Permutation of euclidean distances between 13 row indexes in R

Question

I have 13 Area of Interests (AOIs) with x and y values for each test image. How can I get all possible combination of euclidian distance between pairs of AOIs, ((x-xi)+(y-yi))^(1/2)? Ultimately I am looking for the maximum distance from all possible distances between two AOIs for each test image.

Can this be done without using loops?

> setwd("C:/Users/Data/Desktop") > RawColor <- read.csv(file="13ptColor.csv") > print(RawColor) SN TestImage AOI x y 1 50293253 B 13 0.1597 0.06775 2 50293253 B 12 0.1587 0.06574 3 50293253 B 11 0.1596 0.06715 4 50293253 B 10 0.1594 0.06618 5 50293253 B 9 0.1590 0.06582 6 50293253 B 8 0.1593 0.06638 7 50293253 B 7 0.1589 0.06602 8 50293253 B 6 0.1594 0.06601 9 50293253 B 5 0.1591 0.06552 10 50293253 B 4 0.1587 0.06473 11 50293253 B 3 0.1593 0.06603 12 50293253 B 2 0.1585 0.06481 13 50293253 B 1 0.1588 0.06510 14 50293253 G 13 0.2985 0.60400 15 50293253 G 12 0.2977 0.60440

Cole · Accepted Answer · 2019-12-01 11:33:49Z

See dist(). Since not enough test data was provided, here's an example on iris:

as.matrix( by(data = iris[, c('Sepal.Length', 'Sepal.Width')], INDICES = iris[, 'Species', drop = F], FUN = function(DF) max(dist(DF)) ) ) # [,1] # setosa 2.418677 # versicolor 2.332381 # virginica 3.269557 # or sp_DF <- split(x = iris[, c('Sepal.Length', 'Sepal.Width')], f = iris[, 'Species', drop = F]) sapply(sp_DF, function(DF) max(dist(DF))) # setosa versicolor virginica # 2.418677 2.332381 3.269557

And a similar approach in dplyr

library(dplyr) iris%>% group_by(Species)%>% summarize(max_dist = max(dist(cbind(Sepal.Length, Sepal.Width)))) # A tibble: 3 x 2 Species max_dist <fct> <dbl> 1 setosa 2.42 2 versicolor 2.33 3 virginica 3.27

And data.table:

library(data.table) as.data.table(iris)[, .(max_dist = max(dist(.SD))), .SDcols = c('Sepal.Length', 'Sepal.Width'), by = Species]

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