Compute and plot pairwise distances using dist in R-CodePudding

I have a dataframe with 4 columns.

set.seed(123)
df <- data.frame(A = round(rnorm(1000, mean = 1)),
           B = rpois(1000, lambda = 3),
           C = round(rnorm(1000, mean = -1)),
           D = round(rnorm(1000, mean = 0)))

I would like to compute the distances for every possible combination of my columns (A-B, A-C, A-D, B-C, B-D, C-D) at every row of my dataframe. This would be the equivalent of doing df$A - df$B for every combination.

Can we use the dist() function to compute this efficiently as I have a very large dataset? I would like to then convert the dist object into a data.frame to plot the results with ggplot2. Unless there is a good tidy version of doing the above.

Many Thanks

The closest I got was doing the below, but I am not sure to what the column names refer to.

d <- apply(as.matrix(df), 1, function(e) as.vector(dist(e)))
t(d)

CodePudding user response：

dist will compare every value in a vector to every other value in the same vector, so if you are looking to compare columns row-by-row, this is not what you are looking for.

If you just want to calculate the difference between all columns pairwise, you can do:

df <- cbind(df, 
            do.call(cbind, lapply(asplit(combn(names(df), 2), 2), function(x) {
  setNames(data.frame(df[x[1]] - df[x[2]]), paste(x, collapse = ""))
})))

head(df)
#>   A B  C  D AB AC AD BC BD CD
#> 1 0 1 -2 -1 -1  2  1  3  2 -1
#> 2 1 1 -1  1  0  2  0  2  0 -2
#> 3 3 1 -2 -1  2  5  4  3  2 -1
#> 4 1 3  0 -1 -2  1  2  3  4  1
#> 5 1 3  0  1 -2  1  0  3  2 -1
#> 6 3 3  1  0  0  2  3  2  3  1

^{Created on 2022-06-14 by the reprex package (v2.0.1)}

CodePudding user response：

Using base r:

df_dist <- t(apply(df, 1, dist))
colnames(df_dist) <- apply(combn(names(df), 2), 2, paste0, collapse = "_")

If you really want to use a tidy-approach, you could go with c_across, but this also removes the names, and is much slower if your data is huge