Hi, this is hard to tell without the test object. The way to fix this is with a reprex. See the FAQ. That way, everyone interested in helping can see what you are seeing. Here's what one looks like using a simulated dataset and taking a functional programming approach.
# f(x) = y
# step 1: create fake_data
# x: col 1:7 are loanfile data
# y: col 8:12 are control flags to be set
var_num <- 12
fake_rows <- 90
set.seed(42)
input <- sample(c(TRUE,FALSE),var_num * fake_rows,
replace = TRUE)
m <- matrix(input, nrow = fake_rows, ncol = var_num)
col_header <- c("BLACK","HISPANIC","API","FEMALE",
"lowmod","elder","majblhs","c_minority",
"c_gender", "c_lowmod", "c_elder","c_majblhs")
colnames(m) <- col_header
# control flags intitially set FALSE
m[,8:12] <- FALSE
before = head(m)
# step 2: f to set control flags
m[which(rowSums(m[,1:3]) > 0),8] <- TRUE
m[which(m[,4]),9] <- TRUE
m[which(m[,5]),10] <- TRUE
m[which(m[,6]),11] <- TRUE
m[which(m[,7]),12] <- TRUE
after <- head(m)
# show example of which control flags were changed
# from FALSE to TRUE
(before == after)[,8:12]
#> c_minority c_gender c_lowmod c_elder c_majblhs
#> [1,] FALSE FALSE FALSE TRUE FALSE
#> [2,] FALSE FALSE FALSE FALSE FALSE
#> [3,] FALSE TRUE FALSE TRUE TRUE
#> [4,] FALSE FALSE TRUE TRUE TRUE
#> [5,] TRUE TRUE FALSE FALSE FALSE
#> [6,] TRUE FALSE TRUE FALSE FALSE