(This article was first published on

**Coffee and Econometrics in the Morning**, and kindly contributed to R-bloggers)Suppose you have a data set with two identifiers. For example, maybe you’re studying the relationships among firms in an industry and you have a way to link the firms to one another. Each firm has an id, but the unique unit in your data set is a pairing of ids. Here’s a stylized example of one such data set:

In the example that motivated this post, I only cared that A was linked with B in my data, and if B is linked with A, that’s great, but it does not make A and B any more related. In other words, the order of the link didn’t matter.

In this case, you’ll see that our stylized example has duplicates — id1 = “A” and id2 = “B” is the same as id1=”B” and id2 = “A” for this purpose. What’s a simple way to get a unique identifier? There’s an apply command for that!

Thinking of each row of the identifier data as a vector, we could

**alphabetize**(using sort(), so c(“B”, “A”) becomes c(“A”, “B”)), and then**paste**the the resulting vector together into one identifier (paste, using collapse). I call our worker function idmaker():idmaker = function(vec){return(paste(sort(vec), collapse=””))}

Then, all we need to do is use the apply command to apply this function to the rows of the data, returning a vector of results. Here’s how my output looks.

To get a data frame of unique links, all we need to do is cbind() the resulting vector of indices to the original data frame (and strip the duplicates). Here’s some code:

co_id = apply(as.matrix(df[, c(“id1”, “id2”)]), 1, idmaker)

df = cbind(df, co_id)

df = df[!duplicated(df[,”co_id”]),]

Here is the resulting data frame with only unique pairs.

To

**leave a comment**for the author, please follow the link and comment on their blog:**Coffee and Econometrics in the Morning**.R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...