我有一家公司的数据，我想通过树形图进行可视化。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 上有很多关于如何创建树状图的线程，但我没有找到适用于我的数据的代码片段（例如如何将 data.frame 转换为树状结构对象，例如 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。这样我使用更通用的术语“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))
我很感激任何帮助。