Calculate minimum distance between groups of points in data frame

user_7768165 0
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my data frame looks like this:

Time, Value, Group
0, 1.0, A
1, 2.0, A
2, 3.0, A
0, 4.0, B
1, 6.0, B
2, 6.0, B
0, 7.0, C
1, 7.0, C
2, 9.0, C

I need to find for each combination (A, B), (A, C), (B, C) the maximum difference over each corresponding Time points.

So comparing A and B has maximum distance for t=1 which is 6 (B) - 2 (A) = 4.

The full output should be something like this:

combination,time,distance
AB, 0, 4
AC, 0, 6
BC, 0, 3
r
2 Answers

One way in base R using combn :

do.call(rbind, combn(unique(df$Group), 2, function(x) {
  df1 <- subset(df, Group == x[1])
  df2 <- subset(df, Group == x[2])
  df3 <- merge(df1, df2, by = 'Time')
  value <- abs(df3$Value.x - df3$Value.y)
  data.frame(combn = paste(x, collapse = ''), 
             time = df3$Time[which.max(value)],
             max_difference = max(value))
}, simplify = FALSE))

#  combn time max_difference
#1    AB    1              4
#2    AC    0              8
#3    BC    0              5

We create all combination of unique Group values, subset the data for them and merge them on Time. Subtract the corresponding value columns and return the max difference between them.

data

df <- structure(list(Time = c(0L, 1L, 2L, 0L, 1L, 2L, 0L, 0L, 0L), 
    Value = c(1, 2, 3, 4, 6, 6, 7, 7, 9), Group = c("A", "A", 
    "A", "B", "B", "B", "C", "C", "C")), 
    class = "data.frame", row.names = c(NA, -9L))
user_39629140

One dplyr option could be:

df %>%
 inner_join(df, by = "Time") %>%
 filter(Group.x != Group.y) %>%
 group_by(Time,
          Group = paste(pmax(Group.x, Group.y), pmin(Group.x, Group.y), sep = "-")) %>%
 summarise(Max_Distance = abs(max(Value.x[Group.x == first(Group.x)]) -  max(Value.y[Group.y == first(Group.y)])))

   Time Group Max_Distance
  <int> <chr>        <dbl>
1     0 B-A              3
2     0 C-A              8
3     0 C-B              5
4     1 B-A              4
5     2 B-A              3
user_59645570
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