| Column | Key or value? | Type | Levels | Role in my charts |
|---|---|---|---|---|
| country | Key | Categorical | 50 | Separates the bars; rows of the matrix |
| code | Key (redundant) | Categorical | 50 | 1:1 with country; not drawn |
| region | Attribute of key 1 | Categorical | 7 | Colours the parallel-coordinates lines |
| year | Key | Ordinal | 8 | Stacked/grouped segments; matrix columns |
| population_millions | Value | Quantitative | – | Parallel-coordinates axis |
| gdp_per_capita | Value | Quantitative | – | Parallel-coordinates axis |
| co2_per_capita | Value | Quantitative | – | Bar length, cell colour, radial bars, PC axis; basis of derived % change |
| co2_total_mt | Value | Quantitative | – | Not drawn |
| renewable_pct | Value | Quantitative | – | Parallel-coordinates axis (moved in Q3B) |
| energy_per_capita | Value | Quantitative | – | Parallel-coordinates axis |
A key is a categorical label with no inherent magnitude or order, so placing it at a spatial position would imply a quantity or ranking that isn’t in the data; position can only faithfully carry a value. Instead the chapter has us separate the table into regions (one per key level), order them deliberately, and align them on a common axis so values can be compared.