Arrange by multiple conditions in R - r

Question

Having the following dataset:
test <- data.frame(name= c("A", "B", "C", "D", "E"), v1 = c(2, 4, 1, 1, 2), v2 = c(3, 4, 2, 1, 5))
name v1 v2
A 2 3
B 4 4
C 1 2
D 1 1
E 2 5
I want to test a concept of actor/node dominance, which means that for each row/entry I want to see if it has the highest value in the dataset. For example B is higher than A, C and D for both v1 and v2. So it "dominates" all the other rows. E for example is only higher than A, C and D, so it dominates those 3 rows.
Mathematically speaking, what I'm searching for is i >= j, for v1_i >= v1_j and v2_i >= v2_j.
Arranging or sorting by columns doesn't work because it doesn't really show how one row will impose another as it sorts first by one column and then by another.
EDIT:Just to add an example an end output would be:
B dominates A, C, D
E dominates A, D, C
C dominates D
A dominates C, D
Doesn't really matter how it would look like. If it's in the form of a directed network/matrix or a table with a variable with all the letters.

Answer 1

i got a way out hope this helps :)
c$v3= rowSums(x = c[,-1])
c = c[order(c$v3,decreasing = T),]
k = length(c$name)
for (i in 1:k ) {
if (i == k) {
}else {
a = c$name[i+1:k]
a = as.character(a[!is.na(a)])
b = c$name[i]
b = as.character(b[!is.na(b)])
cat(b,"greater than ", a,"\n",sep=" ")
}
}
so your output will be
B greater than E A C D
E greater than A C D
A greater than C D
C greater than D

Answer 2

for loop are very inefficient in R. Please, avoid them !
You can simply do it with apply:
# Names column
names = c("A", "B", "C", "D", "E")
# Dataframe
test <- data.frame(name= names, v1 = c(2, 4, 1, 1, 2), v2 = c(3, 4, 2, 1, 5))
# Display function
findLowerValues <- function(row, test, names) {
rep <- test$v1 <= row["v1"] & test$v2 <= row["v2"] & test$name != row["name"]
cat(row["name"], 'dominates', names[rep], "\n")
}
# Apply the display function
# axis : row
# Extra args: the full dataset and names
apply(test, 1, findLowerValues, test=test, names=names)
# A dominates C D
# B dominates A C D
# C dominates D
# D dominates
# E dominates A C D
# NULL

Answer 3

Here is a suggestion. It's probably not the most elegant solution.
We can have a function compare that checks if one letter dominates the other (unless it is the identical letter), and then use two nested sapplys.
my_letters <- c("A", "B", "C", "D", "E")
test <- data.frame(name= my_letters, v1 = c(2, 4, 1, 1, 2), v2 = c(3, 4, 2, 1, 5))
get_row<-function(letter){
test[test$name==letter,2:ncol(test)]
}
compare<-function(letter,i){
if(letter!=i){
if(!sum(get_row(letter) < get_row(i))){
return(i)
}
}
}
result <- sapply(my_letters, function(let) unlist(sapply(my_letters, compare, letter=let)))
results in a list:
$A
C D
"C" "D"
$B
A C D
"A" "C" "D"
$C
D
"D"
$D
NULL
$E
A C D
"A" "C" "D"

Answer 4

We first split every row into list of rows and pass it to mapply, repeat each row nrow(test) times, compare them with the entire dataframe test and select the name which has all values greater than that row. As this will also match rows with itself as well we use setdiff to remove those name values.
mapply(function(x, y) setdiff(
test$name[rowSums(x[rep(1, nrow(test)),] >= test[-1]) == ncol(test) - 1], y),
split(test[-1], test$name), test$name)
#$A
#[1] "C" "D"
#$B
#[1] "A" "C" "D"
#$C
#[1] "D"
#$D
#character(0)
#$E
#[1] "A" "C" "D"
data
test <- data.frame(name= c("A", "B", "C", "D", "E"), v1 = c(2, 4, 1, 1, 2),
v2 = c(3, 4, 2, 1, 5), stringsAsFactors = FALSE)

Answer 5

If you don't mind a data.table solution, a possibility is using non-equi joins as follows:
library(data.table)
setDT(test)
test[test, on=.(v1<=v1, v2<=v2), .(actor=i.name, node=x.name), by=.EACHI, allow.cartesian=TRUE][
actor!=node, .(actor, node)]
output:
actor node
1: A C
2: A D
3: B A
4: B C
5: B D
6: C D
7: E A
8: E C
9: E D