Deduce informations with pairs using programmation - r

Question

I would like to analyze data.
My Database is composed of 1408 (704 for type 1 and 704 for type 2) observations and 49 variables. Here is part of my database.
The point is that I want to analyze gender of type 1(sellers) who overcharged.
Data
Subject ID Gender Period Matching group Group Type Overcharging
654 1 1 73 1 1 NA
654 1 2 73 1 1 NA
654 1 3 73 1 1 NA
654 1 4 73 1 1 NA
708 0 1 73 1 2 1
708 0 2 73 1 2 0
708 0 3 73 1 2 0
708 0 4 73 1 2 1
435 1 1 73 2 1 NA
435 1 2 73 2 1 NA
435 1 3 73 2 1 NA
435 1 4 73 2 1 NA
546 0 1 73 2 2 0
546 0 2 73 2 2 0
546 0 3 73 2 2 1
546 0 4 73 2 2 0
For example, if you take a look at matching group =73, there are 2 groups (1 and 2).And in each group, there are two types (1 and 2). For each type 1 (seller) we do not have information about what he did (overcharge or not). But we have informations about buyers (type 2) who were overcharged or not.
If I can identify the buyer who were over-treated, then, this means that the seller this buyer is interacting has over-treated the buyer. So all I need to look at is the gender of the seller in the same group as the buyer.
In matching group 73 we know for instance that at period 1 subject 708 was overcharged (the one in group 1). As I know that this men belongs to group 1 and matching group 73, I am able to identify the seller who has overcharged him : subject 654 with gender =1.
In group 2 (matching group 73), we know that at period 3, agent 546 was overcharged. As I know that this men belongs to group 1 and matching group 73, I am able to identify the seller who has overcharged him : subject 435 with gender =1.
....
I would do this for all the observations I have.
However I really don't know how to proceed to code and make this condition on R.
This is what I tried to do, but doesn't fit my needs !
for (matchinggroup[type==1]==matchinggroup[type==2] &
group[type==1]==group[type==2] & period[type==1]==period[type==2])
{
if ((overtreatment==1), na.rm=TRUE)
sum(gender==1[type==1], na.rm=TRUE)
}
The expected output I would like to have is :
sum(overcharging==1[gender==1&type==1])
>3
sum(overcharging==1[gender==0&type==1])
>0
sum(overcharging==0[gender==1&type==1])
>5
sum(overcharging==0[gender==0&type==1])
>0

Answer 1

Not exactly sure what your desired output is, but consider this:
Data <- read.table(header = T,
text = "Subject_ID Gender Period Matching_group Group Type Overcharging
654 1 1 73 1 1 NA
654 1 2 73 1 1 NA
654 1 3 73 1 1 NA
654 1 4 73 1 1 NA
708 0 1 73 1 2 1
708 0 2 73 1 2 0
708 0 3 73 1 2 0
708 0 4 73 1 2 1
435 1 1 73 2 1 NA
435 1 2 73 2 1 NA
435 1 3 73 2 1 NA
435 1 4 73 2 1 NA
546 0 1 73 2 2 0
546 0 2 73 2 2 0
546 0 3 73 2 2 1
546 0 4 73 2 2 0
")
dat1 <- subset(Data, Overcharging==1)
This will find all the Overcharging sellers. And then you could find each matching buyer using this loop:
out <- data.frame()
for(i in 1:nrow(dat1)){
dat2 <- dat1[i,]
df <- Data[Data$Period==dat2$Period & Data$Matching_group==dat2$Matching_group &
Data$Group==dat2$Group & Data$Type==1,]
out <- rbind(out, df)
}
Which will give you:
Subject_ID Gender Period Matching_group Group Type Overcharging
1 654 1 1 73 1 1 NA
4 654 1 4 73 1 1 NA
11 435 1 3 73 2 1 NA

Answer 2

I think "for loop" solution is not suitable in R.
I developed another solution for you with data.table by seperating sellers and buyers, and then joining them.
library(data.table)
Data <- data.table(read.table(header = T,
text = "Subject_ID Gender Period Matching_group Group Type Overcharging
654 1 1 73 1 1 NA
654 1 2 73 1 1 NA
654 1 3 73 1 1 NA
654 1 4 73 1 1 NA
708 0 1 73 1 2 1
708 0 2 73 1 2 0
708 0 3 73 1 2 0
708 0 4 73 1 2 1
435 1 1 73 2 1 NA
435 1 2 73 2 1 NA
435 1 3 73 2 1 NA
435 1 4 73 2 1 NA
546 0 1 73 2 2 0
546 0 2 73 2 2 0
546 0 3 73 2 2 1
546 0 4 73 2 2 0
")
)
Data[, SubjectType := ifelse(Type==1, "Seller", "Buyer")]
Subjects <- unique(Data[, .(Subject_ID, Gender)])
Matches <- dcast(Data, Matching_group+Group~SubjectType, value.var="Subject_ID", fun.aggregate = mean)
Buys <- Data[!is.na(Overcharging), .(Buyer = Subject_ID, BuyerGender = Gender, Period, Matching_group, Group, Overcharging)]
Buys <- merge(Buys, Matches, by=c("Buyer", "Matching_group", "Group"), all.x = T)
Buys <- merge(Buys, Subjects[, .(Seller = Subject_ID, SellerGender = Gender)], by="Seller", all.x = T)
Buys[Overcharging==0, .N, .(BuyerGender, SellerGender)]
Buys[Overcharging==1, .N, .(BuyerGender, SellerGender)]