45:["$","title",null,{"children":"Tick Mark / Observable Plot | 1907040205 | Observable"}] 4d:[{"id":"3ccff97fd2d93da734e76829b2b066eafdaac6a1fafdec0faf6ebc443271cfc109d29e80dd217468fcb2aff1e6bffdc73f356cc48feb657f35378e6abbbb63b9","href":"https://static.observableusercontent.com/files/3ccff97fd2d93da734e76829b2b066eafdaac6a1fafdec0faf6ebc443271cfc109d29e80dd217468fcb2aff1e6bffdc73f356cc48feb657f35378e6abbbb63b9","name":"aapl.csv","type":"text/csv","lastModified":1637249798139.406,"size":92399},{"id":"acdb539785511895ff780946e2f870005c718d1f63ecda757414220af0582ba177a2b5e7c44a5dd544a07b85a3f0195f9f3bee5195d36ca4e922515f62f6bcb9","href":"https://static.observableusercontent.com/files/acdb539785511895ff780946e2f870005c718d1f63ecda757414220af0582ba177a2b5e7c44a5dd544a07b85a3f0195f9f3bee5195d36ca4e922515f62f6bcb9","name":"morley.csv","type":"text/csv","lastModified":1637249798139.406,"size":874},{"id":"81d7bd5e9551f005d7a4764e2dcb46c44f04b5918551ce19dba191a8799b498beddb5ef2da994047586fc7231749e8911c825b1967f22814a57bee58c590c86e","href":"https://static.observableusercontent.com/files/81d7bd5e9551f005d7a4764e2dcb46c44f04b5918551ce19dba191a8799b498beddb5ef2da994047586fc7231749e8911c825b1967f22814a57bee58c590c86e","name":"us-population-state-age.csv","type":"text/csv","lastModified":1637249798139.406,"size":3516}] 49:{"id":"576e1fde3cba9e4b","slug":"tick-mark-observable-plot","title":"Tick Mark / Observable Plot","thumbnail":"a4ac1b2559c24d6a300e74e9d979b15ef5044941b1dcd2e9f65b8c2e4fb48412","access_level":"public","create_time":"$D2021-11-18T15:36:38.139Z","update_time":"$D2021-11-18T16:29:17.092Z","view_time":null,"first_public_time":"$D2021-11-23T00:31:30.791Z","has_unpublished_edits":null,"fork":{"id":"fd877a47abae7323","slug":"plot-tick","title":"Tick Mark / Observable Plot","owner":{"id":"f35c755083683fe5","login":"observablehq","name":"Observable"}},"stars":0,"starred":false,"owner":{"type":"team","id":"a4b7eada444d9b53","login":"1907040205","name":"","avatar_url":null,"tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":161,"publish_version":161,"role":null,"authored":false,"authors":[{"type":"individual","id":"3fdc14ae238bd407","login":"1907040205","name":"","avatar_url":null,"tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"3fdc14ae238bd407","login":"1907040205","name":"","avatar_url":null,"tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Tick Mark / Observable Plot","cells":[{"id":0,"value":"md`

Tick Mark / Observable Plot

Observable Plot › Marks › Tick · API

\n\n# Plot: Tick\n\n\nTicks是水平线或垂直线。 **tickY**↔︎ 具有 *y* 值，而 **tickX**↕︎ 具有 *x* 值。 Ticks 有一个可选的二级位置维度（*x* 代表 tickY，*y* 代表 tickX）；这第二个维度是有序的，与 [rule](/@observablehq/plot-rule) 不同，它需要一个 [band scale](/@observablehq/plot-scales)。刻度通常用于显示一维分布，如下面的“条形码”图显示了美国各州每个年龄段的人口比例。`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":141,"value":"states = FileAttachment(\"us-population-state-age.csv\").csv({typed: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":144,"value":"ages = states.columns.slice(1)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"stateages = ages.flatMap(age => states.map(d => ({state: d.name, age, population: d[age]}))) // pivot","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"Plot.plot({\n marginLeft: 50,\n grid: true,\n x: {\n axis: \"top\",\n label: \"Percent (%) →\",\n transform: d => d * 100\n },\n y: {\n domain: ages,\n label: \"Age\"\n },\n marks: [\n Plot.ruleX([0]),\n Plot.tickX(stateages, Plot.normalizeX({basis: \"sum\", z: \"state\", x: \"population\", y: \"age\"}))\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":75,"value":"md`\n另一个例子，下面用ticks绘制了一个随机正态分布。当ticks重合时，降低不透明度可以更好地感知密度。`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":73,"value":"Plot.plot({\n x: {\n domain: [-4, 4]\n },\n marks: [\n Plot.tickX({length: 500}, {x: d3.randomNormal(), strokeOpacity: 0.2}),\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":95,"value":"md`\nTicks也可以用在 [box plot](/@observablehq/plot-box) 中来标记每组的中值（灰色条内的黑色粗竖线代表[interquartile range](https://en.wikipedia.org/wiki/Interquartile_range))。在这里，reduce transform用于通过实验对观察进行分组，然后计算汇总统计量。`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"morley = FileAttachment(\"morley.csv\").csv({typed: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":30,"value":"Plot.plot({\n x: {\n grid: true,\n inset: 6\n },\n marks: [\n Plot.ruleY(morley, Plot.groupY({x1: iqr1, x2: iqr2}, {x: \"Speed\", y: \"Expt\"})),\n Plot.barX(morley, Plot.groupY({x1: quartile1, x2: quartile3}, {x: \"Speed\", y: \"Expt\", fill: \"#ccc\"})),\n Plot.tickX(morley, Plot.groupY({x: \"median\"}, {x: \"Speed\", y: \"Expt\", strokeWidth: 2})),\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":63,"value":"md`\n一维tick——*i.e*，只有*x* 通道的tickX 或只有*y* 通道的tickY——等价于一维[rule](/@observablehq/plot-rule)。虽然等效，但一维rule应该比二维图中的tick更受欢迎，“rule”这个名称更具描述性。`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"md`\n当用作注释时，ticks通常具有硬编码数据（以下为零值）。 tick的默认位置定义（*x* 表示tickX，*y* 表示tickY）是恒等函数，允许ticks的数据是值的文字数组。`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":59,"value":"aapl = FileAttachment(\"aapl.csv\").csv({typed: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":58,"value":"Plot.plot({\n y: {\n grid: true\n },\n marks: [\n Plot.tickY([0], {stroke: \"red\"}),\n Plot.line(aapl, {x: \"Date\", y: \"Close\"})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":122,"value":"md`Next \nAPI Reference`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"md`---\n\n## 附录`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"iqr1 = V => Math.max(d3.min(V), quartile1(V) * 2.5 - quartile3(V) * 1.5)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"iqr2 = V => Math.min(d3.max(V), quartile3(V) * 2.5 - quartile1(V) * 1.5)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"quartile1 = V => d3.quantile(V, 0.25)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":36,"value":"quartile3 = V => d3.quantile(V, 0.75)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4d","stdlib":"1"}} 44:null