Calculate euclidean distance between multiple vectors in R

Question

I'm trying to calculate the euclidean distances between one vector on the one hand and multiple vectors on the other hand using R.

So far, I've been following this documentation https://cran.r-project.org/web/packages/neighbr/neighbr.pdf and used distance(x, y, "euclidean"). This works perfectly well if I only calculate the distance between two vectors, ie when I have one row of data in both x and y. However, in my original dataset, I have multiple rows in y and I'd like to calculate the distances between each of these rows and the single row in x.

How is it possible to do this?

x = structure(list(`Feature I` = 0.85649790378586, `Feature II` = 0.851856356221207, `Feature III` = 0.799580263077569, `Feature IV` = 0.895081402129565, `Feature V` = 0.920173237422567), row.names = c(NA, -1L), class = c("tbl_df", "tbl", "data.frame"))

y = structure(list(`Feature I` = c(0.0444280626160322, 0.00326398594129033, 0.0218000692329814), `Feature II` = c(0.0481646509894741, 0.00509786237104908, 0.0276902769176258), `Feature III` = c(0.0456380620204004, 0.00422956673025977, 0.0347273727088683), `Feature IV` = c(0.0365954415011219, 0.00422974884164406, 0.0328151120410415), `Feature V` = c(0.0384331094111439, 0.00362614754925969, 0.0260414956219995)), row.names = c(NA, -3L), class = c("tbl_df", "tbl", "data.frame"))

Answer 1

Adapting this answer to your data:

y$dist_from_x = t(outer(
  1:nrow(x),
  1:nrow(y),
  FUN = Vectorize(function(xi,yi) dist(rbind(x[xi,],y[yi,])))
))

y
#     Feature I  Feature II Feature III  Feature IV   Feature V dist_from_x
# 1 0.044428063 0.048164651 0.045638062 0.036595442 0.038433109    1.840726
# 2 0.003263986 0.005097862 0.004229567 0.004229749 0.003626148    1.926465
# 3 0.021800069 0.027690277 0.034727373 0.032815112 0.026041496    1.871883

Since x has one row, this would be a little more efficient:

# reset definition of y (or remove the dist_from_x column)
x_expanded = x[rep(1, nrow(y)), ]
y$dist_from_x = sqrt(rowSums((x_expanded - y)^2))
# same result as above