创建树形图

Question

我有一家公司的数据，我想通过树状图进行可视化。这意味着，我将他们的品牌嵌套在类中、嵌套在子组中、嵌套在部门中 (http://www.bayer.com/en/products-from-a-to-z.aspx#B)。

尽管stackoverflow上有很多线程如何创建树状图，但我没有找到适用于我的数据的代码sn-p（例如how to convert a data.frame to tree structure object such as dendrogram）。

编辑： 如果我是对的，“树状图”在这种情况下不是正确的术语，因为我没有/考虑任何距离测量。这样我就使用了更通用的术语“树形图”

df <- structure(list(Brand = structure(c(73L, 122L, 131L, 44L, 6L, 
7L, 8L, 27L, 52L, 84L, 95L, 101L, 121L, 142L, 17L, 21L, 53L, 
86L, 99L, 112L, 139L, 4L, 97L, 76L, 47L, 113L, 146L, 71L, 109L, 
147L, 148L, 149L, 14L, 80L, 93L, 114L, 3L, 15L, 25L, 26L, 35L, 
36L, 37L, 39L, 42L, 56L, 57L, 59L, 60L, 61L, 62L, 63L, 65L, 66L, 
67L, 68L, 69L, 72L, 90L, 92L, 117L, 28L, 91L, 9L, 16L, 81L, 45L, 
51L, 10L, 130L, 138L, 46L, 74L, 116L, 128L, 137L, 11L, 77L, 82L, 
83L, 94L, 111L, 123L, 124L, 134L, 136L, 141L, 18L, 23L, 48L, 
75L, 79L, 87L, 88L, 100L, 102L, 126L, 129L, 50L, 54L, 115L, 5L, 
22L, 85L, 98L, 118L, 127L, 19L, 38L, 107L, 132L, 58L, 120L, 96L, 
2L, 49L, 55L, 20L, 106L, 135L, 143L, 145L, 1L, 133L, 125L, 108L, 
119L, 12L, 24L, 33L, 78L, 103L, 104L, 105L, 13L, 29L, 30L, 31L, 
32L, 34L, 40L, 41L, 43L, 70L, 89L, 110L, 144L, 64L, 140L), .Label = c("3.0T Prostate eCoil", 
"A1CNow", "Acclaim Polyol", "Adalat", "Admire", "Advantage", 
"Advantix", "Advocate", "Aleve", "Alka-Seltzer", "Antracol", 
"Apec", "Arcol", "Arize", "Artwalk", "Aspirin", "Aspirin Cardio", 
"Atlantis", "Attribut", "Avanta", "Avelox", "Bariton", "Basta", 
"Bayblend", "Baybond", "Baycoll", "Baycox", "Baycusan", "Baydur", 
"Bayfill", "Bayfit", "Bayflex", "Bayfol", "Baygalÿ/Baymidur", 
"Bayhydrol", "Bayhydur", "Bayhytherm", "Bayleton", "Baymer", 
"Baynat", "Baypreg", "Baypren", "Baytec", "Baytril", "Bepanthenÿ/Bepanthol", 
"Berocca", "Betaferonÿ/ Betaseron", "Betanal", "Breezeÿ2", "Calypso", 
"Canesten", "Catosal", "Cipro", "Confidor", "Contourÿ/Contour Linkÿ/Contour TS", 
"CreKat", "Crelan", "Decis", "Desavin", "Desmocap", "Desmocoll", 
"Desmoderm", "Desmodur", "Desmoflex", "Desmolac", "Desmolith", 
"Desmolux", "Desmomelt", "Desmopan", "Desmophen", "Diane", "Dispercoll", 
"Drontal", "Elevit", "Equip", "Eylea", "Fandango", "Fantasia", 
"Fenikan", "FiberMax", "Flanaxÿ/ÿApronax", "Flint", "Folicur", 
"Gadovist", "Gaucho", "Glucobay", "Hoestar", "Husar", "Hyperlite", 
"Imprafix", "Impranil", "Impraperm", "InVigor", "Input", "Iopamiron", 
"K-Othrine", "Kogenate", "Lamardor", "Levitra", "Liberty", "Magnevist", 
"MaisTer", "Makroblend", "Makrofol", "Makrolon", "Mark V ProVis", 
"Maxforce", "Microlet 2", "Mirena", "Multitec", "Nativo", "Nebido", 
"Nexavar", "Nunhems", "Oberon", "One-A-Day", "Pergut", "Poncho", 
"Possis Angio Jet", "Premise", "Primovist", "Profender", "Proline", 
"Prosaro", "Pulsar", "Puma", "Raxil", "Redoxon", "Rely", "Rennie", 
"Rompun", "Ronstar", "Solaris", "Sphere", "Stellant", "Stratego", 
"Supradyn", "Talcid", "Testogel", "Texin", "Twist", "Ultravist", 
"Vistron CT", "Vulkollan", "XDS", "Xarelto", "Yasmin", "Yasminelle", 
"Yaz"), class = "factor"), Class = structure(c(6L, 6L, 6L, 7L, 
8L, 8L, 8L, 13L, 13L, 10L, 11L, 11L, 10L, 11L, 12L, 17L, 17L, 
17L, 18L, 18L, 18L, 20L, 20L, 29L, 33L, 34L, 39L, 43L, 43L, 43L, 
43L, 43L, 1L, 2L, 2L, 41L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
31L, 37L, 3L, 3L, 3L, 9L, 14L, 16L, 16L, 16L, 42L, 42L, 42L, 
42L, 42L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 26L, 26L, 
26L, 27L, 32L, 32L, 32L, 32L, 32L, 21L, 15L, 19L, 21L, 26L, 36L, 
40L, 4L, 4L, 4L, 22L, 22L, 23L, 23L, 23L, 24L, 24L, 25L, 28L, 
38L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 31L, 31L, 
31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 35L, 35L), .Label = c("Agricultural Seed", 
"Agricultural seed", "Analgesics", "Blood Glucose Monitoring Systems", 
"Coatings, adhesives, specialties", "Companion Animal", "Companion Animal / Food Animal Product", 
"Companion animals", "Dermatologicals", "Diagnostic Imaging", 
"Diagnostic imaging", "Drug product for protection against heart attack", 
"Food Animal Product", "Fungal infections", "Fungicides", "Gastrointestinals", 
"General Medicine", "General Medicine / Mens HealthCare", "General insect control", 
"Hematology/cardiology", "Herbicides", "Injection Systems / Cardiology", 
"Injection Systems / Computer Tomography", "Injection Systems / Magnetic Resonance Tomography", 
"Injections Systems Ultrasound", "Insecticides", "Insecticides/seed treatment", 
"Lancing Devices", "Ophthalmology", "Polycarbonates", "Polyurethanes", 
"Seed treatment", "Specialty Medicine", "Specialty Medicine / Onkology", 
"TPU granules", "Termiticides", "Textile coating", "Thrombectomy", 
"Thromboembolic diseases", "Vector and locust control", "Vegetable seed", 
"Vitamins", "Women's healthcare"), class = "factor"), Subgroup = structure(c(2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L), .Label = c("Bayer CropScience", "Bayer HealthCare", 
"Bayer MaterialScience"), class = "factor"), Division = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 8L, 8L, 8L, 8L, 8L, 
8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L), .Label = c("Animal Health", 
"Bayer HealthCare Pharmaceuticals, Germany", "BioScience", "Coatings, Adhesives, Specialties", 
"Consumer Care", "Crop Protection", "Crop Protection/Environmental Science", 
"Environmental Science", "Medical Care", "Polycarbonates", "Polyurethanes", 
"Thermoplastic Polyurethanes"), class = "factor")), .Names = c("Brand", 
"Class", "Subgroup", "Division"), class = "data.frame", row.names = c(NA, 
-149L))

感谢您的帮助。

Answer 1

使用包ape，并假设你的数据是olddat：

library(ape)
newdata <- as.phylo(x=~Division/Subgroup/Class/Brand,data=olddat)
plot.phylo(x=newdata,show.tip.label=TRUE,show.node.label=TRUE,no.margin=TRUE)

您需要使用各种情节选项，但我认为这会让您朝着正确的方向前进。如果我能让情节看起来不错，我会看看更新答案。