查找不同列中不同行的总和

Question

I'm trying to find the sum of different rows in different columns. For example, in the made-up data below I would like to know the sum of row 3-5 from exp1, 6-8 from exp2, and 2-4 from exp3, returning 12, 51, 69 for each column respectively.

 > data
   exp1 exp2 exp3
1     1   11   21
2     2   12   22
3     3   13   23
4     4   14   24
5     5   15   25
6     6   16   26
7     7   17   27
8     8   18   28
9     9   19   29
10   10   20   30

I have the range of the row index that I want as two separate named numeric objects, lower being one value smaller than my intended row index because of what I'm doing next.

> upper
exp1 exp2 exp3 
   5    8    4 
> lower
exp1 exp2 exp3 
   2    5    1 

What I've tried is to slice out row 1:upper and row 1:lower with a loop.

output <- list()
for (i in 1:length(data)) {
  temp <- data[i]
  output[[i]] <- slice(temp, 1:upper[i])
}

This creates two new dataframes, allowing me to find colSums of both of them, then subtracting 1:lower from 1:upper. So I did manage to get a sum for each column, but even to someone new to R this seems to be a really convoluted way of doing things, so I'm wondering if there's a cleaner way to achieve the same outcome.

Any advice would be very appreciated, thank you!

Answer 1

You can use mapply :

mapply(function(x, y, z) sum(z[x:y]), lower + 1, upper, data)

#exp1 exp2 exp3 
#  12   51   69 

data

data <- structure(list(exp1 = 1:10, exp2 = 11:20, exp3 = 21:30), 
class = "data.frame", row.names = c(NA, -10L))
upper <- c('exp1' = 5, 'exp2' = 8, 'exp3' = 4)
lower <- c('exp1' = 2, 'exp2' = 5, 'exp3' = 1)

Answer 2

Using pmap

library(purrr)
pmap_dbl(list(data, lower+1, upper), ~ sum(..1[..2:..3]))
# exp1 exp2 exp3 
#  12   51   69 

data

data <- structure(list(exp1 = 1:10, exp2 = 11:20, exp3 = 21:30), 
class = "data.frame", row.names = c(NA, -10L))
upper <- c('exp1' = 5, 'exp2' = 8, 'exp3' = 4)
lower <- c('exp1' = 2, 'exp2' = 5, 'exp3' = 1)