Sum values larger than the current value, by group - r

Question

I have measured basal area of trees in different plots. Here's a small example with two plots with 4 trees each:
Plot Tree BasalArea
1 1 4
1 2 5
1 3 7
1 4 3
2 1 4
2 2 6
2 3 9
2 4 5
Within each plot, I want calculate the sum of basal area of the trees that have basal area larger than the focal tree.
For example, Tree 1 in Plot 1 has an area of 4. Within that plot there are 2 trees with an area larger than tree 1: Tree 2 and Tree 3 with area 5 and 7, respectively. So, "BA_Larger" for tree 1 is 5 + 7 = 12.
Tree 2 in the same plot has basal area = 5. Within plot 1 there is only one tree with a larger area than tree 2: tree 3 with area 7. Thus, "BA_Larger" for tree 2 is 7.
Finally, the data frame should be like this:
Plot Tree BasalArea BA_Larger
1 1 4 12
1 2 5 7
1 3 7 0
1 4 3 16
2 1 4 20
2 2 6 9
2 3 9 0
2 4 5 15
The data set is very large. I have tried to calculate the "BA_Larger", without success. Any help is highly appreciated.

Answer 1

The base R solution with ave():
within(df, BA_Larger <- ave(BasalArea, Plot, FUN = function(x) sapply(x, function(y) sum(x[x > y]))))
With a tidyverse style, you can also use map_int() or map_dbl() from purrr.
library(dplyr)
library(purrr)
df %>%
group_by(Plot) %>%
mutate(BA_Larger = map_int(BasalArea, ~ sum(BasalArea[BasalArea > .]))) %>%
ungroup()
Output
# # A tibble: 8 x 4
# Plot Tree BasalArea BA_Larger
# <int> <int> <int> <int>
# 1 1 1 4 12
# 2 1 2 5 7
# 3 1 3 7 0
# 4 1 4 3 16
# 5 2 1 4 20
# 6 2 2 6 9
# 7 2 3 9 0
# 8 2 4 5 15
Data
df <- structure(list(Plot = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L), Tree = c(1L,
2L, 3L, 4L, 1L, 2L, 3L, 4L), BasalArea = c(4L, 5L, 7L, 3L, 4L,
6L, 9L, 5L)), class = "data.frame", row.names = c(NA, -8L))

Answer 2

Another solution
library(tidyverse)
df %>%
group_by(Plot) %>%
arrange(BasalArea, .by_group = T) %>%
mutate(res = sum(BasalArea) - cumsum(BasalArea)) %>%
arrange(Tree, .by_group = T) %>%
ungroup()
# A tibble: 8 x 4
Plot Tree BasalArea res
<int> <int> <int> <int>
1 1 1 4 12
2 1 2 5 7
3 1 3 7 0
4 1 4 3 16
5 2 1 4 20
6 2 2 6 9
7 2 3 9 0
8 2 4 5 15

Answer 3

Using a non-equi join with data.table. Calculate sum for each match.
library(data.table)
setDT(d)
d[ , ba2 := d[d, on = .(Plot, BasalArea > BasalArea), sum(x.BasalArea), by = .EACHI]$V1]
# Plot Tree BasalArea ba2
# 1: 1 1 4 12
# 2: 1 2 5 7
# 3: 1 3 7 NA
# 4: 1 4 3 16
# 5: 2 1 4 20
# 6: 2 2 6 9
# 7: 2 3 9 NA
# 8: 2 4 5 15

Answer 4

Actually you don't need a package to do this. Using by you may split the data on the Plot column, then compare the specific tree i to the other values in the split-subset and exclude i in the sum. Finally unsplit the result according to the df1$Plot column.
res <- unsplit(by(df1, df1$Plot, function(x)
transform(x, BA_Larger=sapply(1:nrow(x), function(i)
sum(x[x[, 3] > x[i, 3], 3])))), df1$Plot)
res
# Plot Tree BasalArea BA_Larger
# 1 1 1 4 12
# 2 1 2 5 7
# 3 1 3 7 0
# 4 1 4 3 16
# 5 2 1 4 20
# 6 2 2 6 9
# 7 2 3 9 0
# 8 2 4 5 15
Data:
df1 <- structure(list(Plot = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L), Tree = c(1L,
2L, 3L, 4L, 1L, 2L, 3L, 4L), BasalArea = c(4L, 5L, 7L, 3L, 4L,
6L, 9L, 5L)), class = "data.frame", row.names = c(NA, -8L))