Better way to adding elements in data frame without looping in R - r

Question

I want to create a dataframe that calculates the odds ratio with the standard error and confidence intervals in R.
I have a dataset similar to the one like so:
dat <- read.table(header = TRUE, text = "
f1 f2 f3 f4 f5 f6 f7 f8 f9 f10 f11 target
0 0 1 0 0 0 0 0 0 0 0 0
1 1 1 0 0 0 0 0 1 0 0 1
0 0 0 0 0 0 0 0 0 0 0 1
1 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 1 1 0 1
1 1 1 1 1 0 0 0 0 0 0 0")
And create a function that calculates everything I need in the dataframe for a particular future in the data set like so:
get_ci <- function(df, feature) {
tab <- table(df[[feature]], df$target)
a <- tab[1,1]
b <- tab[1,2]
c <- tab[2,1]
d <- tab[2,2]
odds_ratio <- (a/b)/(c/d)
standard_error <- sqrt(1/a + 1/b + 1/c + 1/d)
log_ci_lower <- log(odds_ratio) - 1.96 + standard_error
log_ci_upper <- log(odds_ratio) - 1.96 + standard_error
ci_lower <- exp(log_ci_lower)
ci_upper <- exp(log_ci_upper)
df <- data.frame(Feature = feature,
`Odds Ratio` = odds_ratio,
`Standard Error` = standard_error,
`Lower Bound CI` = ci_lower,
`Upper Bound CI` = ci_upper
)
}
I want to create a DF that computes the odds ratio, standard error, and confidence interval for each features (f1-f11). What is the most efficient way to do this?
I am currently creating an empty dataframe and looping through the features in the df to populate one but I feel like this is not the right way to do it. I was looking at the apply functions, but not sure how I can apply that with my function I created

Answer 1

I think the first table line in the function should be :
tab <- table(factor(df[[feature]], levels = 0:1), df$target)
otherwise, if you have all 1's and all 0's in a particular column the next lines would break.
With that change, you can use lapply passing the column names
result <- do.call(rbind, lapply(paste0('f', 1:11), get_ci, df = dat))
Or using purrr's map_df
result <- map_df(paste0('f', 1:11), get_ci, df = dat)

Answer 2

Here's another solution.
get_ci <- function(x, target) {
tab <- table(factor(x, levels=0:1), target) #changed
...
ci_upper <- exp(log_ci_upper)
c(`Odds Ratio` = odds_ratio, # changed
`Standard Error` = standard_error,
`Lower Bound CI` = ci_lower,
`Upper Bound CI` = ci_upper
)
}
as.data.frame(apply(dat[,1:11], 2, function(x) { get_ci(x, dat$target) })) #changed