The category that eats the chart

channel on support-tickets has 4 values, and one of them is 34.2% of the data.

• email — 1,368 rows, 34.2%
• chat — 1,191 rows, 29.8%
• in-app — 952 rows, 23.8%
• phone — 489 rows, 12.2%

The top three take 87.8% between them. With only 4 values there is no tail to worry about, which makes this a genuinely easy column to chart.

select channel,
       count(*)                                      as rows,
       round(100.0 * count(*) / sum(count(*)) over (), 1) as pct,
       round(avg(first_response_min)::numeric, 2)             as avg_first_response_min
from support_tickets
group by 1
order by rows desc;

The second column is the one that matters. Share of rows tells you what is common; avg_first_response_min tells you whether the common thing is the important thing. They disagree more often than not, and a chart that shows only the first is answering the easier question.

Decide what happens to the tail before you plot it. "Other" as an explicit bucket is honest; twelve slivers is not, and neither is silently taking the top eight.