3e:null 3f:["$","title",null,{"children":"Практическая работа №4 | Yulvin COOL | Observable"}] 45:[{"id":"8ea73ccb77df85ae94ff8af9fc5d1ea114c387d384b0007f40574e13f605d3d9ae2d8e413f43a0c37957d84d65c89cac99a8994dd677c75cdae4f645b2278465","href":"https://static.observableusercontent.com/files/8ea73ccb77df85ae94ff8af9fc5d1ea114c387d384b0007f40574e13f605d3d9ae2d8e413f43a0c37957d84d65c89cac99a8994dd677c75cdae4f645b2278465","name":"competition.xlsx","type":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","lastModified":1789828705538,"size":98419}] 43:{"id":"038deae133959de0","slug":"prakticheskaya-rabota-4","title":"Практическая работа №4","thumbnail":"126e08ed06eb0a3958042ed7c16d4385a477f8698d0f6c04789da3de1cf94b69","access_level":"public","create_time":"$D2026-09-19T15:12:04.601Z","update_time":"$D2026-10-15T15:42:16.596Z","view_time":null,"first_public_time":"$D2026-10-15T15:42:27.072Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"4b0ed8c6259c6ad3","login":"yulvincool","name":"Yulvin COOL","avatar_url":"https://avatars-next.observableusercontent.com/3c049d548b3e6cd7c0e5d033a80b61b082aafbbdf7d23f4d005f61fb86bff82b","tier":"starter_2024","bio":""},"version":349,"publish_version":349,"role":null,"authored":false,"authors":[{"type":"individual","id":"2761665ddc95bf45","login":"yulvincool","name":"Yulvin COOL","avatar_url":"https://avatars-next.observableusercontent.com/3c049d548b3e6cd7c0e5d033a80b61b082aafbbdf7d23f4d005f61fb86bff82b","tier":null,"bio":""}],"members":[{"type":"team","id":"4b0ed8c6259c6ad3","login":"yulvincool","name":"Yulvin COOL","avatar_url":"https://avatars-next.observableusercontent.com/3c049d548b3e6cd7c0e5d033a80b61b082aafbbdf7d23f4d005f61fb86bff82b","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Практическая работа №4","cells":[{"id":1,"value":"

\n Практическая работа №4\n

\n тема: Группировка данных для визуализации\n

\n Выполнила: Сизинцева Ю.С. 4.606Мз-3\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2,"value":"Загружаем данные: xlsx-файл для выполнения экспериментов:","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"workbook = FileAttachment(\"competition.xlsx\").xlsx()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"sport = workbook.sheet(\"спорт\", {headers: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"countries = workbook.sheet(\"страны\", {headers: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"sport[0]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"Object.keys(sport[0] ?? {})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"Array.from(new Set(sport.map(d => d[\"Пол\"])))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":26,"value":"sportData = sport\n .filter((d) => {\n const p = (d[\"Пол\"] || \"\").toString().trim().toLowerCase();\n return p === \"м\" || p === \"ж\";\n })\n .map(function (d) {\n return {\n fam: d[\"Фамилия\"],\n name: d[\"Имя\"],\n data_b: Number(d[\"Год рождения\"]),\n pol: (d[\"Пол\"]).trim().substring(0, 1).toUpperCase(),\n age: Number(d[\"Год рождения\"]),\n count_b: Number(d[\"Прыжок в длину с места, см \"]),\n run1000: Number(d[\"Бег 1000 метров, мин. и сек.\"]),\n run30: Number(d[\"Бег 30 метров, сек.\"])\n };\n })\n .sort((a, b) => a.data_b - b.data_b)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":52,"value":"#### Исходная таблица","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"viewof table_sportData = Inputs.table(sportData, {maxHeight: 300})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"countriesData = countries.map(function (d) {\n return {\n city: d[\"Город\"],\n population: Number(d[\"Численность население\"]) * 1000,\n country: d[\"Страна\"]\n };\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"viewof table_countriesData = Inputs.table(countriesData, {maxHeight: 300})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"sport_1998 = sportData.filter(d => d[\"data_b\"] == 1998)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":32,"value":"Plot.plot({\n width: 900,\n height: 320,\n grid: true,\n title: \"Прыжок в длину с места — спортсмены 1998 года рождения\",\n x: { label: \"Прыжок, см\", domain: [170, 240] },\n y: { label: \"Количество человек\" },\n facet: {\n data: sport_1998,\n x: \"pol\",\n label: \"Пол\",\n margin: 30\n },\n marks: [\n Plot.rectY(sport_1998, Plot.binX(\n {y: \"count\", thresholds: 15},\n {x: \"count_b\", fill: \"pol\"}\n )),\n Plot.ruleY([0])\n ],\n color: { legend: true, label: \"Пол\" },\n marginTop: 30\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"На гистограмме видно, как различаются результаты у мужчин и женщин:\n\n- **Женщины (Ж):** основная масса результатов сосредоточена в диапазоне 180-210 см, с чётким пиком около 200-205 см. Распределение узкое - большинство прыгает примерно одинаково.\n- **Мужчины (M):** результаты распределены шире - от 175 до 240 см. Есть несколько локальных пиков (около 190, 205 и 225-230 см), а также заметный «хвост» вправо - отдельные спортсмены прыгают заметно дальше остальных.\n\n**Вывод:** у мужчин результаты в среднем выше и разброс больше, у женщин - кучнее и скромнее.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":36,"value":"sportDataNew = sport\n .filter(d => d[\"Пол\"])\n .map(d => {\n const p = String(d[\"Пол\"]).trim().toLowerCase();\n\n const raw = String(d[\"Бег 1000 метров, мин. и сек.\"]);\n const [min, sec] = raw.split(\".\");\n const seconds = sec ? Number(sec.padEnd(2, \"0\")) : 0;\n const run1000 = Number(min) * 60 + seconds;\n\n return {\n fam: d[\"Фамилия\"],\n name: d[\"Имя\"],\n pol: p === \"м\" ? \"M\" : \"F\",\n year: Number(d[\"Год рождения\"]),\n run1000: run1000,\n run30: Number(d[\"Бег 30 метров, сек.\"]),\n jump: Number(d[\"Прыжок в длину с места, см\"])\n };\n })\n .sort((a, b) => a.year - b.year)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":39,"value":"sportData2 = sportDataNew.map(d => ({...d, unit: 1}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"[...new Set(sportData2.map(d => d.year))]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":42,"value":"### Распределение спортсменов по полу и году рождения","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":41,"value":"Plot.plot({\n width: 900,\n height: 400,\n facet: {\n data: sportData2,\n x: \"year\",\n margin: 40,\n label: \"Год рождения\"\n },\n x: {\n label: \"Пол\",\n tickFormat: d => d === \"F\" ? \"Ж\" : \"М\"\n },\n y: { label: \"Количество человек\" },\n marks: [\n Plot.ruleY([0]),\n Plot.barY(\n sportData2,\n Plot.groupX(\n { y: \"count\" },\n { x: \"pol\", fill: \"pol\" }\n )\n )\n ],\n color: {\n legend: true,\n label: \"Пол\",\n domain: [\"F\", \"M\"],\n range: [\"#4e79a7\", \"#f28e2b\"],\n tickFormat: d => d === \"F\" ? \"Ж\" : \"М\"\n },\n grid: true\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":91,"value":"**Задача:** сравнить численность мужчин и женщин в каждой возрастной когорте (1996-1999) и выявить возможный гендерный дисбаланс выборки.\n\n**Метод:** столбчатая диаграмма с фасетом по году рождения. Внутри каждой панели строится по два столбика - Ж и М, высота которых равна количеству человек соответствующего пола (группировка `Plot.groupX` с ключом count).","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":37,"value":"**Что видно:**\n- В каждом году мужчин в выборке больше, чем женщин.\n- Пик по обоим полам приходится на 1998–1999 годы.\n- Соотношение М/Ж колеблется примерно от 1.2 до 1.7 — то есть выборка смещена в сторону мужчин.","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":43,"value":"### Доля групп «пол × год рождения» в общей выборке","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":44,"value":"Plot.plot({\n width: 1000,\n grid: true,\n x: { label: \"Год рождения\" },\n y: {\n percent: true,\n domain: [0, 18],\n label: \"Доля от всех спортсменов, %\"\n },\n color: {\n legend: true,\n label: \"Пол\",\n domain: [\"F\", \"M\"],\n range: [\"#f28e2b\", \"#4e79a7\"],\n tickFormat: d => d === \"F\" ? \"Ж\" : \"М\"\n },\n marks: [\n Plot.barY(\n sportData2,\n Plot.groupX(\n { y: \"proportion\" },\n { x: \"year\", y: \"unit\", fill: \"pol\" }\n )\n ),\n Plot.text(\n sportData2,\n Plot.groupX(\n { y: \"proportion\", text: \"count\" },\n { x: \"year\", y: \"unit\", fill: \"pol\", dy: -5, text: \"unit\" }\n )\n ),\n Plot.ruleY([0])\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":48,"value":"**Задача:** выяснить, какая доля от всех спортсменов приходится на каждую комбинацию «год рождения х пол», и сравнить эти доли между годами.\n\n**Метод:** столбчатая диаграмма с накоплением. Группировка `Plot.groupX({y: \"proportion\"})` считает долю группы как отношение суммы наблюдений группы к общей сумме по всей выборке. Над столбиками выводятся абсолютные количества (`Plot.text`).\n\n**Что видно на диаграмме:**\n- **Синий** - мужчины (М), **жёлтый** - женщины (Ж).\n- Доли каждой группы колеблются в диапазоне примерно 5–8 % от всей выборки.\n- Наибольший вклад дают группы 1998 и 1999 годов - как среди мужчин, так и среди женщин.\n- Доля мужчин в каждом году стабильно выше доли женщин - подтверждает перекос выборки в сторону мужчин.","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"### Выберите год:","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":47,"value":"viewof selectYear = Inputs.select(\n [...new Set(sportData2.map(d => d.year))].sort(),\n { label: \"Год рождения\" }\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":45,"value":"sel = sportData2.filter(d => d.year === selectYear)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":50,"value":"### Доля мужчин и женщин среди спортсменов выбранного года рождения","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":46,"value":"Plot.plot({\n width: 600,\n height: 400,\n grid: true,\n x: { label: \"Пол\", tickFormat: d => d === \"F\" ? \"Ж\" : \"М\" },\n y: { percent: true, domain: [0, 80], label: \"Доля, %\" },\n marks: [\n Plot.barY(\n sel,\n Plot.groupX({ y: \"proportion\" }, { x: \"pol\", y: \"unit\", fill: \"pol\" })\n ),\n Plot.text(\n sel,\n Plot.groupX(\n { y: \"proportion\", text: \"count\" },\n { x: \"pol\", y: \"unit\", fill: \"pol\", dy: -5, text: \"unit\" }\n )\n ),\n Plot.ruleY([0])\n ],\n color: {\n legend: true,\n label: \"Пол\",\n domain: [\"F\", \"M\"],\n range: [\"#4e79a7\", \"#f28e2b\"],\n tickFormat: d => d === \"F\" ? \"Ж\" : \"М\"\n }\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":51,"value":"**Задача:** оценить гендерный баланс внутри конкретного года рождения и увидеть, насколько велик перекос в ту или иную сторону.\n\n**Метод:** столбчатая диаграмма, зависящая от селектора `selectYear`. Для выбранного года строятся два столбика - Ж и М. Ключ `proportion` вычисляет долю группы внутри года (сумма двух столбиков ≈ 100 %). Над столбиками выводится абсолютное количество человек.\n\n**Что видно на диаграмме:**\n- При смене года в селекторе столбики перестраиваются - видно, как меняется баланс.\n- В каждом году мужчин больше: обычно 55-65 % против 35-45 % у женщин.\n- Разница особенно заметна в когортах 1998-1999 годов.\n- Абсолютные числа над столбиками позволяют оценить, насколько выборка представительна для данного года.","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":53,"value":"Создадим ячейку с преобразованием:","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":54,"value":"countryData = countries\n .filter(d => d[\"Страна\"] && d[\"Город\"] && d[\"Численность население\"] != null)\n .map(d => {\n const country = String(d[\"Страна\"]).trim();\n const city = String(d[\"Город\"]).trim();\n const population = Number(String(d[\"Численность население\"]).replace(\",\", \".\"));\n return {\n country,\n city,\n population,\n path: `${country}/${city}`,\n label: `${city} (${population.toLocaleString(\"ru-RU\")})`\n };\n })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":55,"value":"viewof tableCountry = Inputs.table(countryData, {maxHeight: 200})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":56,"value":"Импортируем компонент Pack","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":57,"value":"import {Pack} from \"@d3/pack\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":79,"value":"Импортируем компонент Sunburst","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":77,"value":"import {Sunburst} from \"@d3/sunburst\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":58,"value":"countries[0]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":76,"value":"countryData.length","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":80,"value":"function hashCode(s) {\n let h = 0;\n for (let i = 0; i < s.length; i++) h = (h * 31 + s.charCodeAt(i)) | 0;\n return h;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":81,"value":"function colorByName(name) {\n return d3.schemeTableau10[Math.abs(hashCode(name ?? \"\")) % 10];\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"colorByName(\"Египет\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":89,"value":"### Солнечная диаграмма (Sunburst) - население стран и городов","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":83,"value":"Sunburst(countryData, {\n path: d => d.path,\n value: d => d?.population,\n label: d => d?.label,\n width: 800,\n height: 700\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":90,"value":"Диаграмма отображает двухуровневую структуру данных в виде концентрических колец:\n\n- **Внутреннее кольцо** - корень (вся выборка городов).\n- **Среднее кольцо** - страны.\n- **Внешнее кольцо** - города каждой страны.\n- **Угол сектора** пропорционален численности населения.\n- **Цвет** присваивается автоматически из стандартной палитры d3.\n\n**Что видно на диаграмме:**\n- Самые широкие сектора соответствуют странам с наибольшим суммарным населением городов - Россия, Египет, Турция.\n- Внутри каждой страны видно, насколько неравномерно распределено население между её городами: один-два крупных сектора и «гребёнка» мелких.\n- У небольших стран (Мальта, Австрия, Бельгия) сектора короткие, но их много - состав страны раздроблен на множество мелких городов.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":87,"value":"### Диаграмма упаковки (Pack) — население городов по странам","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":86,"value":"Pack(countryData, {\n path: d => d.path,\n value: d => d?.population,\n label: d => d?.label,\n width: 800,\n height: 800,\n fill: \"#D8BFD8\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":88,"value":"Диаграмма упаковки (Pack) показывает двухуровневую структуру данных:\n\n- **Внешний круг** - страна. Его площадь равна сумме населения всех её городов, попавших в выборку.\n- **Внутренние круги** - города этой страны. Размер каждого пропорционален численности его населения.\n- **Подпись** внутри круга - название города и численность населения в скобках.\n- **Наведение курсора** показывает полный путь и точное значение.\n\n**Что видно на диаграмме:**\n- Самые крупные «кластеры» - Россия, Египет и Турция: у них много городов и большое суммарное население.\n- Внутри крупных стран видно по 1-2 доминирующих города (Москва, Санкт-Петербург, Каир, Стамбул) - остальные города заметно меньше.\n- У ряда стран (Мальта, Бельгия, Австрия) кластеры компактные и состоят из множества мелких городов примерно одного размера.","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W45","stdlib":"2"}} 44:"$43:body"