An extended artificial dataset inspired by a similar dataset pizza.sav in Arbeitsbuch zur deskriptiven und induktiven Statistik by Toutenburg et al. The data describe a pizza delivery service in London serving three areas, each record being one order and its associated characteristics.
Format
A data frame with 1209 observations on 22 variables:
- index
integer, index of the record, complete by construction.
- date
date of the delivery.
- week
numeric, the week of the year.
- weekday
numeric, the day of the week.
- area
factor with the levels
Brent,CamdenandWestminster.- count
integer, the number of pizzas delivered.
- rebate
logical,
TRUEif a rebate was given.- price
numeric, the total price of the pizzas delivered.
- operator
factor with three levels, the operator taking the order.
- driver
factor with seven levels, the driver delivering the order.
- delivery_min
numeric, the delivery time in minutes.
- temperature
numeric, the temperature in degrees Celsius on delivery.
- wine_ordered
integer,
1if wine was ordered,0if not.- wine_delivered
integer,
1if wine was delivered,0if not.- wrongpizza
logical,
TRUEif a wrong pizza was delivered.- quality
ordered factor with the levels
low < medium < high, the quality of the pizza on delivery.- vegetarian
integer,
1if the order was vegetarian,0if not.- nps
numeric, the Net Promoter Score from 1 to 10, an ordinal customer rating.
- complaint
integer,
1if a complaint was filed,0if not.- style
character, the type of pizza, e.g.
italian,american,gourmetorvegan.- channel
character, the order channel,
app,weborphone.- tip
numeric, the tip in monetary units.
Details
Compared to the original dataset, this extended version includes additional behavioural and outcome variables such as customer satisfaction, Net Promoter Score (NPS), complaints, dietary choices and tipping behaviour. These variables are generated using probabilistic models to resemble realistic business data, including noise, imperfect relationships and heterogeneous customer behaviour.
The dataset is designed to be realistically complex. It contains the data
types commonly met in practice: numerics, integers, factors, ordered factors,
logicals, characters and dates. Missing values occur both systematically and
at random, in every variable except index.
The variable nps is a simulated Net Promoter Score from 1 to 10,
calibrated to resemble realistic customer feedback distributions, including
asymmetric lower-tail behaviour.
The variable complaint is generated using a probabilistic model depending
on delivery time, order correctness and additional noise, ensuring that
complaints are not deterministically linked to single factors.
The variable tip is based on a percentage of the order price and is
influenced by customer satisfaction (nps), delivery performance and
driver-specific effects. Tips are zero for complaints or very low
satisfaction, and otherwise increase monotonically with customer
satisfaction while retaining stochastic variation.
Overall, the dataset is designed to provide a realistic benchmark for statistical modelling, including classification (binary and ordinal), regression and performance evaluation, e.g. ROC curves and AUC with confidence intervals.
Every variable carries a label attribute with its description, so that
the labels can be used in tables and plots without repeating them in the
code.
References
Toutenburg H, Schomaker M, Wissmann M, Heumann C (2009): Arbeitsbuch zur deskriptiven und induktiven Statistik Springer, Berlin Heidelberg.
See also
Other datasets:
Cards,
Roulette,
Tarot,
courseData()
Examples
str(bedrock::Pizza)
#> 'data.frame': 1209 obs. of 22 variables:
#> $ index : int 1 2 3 4 5 6 7 8 9 10 ...
#> ..- attr(*, "label")= Named chr "Numeric index of the record."
#> .. ..- attr(*, "names")= chr "index"
#> $ date : Date, format: "2014-03-01" "2014-03-01" ...
#> $ week : num 9 9 9 9 9 9 9 9 9 9 ...
#> ..- attr(*, "label")= Named chr "Week number."
#> .. ..- attr(*, "names")= chr "week"
#> $ weekday : num 6 6 6 6 6 6 6 6 6 6 ...
#> ..- attr(*, "label")= Named chr "Weekday (integer)."
#> .. ..- attr(*, "names")= chr "weekday"
#> $ area : Factor w/ 3 levels "Brent","Camden",..: 2 3 3 1 1 2 2 1 3 1 ...
#> ..- attr(*, "label")= Named chr "Factor with levels Brent, Camden, Westminster."
#> .. ..- attr(*, "names")= chr "area"
#> $ count : int 5 2 3 2 5 1 4 NA 3 6 ...
#> ..- attr(*, "label")= Named chr "Number of pizzas delivered."
#> .. ..- attr(*, "names")= chr "count"
#> $ rebate : logi TRUE FALSE FALSE FALSE TRUE FALSE ...
#> ..- attr(*, "label")= Named chr "Logical, TRUE if a rebate was given."
#> .. ..- attr(*, "names")= chr "rabate"
#> $ price : num 65.7 27 41 26 57.6 ...
#> ..- attr(*, "label")= Named chr "Total price of delivered pizzas."
#> .. ..- attr(*, "names")= chr "price"
#> $ operator : Factor w/ 3 levels "Allanah","Maria",..: 3 3 1 1 3 1 3 1 1 3 ...
#> ..- attr(*, "label")= Named chr "Factor indicating the operator."
#> .. ..- attr(*, "names")= chr "operator"
#> $ driver : Factor w/ 7 levels "Butcher","Carpenter",..: 7 1 1 7 3 7 7 7 7 3 ...
#> ..- attr(*, "label")= Named chr "Factor indicating the driver."
#> .. ..- attr(*, "names")= chr "driver"
#> $ delivery_min : num 20 19.6 17.8 37.3 21.8 48.7 49.3 25.6 26.4 24.3 ...
#> ..- attr(*, "label")= Named chr "Delivery time in minutes."
#> .. ..- attr(*, "names")= chr "delivery_min"
#> $ temperature : num 53 56.4 36.5 NA 50 27 33.9 54.8 48 54.4 ...
#> ..- attr(*, "label")= Named chr "Temperature in degrees Celsius when delivered."
#> .. ..- attr(*, "names")= chr "temperature"
#> $ wine_ordered : int 0 0 0 0 0 0 1 NA 0 1 ...
#> ..- attr(*, "label")= Named chr "Integer, 1 if wine was ordered, 0 if not"
#> .. ..- attr(*, "names")= chr "wine_ordered"
#> $ wine_delivered: int 0 0 0 0 0 0 1 NA 0 1 ...
#> ..- attr(*, "label")= Named chr "Integer, 1 if wine was delivered, 0 if not"
#> .. ..- attr(*, "names")= chr "wine_delivered"
#> $ wrongpizza : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
#> ..- attr(*, "label")= Named chr "Logical, TRUE if a wrong pizza was delivered"
#> .. ..- attr(*, "names")= chr "wrongpizza"
#> $ quality : Ord.factor w/ 3 levels "low"<"medium"<..: 2 3 NA NA 2 1 1 3 3 2 ...
#> ..- attr(*, "label")= Named chr "Ordered factor with levels low < medium < high"
#> .. ..- attr(*, "names")= chr "quality"
#> $ vegetarian : int 0 0 0 NA 0 0 0 NA 0 0 ...
#> ..- attr(*, "label")= Named chr "Binary indicator whether the order was vegetarian."
#> .. ..- attr(*, "names")= chr "vegetarian"
#> $ nps : num 4 8 NA NA 6 3 6 9 10 7 ...
#> ..- attr(*, "label")= Named chr "Net Promoter Score (1–10), ordinal customer rating."
#> .. ..- attr(*, "names")= chr "nps"
#> $ complaint : int 0 0 NA NA 0 1 1 NA 0 0 ...
#> ..- attr(*, "label")= Named chr "Binary indicator whether a complaint was filed."
#> .. ..- attr(*, "names")= chr "complaint"
#> $ style : chr "american" "italian" "italian" "italian" ...
#> ..- attr(*, "label")= Named chr "Type of pizza (e.g. italian, american, gourmet, vegan)."
#> .. ..- attr(*, "names")= chr "pizza_style"
#> $ channel : chr "app" "web" "web" "app" ...
#> ..- attr(*, "label")= Named chr "Order channel (app, web, phone)."
#> .. ..- attr(*, "names")= chr "order_channel"
#> $ tip : num 5.14 2.37 NA NA 4.56 0 0 NA 5.89 5.6 ...
#> ..- attr(*, "label")= Named chr "Tip amount in monetary units, derived from price and customer behaviour."
#> .. ..- attr(*, "names")= chr "tip"
summary(bedrock::Pizza$delivery_min)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8.80 17.40 24.40 25.65 32.50 65.60
table(bedrock::Pizza$area, bedrock::Pizza$channel)
#>
#> app phone web
#> Brent 265 48 146
#> Camden 198 30 109
#> Westminster 224 38 109
# the missing values are part of the design
colSums(is.na(bedrock::Pizza))
#> index date week weekday area
#> 0 32 32 32 10
#> count rebate price operator driver
#> 12 12 12 8 5
#> delivery_min temperature wine_ordered wine_delivered wrongpizza
#> 0 39 12 12 4
#> quality vegetarian nps complaint style
#> 201 51 105 129 22
#> channel tip
#> 32 81