Как извлечь соседей из определенных узлов с помощью igraph?

Question

У меня есть .csv файл edgelist людей с их болезнью.

Person Diseases Age gender location  
    Person1 Asthma 25 Female Location1 
    Person2 Pneumonia 35 Male Location2 
    Person3 Typhoid 40 Male Location3 
    Person4 TB 36 Male Location4 
    Person5 Asthma 25 Female Location5 
    Person6 Pneumonia 50 Male Location21 
    Person7 Typhoid 24 Female Location22 
    Person8 TB 30 Female Location23 
    Person9 Pneumonia 40 Male Location24 
    Person10 Pneumonia 30 Male Location25 
    Person11 Pneumonia 45 Female Location26 
    Person12 Pneumonia 25 Male Location3 
    Person13 Pneumonia 45 Male Location4 
    Person14 Pneumonia 30 Male Location5 
    Person15 Pneumonia 65 Male Location15 
    Person16 Pneumonia 40 Female Location16 
    Person17 TB 55 Male Location17 
    Person18 TB 21 Male Location18 
    Person19 TB 35 Female Location11 
    Person20 TB 32 Male Location12 
    Person21 TB 42 Male Location13 
    Person22 TB 55 Female Location14 
    Person23 TB 41 Male Location21 
    Person24 TB 22 Female Location22 
    Person25 Typhoid 31 Female Location23 
    Person26 Typhoid 25 Female Location24 
    Person27 Typhoid 67 Male Location25 
    Person28 Typhoid 20 Female Location26 
    Person29 Typhoid 36 Male Location19 
    Person30 Typhoid 65 Female Location20 
    Person31 Asthma 33 Female Location6 
    Person32 Asthma 21 Male Location7 
    Person33 Asthma 20 Female Location8 
    Person34 Asthma 55 Male Location9 
    Person35 Asthma 40 Female Location10 
    Person36 Asthma 47 Male Location11 
    Person37 Asthma 35 Male Location12 
    Person38 Typhoid 30 Male Location13 
    Person39 Typhoid 39 Female Location23 
    Person40 Typhoid 21 Male Location24 
    Person41 Asthma 40 Female Location25 
    Person42 Asthma 19 Male Location26 
    Person43 Asthma 29 Male Location1 
    Person44 TB 49 Female Location2 
    Person45 TB 24 Female Location3 
    Person46 TB 68 Female Location4 
    Person47 Typhoid 38 Female Location5 
    Person48 Typhoid 59 Female Location6 
    Person49 Typhoid 64 Male Location7 
    Person50 Typhoid 31 Female Location8 
    Person51 Typhoid 64 Female Location20 
    Person52 Typhoid 32 Female Location21 
    Person53 Asthma 30 Male Location22 
    Person54 Asthma 36 Female Location23 
    Person55 Asthma 40 Female Location24 
    Person56 TB 20 Female Location25 
    Person57 TB 26 Female Location26 
    Person58 TB 42 Female Location1 
    Person59 Typhoid 24 Female Location2 
    Person60 Typhoid 48 Female Location3 
    Person61 Typhoid 62 Male Location4 
    Person62 Typhoid 42 Female Location5 
    Person63 Typhoid 27 Female Location6 
    Person64 Typhoid 36 Female Location7 
    Person65 Asthma 67 Male Location8 
    Person66 Asthma 23 Male Location9 
    Person67 TB 21 Male Location10 
    Person68 TB 45 Female Location11 
    Person1 Arthritis 25 Female Location1 
    Person2 heart attack 35 Male Location2 
    Person3 diabetes 40 Female Location3 
    Person4 heart attack 36 Male Location4 
    Person5 Arthritis 25 Male Location5 
    Person6 heart attack 50 Female Location21 
    Person7 heart attack 24 Male Location22 
    Person8 diabetes 30 Female Location23 
    Person9 heart attack 40 Male Location24 
    Person10 Arthritis 30 Male Location25 
    Person11 heart attack 45 Female Location26 
    Person12 Arthritis 25 Male Location3 
    Person13 heart attack 45 Male Location4 
    Person14 diabetes 30 Female Location5 
    Person15 heart attack 65 Female Location15 
    Person16 Arthritis 40 Female Location16 
    Person17 heart attack 55 Female Location17 
    Person18 heart attack 21 Female Location18 
    Person19 Arthritis 35 Male Location11 
    Person20 heart attack 32 Female Location12 
    Person21 heart attack 42 Male Location13 
    Person22 diabetes 55 Female Location14 
    Person23 Arthritis 41 Female Location21 
    Person24 heart attack 22 Female Location22 
    Person25 diabetes 31 Female Location23 
    Person26 heart attack 25 Female Location24 
    Person27 Arthritis 67 Female Location25 
    Person28 heart attack 20 Male Location26 
    Person29 heart attack 36 Female Location19 
    Person30 Arthritis 65 Female Location20 
    Person31 heart attack 25 Female Location6 
    Person32 heart attack 24 Male Location7 

я попробовал эту строку кода, чтобы сделать двудольный сеть человека и их болезни, я показывая человека «местоположение» атрибут этого кода

V(g)$location <- d_el$location 
V(g)$color=V(g)$location  
V(g)$Person<- d_el$Person 

Теперь я хочу, чтобы извлечь все соседи узлы узла ТБ i использовали эту строку кода

V(g)$location[which(V(g)$Person %in% neighbors(g, "TB"))] 
[1] 20 22 14 16 17 20 8 15 16 17 18 19 11 13 24 25 20 21 20 22 14 

Что это за вывод выше?

Answer 1

Это должно работать (ваши места являются факторами, поэтому оно было показано, как целые числа, преобразовывать их в символы):

V(g)$location <- as.character(d_el$location) 
V(g)$Person <- as.character(d_el$Person) 
V(g)$location[which(V(g)$Person %in% names(neighbors(g, "TB")))] 
# [1] "Location4" "Location23" "Location17" "Location18" "Location11" "Location12" "Location13" "Location14" "Location21" "Location22" "Location2" "Location3" "Location4" "Location25" 
# [15] "Location26" "Location1" "Location10" "Location11" "Location4"