Calculate the row-wise weighted sum for a set of columns

I have, say, the following data frame:

> library(tidyverse)
> dd <- tibble(a = rep(1,10), b = rep(1,10), c = rep(1,10))
> dd
# A tibble: 10 × 3
a     b     c
<dbl> <dbl> <dbl>
1     1     1     1
2     1     1     1
3     1     1     1
4     1     1     1
5     1     1     1
6     1     1     1
7     1     1     1
8     1     1     1
9     1     1     1
10     1     1     1

and a vector of weights:

> weight <- c(1, 5, 10)
> weight
[1]  1  5 10

when I want to calculate the row-wise weighted sum for all the columns of the dataframe together, I do this:

> dd %>% mutate(m = rowSums(map2_dfc(dd, weight,`*`)))
# A tibble: 10 × 4
a     b     c     m
<dbl> <dbl> <dbl> <dbl>
1     1     1     1    16
2     1     1     1    16
3     1     1     1    16
4     1     1     1    16
5     1     1     1    16
6     1     1     1    16
7     1     1     1    16
8     1     1     1    16
9     1     1     1    16
10     1     1     1    16

but I don't know how to calculate the row-wise weighted sum for a subset of the data frame. I tried the code below, but it gives messy results:

> dd %>% rowwise() %>% mutate(m = rowwise(map2_dfc(c_across(b:c), weight[2:3],`*`)))
New names:
• `` -> `...1`
• `` -> `...2`
New names:
• `` -> `...1`
• `` -> `...2`
New names:
• `` -> `...1`
• `` -> `...2`
New names:
• `` -> `...1`
• `` -> `...2`
New names:
• `` -> `...1`
• `` -> `...2`
New names:
• `` -> `...1`
• `` -> `...2`
New names:
• `` -> `...1`
• `` -> `...2`
New names:
• `` -> `...1`
• `` -> `...2`
New names:
• `` -> `...1`
• `` -> `...2`
New names:
• `` -> `...1`
• `` -> `...2`
# A tibble: 10 × 4
# Rowwise: 
a     b     c m$...1 $...2
<dbl> <dbl> <dbl>  <dbl> <dbl>
1     1     1     1      5    10
2     1     1     1      5    10
3     1     1     1      5    10
4     1     1     1      5    10
5     1     1     1      5    10
6     1     1     1      5    10
7     1     1     1      5    10
8     1     1     1      5    10
9     1     1     1      5    10
10     1     1     1      5    10

Can someone please give me a hint as to how to approach this problem?

This is matrix multiplication. Your original is equivalent to as.matrix(dd) %*% weight. For a subset inside mutate you can do this:

dd %>% mutate(m = (across(b:c) %>% as.matrix()) %*% weight[1:2])

Using tidyverse methods, we can create a named vector for 'weight', loop across the columns 'b' to 'c', subset the 'weight' value based on the column name (cur_column()), multiply and get the rowSums

library(dplyr)
names(weight) <- names(dd)
dd %>% 
mutate(m = rowSums(across(b:c,  ~ .x * weight[cur_column()])))

-output

# A tibble: 10 × 4
a     b     c     m
<dbl> <dbl> <dbl> <dbl>
1     1     1     1    15
2     1     1     1    15
3     1     1     1    15
4     1     1     1    15
5     1     1     1    15
6     1     1     1    15
7     1     1     1    15
8     1     1     1    15
9     1     1     1    15
10     1     1     1    15

Or if we want to use rowwise (not recommended as it is slower)

dd %>% 
rowwise %>%
mutate(m = sum(c_across(b:c) * weight[2:3])) %>%
ungroup

Or use crossprod

dd %>%
mutate(m = crossprod(t(pick(b:c)), weight[2:3])[,1])

Or with base R

dd$m <-  rowSums(dd[2:3] * weight[2:3][col(dd[2:3])])