Matching negative and positive values using For Loop in R - r

Question

This is my first post so I hope it is not too elementary. I am trying to match observations which have a negative Amount to counterparts that have a positive Amount and an equal abs(Amount). Furthermore, I want to check that the Amounts are both from the same Account. To do this, I am trying to use a for loop, but am getting the following error: "Operations are possibly only for numeric, logical or complex types." This is my code so far:
for(i in 1:nrow(data)){
for(j in 1:nrow(data)){
if ((data$Amount[i]=abs(data$Amount[j]))&(data$Amount[i]!=data$Amount[j])&(data$Account[i]=data$Account[j]))
{data$debit[i]<-1}}}
Does anyone have any idea why this is happening, or know of a better way using the Apply function family? Thank you in advance!
EDIT:
Below is a toy data set: to illustrate this example. For instance, on this data set, I want to create an indicator variable which would be 0 except for ID=3 because for the observation, 4.7=abs(-4.7) and "abc1"="abc1" .
Data <- " ID Amount Account
1 5.0 abc1
2 -5.0 abc9
3 4.7 abc1
4 4.6 abc7
5 5.0 abc8
6 -4.7 abc1 "

Answer 1

Here's an alternative method of achieving the same result with a lot less code (and I think it's easier to read too)
library(dplyr)
Data <- Data %>%
group_by(Account) %>%
mutate(
debit = (Amount > 0 & -Amount %in% unique(Amount)) * 1
) %>%
ungroup()
If you aren't familiar with the pipe operator (%>%), it allows us to avoid nesting a lot of functions inside one another. It works by taking the output of the previous function, and entering it as the first argument of the next function. So this code takes the data set (Data), groups it by the Account, adds a new column with the indicator variable with the desired criterion, and then ungroups the data so it's back to its normal format.
The looping is done within these function calls, which allows them to be implemented in compiled languages (usually C++) - which can be a lot faster than R.

Answer 2

You need to use the == operator (= is an assignment operator) and the && rather than the & operator for your logical condition:
## Assignment (incorrect in this case!)
1 = 1
# Error in 1 = 1 : invalid (do_set) left-hand side to assignment
a <- 1
a = a
Note that with a = a there is no logical checked (just the equivalent of a <- a; see more here).
## Checking equivalence (returns a logical)
1 == 1
# [1] TRUE
a == a
# [1] TRUE
For the difference between & and &&, the second evaluates the full condition and the first each element (see here).
Also it might be more elegant to check whether the sum of data$Amount[i] and data$Amount[j] is null rather than to check if they have the first absolute value but not the same signed value.
## Your example
for(i in 1:nrow(data)){
for(j in 1:nrow(data)){
if ( (sum(c(data$Amount[i], data$Amount[j])) == 0) && (data$Account[i] == data$Account[j]) ) {
data$debit[i]<-1
}
}
}