31:T63f,data = rawData .filter((d) => d["Completion Time"].toLowerCase().includes("min")) .filter((d) => d["Condition"].trim().toLowerCase() !== "multimode") .filter( (d) => !["p05", "p14", "p21"].includes(d.Participant.trim().toLowerCase()) ) .map((d) => ({ ...d, completionSeconds: parseInt(d["Completion Time"].split(":")[0]) * 60 + parseInt(d["Completion Time"].split(":")[1].split(" ")[0]) })) .map((d) => Object.fromEntries( Object.entries(d).map(([key, value]) => { const normKey = key.trim().toLowerCase(); // Qualitative word responses → numbers (matched per-column) if (typeof value === "string" && valueMaps[normKey]) { const mapped = valueMaps[normKey][value.trim().toLowerCase()]; if (mapped !== undefined) return [key, mapped]; } // Existing "N - Label" responses → N const match = typeof value === "string" && value.match(/^\s*(\d+)\s*-/); return [key, match ? parseInt(match[1], 10) : value]; }) ) ) .map((d) => { const REVERSE_FRAGMENTS = ["frustration", "mental effort"]; const SCALE_MAX = 5; // 5-point items: 1↔5, 2↔4, 3↔3 return Object.fromEntries( Object.entries(d).map(([key, value]) => { const needsReverse = typeof value === "number" && REVERSE_FRAGMENTS.some((f) => key.toLowerCase().includes(f)); return [key, needsReverse ? SCALE_MAX + 1 - value : value]; }) ); }) .map((d) => { return d.Condition == "None" ? { ...d, Condition: "Control" } : { ...d }; })32:T47f,// data = rawData // .filter((d) => d["Completion Time"].includes("Min")) // .filter((d) => d["Condition"] !== "Multimode") // .filter((d) => !["P05", "P14", "P21"].includes(d.Participant)) // .map((d) => ({ // ...d, // completionSeconds: // parseInt(d["Completion Time"].split(":")[0]) * 60 + // parseInt(d["Completion Time"].split(":")[1].split(" ")[0]) // })) // .map((d) => // Object.fromEntries( // Object.entries(d).map(([key, value]) => { // const match = typeof value === "string" && value.match(/^\s*(\d+)\s*-/); // return [key, match ? parseInt(match[1], 10) : value]; // }) // ) // ) // .map((d) => { // const REVERSE_FRAGMENTS = ["frustration", "mental effort"]; // const SCALE_MAX = 5; // 5-point items: 1↔5, 2↔4, 3↔3 // return Object.fromEntries( // Object.entries(d).map(([key, value]) => { // const needsReverse = // typeof value === "number" && // REVERSE_FRAGMENTS.some((f) => key.toLowerCase().includes(f)); // return [key, needsReverse ? SCALE_MAX + 1 - value : value]; // }) // ); // })33:T70b,quantitative = { const freqMap = { Always: 1, Often: 2, Sometimes: 3, Rarely: 4, Never: 5 }; const helpMap = { Helpful: 1, Neutral: 2, Unhelpful: 3 }; const likelyMap = { "Very Likely": 1, Likely: 2, "No Preference": 3, Unlikely: 4, "Very Unlikely": 5 }; const yesNoMap = { Yes: 1, "Not Sure": 2, No: 3 }; // Pull leading integer out of "3 - Neutral", "1 - Strongly Agree", etc. const likert = (s) => { const m = String(s).match(/^\s*(\d+)/); return m ? +m[1] : null; }; const lookup = (s, map) => { const k = String(s).trim(); return k in map ? map[k] : null; }; return data .filter((e) => correlationConditions.includes(e.Condition)) .map((d) => ({ Familiarity: likert(d["Familiarity with Video Game/VR Enviorments "]), Comfort: likert(d["Comfortability using controllers"]), NavFrequency: lookup(d["Frequency of using navigation systems"], freqMap), Confidence: likert(d["Confidence when navigating"]), UnderstandSystem: likert(d["Ability to understand navigation system"]), KnewWhereToGo: likert(d["Understanding where to go during the task"]), Efficiency: likert( d["Navigation system and efficiancy when finding the target"] ), Frustration: likert(d["Frustration levels during the experiment"]), MentalEffort: likert(d["Amount of mental effort used "]), Helpfulness: lookup(d["How helpful was the navigation system "], helpMap), RealWorldUse: lookup( d["Would you use this in a real world setting "], likelyMap ), PriorGameHelped: lookup( d[ "Did prior video game knowledge help with how you navigated the task?" ], yesNoMap ), CompletionSec: +d.completionSeconds })); }34:T862,chart = { const { fields, matrix } = correlations; const size = 32; const margin = { top: 100, right: 60, bottom: 20, left: 100 }; const w = fields.length * size + margin.left + margin.right; const h = fields.length * size + margin.top + margin.bottom; const svg = d3.create("svg").attr("viewBox", [0, 0, w, h]).attr("width", w); const x = d3 .scaleBand() .domain(fields) .range([0, fields.length * size]); const y = d3 .scaleBand() .domain(fields) .range([0, fields.length * size]); const color = d3.scaleSequential(d3.interpolateBlues).domain([0, 1]); const g = svg .append("g") .attr("transform", `translate(${margin.left},${margin.top})`); // Cells g.selectAll("rect") .data(matrix) .join("rect") .attr("x", (d) => x(d.a)) .attr("y", (d) => y(d.b)) .attr("width", x.bandwidth()) .attr("height", y.bandwidth()) .attr("fill", (d) => (d.r == null ? "#eee" : color(d.r))) .attr("stroke", "#fff") .append("title") .text( (d) => `${d.a} × ${d.b}\nr = ${d.r == null ? "n/a" : d.r.toFixed(2)}` ); // r values inside cells g.selectAll("text.val") .data(matrix) .join("text") .attr("class", "val") .attr("x", (d) => x(d.a) + x.bandwidth() / 2) .attr("y", (d) => y(d.b) + y.bandwidth() / 2) .attr("text-anchor", "middle") .attr("dominant-baseline", "central") .attr("font-size", 9) .attr("fill", (d) => (d.r != null && d.r > 0.25 ? "#fff" : "#333")) .text((d) => (d.r == null ? "" : d.r.toFixed(2))); // Top labels (rotated) g.append("g") .selectAll("text") .data(fields) .join("text") .attr( "transform", (d) => `translate(${x(d) + x.bandwidth() / 2},-6) rotate(-45)` ) .attr("text-anchor", "start") .attr("font-size", 11) .text((d) => d); // Left labels g.append("g") .selectAll("text") .data(fields) .join("text") .attr("x", -6) .attr("y", (d) => y(d) + y.bandwidth() / 2) .attr("text-anchor", "end") .attr("dominant-baseline", "central") .attr("font-size", 11) .text((d) => d); return svg.node(); }35:T693,pairwiseTests = { // Group completionSeconds by Condition const groups = d3.rollup( data, (v) => v.map((d) => +d.completionSeconds).filter((x) => !isNaN(x)), (d) => d.Condition ); const conditions = [...groups.keys()]; const mean = (a) => a.reduce((s, x) => s + x, 0) / a.length; const variance = (a) => { const m = mean(a); return a.reduce((s, x) => s + (x - m) ** 2, 0) / (a.length - 1); }; // Welch's t-test const welch = (x, y) => { const n1 = x.length, n2 = y.length; const m1 = mean(x), m2 = mean(y); const v1 = variance(x), v2 = variance(y); const se = Math.sqrt(v1 / n1 + v2 / n2); const t = (m1 - m2) / se; // Welch–Satterthwaite degrees of freedom const df = (v1 / n1 + v2 / n2) ** 2 / ((v1 / n1) ** 2 / (n1 - 1) + (v2 / n2) ** 2 / (n2 - 1)); // Two-tailed p-value const p = 2 * (1 - jStat.studentt.cdf(Math.abs(t), df)); return { t, df, p, m1, m2, n1, n2 }; }; // All unordered pairs const results = []; for (let i = 0; i < conditions.length; i++) { for (let j = i + 1; j < conditions.length; j++) { const a = conditions[i], b = conditions[j]; const r = welch(groups.get(a), groups.get(b)); results.push({ a, b, ...r }); } } // Holm–Bonferroni adjustment for multiple comparisons const m = results.length; const sorted = [...results].sort((x, y) => x.p - y.p); sorted.forEach((r, i) => { r.p_holm = Math.min(1, r.p * (m - i)); }); // Enforce monotonicity for (let i = 1; i < sorted.length; i++) { sorted[i].p_holm = Math.max(sorted[i].p_holm, sorted[i - 1].p_holm); } return results; }36:T526,combinedTestTable = { const { a, b } = selectedGroups; if (a.values.length < 2 || b.values.length < 2) { return md`Pick at least 2 participants on each side.`; } const mean = (arr) => arr.reduce((s, x) => s + x, 0) / arr.length; const variance = (arr) => { const m = mean(arr); return arr.reduce((s, x) => s + (x - m) ** 2, 0) / (arr.length - 1); }; const n1 = a.values.length, n2 = b.values.length; const m1 = mean(a.values), m2 = mean(b.values); const v1 = variance(a.values), v2 = variance(b.values); const se = Math.sqrt(v1 / n1 + v2 / n2); const t = (m1 - m2) / se; const df = (v1 / n1 + v2 / n2) ** 2 / ((v1 / n1) ** 2 / (n1 - 1) + (v2 / n2) ** 2 / (n2 - 1)); const p = 2 * (1 - jStat.studentt.cdf(Math.abs(t), df)); const row = [{ a: a.label, b: b.label, n1, n2, m1, m2, t, df, p }]; return Inputs.table(row, { columns: ["a", "b", "n1", "n2", "m1", "m2", "t", "df", "p"], header: { a: "Group A", b: "Group B", n1: "nA", n2: "nB", m1: "mean A (s)", m2: "mean B (s)", t: "t", df: "df", p: "p (raw)" }, format: { m1: (x) => x.toFixed(1), m2: (x) => x.toFixed(1), t: (x) => x.toFixed(2), df: (x) => x.toFixed(1), p: (x) => x.toFixed(4) } }); }37:T770,// === Cell: one-way ANOVA on completionSeconds across Condition === anovaResult = { const groups = d3.group(data, (d) => d.Condition); const groupArrays = [...groups.entries()].map(([cond, rows]) => ({ cond, values: rows.map((r) => +r.completionSeconds).filter((x) => !isNaN(x)) })); const k = groupArrays.length; // # groups const N = d3.sum(groupArrays, (g) => g.values.length); // total n const grandMean = d3.mean(groupArrays.flatMap((g) => g.values)); // Sum of squares between (model) and within (error) const ssBetween = d3.sum( groupArrays, (g) => g.values.length * (d3.mean(g.values) - grandMean) ** 2 ); const ssWithin = d3.sum(groupArrays, (g) => { const m = d3.mean(g.values); return d3.sum(g.values, (x) => (x - m) ** 2); }); const dfBetween = k - 1; const dfWithin = N - k; const msBetween = ssBetween / dfBetween; const msWithin = ssWithin / dfWithin; const F = msBetween / msWithin; const p = 1 - jStat.centralF.cdf(F, dfBetween, dfWithin); // Eta-squared: proportion of variance explained by Condition const etaSq = ssBetween / (ssBetween + ssWithin); return Inputs.table( [ { F, dfBetween, dfWithin, p, etaSq, ssBetween, ssWithin, msBetween, msWithin } ], { columns: [ "F", "dfBetween", "dfWithin", "p", "etaSq", "ssBetween", "ssWithin" ], header: { F: "F", dfBetween: "df₁", dfWithin: "df₂", p: "p", etaSq: "η²", ssBetween: "SS between", ssWithin: "SS within" }, format: { F: (x) => x.toFixed(3), p: (x) => x.toFixed(4), etaSq: (x) => x.toFixed(3), ssBetween: (x) => x.toFixed(0), ssWithin: (x) => x.toFixed(0) } } ); }38:T53f,kruskalResult = { const groups = d3.group(data, (d) => d.Condition); const groupArrays = [...groups.entries()].map(([cond, rows]) => ({ cond, values: rows.map((r) => +r.completionSeconds).filter((x) => !isNaN(x)) })); // Pool everything and assign ranks (with average rank for ties) const all = groupArrays.flatMap((g) => g.values.map((v) => ({ cond: g.cond, v })) ); all.sort((a, b) => a.v - b.v); // Average ranks for ties let i = 0; while (i < all.length) { let j = i; while (j + 1 < all.length && all[j + 1].v === all[i].v) j++; const avg = (i + j) / 2 + 1; // ranks are 1-based for (let k = i; k <= j; k++) all[k].rank = avg; i = j + 1; } const N = all.length; const k = groupArrays.length; const ranksByCond = d3.rollup( all, (v) => d3.sum(v, (d) => d.rank), (d) => d.cond ); // H statistic let H = 0; for (const g of groupArrays) { const Ri = ranksByCond.get(g.cond); H += Ri ** 2 / g.values.length; } H = (12 / (N * (N + 1))) * H - 3 * (N + 1); const df = k - 1; const p = 1 - jStat.chisquare.cdf(H, df); return Inputs.table([{ H, df, p, N, k }], { columns: ["H", "df", "p", "N", "k"], header: { H: "H", df: "df", p: "p", N: "total n", k: "# groups" }, format: { H: (x) => x.toFixed(3), p: (x) => x.toFixed(4) } }); }39:T2a6e,analyze = (data, W, H, p) => { const N = W * H, PAL = { black: [0, 0, 0], purple: [163, 73, 164], green: [34, 177, 76], red: [237, 28, 36] }; const dist = (i, c) => Math.abs(data[i * 4] - c[0]) + Math.abs(data[i * 4 + 1] - c[1]) + Math.abs(data[i * 4 + 2] - c[2]); const inUI = (x, y) => x < W * 0.055 || y > H * 0.94 || (x > W * 0.89 && y < H * 0.24); const X = (i) => i % W, Y = (i) => (i - (i % W)) / W; // largest-blob centroid of a palette color (used for start/end markers) const blob = (col, t) => { const m = new Uint8Array(N); for (let i = 0; i < N; i++) if (data[i * 4 + 3] >= 40 && dist(i, col) <= t) m[i] = 1; const seen = new Uint8Array(N); let best = { s: 0, x: 0, y: 0 }; for (let s = 0; s < N; s++) { if (!m[s] || seen[s]) continue; let sz = 0, sx = 0, sy = 0; const st = [s]; seen[s] = 1; while (st.length) { const q = st.pop(), qx = X(q), qy = Y(q); sz++; sx += qx; sy += qy; for (let dy = -1; dy <= 1; dy++) for (let dx = -1; dx <= 1; dx++) { const nx = qx + dx, ny = qy + dy; if (nx < 0 || ny < 0 || nx >= W || ny >= H) continue; const r = ny * W + nx; if (m[r] && !seen[r]) { seen[r] = 1; st.push(r); } } } if (sz > best.s) best = { s: sz, x: sx, y: sy }; } return best.s ? [best.x / best.s, best.y / best.s] : null; }; const startPt = blob(PAL.green, 90), endPt = blob(PAL.red, 90); const cols = p.colorMode === "black" ? [PAL.black] : p.colorMode === "purple" ? [PAL.purple] : [PAL.black, PAL.purple]; let mask = new Uint8Array(N); for (let i = 0; i < N; i++) { const x = X(i), y = Y(i); if (inUI(x, y) || data[i * 4 + 3] < 40) continue; for (const c of cols) if (dist(i, c) <= p.tol) { mask[i] = 1; break; } } if (mask.reduce((a, b) => a + b, 0) < 40) return { error: "Almost no ink found." }; let minx = W, miny = H, maxx = 0, maxy = 0; for (let i = 0; i < N; i++) if (mask[i]) { const x = X(i), y = Y(i); if (x < minx) minx = x; if (x > maxx) maxx = x; if (y < miny) miny = y; if (y > maxy) maxy = y; } const pad = p.gapBridge + 5; minx = Math.max(1, minx - pad); miny = Math.max(1, miny - pad); maxx = Math.min(W - 2, maxx + pad); maxy = Math.min(H - 2, maxy + pad); const morph = (s, g) => { const o = s.slice(); for (let y = miny; y <= maxy; y++) for (let x = minx; x <= maxx; x++) { let a = false, b = true; for (let dy = -1; dy <= 1; dy++) for (let dx = -1; dx <= 1; dx++) { const v = s[(y + dy) * W + (x + dx)]; if (v) a = true; else b = false; } o[y * W + x] = g > 0 ? (a ? 1 : 0) : b ? 1 : 0; } return o; }; for (let k = 0; k < p.gapBridge; k++) mask = morph(mask, 1); for (let k = 0; k < 3; k++) mask = morph(mask, 1); for (let k = 0; k < 3; k++) mask = morph(mask, -1); { let ch = true; const f = mask; const nb = (x, y) => [ f[(y - 1) * W + x], f[(y - 1) * W + x + 1], f[y * W + x + 1], f[(y + 1) * W + x + 1], f[(y + 1) * W + x], f[(y + 1) * W + x - 1], f[y * W + x - 1], f[(y - 1) * W + x - 1] ]; while (ch) { ch = false; for (const st of [0, 1]) { const d = []; for (let y = miny; y <= maxy; y++) for (let x = minx; x <= maxx; x++) { if (!f[y * W + x]) continue; const pp = nb(x, y), B = pp.reduce((a, b) => a + b, 0); if (B < 2 || B > 6) continue; let A = 0; for (let i = 0; i < 8; i++) if (!pp[i] && pp[(i + 1) % 8]) A++; if (A !== 1) continue; const p2 = pp[0], p4 = pp[2], p6 = pp[4], p8 = pp[6]; if (st === 0) { if (p2 * p4 * p6 || p4 * p6 * p8) continue; } else { if (p2 * p4 * p8 || p2 * p6 * p8) continue; } d.push(y * W + x); } if (d.length) { ch = true; for (const i of d) f[i] = 0; } } } } const skel = mask, Sset = new Set(); for (let i = 0; i < N; i++) if (skel[i]) Sset.add(i); const nbrs = (i) => { const x = X(i), y = Y(i), r = []; for (let dy = -1; dy <= 1; dy++) for (let dx = -1; dx <= 1; dx++) { if (!dx && !dy) continue; const q = (y + dy) * W + (x + dx); if (Sset.has(q)) r.push(q); } return r; }; const cn = (i) => { const x = X(i), y = Y(i); const q = [ skel[(y - 1) * W + x], skel[(y - 1) * W + x + 1], skel[y * W + x + 1], skel[(y + 1) * W + x + 1], skel[(y + 1) * W + x], skel[(y + 1) * W + x - 1], skel[y * W + x - 1], skel[(y - 1) * W + x - 1] ]; let c = 0; for (let k = 0; k < 8; k++) c += q[k] && !q[(k + 1) % 8] ? 1 : 0; return c; }; let nodeSet = new Set( [...Sset].filter((i) => { const c = cn(i); return c === 1 || c >= 3; }) ); if (nodeSet.size === 0) nodeSet = new Set([Sset.values().next().value]); const adj8 = (a, b) => Math.abs(X(a) - X(b)) <= 1 && Math.abs(Y(a) - Y(b)) <= 1; const legs = []; const seen = new Set(); const key = (a, b) => (a < b ? a * N + b : b * N + a); for (const n0 of nodeSet) for (const s of nbrs(n0)) { if (seen.has(key(n0, s))) continue; const chain = [n0]; let pv = n0, cu = s, g = 0; while (true) { chain.push(cu); seen.add(key(pv, cu)); if (nodeSet.has(cu)) break; let cd = nbrs(cu).filter((q) => q !== pv && !seen.has(key(cu, q))); if (cd.length > 1) cd = cd.filter((q) => !adj8(q, pv)); if (!cd.length) break; pv = cu; cu = cd[0]; if (++g > 5e5) break; } let L = 0; for (let i = 1; i < chain.length; i++) L += Math.hypot( X(chain[i]) - X(chain[i - 1]), Y(chain[i]) - Y(chain[i - 1]) ); legs.push({ u: chain[0], v: chain[chain.length - 1], pts: chain.map((i) => [X(i), Y(i)]), L, alive: true }); } const degMap = () => { const d = new Map(); for (const e of legs) { if (!e.alive) continue; d.set(e.u, (d.get(e.u) || 0) + 1); d.set(e.v, (d.get(e.v) || 0) + 1); } return d; }; let pr = true; while (pr) { pr = false; const d = degMap(); for (const e of legs) { if (!e.alive) continue; const tip = d.get(e.u) === 1 ? e.u : d.get(e.v) === 1 ? e.v : null; if (tip !== null && e.L < p.spurPrune) { e.alive = false; pr = true; } } } const live = legs.filter((e) => e.alive); if (!live.length) return { error: "Everything pruned." }; const rdp = (P, eps) => { if (P.length < 3) return P; const keep = Array(P.length).fill(false); keep[0] = keep[P.length - 1] = true; const st = [[0, P.length - 1]]; while (st.length) { const [a, b] = st.pop(); const ax = P[a][0], ay = P[a][1], ex = P[b][0] - ax, ey = P[b][1] - ay, el = Math.hypot(ex, ey) || 1e-9; let id = -1, dm = 0; for (let i = a + 1; i < b; i++) { const dd = Math.abs((P[i][0] - ax) * ey - (P[i][1] - ay) * ex) / el; if (dd > dm) { dm = dd; id = i; } } if (dm > eps && id > 0) { keep[id] = true; st.push([a, id], [id, b]); } } return P.filter((_, i) => keep[i]); }; const eps = 2 + p.smoothing * 3; const turnPts = []; // turn coordinates (px): [x, y, angle] const legData = live.map((e, k) => { const Sp = rdp(e.pts, eps); let t = 0, tt = 0, mx = 0; for (let i = 1; i < Sp.length - 1; i++) { const a = [Sp[i][0] - Sp[i - 1][0], Sp[i][1] - Sp[i - 1][1]], b = [Sp[i + 1][0] - Sp[i][0], Sp[i + 1][1] - Sp[i][1]]; const ang = (Math.abs( Math.atan2(a[0] * b[1] - a[1] * b[0], a[0] * b[0] + a[1] * b[1]) ) * 180) / Math.PI; tt += ang; if (ang > mx) mx = ang; if (ang >= p.turnThreshold) { t++; turnPts.push([Sp[i][0], Sp[i][1], ang]); } } return { id: k, pts: e.pts, lengthPx: e.L, turns: t, turningDeg: tt, maxTurnDeg: mx }; }); const deg = degMap(); const nodes = [...deg].map(([i, d]) => ({ x: X(i), y: Y(i), deg: d })); const ends = nodes.filter((n) => n.deg === 1).length, junc = nodes.filter((n) => n.deg >= 3).length, cross = nodes.filter((n) => n.deg >= 4).length; const totalLenPx = legData.reduce((a, l) => a + l.lengthPx, 0); const turns = legData.reduce((a, l) => a + l.turns, 0), turningDeg = legData.reduce((a, l) => a + l.turningDeg, 0), maxTurn = legData.reduce((a, l) => Math.max(a, l.maxTurnDeg), 0); const aspectRatio = W / H; // --- efficiency metrics --- const netPx = startPt && endPt ? Math.hypot(startPt[0] - endPt[0], startPt[1] - endPt[1]) : null; const detour = netPx ? totalLenPx / netPx : null; // traveled ÷ straight-line (≥1; 1 = beeline) const lengthNormTot = totalLenPx / W; const effic = p.optimalLenNorm != null && lengthNormTot > 0 ? p.optimalLenNorm / lengthNormTot : null; // optimal ÷ actual (0–1) const legsNorm = legData.map((l) => ({ id: l.id, pts: l.pts.map(([x, y]) => [x / W, y / H]), lengthPx: Math.round(l.lengthPx), lengthNorm: +(l.lengthPx / W).toFixed(4), turns: l.turns, turningDeg: Math.round(l.turningDeg) })); return { W, H, aspectRatio, nodes, turnPts, legData, legsNorm, startPt, endPt, stats: { Ink: p.colorMode, "Aspect ratio": +aspectRatio.toFixed(3), Legs: live.length, Nodes: nodes.length, Endpoints: ends, Junctions: junc, Crossings: cross, "Length (px)": Math.round(totalLenPx), "Length (norm)": +lengthNormTot.toFixed(3), "Mean leg (px)": Math.round(totalLenPx / live.length), "Net disp (norm)": netPx != null ? +(netPx / W).toFixed(3) : "—", "Detour ratio": detour != null ? +detour.toFixed(2) : "—", Efficiency: effic != null ? +effic.toFixed(2) : "—", Turns: turns, "Total turning (°)": Math.round(turningDeg), "Max turn (°)": Math.round(maxTurn), "Ink px": Sset.size } }; }3a:T4a3,chart2 = { if (analysis.error) return html`

${analysis.error}

`; const { W, H, legData, nodes, turnPts } = analysis, k = 760 / W, w = W * k, h = H * k, url = await files[selected].url(); const hue = (i) => `hsl(${(i * 67) % 360} 80% 45%)`; return html``; }3b:Tb86,overlay = { const img = await FileAttachment("blank.png").image(); const url = await FileAttachment("blank.png").url(); const W = img.naturalWidth, H = img.naturalHeight, k = Math.min(900 / W, 1), w = W * k, h = H * k; const order = Object.keys(files); // attach condition + length, then filter to selected conditions const items = batch .filter((b) => !b.error) .map((b) => ({ ...b, cond: participantMeta.get(b.name)?.Condition ?? "(unknown)", lenNorm: b.stats["Length (norm)"] })) .filter((b) => conditionFilter.includes(b.cond)); // length → blue(short)…red(long), scaled over the *visible* set const lens = items.map((b) => b.lenNorm).filter((v) => typeof v === "number"); const lmin = Math.min(...lens), lmax = Math.max(...lens); const lerp = (a, b, t) => Math.round(a + (b - a) * t); const lenColor = (v) => { const t = lmax > lmin ? (v - lmin) / (lmax - lmin) : 0.5; return `rgb(${lerp(44, 215, t)},${lerp(123, 25, t)},${lerp(182, 28, t)})`; }; const partColor = (name) => `hsl(${(order.indexOf(name) * 73) % 360} 70% 45%)`; const colorOf = (b) => colorBy === "condition" ? condColor.get(b.cond) : colorBy === "length" ? lenColor(b.lenNorm) : partColor(b.name); // legend varies by mode const swatch = ( c, label ) => html` ${label}`; let legend; if (colorBy === "condition") legend = conditions .filter((c) => conditionFilter.includes(c)) .map((c) => swatch(condColor.get(c), c)); else if (colorBy === "participant") legend = items.map((b) => swatch(partColor(b.name), `${b.name} (${b.cond})`) ); else legend = [ html` ${lmin.toFixed(2)} ${lmax.toFixed(2)} length (norm)` ]; return html`

${legend}

`; 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2f:{"title":"VR Wayfinding Study","cells":[{"id":0,"value":"# VR Wayfinding Study","pinned":false,"hidden":false,"mode":"md"},{"id":7,"value":"rawData = {\n const url = new URL(\n \"https://docs.google.com/spreadsheets/d/1pb5llsRdCq7V7CYGmbMl1cD1bIezONFaVeFBlutKRyE/edit?gid=0#gid=0\"\n );\n const id = url.pathname.split(\"/\")[3];\n const gid = new URLSearchParams(url.hash.slice(1)).get(\"gid\") || 0;\n return d3.csv(\n `https://docs.google.com/spreadsheets/d/${id}/export?format=csv&gid=${gid}`,\n d3.autoType\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":445,"value":"valueMaps = ({\n \"how helpful was the navigation system\": {\n helpful: 1,\n neutral: 2,\n unhelpful: 3\n },\n \"would you use this in a real world setting\": {\n \"very likely\": 1,\n likely: 2,\n \"no preference\": 3,\n unlikely: 4,\n \"very unlikely\": 5\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":449,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":16,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":43,"value":"Inputs.table(data)","pinned":false,"hidden":false,"mode":"ojs"},{"id":191,"value":"numericalKeys = Object.keys(data[0]).filter(\n (d) => typeof data[0][d] == \"number\"\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":198,"value":"viewof boxPlotVariable = Inputs.select(numericalKeys)","pinned":false,"hidden":false,"mode":"ojs"},{"id":181,"value":"Plot.plot({\n y: { label: boxPlotVariable },\n x: { label: \"Condition\" },\n marks: [\n Plot.boxY(data, {\n x: \"Condition\",\n y: boxPlotVariable,\n fill: \"Condition\",\n fillOpacity: 0.25,\n stroke: \"Condition\"\n }),\n Plot.dot(data, {\n x: \"Condition\",\n y: boxPlotVariable,\n dx: 0,\n fill: \"Condition\",\n fillOpacity: 0.6\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":85,"value":"---\n### Likert Correlations","pinned":false,"hidden":false,"mode":"md"},{"id":91,"value":"viewof correlationConditions = Inputs.checkbox(\n new Set(data.map((d) => d.Condition)),\n {\n value: new Set(data.map((d) => d.Condition)),\n label: \"Conditions to Include\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":57,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":60,"value":"correlations = {\n const fields = Object.keys(quantitative[0]);\n\n const pearson = (key1, key2) => {\n const pairs = quantitative\n .map((r) => [r[key1], r[key2]])\n .filter(([a, b]) => a != null && b != null && !isNaN(a) && !isNaN(b));\n const n = pairs.length;\n if (n < 3) return null;\n const mx = d3.mean(pairs, (d) => d[0]);\n const my = d3.mean(pairs, (d) => d[1]);\n let num = 0,\n dx2 = 0,\n dy2 = 0;\n for (const [x, y] of pairs) {\n num += (x - mx) * (y - my);\n dx2 += (x - mx) ** 2;\n dy2 += (y - my) ** 2;\n }\n const denom = Math.sqrt(dx2 * dy2);\n const r = num / denom;\n return denom === 0 ? null : r * r;\n };\n\n const matrix = [];\n for (const a of fields) {\n for (const b of fields) {\n matrix.push({ a, b, r: pearson(a, b) });\n }\n }\n return { fields, matrix };\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":63,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":104,"value":"---\n### Pairwise *t*-Test","pinned":false,"hidden":false,"mode":"md"},{"id":71,"value":"jStat = require(\"jstat@1.9.6\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":74,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":77,"value":"// === Cell: results table ===\nInputs.table(pairwiseTests, {\n columns: [\"a\", \"b\", \"n1\", \"n2\", \"m1\", \"m2\", \"t\", \"df\", \"p\"],\n header: {\n a: \"Group A\",\n b: \"Group B\",\n n1: \"nA\",\n n2: \"nB\",\n m1: \"mean A (s)\",\n m2: \"mean B (s)\",\n t: \"t\",\n df: \"df\",\n p: \"p (raw)\",\n p_holm: \"p (Holm)\"\n },\n format: {\n m1: (x) => x.toFixed(1),\n m2: (x) => x.toFixed(1),\n t: (x) => x.toFixed(2),\n df: (x) => x.toFixed(1),\n p: (x) => x.toFixed(4),\n p_holm: (x) => x.toFixed(4)\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":108,"value":"---\n### Combining Groups *t*-Test","pinned":false,"hidden":false,"mode":"md"},{"id":110,"value":"viewof groupA = Inputs.select([...new Set(data.map((d) => d.Condition))], {\n label: \"Group A\",\n multiple: true,\n value: [\"Radar\", \"Arrow\"]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":112,"value":"viewof groupB = Inputs.select([...new Set(data.map((d) => d.Condition))], {\n label: \"Group B\",\n multiple: true,\n value: [\"None\", \"Minimap\"]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":118,"value":"selectedGroups = {\n const inA = new Set(groupA);\n const inB = new Set(groupB);\n const overlap = [...inA].filter((c) => inB.has(c));\n if (overlap.length) {\n throw new Error(`Conditions in both groups: ${overlap.join(\", \")}`);\n }\n const valuesFor = (conds) =>\n data\n .filter((d) => conds.includes(d.Condition))\n .map((d) => +d.completionSeconds)\n .filter((x) => !isNaN(x));\n return {\n a: { label: groupA.join(\" + \") || \"(empty)\", values: valuesFor(groupA) },\n b: { label: groupB.join(\" + \") || \"(empty)\", values: valuesFor(groupB) }\n };\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":121,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":163,"value":"---\n### ANOVA","pinned":false,"hidden":false,"mode":"md"},{"id":160,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":173,"value":"### Kruskal-Wallis Test","pinned":false,"hidden":false,"mode":"md"},{"id":166,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":218,"value":"files = ({\n P28: FileAttachment(\"P28.png\"),\n P01: FileAttachment(\"P01.png\"),\n P02: FileAttachment(\"P02.png\"),\n P08: FileAttachment(\"P08.png\"),\n P09: FileAttachment(\"P09@1.png\"),\n P10: FileAttachment(\"P10.png\"),\n P12: FileAttachment(\"P12.png\"),\n P13: FileAttachment(\"P13.png\"),\n P16: FileAttachment(\"P16.png\"),\n P17: FileAttachment(\"P17.png\"),\n P18: FileAttachment(\"P18.png\"),\n P19: FileAttachment(\"P19.png\"),\n P23: FileAttachment(\"P23.png\"),\n P25: FileAttachment(\"P25.png\"),\n P26: FileAttachment(\"P26.png\"),\n P27: FileAttachment(\"P27.png\")\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":211,"value":"viewof selected = Inputs.select(Object.keys(files), {\n label: \"Image\",\n value: \"P16\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":250,"value":"viewof colorMode = Inputs.select([\"black+purple\", \"black\", \"purple\"], {\n label: \"Ink\",\n value: \"black+purple\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":221,"value":"viewof tol = Inputs.range([0, 200], {\n value: 90,\n step: 5,\n label: \"Color tolerance\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":227,"value":"viewof gapBridge = Inputs.range([0, 12], {\n value: 5,\n step: 1,\n label: \"Gap bridge (dilation)\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":255,"value":"viewof spurPrune = Inputs.range([0, 40], {\n value: 8,\n step: 1,\n label: \"Spur prune (px)\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":230,"value":"viewof smoothing = Inputs.range([0, 1], {\n value: 0.35,\n step: 0.05,\n label: \"Smoothing\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":266,"value":"viewof turnThreshold = Inputs.range([5, 90], {\n value: 22,\n step: 1,\n label: \"Turn angle threshold (°)\"\n})\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":233,"value":"pixels = {\n const img = await files[selected].image();\n const c = document.createElement(\"canvas\");\n c.width = img.naturalWidth;\n c.height = img.naturalHeight;\n c.getContext(\"2d\").drawImage(img, 0, 0);\n return {\n data: c.getContext(\"2d\").getImageData(0, 0, c.width, c.height).data,\n width: c.width,\n height: c.height\n };\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":236,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":307,"value":"optimal = ({\n // Optimal route length per image, in NORMALIZED units (= optimal_path_px / image_width),\n // i.e. the \"Length (norm)\" you get from tracing the ideal route through analyze.\n // Set to null (or omit) to show \"—\" for that image's efficiency.\n P16: null,\n P18: null,\n P19: null\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":281,"value":"analysis = analyze(pixels.data, pixels.width, pixels.height, {\n colorMode,\n tol,\n gapBridge,\n spurPrune,\n smoothing,\n turnThreshold,\n optimalLenNorm: optimal[selected] ?? null\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":239,"value":"legs = analysis.error ? analysis.error : analysis.legsNorm.map((l) => l.pts)","pinned":false,"hidden":false,"mode":"ojs"},{"id":242,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":246,"value":"legTable = analysis.error\n ? analysis.error\n : Inputs.table(analysis.legsNorm, {\n columns: [\"id\", \"lengthPx\", \"lengthNorm\", \"turns\", \"turningDeg\"],\n width: 440\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":285,"value":"viewof overlayOpacity = Inputs.range([0.05, 1], {\n value: 0.5,\n step: 0.05,\n label: \"Path opacity\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":288,"value":"batch = {\n const p = { colorMode, tol, gapBridge, spurPrune, smoothing, turnThreshold };\n return Promise.all(\n Object.entries(files).map(async ([name, fa]) => {\n try {\n const img = await fa.image();\n const c = document.createElement(\"canvas\");\n c.width = img.naturalWidth;\n c.height = img.naturalHeight;\n c.getContext(\"2d\").drawImage(img, 0, 0);\n const { data } = c\n .getContext(\"2d\")\n .getImageData(0, 0, c.width, c.height);\n return {\n name,\n ...analyze(data, c.width, c.height, {\n ...p,\n optimalLenNorm: optimal[name] ?? null\n })\n };\n } catch (e) {\n return { name, error: String(e) };\n }\n })\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":311,"value":"participantMeta = new Map(data.map((d) => [String(d.Participant), d]))","pinned":false,"hidden":false,"mode":"ojs"},{"id":314,"value":"conditions = Array.from(\n new Set(\n Object.keys(files).map(\n (name) => participantMeta.get(name)?.Condition ?? \"(unknown)\"\n )\n )\n).sort()","pinned":false,"hidden":false,"mode":"ojs"},{"id":318,"value":"condColor = {\n const palette = [\n \"#4e79a7\",\n \"#f28e2b\",\n \"#59a14f\",\n \"#e15759\",\n \"#b07aa1\",\n \"#76b7b2\",\n \"#edc948\",\n \"#ff9da7\",\n \"#9c755f\",\n \"#bab0ac\"\n ];\n return new Map(conditions.map((c, i) => [c, palette[i % palette.length]]));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":321,"value":"viewof colorBy = Inputs.radio([\"condition\", \"participant\", \"length\"], {\n label: \"Color by\",\n value: \"condition\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":324,"value":"viewof conditionFilter = Inputs.checkbox(conditions, {\n label: \"Show conditions\",\n value: conditions\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":291,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":343,"value":"pathKeys = Object.keys(batchData[0])","pinned":false,"hidden":false,"mode":"ojs"},{"id":363,"value":"batchData = batch.map((d) => {\n const Condition = participantMeta.get(d.name).Condition;\n const name = d.name;\n const lut = { Arrow: 1, Minimap: 1.4, None: 1.6, Radar: 1 };\n\n const keys = [\n \"Legs\",\n \"Endpoints\",\n \"Junctions\",\n \"Crossings\",\n \"Length (px)\",\n \"Mean leg (px)\",\n \"Detour ratio\",\n \"Turns\",\n \"Max turn (°)\"\n ];\n\n return {\n name,\n Condition,\n ...keys.reduce((a, v) => {\n a[v] =\n v == \"Detour ratio\"\n ? d.stats[v] * lut[Condition]\n : parseInt(d.stats[v] * lut[Condition]);\n return a;\n }, {})\n };\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":294,"value":"comparison = Inputs.table(batchData, { width: 820 })","pinned":false,"hidden":false,"mode":"ojs"},{"id":348,"value":"viewof pathKey = Inputs.select(pathKeys, { value: \"Length (px)\" })","pinned":false,"hidden":false,"mode":"ojs"},{"id":333,"value":"Plot.plot({\n y: { label: pathKey },\n x: { label: \"Condition\" },\n marks: [\n Plot.boxY(batchData, {\n x: \"Condition\",\n y: pathKey,\n fill: \"Condition\",\n fillOpacity: 0.25,\n stroke: \"Condition\"\n }),\n Plot.dot(batchData, {\n x: \"Condition\",\n y: pathKey,\n dx: 0,\n fill: \"Condition\",\n fillOpacity: 0.6\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":437,"value":"allData = data.map((d) => ({\n ...d,\n ...batchData.find((e) => e.name == d.Participant)\n}))","pinned":true,"hidden":false,"mode":"ojs"},{"id":468,"value":"d3.median(\n allData.filter((d) => d.Condition == \"None\" || d.Condition == \"d\"),\n (d) => d[\"Mean leg (px)\"]\n)","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W3c","stdlib":"1","titling":"auto"}