Nested For loops in R not giving the desired output

Question

Hi i am using nested for loops to find compatible blood types in two data sets. My datasets :

#IDR= c(seq(1,4))
#BTR=c("A","B","AB","O")
#data_R=data.frame(IDR,BTR,stringsAsFactors=FALSE)

#IDD= c(seq(1,8))
#BTD= c(rep("A", each=2),rep("B", each=2),rep("AB", each=2),rep("O", each=2))
#WD= c(rep(0.25, each=2),rep(0.125, each=2),rep(0.125, each=2),rep(0.5, each=2))
#data_D=data.frame(IDD,BTD,WD,stringsAsFactors=FALSE)

# data_R

  IDR BTR
1   1   A
2   2   B
3   3  AB
4   4   O

# data_D

  IDD BTD    WD
1   1   A 0.250
2   2   A 0.250
3   3   B 0.125
4   4   B 0.125
5   5  AB 0.125
6   6  AB 0.125
7   7   O 0.500
8   8   O 0.500

What i am trying to do is to verify for each row from data_R that i have a compatible blood type in data_D,For instance: if i have BTR=AB then i would like to print all the values of WD in data_D (because AB is compatible with A,B,AB and O), if i have BTR=A then i would like to print the values of WD in data_D that corresponds to only A and O, if i have BTR=B then i would like to print the values of WD in data_D that corresponds to only B and O, And finally if i have BTR=O then i would like to print only the values of WD in data_D that corresponds to O.

here is the code i wrote but the output does not have the results i wanted

for (i in 1:nrow(data_R)) {
  for (j in 1:nrow(data_D)) {
    if(BTR[i] =="AB"){
      if(BTD[j]=="A" || BTD[j]=="B" || BTD[j]=="AB" || BTD[j]=="O"){
        output=as.vector(WD)
      }
    }else if(BTR[i] =="A"){
      if(BTD[j]=="A" || BTD[j]=="O"){
        output=as.vector(WD)
      }
    }else if(BTR[i] =="B"){
      if(BTD[j]=="B" || BTD[j]=="O"){
        output=as.vector(WD)
      }
      
    }else if(BTR[i] =="O"){
      if(BTD[j] =="O"){
        output=as.vector(WD)
      }
      
    }
    
  }
  
}
output

and that is the output i got: [1] 0.250 0.250 0.250 0.250 0.125 0.125 0.500 0.500

I was only able to get the output (WRONG), and i would appreciate any help to fix this problem and display more readable (taking info from both datasets) output like :

   BTR BTD output
1    A   A  0.250
2    A   A  0.250
3    A   O  0.500
4    A   O  0.500
5    B   B  0.125
6    B   B  0.125
7    B   O  0.500
8    B   O  0.500
9   AB   A  0.250
10  AB   A  0.250
11  AB   B  0.125
12  AB   B  0.125
13  AB  AB  0.125
14  AB  AB  0.125
15  AB   O  0.500
16  AB   O  0.500
17   O   O  0.500
18   O   O  0.500

I apologize in advance if my question is long i just wanna make sure i explained it well. Thank you in advance for your help.

Answer 1

You just need two merges and an intermediate data.frame:

compatible <- data.frame(
  BTR = c(rep("AB", 4),     rep("A", 2), rep("B", 2), "O"),
  BTD = c("AB","A","B","O", "A","O",     "B","O",     "O")
)
compatible
#   BTR BTD
# 1  AB  AB
# 2  AB   A
# 3  AB   B
# 4  AB   O
# 5   A   A
# 6   A   O
# 7   B   B
# 8   B   O
# 9   O   O

The first step provides all possible donors for each recipient:

tmp <- merge(data_R, compatible, by = "BTR", all.x = TRUE, sort = FALSE)
tmp
#   BTR IDR BTD
# 1   A   1   A
# 2   A   1   O
# 3   B   2   B
# 4   B   2   O
# 5  AB   3  AB
# 6  AB   3   A
# 7  AB   3   B
# 8  AB   3   O
# 9   O   4   O

The second merge brings in the available donors:

merge(tmp, data_D, by = "BTD")
#    BTD BTR IDR IDD    WD
# 1    A   A   1   1 0.250
# 2    A   A   1   2 0.250
# 3    A  AB   3   1 0.250
# 4    A  AB   3   2 0.250
# 5   AB  AB   3   5 0.125
# 6   AB  AB   3   6 0.125
# 7    B   B   2   3 0.125
# 8    B   B   2   4 0.125
# 9    B  AB   3   3 0.125
# 10   B  AB   3   4 0.125
# 11   O   B   2   7 0.500
# 12   O   B   2   8 0.500
# 13   O   O   4   7 0.500
# 14   O   O   4   8 0.500
# 15   O   A   1   7 0.500
# 16   O   A   1   8 0.500
# 17   O  AB   3   7 0.500
# 18   O  AB   3   8 0.500

Note that the order is different but your expected output is in there.

While this is using base R, other packages provide some more control over merging. I suggest you look at How to join (merge) data frames (inner, outer, left, right) and https://stackoverflow.com/a/6188334/3358272 to learn about joins (they are a very powerful data-manipulation mechanism!), and consider dplyr or data.table for facilitating this flow:

library(dplyr)
left_join(data_R, compatible, by = "BTR") %>%
  left_join(data_D, by = "BTD")

library(data.table)
data_RDT <- as.data.table(data_R)
data_DDT <- as.data.table(data_D)
compatible <- as.data.table(compatible)
compatible[data_RDT, on = .(BTR)][data_DDT, on = .(BTD), allow.cartesian = TRUE]