2d:["$","title",null,{"children":"Dynamic Top N and Binning | Heman Robinson | Observable"}] 32:Tdfd,{ // Calculate and draw the histogram. let onXAggregate = ( event, value ) => { // setXDomain( xScale.domain()); xAggregate = event ? +event.target.value : value; // Calculate the X bins. const thresholds = Graph.getThresholds( data, columnIndex, xScale, xAggregate ); const histogram = d3.histogram() .value( d => d[ columnIndex ]) .domain( xScale.domain()) .thresholds( thresholds ); const bins = histogram( data ); // Get the Y scale. const yDomain = [ 0, 1.05 * d3.max( bins, d => d.length )]; // a 5% margin const yScale = d3.scaleLinear() .range([ height - bottom, top ]) .domain( yDomain ); // Draw the bars. svg.selectAll( "*" ).remove(); svg.selectAll( "rect" ) .data( bins ) .enter() .append( "rect" ) .attr( "x", 1 ) .attr( "transform", bin => ( "translate( " + xScale( bin.x0 ) + "," + yScale( bin.length ) + " )" )) .attr( "width", bin => Math.max( 0, (( bin.x1 === bin.x0 ) ? 0 : ( xScale( bin.x1 ) - xScale( bin.x0 ) - 1 )))) .attr( "height", bin => Math.max( 0, ( height - bottom - yScale( bin.length )))) .style( "fill", "#99bbdd" ); // Draw the axes. Graph.drawAxes( svg, width, height, margin, padding, 0, xScale, yScale, xDomain, yDomain, "Random Normal, Bimodal", "Frequency", thresholds ); }; // Initialization. const width = 650, height = 400, sliderOffset = 7, padding = { top: 20, right: 20, bottom: 0, left: 20 }, margin = { top: 0, right: 10, bottom: 50, left: 50 }, top = margin.top + padding.top, right = margin.right + padding.right, bottom = margin.bottom + padding.bottom, left = margin.left + padding.left; // Get the data. const data = dataNormal, columnIndex = 0; // Get the X domain. // TODO: Calculate margins based on minimum and maximum thresholds. const xMin = d3.min( data, d => d[ columnIndex ]), xMax = d3.max( data, d => d[ columnIndex ]), xMargin = 0.1 * ( xMax - xMin ); const xDomain = [ xMin - xMargin, xMax + xMargin ]; // Get the X scale. const xScale = d3.scaleLinear() .range([ left, width - right ]) .domain( xDomain ) .nice(); // Create the elements. let onMouseOver = ( event ) => { slider.style( "display", "inline" ); }; let onMouseOut = ( event ) => { slider.style( "display", "none" ); }; let xAggregate = Graph.getDefaultAggregate( data, columnIndex, xScale ); const div = d3 .create( "div" ) .on( "mouseover", onMouseOver ) .on( "mouseout", onMouseOut ) .style( "width", "" + width + "px" ) .style( "height", "" + height + "px" ); const svg = div.append( "svg" ) .attr( "width", width ) .attr( "height", height ) .attr( "viewBox", [ 0, 0, width, height ]) .style( "border", "1px solid #939ba1" ) .style( "background-color", "#ffffff" ) .style( "z-index", 1 ); const slider = div.append( "input" ) .attr( "type", "range" ) .attr( "min", 0 ) .attr( "max", 1 ) .attr( "step", 0.01 ) .attr( "value", xAggregate ) .on( "input", onXAggregate ) .style( "position", "absolute" ) .style( "width", "" + ( width - left - right + 1 ) + "px" ) .style( "top", "" + ( height - margin.bottom - sliderOffset ) + "px" ) .style( "left", "" + ( left - 1 ) + "px" ) .style( "z-index", 2 ) .style( "display", "none" ); // Calculate and draw the histogram. onXAggregate( undefined, xAggregate ); // Return the graph. return div.node(); }33:T17d8,{ // Calculate and draw the bar chart. let onXAggregate = ( event, value ) => { // setXDomain( xScale.domain()); xAggregate = event ? +event.target.value : value; // Get the data. let data = dataSales; // Calculate the bars and sort them. let bars = Array.from( d3.rollup( data, v => d3.sum( v, d => d[ 1 ]), d => d[ 0 ])); bars.sort(( a, b ) => ( b[ 1 ] - a[ 1 ])); // Combine bars if requested. let n = Math.round( xAggregate * bars.length ); if( 1 < n ) { let total = 0; for( let i = 0; ( i < n ); i++ ) { total += bars[ bars.length - i - 1 ][ 1 ]; } bars.splice( bars.length - n, n ); bars.push([ "Other", total ]); } // Set the X domain. const xDomain0 = bars.map( x => x[ 0 ]); const xScale = d3.scaleBand().domain( xDomain0 ).range([ left, width - right ]).padding( 0.2 ); // Get the Y scale. const yDomain0 = [ 0, ( 1 + yMargin ) * d3.max( bars, d => d[ 1 ])]; const yScale = d3.scaleLinear() .domain( yDomain0 ) .range([ height - bottom, top ]); // If the "Other" bar is long, shorten it. n = bars.length; let yScale1 = yScale, otherLength; const maxLength = d3.max( bars.slice( 0, n - 1 ), d => d[ 1 ]), isOtherLong = ( n > 1 ) && ( bars[ n - 1 ][ 0 ] === "Other" ) && ( bars[ n - 1 ][ 1 ] > otherPercent * maxLength ); if( isOtherLong ) { // Save the "Other" bar length. otherLength = bars[ n - 1 ][ 1 ]; // Shorten the "Other" bar. bars[ n - 1 ][ 1 ] = otherPercent * maxLength; yScale1 = d3.scaleLinear() .domain([ 0, ( 1 + yMargin ) * bars[ n - 1 ][ 1 ]]) .range([ height - bottom, top ]); } // Draw the bars. svg.selectAll( "*" ).remove(); svg.selectAll( "rect" ) .data( bars ) .enter() .append( "rect" ) .attr( "x", ( d ) => xScale( d[ 0 ])) .attr( "y", ( d ) => yScale1( d[ 1 ])) .attr( "width", xScale.bandwidth()) .attr( "height", ( d ) => (( xScale.domain().indexOf( d[ 0 ]) >= 0 ) ? Math.max( 0, height - yScale1( d[ 1 ])) : 0 )) .style( "fill", "#99bbdd" ); // Draw the axes. Graph.drawAxes( svg, width, height, margin, padding, 0, xScale, yScale1, xDomain0, yDomain0, "Model", "Cars Sold, 2022 Q1" ); // If the "Other" bar is long, draw the second Y axis. if( isOtherLong ) { // Clear the margin. const d = 10, y = yScale1( maxLength ) - 2 * d, colorLight = "#ebeeef"; svg.append( "rect" ) .attr( "x", 0 ) .attr( "y", top ) .attr( "width", margin.left ) .attr( "height", y - top ) .style( "fill", colorLight ); // Draw the second Y axis. let yScale2 = d3.scaleLinear() .domain([( 1 + yMargin ) * maxLength, ( 1 + yMargin ) * otherLength ]) .range([ y, top ]); svg.append( "g" ) .attr( "class", "axis" ) .attr( "transform", "translate( " + margin.left + ", 0 )" ) .call( d3.axisLeft( yScale2 ).ticks( 2 ).tickFormat( t => ( "" + t ))); // Draw the breaks. let breakWidth = 30, x = margin.left - breakWidth / 2; svg.append( "rect" ) .attr( "x", x ) .attr( "y", y ) .attr( "width", breakWidth ) .attr( "height", d ) .style( "fill", "#939ba1" ); svg.append( "rect" ) .attr( "x", x ) .attr( "y", y + 1 ) .attr( "width", breakWidth ) .attr( "height", d - 2 ) .style( "fill", "#ffffff" ); breakWidth = xScale.step(); const barPadding = ( width - left - right - n * breakWidth ); x = width - right - breakWidth - barPadding / 2; svg.append( "rect" ) .attr( "x", x ) .attr( "y", y ) .attr( "width", breakWidth ) .attr( "height", d ) .style( "fill", "#939ba1" ); svg.append( "rect" ) .attr( "x", x ) .attr( "y", y + 1 ) .attr( "width", breakWidth ) .attr( "height", d - 2 ) .style( "fill", "#ffffff" ); // Restore the "Other" bar length. bars[ n - 1 ][ 1 ] = otherLength; } }; // Create the elements. let onMouseOver = ( event ) => { slider.style( "display", "inline" ); }; let onMouseOut = ( event ) => { slider.style( "display", "none" ); }; let xAggregate = 0; const yMargin = 0.05, otherPercent = 1.3, width = 650, height = 400, sliderOffset = 7, padding = { top: 20, right: 20, bottom: 0, left: 20 }, margin = { top: 0, right: 0, bottom: 120, left: 60 }, top = margin.top + padding.top, right = margin.right + padding.right, bottom = margin.bottom + padding.bottom, left = margin.left + padding.left; const div = d3 .create( "div" ) .on( "mouseover", onMouseOver ) .on( "mouseout", onMouseOut ) .style( "width", "" + width + "px" ) .style( "height", "" + height + "px" ); const svg = div.append( "svg" ) .attr( "width", width ) .attr( "height", height ) .attr( "viewBox", [ 0, 0, width, height ]) .style( "border", "1px solid #939ba1" ) .style( "background-color", "#ffffff" ) .style( "z-index", 1 ); const slider = div.append( "input" ) .attr( "type", "range" ) .attr( "min", 0 ) .attr( "max", 1 ) .attr( "step", 0.01 ) .attr( "value", xAggregate ) .on( "input", onXAggregate ) .style( "position", "absolute" ) .style( "width", "" + ( width - left - right + 1 ) + "px" ) .style( "top", "" + ( height - margin.bottom - sliderOffset ) + "px" ) .style( "left", "" + ( left - 1 ) + "px" ) .style( "z-index", 2 ) .style( "display", "none" ); // Calculate and draw the bar chart. onXAggregate( undefined, xAggregate ); // Return the graph. return div.node(); }34:T1905,{ // Set the X aggregate. let onXAggregate = ( event, value ) => { // setXDomain( xScale.domain()); xAggregate = event ? +event.target.value : value; draw(); } // Set the Y aggregate. let onYAggregate = ( event, value ) => { // setYDomain( xScale.domain()); yAggregate = event ? +event.target.value : value; draw(); } // Calculate and draw the heatmap. let draw = () => { // Get the unique Y values and sort them. let values = Array.from( d3.rollup( data, v => d3.sum( v, d => d[ zIndex ]), d => d[ yIndex ])); values.sort(( a, b ) => ( b[ 1 ] - a[ 1 ])); yDomain0 = values.map( x => x[ 0 ]); // Get the Y scale. const yDomain = yDomain0; yScale = d3.scaleBand().domain( yDomain ).range([ height - bottom, top ]); // Calculate the X bins. const thresholds = Graph.getThresholds( data, xIndex, xScale, xAggregate ); const histogram = d3.histogram() .value( d => d[ xIndex ]) .domain( xScale.domain()) .thresholds( thresholds ); bins = histogram( data ); // Calculate the tiles and sort them. tiles = []; for( let i = 0; ( i < yDomain0.length ); i++ ) { tiles[ i ] = []; } let j = 0; bins.forEach(( bin ) => { let t = []; for( let i = 0; ( i < yDomain0.length ); i++ ) { t[ i ] = 0; } bin.forEach(( b ) => { let k = yDomain0.indexOf( b[ yIndex ]); t[ k ] += b[ zIndex ]; }) for( let i = 0; ( i < yDomain0.length ); i++ ) { tiles[ i ][ j ] = t[ i ]; } j++; }); tiles.sort(( a, b ) => ( d3.sum( b ) - d3.sum( a ))); // Flatten the tiles into one Array for drawing. let myTiles = []; for( let i = 0; ( i < tiles.length ); i++ ) { for( let j = 0; ( j < bins.length ); j++ ) { myTiles[ i + j * tiles.length ] = tiles[ i ][ j ]; } } tiles = myTiles; // Combine tiles if requested. let n = Math.round( yAggregate * yDomain0.length ); if( 1 < n ) { for( let j = bins.length; ( j > 0 ); j-- ) { let total = 0; for( let i = 1; ( i <= n ); i++ ) { total += tiles[ j * yDomain0.length - i ]; } tiles.splice( j * yDomain0.length - n, n, total ); } yDomain0.splice( yDomain0.length - n, n, "Other" ); } // Assign the Y domain. yScale.domain( yDomain0 ); // Draw the tiles. const nY = yDomain0.length; let colorScale = d3.scaleLinear().domain([ 0, d3.max( tiles, t => t )]).range([ "#99bbdd", "#ff0000" ]); svg.selectAll( "*" ).remove(); svg.selectAll( "rect" ) .data( tiles ) .enter() .append( "rect" ) .attr( "x", ( d, i ) => xScale( bins[( i / nY ) >> 0 ].x0 )) .attr( "y", ( d, i ) => yScale( yDomain0[ i % nY ]) + 1 ) .attr( "width", ( d, i ) => xScale( bins[( i / nY ) >> 0 ].x1 ) - xScale( bins[( i / nY ) >> 0 ].x0 ) - 1 ) .attr( "height", ( d ) => ( d > 0 ) ? yScale.bandwidth() - 1 : 0 ) .style( "fill", ( d ) => colorScale( d )); // Draw the axes. Graph.drawAxes( svg, width, height, margin, padding, 0, xScale, yScale, xDomain0, yDomain0, "Week Number", "Google Trends 2020", thresholds ); }; // Initialization. const width = 400, height = 650, sliderOffset = 7, padding = { top: 20, right: 20, bottom: 10, left: 10 }, margin = { top: 0, right: 0, bottom: 50, left: 140 }, top = margin.top + padding.top, right = margin.right + padding.right, bottom = margin.bottom + padding.bottom, left = margin.left + padding.left; // Get the data. const data = dataTrends; // Initialization. let xIndex = 0, yIndex = 2, zIndex = 3, xDomain0, yDomain0, xScale, yScale, histogram, bins, tiles; // Get the X domain. // TODO: Calculate margins based on minimum and maximum thresholds. const xMin = d3.min( data, d => d[ xIndex ]), xMax = d3.max( data, d => d[ xIndex ]), xMargin = 0.1 * ( xMax - xMin ); xDomain0 = [( Math.abs( xMin ) < xMargin ) ? xMin : xMin - xMargin, xMax + xMargin ]; // Get the X scale. const xDomain = xDomain0; xScale = d3.scaleLinear() .range([ left, width - right ]) .domain( xDomain ) .nice(); // Create the elements. let onMouseOver = ( event ) => { sliderX.style( "display", "inline" ); sliderY.style( "display", "inline" ); }; let onMouseOut = ( event ) => { sliderX.style( "display", "none" ); sliderY.style( "display", "none" ); }; let xAggregate = Graph.getDefaultAggregate( data, xIndex, xScale ), yAggregate = 0; const div = d3 .create( "div" ) .on( "mouseover", onMouseOver ) .on( "mouseout", onMouseOut ) .style( "width", "" + width + "px" ) .style( "height", "" + height + "px" ); const svg = div.append( "svg" ) .attr( "width", width ) .attr( "height", height ) .attr( "viewBox", [ 0, 0, width, height ]) .style( "border", "1px solid #939ba1" ) .style( "background-color", "#ffffff" ) .style( "z-index", 1 ); const sliderX = div.append( "input" ) .attr( "type", "range" ) .attr( "min", 0 ) .attr( "max", 1 ) .attr( "step", 0.01 ) .attr( "value", xAggregate ) .on( "input", onXAggregate ) .style( "position", "absolute" ) .style( "width", "" + ( width - left - right + 1 ) + "px" ) .style( "top", "" + ( height - margin.bottom - sliderOffset ) + "px" ) .style( "left", "" + ( left - 1 ) + "px" ) .style( "z-index", 2 ) .style( "display", "none" ); const sliderY = div.append( "input" ) .attr( "type", "range" ) .attr( "min", 0 ) .attr( "max", 1 ) .attr( "step", 0.01 ) .attr( "orient", "vertical" ) // for Firefox .attr( "value", yAggregate ) .on( "input", onYAggregate ) .style( "appearance", "slider-vertical" ) .style( "position", "absolute" ) .style( "height", "" + ( height - top - bottom + 1 ) + "px" ) .style( "width", "16px" ) .style( "top", "" + ( top + 1 ) + "px" ) .style( "left", "" + ( margin.left - sliderOffset - 1 ) + "px" ) .style( "z-index", 2 ) .style( "display", "none" ); // Calculate and draw the heatmap. draw(); // Return the graph. return div.node(); }35:T574,/* * Returns default aggregate value using Scott's rule. * * @param {Array[]} data data set * @param {number} columnIndex index of column to be binned * @param {D3Scale} xScale X scale * @return {number} default aggregate value, between 0 and 1 */ Graph.getDefaultAggregate = ( data, columnIndex, xScale ) => { // Get the scale information. const ticks = xScale.ticks(); const domain = xScale.domain(); const range = xScale.range(); // Ensure that the minimum bin width is visible. let minWidth = ( ticks[ 1 ] - ticks[ 0 ]) / 16; minWidth = Math.max( minWidth, 2 * ( domain[ 1 ] - domain[ 0 ]) / ( range[ 1 ] - range[ 0 ])); // Get the values. let values = []; data.forEach( d => values.push( d[ columnIndex ])); // Get the initial bin width using Scott's rule. let bins = d3.bin().thresholds( d3.thresholdScott )( values ); let binWidth = ( domain[ 1 ] - domain[ 0 ]) / bins.length; // Calculate the default aggregate value from the binWidth. const k = Math.round(( domain[ 1 ] - domain[ 0 ]) / minWidth ); let aggregate = 1 - (( domain[ 1 ] - domain[ 0 ]) / binWidth - 1 ) / ( k - 1 ); // Transform the aggregate value. let myAggregate = aggregate * aggregate * aggregate * aggregate; // Return the aggregate value. return myAggregate; };36:T91e,/** * Returns bin thresholds for a continuous set of values and specified aggregate value. * * @param {Array[]} data data set * @param {number} columnIndex index of column to be binned * @param {D3Scale} xScale X scale * @param {number} aggregate aggregate value, between 0 and 1 * @return {number[][]} bin thresholds */ Graph.getThresholds = ( data, columnIndex, xScale, aggregate ) => { // Get the scale information. const ticks = xScale.ticks(); const domain = xScale.domain(); const range = xScale.range(); // Ensure that the minimum bin width is visible. let minWidth = ( ticks[ 1 ] - ticks[ 0 ]) / 16; minWidth = Math.max( minWidth, 4 * ( domain[ 1 ] - domain[ 0 ]) / ( range[ 1 ] - range[ 0 ])); // Transform the aggregate value. // Without a transform, changing the number of bins has a disproportionate effect when there are fewer bins. // I tried a log transform, but that does not sufficiently reduce this effect. let myAggregate = Math.sqrt( Math.sqrt( aggregate )); // Calculate the bin width from the aggregate value. const k = Math.round(( domain[ 1 ] - domain[ 0 ]) / minWidth ); const d = Math.round( 1 + ( k - 1 ) * ( 1 - myAggregate )); let binWidth = ( domain[ 1 ] - domain[ 0 ]) / d; // Calculate the nearest "nice" tick step. let mag = Math.floor( Math.log10( binWidth )); const pow = Math.pow( 10, mag ); const m = [ 1, 2, 4, 5, 8 ]; let niceStep = Number.POSITIVE_INFINITY; for( let i = 0; ( i < m.length ); i++ ) { if( Math.abs( binWidth - pow * m[ i ]) < Math.abs( binWidth - niceStep )) { niceStep = pow * m[ i ]; } } // Return the thresholds. let thresholds = []; if( aggregate > 0.95 ) { const epsilon = 1000 * Number.EPSILON * ( domain[ 1 ] - domain[ 0 ]); thresholds = [ d3.min( data, d => d[ columnIndex ]), ( 1 + epsilon ) * d3.max( data, d => d[ columnIndex ])]; } else { const minStep = niceStep * Math.floor( domain[ 0 ] / niceStep ), maxStep = niceStep * Math.floor( domain[ 1 ] / niceStep ); for( let step = minStep; ( step <= maxStep ); step += niceStep ) { thresholds.push( step ); } } return thresholds; };37:T14fe,/** * Draws the axes. * * @param {Object} svg SVG element * @param {number} width width, in pixels * @param {number} height height, in pixels * @param {Box} margin margin * @param {Box} padding padding * @param {number} xScrollSize scroll size in the X dimension, or 0 for default * @param {D3Scale} xScale X scale * @param {D3Scale} yScale Y scale * @param {Array} xDomain0 Initial X domain * @param {Array} yDomain0 Initial Y domain * @param {string} xLabel X axis label * @param {string} yLabel Y axis label * @param {number[]} xThresholds bin thresholds in the X dimension, or undefined for none * @param {number[]} yThresholds bin thresholds in the Y dimension, or undefined for none */ Graph.drawAxes = ( svg, width, height, margin, padding, xScrollSize, xScale, yScale, xDomain0, yDomain0, xLabel, yLabel, xThresholds, yThresholds ) => { // Initialization. const scrollSize = 15, colorLight = "#ebeeef"; // Clear the margins. svg.append( "rect" ) .attr( "x", 0 ) .attr( "y", 0 ) .attr( "width", width ) .attr( "height", padding.top ) .style( "fill", colorLight ); svg.append( "rect" ) .attr( "x", width - padding.right ) .attr( "y", 0 ) .attr( "width", padding.right ) .attr( "height", height + xScrollSize ) .style( "fill", colorLight ); svg.append( "rect" ) .attr( "x", 0 ) .attr( "y", height - margin.bottom ) .attr( "width", width ) .attr( "height", margin.bottom ) .style( "fill", colorLight ); svg.append( "rect" ) .attr( "x", 0 ) .attr( "y", 0 ) .attr( "width", margin.left ) .attr( "height", height + xScrollSize ) .style( "fill", colorLight ); // Get the tick values from the thresholds. let xTickValues = null; let xTickFormat = null; if( xThresholds ) { // Add or remove one tick at the end if needed. const n = xThresholds.length, nextTickValue = xThresholds[ n - 1 ] + xThresholds[ n - 1 ] - xThresholds[ n - 2 ], xValues = xThresholds.concat(); if( nextTickValue <= xScale.domain()[ 1 ]) { xValues.push( nextTickValue ); } if( xValues[ 0 ] < xScale.domain()[ 0 ]) { xValues.splice( 0, 1 ); } // Thin the X tick values if needed to ensure minimum pixels per value. const nTickValues = xValues.length; const nMaxValues = Math.round(( xScale.range()[ 1 ] - xScale.range()[ 0 ]) / 40 ); let inc = 1; let divisors = [ 1, 2, 4, 5, 8 ]; let i = 0; let m = 1; while( nTickValues / inc > nMaxValues ) { inc = divisors[ i ] * m; i++; if( i >= divisors.length ) { i = 0; m *= 10; } } xTickValues = []; for( let i = 0; ( i < nTickValues ); i += inc ) { xTickValues.push( xValues[ i ]); } // Get the precision. let step = xTickValues[ 1 ] - xTickValues[ 0 ]; let precision = d3.precisionFixed( step ); step *= Math.pow( 10, precision ); const epsilon = 1000 * Number.EPSILON * step; if( step - Math.floor( step + epsilon ) > .01 ) { // TODO: This works around a possible bug in d3.precisionFixed. precision++; } // Get the tick format. const s = d3.formatSpecifier( "f" ); s.precision = precision; xTickFormat = d3.format( s ); } // TODO: Similarly for Y. let yTickValues = null; let yTickFormat = null; // Draw the X axis. if( margin.bottom > 50 ) { svg.append( "g" ) .style( "text-anchor", "end" ) .attr( "transform", "translate( 0, " + ( height - margin.bottom ) + " )" ) .call( d3.axisBottom( xScale ).tickSizeOuter( 0 ).ticks( 3 ).tickValues( xTickValues ).tickFormat( xTickFormat )) .selectAll( "text" ) .attr( "transform", ( margin.bottom > 50 ) ? "translate( -13, 8 ) rotate( -90 )" : "" ); } else { svg.append( "g" ) .attr( "transform", "translate( 0, " + ( height - margin.bottom ) + " )" ) .call( d3.axisBottom( xScale ).tickSizeOuter( 0 ).ticks( 3 ).tickValues( xTickValues ).tickFormat( xTickFormat )) .selectAll( "text" ) .attr( "transform", ( margin.bottom > 50 ) ? "translate( -13, 8 ) rotate( -90 )" : "" ); } svg.append( "text" ) .attr( "transform", "translate( " + ( width / 2 ) + " ," + ( height - 1.5 * scrollSize ) + ")" ) .style( "text-anchor", "middle" ) .style( "font-size", "12px" ) .text( xLabel ); // Draw the Y axis. svg.append( "g" ) .attr( "class", "axis" ) .attr( "transform", "translate( " + margin.left + ", 0 )" ) .call( d3.axisLeft( yScale ).tickSizeOuter( 0 ).ticks( 3 ).tickValues( yTickValues ).tickFormat( yTickFormat )); svg.append( "text" ) .attr( "x", margin.left ) .attr( "y", margin.top + padding.top * 0.7 ) .style( "text-anchor", "middle" ) .style( "font-size", "12px" ) .text( yLabel ); };38:T145f, // Random normal, bimodal. dataNormal = [ [-0.08], [-0.02], [-0.35], [0.21], [-0.6], [0], [-0.46], [0.25], [-0.3], [0.2], [-0.54], [-0.07], [-0.03], [0.14], [-0.34], [0.21], [0.02], [-0.27], [0.02], [0.16], [-0.02], [0.11], [-0.12], [0.59], [-0.22], [0.06], [0.03], [-0.16], [-0.22], [0.03], [0.08], [0.24], [0.01], [0.04], [0.46], [0.06], [-0.15], [0.31], [0.14], [0.25], [-0.27], [0.6], [-0.21], [0.29], [-0.31], [0.22], [0.16], [0.09], [-0.07], [0.05], [-0.3], [0.19], [-0.44], [-0.25], [-0.41], [0.16], [-0.4], [0.08], [-0.26], [0.17], [-0.49], [0.35], [-0.21], [-0.31], [-0.12], [0.09], [-0.21], [0.12], [-0.39], [0.21], [-0.59], [0.2], [-0.01], [0.38], [0.2], [0.27], [-0.16], [-0.06], [0.08], [0.17], [-0.33], [0.33], [-0.09], [-0.04], [-0.04], [0.08], [-0.06], [0.25], [0.14], [-0.01], [-0.01], [-0.33], [-0.23], [-0.26], [-0.32], [-0.03], [-0.2], [0.12], [0.1], [0.01], [-0.18], [0], [-0], [0.35], [-0.22], [0.2], [0.1], [0.37], [-0.05], [0.09], [0.42], [0.09], [0.13], [0.44], [-0.01], [-0.17], [-0.12], [0.14], [-0.16], [0.36], [0.08], [0.28], [-0.36], [0.07], [-0.28], [0.09], [-0.22], [-0], [-0.16], [-0.08], [-0.07], [0.09], [-0.19], [0.26], [0.01], [-0.34], [-0.28], [0.59], [-0.04], [0.08], [-0.56], [0.67], [-0.28], [0.26], [-0.35], [-0], [0.21], [-0.21], [-0.14], [0.36], [0.01], [0.49], [-0.1], [-0.07], [-0.42], 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[-0.03], [0.04], [-0.22], [-0.19], [-0.08], [0.39], [-0.22], [0.33], [-0.32], [0.1], [0.13], [0.23], [-0.07], [0.03], [-0.26], [0.47], [-0.03], [0.17], [0.32], [0.07], [0.15], [0.11], [-0.37], [0.06], [-0.15], [0.13], [-0.13], [0.23], [-0.18], [0.08], [-0.04], [0.38], [0.23], [-0.01], [-0.18], [-0.1], [-0.79], [0.41], [-0.06], [-0.03], [0.1], [0.21], [-0.3], [-0.06], [-0.28], [0.04], [-0.37], [0.02], [-0.21], [0.21], [-0.44], [0.21], [-0.35], [0.41], [-0.02], [0.32], [-0.05], [0.36], [0], [0.1], [-0.29], [-0.23], [-0.18], [-0.24], [-0.02], [-0.28], [-0.25], [0.23], [0.07], [0.27], [-0.05], [-0.07], [-0.35], [0.28], [-0.01], [0.31], [-0.18], [0.38], [-0.06], [0.05], [-0.13], [0.55], [0.23], [0.12], [-0.09], [0.43], [-0.44], [0.14], [-0.45], [0.35], [-0.1], [0.03], [-0.1], [-0.09], [-0.3], [0.35], [-0.26], [0.6], [-0.44], [0], [-0.4], [0.26], [0.07], [0.13], [-0.13], [0.01] ];39:T1f84,// US Car Sales (https://www.goodcarbadcar.net/2022-us-passenger-car-sales-figures-by-model/). dataSales = [ [ "Toyota Camry", 19665 ], [ "Toyota Camry", 15612 ], [ "Toyota Camry", 26228 ], [ "Toyota Corolla", 15368 ], [ "Toyota Corolla", 15222 ], [ "Toyota Corolla", 16911 ], [ "Honda Accord", 10410 ], [ "Honda Accord", 13971 ], [ "Honda Accord", 17658 ], [ "Tesla Model 3", 13739 ], [ "Tesla Model 3", 13230 ], [ "Tesla Model 3", 13230 ], [ "Honda Civic", 13599 ], [ "Honda Civic", 14472 ], [ "Honda Civic", 10444 ], [ "Nissan Altima", 12254 ], [ "Nissan Altima", 12254 ], [ "Nissan Altima", 13786 ], [ "Nissan Sentra", 8682 ], [ "Nissan Sentra", 8682 ], [ "Nissan Sentra", 9767 ], [ "Chevrolet Malibu", 8218 ], [ "Chevrolet Malibu", 8218 ], [ "Chevrolet Malibu", 9246 ], [ "Kia Forte", 5748 ], [ "Kia Forte", 8141 ], [ "Kia Forte", 9609 ], [ "Hyundai Elantra", 5081 ], [ "Hyundai Elantra", 6786 ], [ "Hyundai Elantra", 10205 ], [ "Kia K5", 5794 ], [ "Kia K5", 6558 ], [ "Kia K5", 5470 ], [ "Dodge Charger", 4940 ], [ "Dodge Charger", 4940 ], [ "Dodge Charger", 5558 ], [ "Kia Soul", 3890 ], [ "Kia Soul", 5059 ], [ "Kia Soul", 5175 ], [ "Ford Mustang", 5671 ], [ "Ford Mustang", 4700 ], [ "Ford Mustang", 3615 ], [ "Tesla Model S", 3930 ], [ "Tesla Model S", 3785 ], [ "Tesla Model S", 3785 ], [ "Dodge Challenger", 3560 ], [ "Dodge Challenger", 3560 ], [ "Dodge Challenger", 4005 ], [ "Volkswagen Jetta", 3481 ], [ "Volkswagen Jetta", 3481 ], [ "Volkswagen Jetta", 3916 ], [ "Lexus ES", 2944 ], [ "Lexus ES", 3284 ], [ "Lexus ES", 3823 ], [ "Mazda 3", 3470 ], [ "Mazda 3", 2969 ], [ "Mazda 3", 3053 ], [ "Chevrolet Corvette", 2820 ], [ "Chevrolet Corvette", 2820 ], [ "Chevrolet Corvette", 3172 ], [ "Subaru Impreza", 2939 ], [ "Subaru Impreza", 3520 ], [ "Subaru Impreza", 2329 ], [ "BMW 3-Series", 2610 ], [ "BMW 3-Series", 2610 ], [ "BMW 3-Series", 2936 ], [ "BMW 4-Series", 2361 ], [ "BMW 4-Series", 2361 ], [ "BMW 4-Series", 2656 ], [ "Toyota Prius Family", 0 ], [ "Toyota Prius Family", 3080 ], [ "Toyota Prius Family", 3631 ], [ "Chevrolet Camaro", 2147 ], [ "Chevrolet Camaro", 2147 ], [ "Chevrolet Camaro", 2416 ], [ "Kia Rio", 1633 ], [ "Kia Rio", 2045 ], [ "Kia Rio", 2829 ], [ "BMW 5-Series", 1806 ], [ "BMW 5-Series", 1806 ], [ "BMW 5-Series", 2032 ], [ "Subaru Legacy", 1492 ], [ "Subaru Legacy", 1417 ], [ "Subaru Legacy", 2611 ], [ "Mitsubishi Mirage", 1573 ], [ "Mitsubishi Mirage", 1573 ], [ "Mitsubishi Mirage", 1770 ], [ "Mercedes-Benz S", 1541 ], [ "Mercedes-Benz S", 1541 ], [ "Mercedes-Benz S", 1734 ], [ "Hyundai Sonata", 0 ], [ "Hyundai Sonata", 3037 ], [ "Hyundai Sonata", 1716 ], [ "Mercedes-Benz E / CLS", 1514 ], [ "Mercedes-Benz E / CLS", 1514 ], [ "Mercedes-Benz E / CLS", 1703 ], [ "Lexus IS", 1708 ], [ "Lexus IS", 1184 ], [ "Lexus IS", 1570 ], [ "Nissan Leaf", 1399 ], [ "Nissan Leaf", 1399 ], [ "Nissan Leaf", 1574 ], [ "BMW 2-Series", 1288 ], [ "BMW 2-Series", 1288 ], [ "BMW 2-Series", 1449 ], [ "Mini Cooper", 1241 ], [ "Mini Cooper", 1241 ], [ "Mini Cooper", 1396 ], [ "Audi A4", 1233 ], [ "Audi A4", 1233 ], [ "Audi A4", 1387 ], [ "Audi A5", 1229 ], [ "Audi A5", 1229 ], [ "Audi A5", 1383 ], [ "Acura ILX", 1045 ], [ "Acura ILX", 1223 ], [ "Acura ILX", 1493 ], [ "Hyundai Ioniq", 1415 ], [ "Hyundai Ioniq", 1269 ], [ "Hyundai Ioniq", 860 ], [ "Cadillac CT5", 1070 ], [ "Cadillac CT5", 1070 ], [ "Cadillac CT5", 1203 ], [ "Toyota GR86", 1331 ], [ "Toyota GR86", 925 ], [ "Toyota GR86", 1001 ], [ "Kia Stinger", 991 ], [ "Kia Stinger", 1090 ], [ "Kia Stinger", 1026 ], [ "Acura TLX", 224 ], [ "Acura TLX", 985 ], [ "Acura TLX", 1782 ], [ "Genesis G70", 681 ], [ "Genesis G70", 895 ], [ "Genesis G70", 1297 ], [ "Nissan Maxima", 918 ], [ "Nissan Maxima", 918 ], [ "Nissan Maxima", 1033 ], [ "Volvo 60-Series", 1263 ], [ "Volvo 60-Series", 592 ], [ "Volvo 60-Series", 965 ], [ "Chrysler 300", 878 ], [ "Chrysler 300", 878 ], [ "Chrysler 300", 988 ], [ "Hyundai Accent", 507 ], [ "Hyundai Accent", 744 ], [ "Hyundai Accent", 1446 ], [ "Infiniti Q50", 777 ], [ "Infiniti Q50", 777 ], [ "Infiniti Q50", 874 ], [ "Volkswagen Passat", 750 ], [ "Volkswagen Passat", 750 ], [ "Volkswagen Passat", 844 ], [ "Nissan Versa", 746 ], [ "Nissan Versa", 746 ], [ "Nissan Versa", 839 ], [ "Volkswagen Golf", 743 ], [ "Volkswagen Golf", 743 ], [ "Volkswagen Golf", 836 ], [ "Toyota Avalon", 892 ], [ "Toyota Avalon", 537 ], [ "Toyota Avalon", 726 ], [ "Porsche 911", 682 ], [ "Porsche 911", 682 ], [ "Porsche 911", 768 ], [ "Porsche Taycan", 616 ], [ "Porsche Taycan", 616 ], [ "Porsche Taycan", 693 ], [ "Cadillac CT4", 606 ], [ "Cadillac CT4", 606 ], [ "Cadillac CT4", 681 ], [ "Honda Insight", 556 ], [ "Honda Insight", 546 ], [ "Honda Insight", 763 ], [ "Mazda MX-5 Miata", 386 ], [ "Mazda MX-5 Miata", 556 ], [ "Mazda MX-5 Miata", 663 ], [ "BMW 7-Series", 479 ], [ "BMW 7-Series", 479 ], [ "BMW 7-Series", 539 ], [ "BMW 8-Series", 448 ], [ "BMW 8-Series", 448 ], [ "BMW 8-Series", 504 ], [ "Audi A3", 438 ], [ "Audi A3", 438 ], [ "Audi A3", 492 ], [ "Audi A6", 408 ], [ "Audi A6", 408 ], [ "Audi A6", 459 ], [ "Alfa Romeo Giulia", 366 ], [ "Alfa Romeo Giulia", 366 ], [ "Alfa Romeo Giulia", 412 ], [ "Mercedes-Benz A", 360 ], [ "Mercedes-Benz A", 360 ], [ "Mercedes-Benz A", 405 ], [ "Toyota Supra", 370 ], [ "Toyota Supra", 298 ], [ "Toyota Supra", 439 ], [ "Mercedes-Benz CLA", 340 ], [ "Mercedes-Benz CLA", 340 ], [ "Mercedes-Benz CLA", 383 ], [ "Genesis G80", 405 ], [ "Genesis G80", 263 ], [ "Genesis G80", 380 ], [ "Subaru BRZ", 331 ], [ "Subaru BRZ", 349 ], [ "Subaru BRZ", 291 ], [ "Mercedes-Benz AMG GT", 301 ], [ "Mercedes-Benz AMG GT", 301 ], [ "Mercedes-Benz AMG GT", 339 ], [ "Porsche Panamera", 252 ], [ "Porsche Panamera", 252 ], [ "Porsche Panamera", 283 ], [ "Porsche 718", 250 ], [ "Porsche 718", 250 ], [ "Porsche 718", 282 ], [ "Infiniti Q60", 246 ], [ "Infiniti Q60", 246 ], [ "Infiniti Q60", 276 ], [ "Toyota Mirai", 134 ], [ "Toyota Mirai", 175 ], [ "Toyota Mirai", 406 ], [ "Audi e-Tron Sportback", 209 ], [ "Audi e-Tron Sportback", 209 ], [ "Audi e-Tron Sportback", 234 ], [ "Lexus LS", 203 ], [ "Lexus LS", 200 ], [ "Lexus LS", 186 ], [ "Subaru Impreza WRX", 341 ], [ "Subaru Impreza WRX", 31 ], [ "Subaru Impreza WRX", 204 ], [ "Audi e-Tron GT", 183 ], [ "Audi e-Tron GT", 183 ], [ "Audi e-Tron GT", 206 ], [ "Audi A7", 161 ], [ "Audi A7", 161 ], [ "Audi A7", 181 ], [ "Volvo 90-Series", 154 ], [ "Volvo 90-Series", 147 ], [ "Volvo 90-Series", 197 ], [ "Mercedes-Benz C", 158 ], [ "Mercedes-Benz C", 158 ], [ "Mercedes-Benz C", 178 ], [ "Lexus RC", 159 ], [ "Lexus RC", 137 ], [ "Lexus RC", 165 ], [ "Lexus LC", 146 ], [ "Lexus LC", 143 ], [ "Lexus LC", 153 ], [ "Genesis G90", 187 ], [ "Genesis G90", 145 ], [ "Genesis G90", 97 ], [ "Hyundai Veloster", 86 ], [ "Hyundai Veloster", 171 ], [ "Hyundai Veloster", 171 ], [ "Jaguar F-Type", 128 ], [ "Jaguar F-Type", 128 ], [ "Jaguar F-Type", 144 ], [ "Jaguar XF", 128 ], [ "Jaguar XF", 128 ], [ "Jaguar XF", 144 ], [ "Chevrolet Bolt", 115 ], [ "Chevrolet Bolt", 115 ], [ "Chevrolet Bolt", 129 ], [ "Fiat 500", 107 ], [ "Fiat 500", 107 ], [ "Fiat 500", 120 ], [ "Mazda 6", 256 ], [ "Mazda 6", 55 ], [ "Mazda 6", 14 ], [ "BMW Z4", 89 ], [ "BMW Z4", 89 ], [ "BMW Z4", 100 ], [ "Chevrolet Spark", 89 ], [ "Chevrolet Spark", 89 ], [ "Chevrolet Spark", 100 ], [ "Audi A8", 75 ], [ "Audi A8", 75 ], [ "Audi A8", 84 ], [ "Audi TT", 39 ], [ "Audi TT", 39 ], [ "Audi TT", 44 ], [ "Audi R8", 22 ], [ "Audi R8", 22 ], [ "Audi R8", 24 ], [ "Acura NSX", 0 ], [ "Acura NSX", 21 ], [ "Acura NSX", 25 ] ];3a:Te8f5,// Subset of Google Trends 2020 (https://observablehq.com/@observablehq/plot-test-data/). dataTrends = [ [ 1, "2019-12-29", "COVID-19", 0.5 ], [ 2, "2020-01-05", "COVID-19", 0.5 ], [ 3, "2020-01-12", "COVID-19", 0.5 ], [ 4, "2020-01-19", "COVID-19", 1 ], [ 5, "2020-01-26", "COVID-19", 1 ], [ 6, "2020-02-02", "COVID-19", 1 ], [ 7, "2020-02-09", "COVID-19", 1 ], [ 8, "2020-02-16", "COVID-19", 1 ], [ 9, "2020-02-23", "COVID-19", 4 ], [ 10, "2020-03-01", "COVID-19", 10 ], [ 11, "2020-03-08", "COVID-19", 38 ], [ 12, "2020-03-15", "COVID-19", 87 ], [ 13, "2020-03-22", "COVID-19", 97 ], [ 14, "2020-03-29", "COVID-19", 100 ], [ 15, "2020-04-05", "COVID-19", 86 ], [ 16, "2020-04-12", "COVID-19", 67 ], [ 17, "2020-04-19", "COVID-19", 61 ], [ 18, "2020-04-26", "COVID-19", 55 ], [ 19, "2020-05-03", "COVID-19", 51 ], [ 20, "2020-05-10", "COVID-19", 50 ], [ 21, "2020-05-17", "COVID-19", 48 ], [ 22, "2020-05-24", "COVID-19", 42 ], [ 23, "2020-05-31", "COVID-19", 35 ], [ 24, "2020-06-07", "COVID-19", 39 ], [ 25, "2020-06-14", "COVID-19", 47 ], [ 26, "2020-06-21", "COVID-19", 59 ], [ 27, "2020-06-28", "COVID-19", 65 ], [ 28, "2020-07-05", "COVID-19", 66 ], [ 29, "2020-07-12", "COVID-19", 70 ], [ 30, "2020-07-19", "COVID-19", 61 ], [ 31, "2020-07-26", "COVID-19", 59 ], [ 32, "2020-08-02", "COVID-19", 61 ], [ 33, "2020-08-09", "COVID-19", 48 ], [ 34, "2020-08-16", "COVID-19", 42 ], [ 35, "2020-08-23", "COVID-19", 38 ], [ 36, "2020-08-30", "COVID-19", 40 ], [ 37, "2020-09-06", "COVID-19", 36 ], [ 38, "2020-09-13", "COVID-19", 33 ], [ 39, "2020-09-20", "COVID-19", 35 ], [ 40, "2020-09-27", "COVID-19", 46 ], [ 41, "2020-10-04", "COVID-19", 44 ], [ 42, "2020-10-11", "COVID-19", 39 ], [ 43, "2020-10-18", "COVID-19", 42 ], [ 44, "2020-10-25", "COVID-19", 47 ], [ 45, "2020-11-01", "COVID-19", 38 ], [ 46, "2020-11-08", "COVID-19", 62 ], [ 47, "2020-11-15", "COVID-19", 79 ], [ 48, "2020-11-22", "COVID-19", 61 ], [ 49, "2020-11-29", "COVID-19", 61 ], [ 50, "2020-12-06", "COVID-19", 66 ], [ 51, "2020-12-13", "COVID-19", 65 ], [ 52, "2020-12-20", "COVID-19", 66 ], [ 53, "2020-12-27", "COVID-19", 66 ], [ 1, "2019-12-29", "TikTok", 29 ], [ 2, "2020-01-05", "TikTok", 29 ], [ 3, "2020-01-12", "TikTok", 29 ], [ 4, "2020-01-19", "TikTok", 33 ], [ 5, "2020-01-26", "TikTok", 27 ], [ 6, "2020-02-02", "TikTok", 28 ], [ 7, "2020-02-09", "TikTok", 32 ], [ 8, "2020-02-16", "TikTok", 34 ], [ 9, "2020-02-23", "TikTok", 33 ], [ 10, "2020-03-01", "TikTok", 33 ], [ 11, "2020-03-08", "TikTok", 35 ], [ 12, "2020-03-15", "TikTok", 43 ], [ 13, "2020-03-22", "TikTok", 55 ], [ 14, 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Continuous Data

\n For continuous data, there are many rules for determining bin width. D3's supported rules are demonstrated here (Freedman and Diaconis, 1981) (Scott, 1979) (Sturges, 1926).\n

\n These rules produce different results, because this problem doesn't have one right answer. So it's best to let the user explore.\n

\n A slider control on the axis affords adjustment of the bins. In the histogram below, larger bin widths suggest the data might fit a normal distribution. Smaller bin widths suggest a multimodal distribution. (The generated data are in fact bimodal).\n

Categorical Data

\n For categorical data, the smaller categories can be combined into an \"Other\" bin. This clearly displays the \"Top N\" larger categories -- top 5, top 10, or however many the user desires.\n

\n We don't know what the user desires, and it may depend on the data. So as with the continuous case, it's best to let the user explore.\n

\n With modern technology, we can make the \"Other\" bin dynamically adjustable. This is particularly useful with \"long-tailed\" distributions, as in the bar chart below.\n

Multiple Dimensions

\nThis user interface extends readily to multiple dimensions. The heatmap below demonstrates both continuous and categorical binning.\n

User Interface

\n To minimize distraction from the data display, controls are displayed only when they can be used. On desktops, controls are discoverable by hovering over the graph. (On mobile devices, not supported here, a tap might serve this purpose.)\n

\n For continuous data, the default bin width is determined by Scott's binning rule (Scott, 1979). The minimum bin width is the smallest visible width. The maximum bin width puts all the data in one bin. These are natural limits that users expect.\n

\n For categorical data, the default behavior is to show all the categories. At the other extreme, the natural limit is to lump all the data into one category.\n

\n Positioning the slider along the axis conserves screen real estate, but the better reason is that it improves usability. This positioning enables the user to focus their eyes in one place, rather than switching between the graph in one place and the controls in another.\n

Implementation

\nThis project uses React, Material-UI, and d3.\n

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Further Reading

Continuous Data

Categorical Data

Multiple Dimensions

User Interface

Implementation