R: матрица расчетных расстояний между двумя списками строк
Пожалуйста, обратите внимание на репрекс в конце поста. У меня есть два списка фреймов данных. Каждый фрейм данных имеет столбец $keyword, который представляет собой вектор текста. Я ищу эффективный в вычислительном отношении способ получить матрицу расстояний между текстом в столбце ключевых слов list_v1 и list_v2. Конечным результатом должна быть матрица 5 на 10 или 10 на 5 или фрейм данных/таблица. В качестве примера подойдет любая мера из пакета stringdist (например, Levenshtein). Кроме того, я хотел бы снова вычислить индекс Жаккара (см. формулу в репрексе) между текстом в столбце ключевых слов list_v1 и list_v2. Любое предложение приветствуется.
library(tidyverse)
library(stringdist)
#>
#> Attaching package: 'stringdist'
#> The following object is masked from 'package:tidyr':
#>
#> extract
jaccard <- function(a, b) {
intersection = length(intersect(a, b))
union = length(a) + length(b) - intersection
return (intersection/union)
}
list_v1 <- list(structure(list(keyword = c("lieutenant army", "General army",
"air force", "government establishment", "commander", "army",
"lieutenant", "force officer", "force", "officer", "Air", "border",
"employment", "equivalent", "example", "group", "job", "leadership",
"management", "member", "occupations", "person", "rank", "service",
"task", "unit", "variety"), ngram = c(2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), freq = c(1L, 1L, 2L, 1L, 1L, 3L, 1L, 1L,
4L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L), rake = c(2.76190476190476, 2.42857142857143, 2.41666666666667,
2, 1.5, 1.42857142857143, 1.33333333333333, 1.08333333333333,
0.75, 0.333333333333333, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0)), row.names = c(NA, -27L), class = "data.frame"),
structure(list(keyword = c("government establishment", "force occupation",
"force officer", "warrant officer", "force", "rank", "officer",
"activity", "army", "border", "discipline", "employment",
"example", "flight", "group", "job", "member", "occupations",
"person", "service", "task", "variety"), ngram = c(2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), freq = c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L
), rake = c(2, 2, 1.66666666666667, 1.66666666666667, 1,
1, 0.666666666666667, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0)), row.names = c(NA, -22L), class = "data.frame"),
structure(list(keyword = c("government establishment", "air force",
"force example", "force occupation", "force", "Gunner", "army",
"border", "employment", "group", "job", "member", "navy",
"occupations", "officer", "person", "rank", "service", "task",
"variety"), ngram = c(2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), freq = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 1L), rake = c(2, 1.75, 1.75, 1.75, 0.75, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA,
-20L), class = "data.frame"), structure(list(keyword = c("assemblie b",
"committee e", "government Secretary", "government class",
"government department", "community function", "repeal law",
"interest group", "parliament councils", "government", "community",
"councils", "law", "parliament", "policy", "interest", "Legislator",
"administrator", "agency", "agreement", "amend", "amending",
"board", "body", "concern", "constituency", "framework",
"implementation", "information", "interpretation", "legislator",
"matter", "meeting", "member", "occupations", "official",
"opinion", "order", "presiding", "proceeding", "regulation",
"representative", "rule", "service", "state", "statutory"
), ngram = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), freq = c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 5L, 1L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 4L, 1L), rake = c(2, 2, 1.90909090909091,
1.90909090909091, 1.90909090909091, 1.5, 1.5, 1.33333333333333,
1, 0.909090909090909, 0.5, 0.5, 0.5, 0.5, 0.5, 0.333333333333333,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, -46L
), class = "data.frame"), structure(list(keyword = c("Police commissioner",
"government department", "policy matter", "government administration",
"government committee", "government e", "government policy",
"unit Group", "amendment class", "body tasnks", "document briefs",
"service commissioner", "commissioner inspector", "staff member",
"policy programme", "government official", "government manager",
"government legislation", "agency example", "government",
"budget", "control", "policy", "manager", "official", "agency",
"legislation", "Director", "accordance", "activity", "administrator",
"advising", "behalf", "commission", "conjunction", "consultation",
"country", "enterprise", "implementation", "interpretation",
"legislator", "objective", "occupations", "organization",
"preparation", "presentations", "procedure", "programm",
"regulation", "report", "state", "system", "task"), ngram = c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), freq = c(1L, 4L, 1L, 1L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L,
1L, 1L, 2L, 1L, 1L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 3L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L), rake = c(4, 3.02380952380952, 2.375, 2.19047619047619,
2.19047619047619, 2.19047619047619, 2.06547619047619, 2,
2, 2, 2, 2, 2, 2, 1.875, 1.69047619047619, 1.69047619047619,
1.44047619047619, 1.25, 1.19047619047619, 1, 1, 0.875, 0.5,
0.5, 0.25, 0.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, -53L
), class = "data.frame"), structure(list(keyword = c("councils g",
"custom e", "regulation example", "surplus production", "government rule",
"village b", "village care", "village chiefs", "chiefs",
"rule", "village", "allocating", "authority", "community",
"connection", "dispute", "division", "duty", "head", "household",
"land", "member", "occasion", "occupations", "resource",
"responsibility", "rights", "task", "tradition", "use", "variety",
"violation"), ngram = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), freq = c(1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 5L, 1L, 1L, 6L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), rake = c(2,
2, 2, 2, 1.5, 1.375, 1.375, 0.875, 0.5, 0.5, 0.375, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA,
-32L), class = "data.frame"), structure(list(keyword = c("interest group",
"of planning", "policy rule", "funding agency", "forum example",
"charity organization", "organization class", "employer organization",
"interest organization", "rights organization", "sport associations",
"board meeting", "party organization", "director", "interest",
"policy", "organization", "associations", "board", "party",
"act", "behalf", "body", "convention", "determining", "enterprise",
"functioning", "government", "hearing", "implementation",
"member", "membership", "negotiation", "objective", "occasion",
"occupations", "official", "operation", "performance", "procedure",
"regulation", "result", "section", "system"), ngram = c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), freq = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
8L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L), rake = c(2.5,
2.5, 2.25, 2, 2, 1.72222222222222, 1.72222222222222, 1.72222222222222,
1.72222222222222, 1.72222222222222, 1.5, 1.5, 1.22222222222222,
1, 1, 0.75, 0.722222222222222, 0.5, 0.5, 0.5, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0)), row.names = c(NA, -44L), class = "data.frame"), structure(list(
keyword = c("unit Group", "conventions seminar", "unit group",
"staff k", "subordinate staff", "body class", "government department",
"regulation example", "operation undertaken", "organization b",
"organization e", "interest organization", "manager note",
"board meeting", "objective", "sale manager", "body",
"example", "government", "operation", "policy", "sale",
"organization", "manager", "board", "Director", "activity",
"area", "budget", "consult", "coordinate", "director",
"directors", "enterprise", "enterprisis", "executive",
"expenditure", "forum", "function", "functioning", "guideline",
"hearing", "leadership", "legislation", "management",
"member", "negotiation", "occasion", "occupations", "performance",
"planning", "programme", "range", "recommendation", "report",
"resource", "responsibility", "result", "selection",
"support", "use"), ngram = c(2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L),
freq = c(3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 7L, 5L, 3L,
3L, 2L, 1L, 1L, 1L, 1L, 4L, 1L, 6L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L), rake = c(2, 2, 2, 2,
2, 1.5, 1.5, 1.5, 1.5, 1.3, 1.3, 1.3, 1.28571428571429,
1.25, 1, 0.785714285714286, 0.5, 0.5, 0.5, 0.5, 0.5,
0.5, 0.3, 0.285714285714286, 0.25, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA,
-61L), class = "data.frame"), structure(list(keyword = c("management accountant",
"body example", "resource e", "staff h", "selection training",
"operation g", "Finance manager", "finance manager", "organization b",
"operation", "manager", "organization", "budget", "consultation",
"controller", "dealings", "departments", "enterprise", "enterprisis",
"expenditure", "occupations", "performance", "planning",
"procedure", "section", "service", "situation", "use"), ngram = c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), freq = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 4L, 2L, 1L, 1L, 1L,
2L, 4L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L), rake = c(2,
2, 2, 2, 2, 1.75, 1.66666666666667, 1.66666666666667, 1.2,
0.75, 0.666666666666667, 0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, -28L), class = "data.frame"),
structure(list(keyword = c("resource manager", "manager",
"selection training", "relation", "activity e", "safety activity",
"body example", "organizing negotiation", "wage structure",
"safety health", "resource service", "procedure g", "health",
"management", "planning", "policy", "staff", "resource",
"safety", "organization", "procedure", "budget", "compliance",
"concern", "condition", "consult", "consultation", "dealings",
"departments", "determination", "development", "dismissal",
"employment", "enterprise", "enterprisis", "expenditure",
"implementation", "legislation", "level", "occupations",
"opportunity", "performance", "personnel", "practice", "programm",
"standard", "use", "worker"), ngram = c(2L, 1L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), freq = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 4L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 4L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L), rake = c(3.86666666666667, 3.2, 3, 2.33333333333333,
2.33333333333333, 2, 2, 2, 2, 1.66666666666667, 1.66666666666667,
1.33333333333333, 1, 1, 1, 1, 1, 0.666666666666667, 0.666666666666667,
0.4, 0.333333333333333, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA,
-48L), class = "data.frame"))
list_v2 <- list(structure(list(keyword = c("user journey", "market data",
"logistic firm", "management tool", "rights offence", "other shine",
"usability testing", "user experience", "business requirement",
"end journey", "health service", "house team", "stakeholder interest",
"bank design", "design process", "constraint ability", "service design",
"product designer", "product design", "experience", "business",
"designer", "end", "interest", "service", "team", "design", "product",
"ability", "action", "aesthetic", "agency", "app", "artefact",
"attitude", "blueprint", "client", "consultant", "core", "curiosity",
"define", "deliverable", "developer", "discipline", "eye", "flow",
"future", "idea", "insight", "kind", "life", "lot", "mind", "mindset",
"mockup", "money", "need", "opportunity", "output", "passion",
"persona", "practice", "prototype", "reporting", "research",
"role", "sense", "story", "strategy", "thing", "travel", "trend",
"wireframe", "workshop"), ngram = c(2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), freq = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 5L, 1L,
5L, 4L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L
), rake = c(2.25, 2, 2, 2, 2, 2, 2, 1.91666666666667, 1.5, 1.5,
1.5, 1.5, 1.5, 1.44444444444444, 1.44444444444444, 1.16666666666667,
0.944444444444444, 0.833333333333333, 0.777777777777778, 0.666666666666667,
0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.444444444444444, 0.333333333333333,
0.166666666666667, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, -74L), class = "data.frame"),
structure(list(keyword = c("employment Business", "employment agency",
"bank holiday", "construction environment", "pension contribution",
"minimising disruption", "home projects", "home scheme",
"property information", "market leader", "site operation",
"training opportunity", "roofing renewal", "communication skill",
"literacy skill", "principal contractor", "housing sector",
"job type", "site team", "ability", "opportunity", "client team",
"skill", "role", "contractor", "housing", "job", "team",
"client", "CV", "Cladding", "FTC", "access", "appointment",
"appreciation", "benefit", "candidate", "complaint", "customer",
"detail", "experience", "initiative", "issue", "leave", "level",
"location", "mixture", "offer", "party", "phone", "process",
"project", "recruitment", "refurbishment", "relation", "relationship",
"resident", "supply", "survey", "today", "touch", "units",
"vacancy", "value", "view", "worker", "year"), ngram = c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L), freq = c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), rake = c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1.75,
1.75, 1.5, 1.5, 1.5, 1.5, 1, 1, 0.833333333333333, 0.75,
0.666666666666667, 0.5, 0.5, 0.5, 0.5, 0.333333333333333,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA,
-67L), class = "data.frame"), structure(list(keyword = c("department standard",
"Package Benefits", "Wells area", "household name", "stock package",
"customer base", "business objective", "quality plant", "manager",
"product offer", "customer service", "retail business", "day",
"job", "business", "customer", "plant", "service", "role",
"retail", "product", "Assistants", "Tn2", "accessory", "advice",
"aspect", "belief", "candidate", "client", "company", "condition",
"confidence", "desire", "example", "experience", "garden",
"gardener", "gardening", "hand", "horticulturist", "individual",
"interview", "knowledge", "layout", "location", "love", "management",
"order", "passion", "people", "plenty", "position", "purveyor",
"recruit", "reputation", "responsibility", "sale", "sundry",
"support", "team", "thing", "training", "update", "willingness"
), ngram = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), freq = c(1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L,
2L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L
), rake = c(2.5, 2, 2, 2, 2, 1.66666666666667, 1.66666666666667,
1.5, 1.33333333333333, 1.25, 1.16666666666667, 1, 1, 1, 0.666666666666667,
0.666666666666667, 0.5, 0.5, 0.4, 0.333333333333333, 0.25,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0)), row.names = c(NA, -64L), class = "data.frame"),
structure(list(keyword = c("conduct work", "document responsibility",
"industry health", "Groundworkers", "duty", "experience",
"qualification", "reference", "site", "skills", "week"),
ngram = c(2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L),
freq = c(1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L),
rake = c(2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA,
-11L), class = "data.frame"), structure(list(keyword = c("Pension scheme",
"transportation allowances", "job career", "employment condition",
"hour contract", "university degree", "holiday payment",
"hour week", "risk manager", "market risk", "Mrcp team",
"communication skill", "risk team", "team work", "trading activity",
"life balance", "vacation day", "work environment", "programme knowledge",
"portfolio self", "month salary", "it skill", "experience",
"risk", "market", "skill", "team", "management", "day", "environment",
"knowledge", "life", "portfolio", "trading", "it", "salary",
"13th", "Fluent", "ability", "addition", "ambition", "analysis",
"atmosphere", "attention", "attitude", "audit", "business",
"candidate", "capital", "challenge", "change", "choice",
"coach", "colleague", "competence", "concept", "coordinating",
"course", "demand", "departments", "detail", "development",
"difference", "econometric", "economics", "finance", "focus",
"gross", "guideline", "home", "idea", "impact", "initiative",
"instrument", "interest", "listen", "measure", "measurement",
"method", "mindset", "monitoring", "motivat", "other", "part",
"party", "passion", "performance", "personality", "policy",
"possibility", "professional", "profile", "project", "publication",
"regulation", "regulator", "regulatory", "responsibility",
"result", "role", "scope", "stakeholder", "starter", "subject",
"supervisor", "support", "technique", "thing", "think", "thinking",
"time", "understanding", "way"), ngram = c(2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L), freq = c(1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 3L, 3L, 3L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L
), rake = c(2, 2, 2, 2, 2, 2, 2, 2, 2, 1.875, 1.71428571428571,
1.71428571428571, 1.71428571428571, 1.71428571428571, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 1.33333333333333, 1.04761904761905,
1, 1, 0.875, 0.714285714285714, 0.714285714285714, 0.666666666666667,
0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.333333333333333, 0.333333333333333,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0)), row.names = c(NA, -113L), class = "data.frame"))
## see what they look like
list_v1[[5]] |> glimpse()
#> Rows: 53
#> Columns: 4
#> $ keyword <chr> "Police commissioner", "government department", "policy matter…
#> $ ngram <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1,…
#> $ freq <int> 1, 4, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1,…
#> $ rake <dbl> 4.000000, 3.023810, 2.375000, 2.190476, 2.190476, 2.190476, 2.…
list_v2[[5]] |> glimpse()
#> Rows: 113
#> Columns: 4
#> $ keyword <chr> "Pension scheme", "transportation allowances", "job career", "…
#> $ ngram <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
#> $ freq <int> 1, 1, 1, 1, 2, 1, 1, 1, 1, 3, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1,…
#> $ rake <dbl> 2.0000000, 2.0000000, 2.0000000, 2.0000000, 2.0000000, 2.00000…
sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Debian GNU/Linux 12 (bookworm)
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
#>
#> locale:
#> [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
#> [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
#> [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Europe/Brussels
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] stringdist_0.9.12 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
#> [5] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
#> [9] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.5 compiler_4.4.1 reprex_2.1.0 tidyselect_1.2.1
#> [5] parallel_4.4.1 scales_1.3.0 yaml_2.3.8 fastmap_1.1.1
#> [9] R6_2.5.1 generics_0.1.3 knitr_1.46 munsell_0.5.1
#> [13] R.cache_0.16.0 tzdb_0.4.0 pillar_1.9.0 R.utils_2.12.3
#> [17] rlang_1.1.3 utf8_1.2.4 stringi_1.8.4 xfun_0.43
#> [21] fs_1.6.4 timechange_0.3.0 cli_3.6.2 withr_3.0.0
#> [25] magrittr_2.0.3 digest_0.6.35 grid_4.4.1 hms_1.1.3
#> [29] lifecycle_1.0.4 R.methodsS3_1.8.2 R.oo_1.26.0 vctrs_0.6.5
#> [33] evaluate_0.23 glue_1.7.0 styler_1.10.3 fansi_1.0.6
#> [37] colorspace_2.1-0 rmarkdown_2.26 tools_4.4.1 pkgconfig_2.0.3
#> [41] htmltools_0.5.8.1
Created on 2024-06-25 with reprex v2.1.0
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Данный вопрос помечен как решенный
Ответы 3
Сначала инициализируйте как матрицу с размерами, а затем перебирайте каждую пару кадров данных в list_v1 и list_v2. Для каждой пары он извлекает столбцы ключевых слов и рассчитывает расстояние Жаккара. Нравиться:
# Initialize an empty matrix to store distances
n_v1 <- length(list_v1)
n_v2 <- length(list_v2)
dist_matrix <- matrix(NA, nrow = n_v1, ncol = n_v2)
# Loop through list_v1 and list_v2 to calculate distances
for (i in seq_along(list_v1)) {
keywords_v1 <- list_v1[[i]]$keyword
for (j in seq_along(list_v2)) {
keywords_v2 <- list_v2[[j]]$keyword
dist_matrix[i, j] <- calculate_jaccard_distance(keywords_v1, keywords_v2)
}
}
IIUC
sapply(list_v2, \(y)sapply(list_v1, \(x) jaccard(x$keyword, y$keyword)))
[,1] [,2] [,3] [,4] [,5]
[1,] 0.010000000 0.010752688 0.04597701 0.00000000 0.007194245
[2,] 0.021276596 0.011363636 0.03614458 0.00000000 0.000000000
[3,] 0.010752688 0.011627907 0.02439024 0.00000000 0.000000000
[4,] 0.025641026 0.000000000 0.01851852 0.00000000 0.019230769
[5,] 0.007936508 0.000000000 0.00862069 0.00000000 0.012195122
[6,] 0.000000000 0.000000000 0.01052632 0.02380952 0.006944444
[7,] 0.008547009 0.009090909 0.00000000 0.00000000 0.039735099
[8,] 0.000000000 0.000000000 0.05042017 0.00000000 0.041916168
[9,] 0.009900990 0.000000000 0.02222222 0.00000000 0.014388489
[10,] 0.016666667 0.036036036 0.02752294 0.00000000 0.032051282
Спасибо, это очень интересно, и я, вероятно, скоро приму ваш ответ. Один вопрос: могу ли я обмануть карту из purrr, чтобы вывести матрицу или фрейм данных для решения этой проблемы, или я буду работать против этого инструмента?
—
25.06.2024 21:51
@larry77 map_df(list_v1, ~data.frame(t(map_dbl(list_v2, ~jaccard(.x[[1]],.y[[1]]), .y=.x))))
—
25.06.2024 22:20
Ответ принят как подходящий
Вы можете попробовать outer вместе с Vectorize
outer(
list_v1,
list_v2,
Vectorize(\(x, y) jaccard(x$keyword, y$keyword))
)
который дает
[,1] [,2] [,3] [,4] [,5]
[1,] 0.010000000 0.010752688 0.04597701 0.00000000 0.007194245
[2,] 0.021276596 0.011363636 0.03614458 0.00000000 0.000000000
[3,] 0.010752688 0.011627907 0.02439024 0.00000000 0.000000000
[4,] 0.025641026 0.000000000 0.01851852 0.00000000 0.019230769
[5,] 0.007936508 0.000000000 0.00862069 0.00000000 0.012195122
[6,] 0.000000000 0.000000000 0.01052632 0.02380952 0.006944444
[7,] 0.008547009 0.009090909 0.00000000 0.00000000 0.039735099
[8,] 0.000000000 0.000000000 0.05042017 0.00000000 0.041916168
[9,] 0.009900990 0.000000000 0.02222222 0.00000000 0.014388489
[10,] 0.016666667 0.036036036 0.02752294 0.00000000 0.032051282
это, наверное, самый простой и краткий из ответов, которые я получил. есть ли какие-либо проблемы с производительностью Vectorize? Мне нужно будет применить его к спискам, содержащим десятки тысяч элементов.
—
26.06.2024 08:52
@ larry77 Ларри77 Я думаю, что Vectorize это похоже на вложенные циклы для перебора всех точек в сетке параметров, с точки зрения производительности не будет большой разницы по сравнению с обычными массивными вложенными циклами.
—
26.06.2024 09:33
Я выбрал этот ответ из-за его простоты, но все предложенные здесь работают.
—
26.06.2024 10:45
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