A/B sketch preview. The current Weekday/Month bar chart aggregates 70 active days into a handful of totals — losing the bimodal shape that the underlying data has. Two treatments below restore access to the days the current chart erases.
6
spike days (≥6K)
48,518
hits from spikes
64
baseline days
2,931
median baseline day
10,007
hottest day (2026-03-07)
Sketch A — calendar heatmap
90 daily cells. Rows = weekday (Mon top, Sun bottom). Columns = calendar weeks. Colour intensity ∝ hits (log-binned). Hover any cell for the date + value.
What it reveals
The March spike cluster jumps out as a column of dark cells; baseline days form the muted background. Weekday rhythm (if any) reads as horizontal bands. Both axes (weekday + month) coexist in one view, so the Weekday/Month toggle becomes vestigial.
What it loses
Exact numbers are gone — you read pattern, not value. Wrong tool for "what was last Tuesday's count?"; right tool for "is anything anomalous?"
Sketch C — cumulative-volume curve (concentration view)
Active days sorted by hits descending. Curve shows cumulative % of total hits as you walk through the top days first. The dashed 45° line is what perfect equality would look like; the bow off the line is the concentration. Where the curve elbows from steep to shallow is the spike-vs-baseline boundary.
What it reveals
The top 6 days (8.6% of active days) carry 20% of total hits — a Pareto-like concentration. The top 10% of days (7 days) carry 23% of volume. The curve's elbow is the natural spike-threshold; the bar-chart B uses a hardcoded 6K cutoff, but this curve derives the boundary from the data's own shape.
What it loses
Time — both x-axis and y-axis are about distribution, not when. A March-clustering vs April-evenness story is invisible here. Pair with the heatmap or two-mode bar; this curve is a complement, not a substitute.
Sketch B — two-mode bar (spike vs baseline)
Same months on x-axis as today's chart, but each bar is split: red top segment = sum of spike days (≥6K hits), green bottom = baseline days. Hover for the spike-day dates.
What it reveals
How much of each month's total is baseline vs event-driven. March's 111K is ~37% spike, April's 98K is ~7% spike. The story shifts from "March was big" to "March had a 5-day burst on top of a normal floor."
What it loses
The spike threshold (6K) is a categorical decision — obscures values 4-6K. Less honest if the distribution were continuous; works because this data IS bimodal.
Recommendation
Two-mode bar = the upgrade to the existing chart(Dan's pick). Same shape, restored bimodality. Threshold becomes a config knob; tooltip ports the rich bar-tooltip pattern; click-through to a per-day report is the future workstream.
Cumulative-volume curve = the small companion beside the metric strip. Shows how concentrated the distribution is in one curve (6 days carrying 20% of hits). The elbow of the curve naturally argues for the spike-threshold in B — they're two views of the same boundary, mutually reinforcing.
Heatmap = the future investigation surface — when the per-day deep-link lands, each cell becomes a portal. Less urgent than B + C; park as v2.
Together the three views answer different questions: when (heatmap), how much (two-mode bar), how concentrated (cumulative curve). Each earns its slot by answering something the others can't.
Sketch built one-shot from the live dashboard data. Production would re-read on each cron refresh + cache-bust on data change. Generator: ~/Downloads/dare_ab_preview_pagehits_reimagine_build.py.