2a:null 2b:["$","title",null,{"children":"What Ski Areas in the Northeast are the Best and Worst Value? | Camden Blatchly | Observable"}] 30:T4dd,md` # What Ski Areas in the Northeast are the Best and Worst Value? I pulled together a spreadsheet of ski resorts in New Hampshire, Maine, Vermont, and New York that captures each resort's avg. snowfall, vertical drop, skiable acres, lift number, area owner, and season pass price. The first four values I pulled from each ski area's [OnTheSnow](onthesnow.com) page, while I did some googling to determine the latter two values. Because there is a wide variety of season pass offerings (such as senior passes, midweek passes, and others), the season pass price in the dataset represents the cheapest pass option at each resort that would allow an adult (ages 30-65) unlimited skiing for the season, disregarding pre-season sale prices. I would have liked to include day ticket prices as well, but, with many ski areas still figuring out how to handle skier capacity with regard to coronavirus guidelines, only a handful of ski areas had their day pass prices listed. For me, the best value ski area is the one that provides the most vertical drop at the lowest price. Accordingly, I've plotted ski areas against these two values below. At first glance, Smuggler's Notch appears to be a great deal, while Holiday Valley is a clear rip-off. `31:T841,chart = { // let data = edges.map(e=>Object({idx:e, dis:v[e[0]].dis(v[e[1]])})) let data = stat; let trace = [ { y: d3.map(data, function(d) { return d.season_pass; }), x: d3.map(data, function(d) { return d.vertical_drop; }), text: d3.map(data, function(d) { return d.ski_area + ", " + d.state; }), customdata: d3.map(data, function(d) { return d.avg_snowfall; }), marker: { size: d3.map(data, function(d) { return d.skiable_acres; }), sizemode: "area" }, transforms: [ { type: "groupby", groups: d3.map(data, function(d) { return d.owner; }) } ], mode: 'markers', type: 'scatter', hovertemplate: "%{text}
" + "Vertical Drop: %{x} ft
" + "Season Pass Price: %{y}
" + "Skiable Terrain: %{marker.size:,} acres
" + // "Avg. Snowfall: %{customdata:,} in
" + "Owner: %{fullData.name}" + "", hoverlabel: { bgcolor: "white", align: "left", font: { family: 'Raleway' } } } ]; let config = { responsive: true, displayModeBar: false }; let layout = { hovermode: 'closest', legend: { orientation: 'h', y: -0.4 }, margin: { t: 10 }, xaxis: { tickformat: ",.0", zeroline: false, tickfont: { family: 'Raleway', color: '#646468' }, title: { text: "Vertical Drop (ft)", font: { family: 'Raleway', color: 'black' } } }, yaxis: { tickformat: "$,.0", zeroline: false, tickfont: { family: 'Raleway', color: '#646468' }, title: { text: "Season Pass Price (dollars)", font: { family: 'Raleway', color: 'black' }, standoff: 15 } } }; return Plotly.newPlot(DOM.element('div'), trace, layout, config); }32:T8c6,chart_with_lin_regression = { let data = stat; let trace1 = { name: "owner", y: d3.map(data, function(d) { return d.season_pass; }), x: d3.map(data, function(d) { return d.vertical_drop; }), text: d3.map(data, function(d) { return d.ski_area + ", " + d.state; }), customdata: d3.map(data, function(d) { return d.avg_snowfall; }), marker: { size: d3.map(data, function(d) { return d.skiable_acres; }), sizemode: "area" }, transforms: [ { type: "groupby", groups: d3.map(data, function(d) { return d.owner; }) } ], mode: 'markers', type: 'scatter', hovertemplate: "%{text}
" + "Vertical Drop: %{x} ft
" + "Season Pass Price: %{y}
" + "Skiable Terrain: %{marker.size:,} acres
" + // "Avg. Snowfall: %{customdata:,} in" + "Owner: %{fullData.name}" + "", hoverlabel: { bgcolor: "white", font: { family: 'Raleway' }, align: "left" } }; let trace2 = { x: [ski_value_linear_regression[0][0], ski_value_linear_regression[1][0]], y: [ski_value_linear_regression[0][1], ski_value_linear_regression[1][1]], mode: 'lines', name: "linear regression" }; let traces = [trace1, trace2]; let config = { responsive: true, displayModeBar: false }; let layout = { hovermode: 'closest', legend: { orientation: 'h', y: -0.4 }, margin: { t: 10 }, xaxis: { tickformat: ",.0", zeroline: false, tickfont: { family: 'Raleway', color: '#646468' }, title: { text: "Vertical Drop (ft)", font: { family: 'Raleway', color: 'black' } } }, yaxis: { tickformat: "$,.0", zeroline: false, tickfont: { family: 'Raleway', color: '#646468' }, title: { text: "Season Pass Price (dollars)", font: { family: 'Raleway', color: 'black' }, standoff: 15 } } }; return Plotly.newPlot(DOM.element('div'), traces, layout, config); }33:T7c4,best_value_chart = { let data = stat_plus_distance; let trace1 = { y: d3.map(data, function(d) { return d.ski_area + ", " + d.state; }), x: d3.map(data, function(d) { return +d.distance; }), text: d3.map(data, function(d) { return ( "Residual: " + residualFormat(d.distance) + "
" + "Vertical Drop: " + d.vertical_drop + " ft
" + "Season Pass Price: " + d.season_pass + "
" + // "Avg. Snowfall: " + // d.avg_snowfall + // " in
" + "Owner: " + d.owner + "
" ); }), //customdata: d3.map(data, function(d) { return "Verical Drop: " + d.vertical_drop + "
" ; }), transforms: [ // { type: "groupby", groups: d3.map(data, function(d) { return d.owner; })}, { type: 'sort', target: 'x', order: 'total ascending' } ], marker: { color: d3.map(data, function(d) { return d3Colors(d.owner); }) }, type: 'bar', orientation: 'h', hovertemplate: "%{y}
" + "%{text}" + "", hoverlabel: { bgcolor: "white", font: { family: "Raleway" }, align: "left" } }; let traces = [trace1]; let config = { responsive: true, displayModeBar: false }; let layout = { hovermode: "closest", showlegend: false, height: 1200, margin: { t: 10 }, xaxis: { tickformat: "$,.0", tickfont: { family: 'Raleway', color: '#646468' }, title: { font: { family: 'Raleway', color: 'black' } } }, yaxis: { automargin: true, tickfont: { family: 'Raleway', color: '#646468' }, title: { font: { family: 'Raleway', color: 'black' } } } }; return Plotly.newPlot(DOM.element('div'), traces, layout, config); }34:T872,chart_weighted_score = { let data = stat_normalized; let trace = [ { y: d3.map(data, function(d) { return d.season_pass; }), x: d3.map(data, function(d) { return d.weighted_score; }), text: d3.map(data, function(d) { return d.ski_area + ", " + d.state; }), customdata: d3.map(data, function(d) { return [d.avg_snowfall, d.vertical_drop, d.lift_number]; }), marker: { size: d3.map(data, function(d) { return d.skiable_acres; }), sizemode: "area" }, transforms: [ { type: "groupby", groups: d3.map(data, function(d) { return d.owner; }) } ], mode: 'markers', type: 'scatter', hovertemplate: "%{text}
" + "Weighted Score: %{x:,.0f}
" + "Season Pass Price: %{y}
" + "Skiable Terrain: %{marker.size:,} acres
" + "Avg. Snowfall: %{customdata[0]:,} in
" + "Vertical Drop: %{customdata[1]:,} ft
" + "Lift Number: %{customdata[2]:,}" + "", hoverlabel: { bgcolor: "white", font: { family: "Raleway" }, align: "left" } } ]; let config = { responsive: true, displayModeBar: false }; let layout = { hovermode: 'closest', legend: { orientation: 'h', y: -0.4 }, margin: { t: 10 }, xaxis: { tickformat: ",.0", zeroline: false, tickfont: { family: 'Raleway', color: '#646468' }, title: { text: "Weighted Score", font: { family: 'Raleway', color: 'black' } } }, yaxis: { tickformat: "$,.0", zeroline: false, tickfont: { family: 'Raleway', color: '#646468' }, title: { text: "Season Pass Price (dollars)", font: { family: 'Raleway', color: 'black' }, standoff: 15 } } }; return Plotly.newPlot(DOM.element('div'), trace, layout, config); }35:T7cf,best_value_chart_weighted = { let data = stat_normalized_plus_distance; let trace1 = { y: d3.map(data, function(d) { return d.ski_area + ", " + d.state; }), x: d3.map(data, function(d) { return +d.distance; }), text: d3.map(data, function(d) { return ( "Residual: " + residualFormat(d.distance) + "
" + "Vertical Drop: " + d.vertical_drop + " ft
" + "Season Pass Price: " + d.season_pass + "
" + "Avg. Snowfall: " + d.avg_snowfall + " in
" + "Owner: " + d.owner + "
" ); }), //customdata: d3.map(data, function(d) { return "Verical Drop: " + d.vertical_drop + "
" ; }), transforms: [ // { type: "groupby", groups: d3.map(data, function(d) { return d.owner; })}, { type: 'sort', target: 'x', order: 'total ascending' } ], marker: { color: d3.map(data, function(d) { return d3Colors(d.owner); }) }, type: 'bar', orientation: 'h', hovertemplate: "%{y}
" + "%{text}" + "", hoverlabel: { bgcolor: "white", font: { family: "Raleway" }, align: "left" } }; let traces = [trace1]; let config = { responsive: true, displayModeBar: false }; let layout = { hovermode: "closest", showlegend: false, height: 1200, margin: { t: 10 }, xaxis: { tickformat: "$,.0", tickfont: { family: 'Raleway', color: '#646468' }, title: { font: { family: 'Raleway', color: 'black' } } }, yaxis: { automargin: true, tickfont: { family: 'Raleway', color: '#646468' }, title: { font: { family: 'Raleway', color: 'black' } } } }; return Plotly.newPlot(DOM.element('div'), traces, layout, config); }36:T5e9,stat_normalized = { let snowfall_scale = d3 .scaleLinear() .domain([ d3.min(stat, function(d) { return +d.avg_snowfall; }), d3.max(stat, function(d) { return +d.avg_snowfall; }) ]) .range([0, 100]); let vertical_scale = d3 .scaleLinear() .domain([ d3.min(stat, function(d) { return +d.vertical_drop; }), d3.max(stat, function(d) { return +d.vertical_drop; }) ]) .range([0, 100]); let lift_scale = d3 .scaleLinear() .domain([ d3.min(stat, function(d) { return +d.lift_number; }), d3.max(stat, function(d) { return +d.lift_number; }) ]) .range([0, 100]); let terrain_scale = d3 .scaleLinear() .domain([ d3.min(stat, function(d) { return +d.skiable_acres; }), d3.max(stat, function(d) { return +d.skiable_acres; }) ]) .range([0, 100]); console.log( d3.max(stat, function(d) { return d.skiable_acres; }) ); let temp_stat = stat; for (let i = 0; i < stat.length; i++) { let temp = temp_stat[i]; temp_stat[i]["weighted_score"] = slider_inputs["snowfall"] * snowfall_scale(temp["avg_snowfall"]) + slider_inputs["vertical_drop"] * vertical_scale(temp["vertical_drop"]) + slider_inputs["lift_number"] * lift_scale(temp["lift_number"]) + slider_inputs["skiable_terrain"] * terrain_scale(temp["skiable_acres"]); } return temp_stat; }37:[{"id":"3efe7f6d90ef8e85faf67cf51a02ee7fb2fbfe2a0420be205127f7d8dca49f31b4604a2e49c276a0b1a96df7e0ba31508e64ef2130126e2011cfb52a82b535ff","href":"https://static.observableusercontent.com/files/3efe7f6d90ef8e85faf67cf51a02ee7fb2fbfe2a0420be205127f7d8dca49f31b4604a2e49c276a0b1a96df7e0ba31508e64ef2130126e2011cfb52a82b535ff","name":"ski_area_comparison.csv","type":"text/csv","lastModified":1603930551718.548,"size":2289}] 2e:{"id":"b6df8435aabea03e","slug":"what-ski-areas-in-the-northeast-are-the-best-and-worst-value","title":"What Ski Areas in the Northeast are the Best and Worst Value?","thumbnail":"4c7252626610c564538dab7d541d97c26db8802b8b4d78d24986702422408cfc","access_level":"public","create_time":"$D2020-10-29T00:15:24.248Z","update_time":"$D2020-11-01T14:07:04.885Z","view_time":null,"first_public_time":"$D2020-10-31T04:17:10.361Z","has_unpublished_edits":null,"fork":{"id":"644ea80cac055999","slug":"plotly-playground","title":"Plotly Playground","owner":{"id":"e968a466b4f02e15","login":"gustavoquincy","name":"Kaile Yuan"}},"stars":3,"starred":false,"owner":{"type":"team","id":"a2a487c20bd4b3af","login":"camdenblatchly","name":"Camden Blatchly","avatar_url":"https://avatars-next.observableusercontent.com/001eda96ca4dcca562e7236ae19e698720e8ea20a3d23a09a96333702c4b0f7a","tier":"starter_2024"},"version":690,"publish_version":690,"role":null,"authored":false,"authors":[{"type":"individual","id":"c72886761e8ba310","login":"camdenblatchly","name":"Camden Blatchly","avatar_url":"https://avatars-next.observableusercontent.com/001eda96ca4dcca562e7236ae19e698720e8ea20a3d23a09a96333702c4b0f7a","tier":null}],"members":[{"type":"individual","id":"c72886761e8ba310","login":"camdenblatchly","name":"Camden Blatchly","avatar_url":"https://avatars-next.observableusercontent.com/001eda96ca4dcca562e7236ae19e698720e8ea20a3d23a09a96333702c4b0f7a","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":2,"collection":null,"type":"notebook","body":{"title":"What Ski Areas in the Northeast are the Best and Worst Value?","cells":[{"id":91,"value":"$30","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":0,"value":"md`## The Bigger the Mountain, the Bigger the Cost\nVertical Drop, feet, versus Season Pass Price, dollars
\nSkiable terrain, acres, corresponds to circle area`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":89,"value":"md`\nTo better investigate ski area value, I added a linear regression line on top of the same chart as above. The ski areas falling above the line are priced higher than predicted (bad value), while the ski areas below the line are priced lower than predicted (good value).\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":168,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":371,"value":"md`\nBy calculating the distance between the center of each circle and the linear regression line, I can start to answer my initial question: *what ski areas in the Northeast are the best and worst value?*\n\n# Ski Area Value in the Northeast, Best to Worst\nDistance from predicted season pass value, dollars, based on vertical drop\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":196,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":400,"value":"md`\nOf course, there are factors other than vertical drop that make a ski area good or bad. While it's impossible to numerically describe an area's atmosphere, we can account for other factors, such as snowfall, skiable terrain, vertical drop, and uphill capacity. Adjust the weights below to your preferences and see which resorts come out on top.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":478,"value":"viewof slider_inputs = inputsGroup(\n [\n [\n slider({ title: 'Snowfall', name: 'name' }),\n slider({\n title: 'Vertical Drop',\n name: 'name'\n })\n ],\n [\n slider({\n title: 'Lift Number',\n name: 'name'\n }),\n slider({\n title: 'Skiable Terrain',\n name: 'name'\n })\n ]\n ],\n\n [['snowfall', 'vertical_drop'], ['lift_number', 'skiable_terrain']]\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":461,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":629,"value":"md`\nBased upon your weighting of the four inputs, we can now calculate a new ranking of the best and worst value ski areas.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":559,"value":"md`\n# Ski Area Value in the Northeast, Best to Worst\nDistance from predicted season pass value, dollars, based on weighted score\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":556,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":683,"value":"md`\nNo matter which way you spin it, you should probably get a season pass to [Whaleback](https://www.youtube.com/watch?v=5rkQuLb4QUM&feature=emb_title) in Enfield, NH.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":624,"value":"md`\n## Sorted and Normalized Data\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":434,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":301,"value":"sorted_resorts = stat.slice().sort((a, b) => d3.descending(+a.vertical_drop, +b.vertical_drop))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":617,"value":"md`\n## Residual calculation and format\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":186,"value":"function getDistanceFromLine(x0, y0, x1, y1, x2, y2) {\n return ( (y2-y1)*x0 - (x2-x1)*y0 + x2*y1 - y2*x1 ) / Math.sqrt( Math.pow((y2-y1), 2) + Math.pow((x2-x1), 2) )\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":341,"value":"residualFormat = d3.format(\"$.0f\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":611,"value":"md`\n## Mapping Colors to Area Owners\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":284,"value":"function onlyUnique(value, index, self) {\n return self.indexOf(value) === index;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":266,"value":"d3Colors = Plotly.d3.scale.category10()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":281,"value":"owners = d3.map(stat, function(d) { return d.owner; })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":288,"value":"owners_unique = owners.filter(onlyUnique);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":291,"value":"d3Colors.domain(owners_unique);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":591,"value":"md`\n## Trend Lines\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":117,"value":"ski_value_linear_regression = linearRegression(stat)\n\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":539,"value":"weighted_ski_value_linear_regression = linearRegressionWeightedScore(\n stat_normalized\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":588,"value":"md`\n## Linear Regression Functions\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":150,"value":"linearRegression = d3_regression.regressionLinear()\n .x(d => +d.vertical_drop)\n .y(d => +d.season_pass)\n .domain([0, 3750]);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":537,"value":"linearRegressionWeightedScore = d3_regression\n .regressionLinear()\n .x(d => +d.weighted_score)\n .y(d => +d.season_pass)\n .domain([\n 0,\n d3.max(stat_normalized, function(d) {\n return d.weighted_score;\n })\n ])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":578,"value":"md`\n## Adding residuals to datasets\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":309,"value":"stat_plus_distance = {\n let temp = stat\n for (let i = 0; i < stat.length; i++) {\n temp[i][\"distance\"] = getDistanceFromLine(+temp[i].vertical_drop, +temp[i].season_pass, ski_value_linear_regression[0][0], ski_value_linear_regression[0][1], ski_value_linear_regression[1][0], ski_value_linear_regression[1][1])\n }\n return temp\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":549,"value":"stat_normalized_plus_distance = {\n let temp = stat_normalized;\n for (let i = 0; i < stat.length; i++) {\n temp[i][\"distance\"] = getDistanceFromLine(\n +temp[i].weighted_score,\n +temp[i].season_pass,\n weighted_ski_value_linear_regression[0][0],\n weighted_ski_value_linear_regression[0][1],\n weighted_ski_value_linear_regression[1][0],\n weighted_ski_value_linear_regression[1][1]\n );\n }\n return temp;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":582,"value":"md`\n## Links, Imports, and Files\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":54,"value":"stat = FileAttachment(\"ski_area_comparison.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":76,"value":"Plotly = require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":63,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":153,"value":"d3_regression = require(\"https://unpkg.com/d3-regression@1.3.4/dist/d3-regression.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":476,"value":"import { inputsGroup } from \"@bumbeishvili/input-groups\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":484,"value":"import { slider } from \"@jashkenas/inputs\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":81,"value":"style = html`\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W37","stdlib":"1","titling":"auto"}} 2f:"$2e:body"