CODEWITHSUNDEEP
r
Rnovice
Rnovice

Reputation: 79

Splitting dataset into groups in r

I have a dataset where individuals belong to no group, one group, or more than one group. It looks like this

  resp_id     G1     G2      G3       G4       G5      G6
    1         0      1        1        0       0        1
    2         1      1        0        1       0        0
    3         0      0        0        0       0        0
    4         0      1        0        0       0        0
    5         0      1        0        0       0        0

and so on for about 400 rows. Here, '1' signifies groups membership and '0' means they aren't a part of the group. I want to assign individuals to groups such that:

  1. All individuals are in at most 1 group
  2. I have 6 approximately similar-sized groups (~46 people per group. Total number of people who are a part of at least 1 group is 278.)

Is there a way I can do this in R?

Upvotes: 0

Views: 69

Answers (1)

Wimpel
Wimpel

Reputation: 27802

you mean something like this?

#persons
id = 1:400
# draw 6 groups of 46 unique persons
L <- lapply(split(id, cut(id, 6)), sample, 47, replace = FALSE)
names(L) <- paste0("G", 1:6)
L <- lapply(L, as.data.table)
# bind to a data.table
DT <- rbindlist(L, id = "group", use.names = TRUE, fill = TRUE)
# final approach
final <- data.table(id = id)
final[DT, group := i.group, on = .(id = V1)]

answer <- dcast(final, id ~ group, value.var = "id", fun.aggregate = length)

colSums(answer)
# id       NA    G1    G2    G3    G4    G5    G6 
# 80200   118    47    47    47    47    47    47 

head(answer, 20)
#    id NA G1 G2 G3 G4 G5 G6
# 1:  1  0  1  0  0  0  0  0
# 2:  2  0  1  0  0  0  0  0
# 3:  3  0  1  0  0  0  0  0
# 4:  4  0  1  0  0  0  0  0
# 5:  5  0  1  0  0  0  0  0
# 6:  6  0  1  0  0  0  0  0
# 7:  7  0  1  0  0  0  0  0
# 8:  8  0  1  0  0  0  0  0
# 9:  9  0  1  0  0  0  0  0
#10: 10  0  1  0  0  0  0  0
#11: 11  0  1  0  0  0  0  0
#12: 12  0  1  0  0  0  0  0
#13: 13  1  0  0  0  0  0  0
#14: 14  0  1  0  0  0  0  0
#15: 15  0  1  0  0  0  0  0
#16: 16  1  0  0  0  0  0  0
#17: 17  0  1  0  0  0  0  0
#18: 18  0  1  0  0  0  0  0
#19: 19  1  0  0  0  0  0  0
#20: 20  0  1  0  0  0  0  0

Upvotes: 1

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