Six hundred simulated respondent-days with age, sex, the seven impact totals and a GDQS score, for use in examples, vignettes and tests.
Format
A data frame with 600 rows and 11 columns:
- id
Row identifier
- age
Age in years, 0.5 to 65
- sex
Factor with levels `"male"` and `"female"`
- land_total_m2yr, land_arable_m2yr, land_pasture_m2yr
Land use, m2*year
- ghg_kgco2e
Greenhouse gas emissions, kg CO2eq
- eutroph_gpo4e
Eutrophication potential, **g** PO4(3-)eq. Grams, like the EIOF databases and both cut-off tables. Renamed from `eutroph_kgpo4e` and rescaled in 0.1.0; see `vignette("eutrophication")`
- water_litres
Water use, litres
- biodiv_pdf
Biodiversity loss, PDF*year
- gdqs
Global Diet Quality Score
Details
**These are not real survey data.** The values were drawn from lognormal distributions chosen to be roughly plausible. No prevalence computed from this dataset carries any substantive meaning.
A small amount of missingness is included in `biodiv_pdf` and `gdqs` so examples show how it propagates.
The column names are deliberately not the indicator keys
Until October 2026 the impact columns were called `landtotal`, `ghg`, `biodiv` and so on - the same strings the `indicator` argument takes. That made the examples tidy and taught something false: a loop over `eiod_demo[[ind]]` works only because the columns happen to be named after the keys, and readers concluded that reiod required those names.
It never did. reiod takes vectors, so any column name has always worked. The columns now carry survey-realistic names with their units, which forces every example to state the indicator-to-column mapping explicitly. See [eiod_demo_map] and [eiod_absolute_all()].
See also
[eiod_demo_map] for the indicator-to-column mapping. A deliberately broken copy ships as `system.file("extdata", "eiod_broken.csv", package = "reiod")` and is used in `vignette("troubleshooting")`.
Examples
data(eiod_demo)
head(eiod_demo)
#> id age sex land_total_m2yr land_arable_m2yr land_pasture_m2yr ghg_kgco2e
#> 1 1 16.4 male 6.661992 9.1117404 6.257198 7.709933
#> 2 2 33.0 male 6.173535 3.6705075 1.322853 7.400251
#> 3 3 45.5 male 8.260851 0.4574517 9.283150 1.127271
#> 4 4 16.9 male 7.472962 1.2796210 3.753050 1.493590
#> 5 5 51.2 female 1.115144 4.3624452 11.644862 2.790514
#> 6 6 12.7 male 2.946872 2.9984842 2.331456 5.190682
#> eutroph_gpo4e water_litres biodiv_pdf gdqs
#> 1 3.062745 289.1277 1.670692e-12 26.7
#> 2 10.051376 361.7203 5.547457e-13 18.4
#> 3 8.827951 401.3645 9.416969e-13 35.0
#> 4 30.143463 584.7734 2.538843e-13 37.1
#> 5 11.739560 478.3885 6.225413e-12 17.3
#> 6 52.443135 998.4629 1.250413e-12 13.9