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
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))
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
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.