Field note · November 29, 2023
What site actually contains in clinical-trial
900 rows, 6 distinct values, and the one fact about site that changes how you query it.
Working through clinical-trial again. site is the column people trip over, so here is what it actually looks like.
900 rows, no nulls, 6 distinct values. Enrolling site, 1–6.
6 values, and they are not evenly spread: site-2 19.3%, site-4 16.9%, site-3 16.4%, site-6 16.2%. The largest takes 19.3% on its own.
Read the distinct list rather than the distinct count. Casing differences and trailing whitespace produce values that look identical in a report and group separately in SQL, and the count will not show you that — select distinct site order by 1 will, in about a second.
select
count(*) as rows,
count(site) as present,
count(*) - count(site) as nulls,
count(distinct site) as distinct_values
from clinical_trial;Low cardinality, stable values — this is a column you can group by without thinking about it, and a reasonable candidate for a chart facet. Check the distinct list, not just the count, because a stray casing variant hides in the count and shows up in the group-by.
Where this bites: a group-by on this column produces 6 rows today. If it is a column an upstream system can add values to, it produces an unknown number tomorrow, and any dashboard laid out for 6 categories reflows without warning. Values arriving is a schema change that no schema check catches.
The grain is one row per enrolled subject, which is the context every one of those numbers depends on. None of them survive a change of grain, which is why "profile the column" and "profile the table" are the same job. Full schema, and the CSV, on the dataset page.