Clustering / Matching Over Many Dimensions in R - r

Question

I have a very large and complex data set with many observations of companies. Some of the observations of the companies are redundant and I need to make a key to map the redundant observations to a single one. However the only way to tell if they are actually representing the same company is through the similarity of a variety of variables. I think the appropriate approach is a kind of clustering based on a variety of conditions or perhaps even some kind of propensity score matching. Perhaps I just need flexible tools for making a complex kind of similarity matrix.
Unfortunately, I am not quite sure how to go about that in R. Most of the tools I've seen for clustering and categorizing seem to do so with either numerical distance or categorical data, but don't seem to allow multiple conditions or user specified conditions.
Below I've tried to create a smaller, public example of the kind of data I am working with and the result I am trying to produce. There are some conditions that must apply, for example, the location must be the same. There are some features that may associate one with another, for example var1 and var2. Then there are some features that may associate one with another, but they must not conflict, such as var3.
An additional layer of complexity is that the kind of association I am trying to use to map the redundant observation varies. For example, id1 and id2 are the same company redundantly entered into the data twice. In one place its name is "apples" and another "red apples". They share the same location, var1 value and var3 (after adjusting for formatting). Similarly ids 3, 5 and 6, are also really just one company, though much of the input for each is different. Some clusters would identify multiple observations, others would only have one. Ideally I would like to find a way to categorize or associate the observations based on several conditions, for example:
1. Test that the location is the same
2. Test whether var3 is different
3. Test whether the names is a substring of others
4. Test the edit distance of names
5. Test the similarity of var1 and var2 between observations
Anyways, hopefully there are better, more flexible tools for this than what I am finding or someone has experience with this kind of data work in R. Any and all suggestions and advice are much appreciated!
Data
id name location var1 var2 var3
1 apples US 1 abc 12345
2 red apples US 1 NA 12-345
3 green apples Mexico 2 def 235-92
4 bananas Brazil 2 abc NA
5 oranges Mexico 2 NA 23592
6 green apple Mexico NA def NA
7 tangerines Honduras NA abc 3498
8 mango Honduras 1 NA NA
9 strawberries Honduras NA abcd 3498
10 strawberry Honduras NA abc 3498
11 blueberry Brazil 1 abcd 2348
12 blueberry Brazil 3 abc NA
13 blueberry Mexico NA def 1859
14 bananas Brazil 1 def 2348
15 blackberries Honduras NA abc NA
16 grapes Mexico 6 qrs NA
17 grapefruits Brazil 1 NA 1379
18 grapefruit Brazil 2 bcd 1379
19 mango Brazil 3 efaq NA
20 fuji apples US 4 NA 189-35
Result
id name location var1 var2 var3 Result
1 apples US 1 abc 12345 1
2 red apples US 1 NA 12-345 1
3 green apples Mexico 2 def 235-92 3
4 bananas Brazil 2 abc NA 4
5 oranges Mexico 2 NA 23592 3
6 green apple Mexico NA def NA 3
7 tangerines Honduras NA abc 3498 7
8 mango Honduras 1 NA NA 8
9 strawberries Honduras NA abcd 3498 7
10 strawberry Honduras NA abc 3498 7
11 blueberry Brazil 1 abcd 2348 11
12 blueberry Brazil 3 abc NA 11
13 blueberry Mexico NA def 1859 13
14 bananas Brazil 1 def 2348 11
15 blackberries Honduras NA abc NA 15
16 grapes Mexico 6 qrs NA 16
17 grapefruits Brazil 1 NA 1379 17
18 grapefruit Brazil 2 bcd 1379 17
19 mango Brazil 3 efaq NA 19
20 fuji apples US 4 NA 189-35 20
Thanks in advance for your time and help!

Answer 1

library(stringdist)
getMatches <- function(df, tolerance=6){
out <- integer(nrow(df))
for(row in 1:nrow(df)){
dists <- numeric(nrow(df))
for(col in 1:ncol(df)){
tempDist <- stringdist(df[row, col], df[ , col], method="lv")
# WARNING: Matches NA perfectly.
tempDist[is.na(tempDist)] <- 0
dists <- dists + tempDist
}
dists[row] <- Inf
min_dist <- min(dists)
if(min_dist < tolerance){
out[row] <- which.min(dists)
}
else{
out[row] <- row
}
}
return(out)
}
test$Result <- getMatches(test[, -1])
Where test is your data. This probably definitely needs some refining and certainly needs some postprocessing. This creates a column with the index of the closest match. If it can't find a match within the given tolerance, it returns the index of itself.
EDIT: I will attempt some more later.