compute a weighted sum with many variables

I have a large dataset with many x and y columns, but showing an example with 5 below

library(dplyr)
set.seed(505)
df <- data.frame(
x1 = sample(-2 : 4,size = 10, replace = TRUE),
x2 = sample(-2 : 4,size = 10, replace = TRUE),
x3 = sample(-2 : 4,size = 10, replace = TRUE),  
x4 = sample(-2 : 4,size = 10, replace = TRUE), 
x5 = sample(-2 : 4,size = 10, replace = TRUE),   
y1 = sample(-2 : 4,size = 10, replace = TRUE), 
y2 = sample(-2 : 4,size = 10, replace = TRUE),
y3 = sample(-2 : 4,size = 10, replace = TRUE),  
y4 = sample(-2 : 4,size = 10, replace = TRUE),  
y5 = sample(-2 : 4,size = 10, replace = TRUE))

My task can be achieved with the code below

df |> mutate(
  sum1 = x1 * as.numeric(x1 > -1 & y1 > -1) +
         x2 * as.numeric(x2 > -1 & y2 > -1) +
         x3 * as.numeric(x3 > -1 & y3 > -1) +
         x4 * as.numeric(x4 > -1 & y4 > -1) +
         x5 * as.numeric(x5 > -1 & y5 > -1)

Because I have 25 x and y variables, my question is: is there a better way to achieve the same?

>Solution :

xs <- paste0("x", 1:5)
ys <- paste0("y", 1:5)

rowSums(df[xs] * (df[xs] > -1 & df[ys] > -1))

# Alternatively
foo <- \(x, y) replace(x, pmin(x, y) <= -1, 0)
rowSums(mapply(foo, df[xs], df[ys]))


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