A fictional survey of patrons of a turn-of-the-century (c. 1905) general emporium, written in an Edwardian register for colour. It behaves like a real survey export – a latent satisfaction drives the worded attribute ratings and the recommend score together, demographics use standard sex/country categories alongside period social-class and occupation items, and the multi-select and open-text columns mirror the shapes the helpers consume. A companion to podracing_survey for examples that want a second, very different theme.
Format
A tibble with 800 rows and 25 columns:
- patron_id
Unique patron id (character).
- demo_age
Age in years (integer, 18-80).
- demo_gender
Sex (Female / Male, some "Prefer not to answer").
- demo_country
Country of residence (ISO 3166 names).
- region
World region derived from
demo_country.- social_class
Edwardian social class (Upper class .. Poor).
- occupation
Period occupation (Clerk, Domestic servant, ...).
- household_size
Number in the household (integer).
- weekly_spend
Weekly spend at the emporium, in shillings (numeric).
- payment
How they pay: Cash, On account or Barter.
- transport
How they travel to the shop (On foot, Horse and cart, ...).
- visit_frequency
How often they visit (Never .. Always).
- recommend
0-10 "would recommend to a neighbour" rating (integer).
- ratings_goods, ratings_service, ratings_credit, ratings_delivery, ratings_value
Worded 1-5 attribute ratings (Very bad .. Very good).
- reasons_price, reasons_quality, reasons_credit, reasons_proximity, reasons_variety, reasons_service
Multi-select reasons for patronage; each holds its option text when chosen, otherwise
"".- comment
Open-text comment (mostly blank).
See also
Other data:
country_region,
currency_rates,
podracing_survey
Examples
calc_percentage(shopping_survey, social_class, sort = "desc")
#> # A tibble: 6 × 3
#> social_class n pct
#> <fct> <int> <dbl>
#> 1 Skilled working class 232 30
#> 2 Working class 193 25
#> 3 Lower middle class 183 23
#> 4 Upper middle class 82 11
#> 5 Poor 63 8
#> 6 Upper class 26 3
calc_summary(shopping_survey, weekly_spend, by = payment)
#> # A tibble: 3 × 5
#> payment n mean median sd
#> <chr> <int> <dbl> <dbl> <dbl>
#> 1 Barter 63 17.6 17.2 8.30
#> 2 Cash 448 18.4 18 8.15
#> 3 On account 289 19.0 19 8.46