delivered
rabate: logical, TRUE if a rabate has been given
price: numeric, the total price of delivered pizza(s)
operator: a factor with levels Allanah Maria Rhonda
driver: a factor with levels Carpenter Carter Taylor Butcher Hunter Miller Farmer
delivery_min: numeric, the delivery time in minutes (decimal)
temperature: numeric, the temperature of the pizza in degrees Celsius when delivered to the customer
wine_ordered: integer, 1 if wine was ordered, 0 if not
wine_delivered: integer, 1 if wine was delivered, 0 if not
wrongpizza: logical, TRUE if a wrong pizza was delivered
quality: ordered factor with levels low < medium < high, defining the quality of the pizza when delivered

Details

The dataset contains NAs randomly scattered.

References

Toutenburg H, Schomaker M, Wissmann M, Heumann C (2009): Arbeitsbuch zur deskriptiven und induktiven Statistik Springer, Berlin Heidelberg

Examples

Run this code

str(d.pizza)
head(d.pizza)

Desc(d.pizza)

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