How to find best GLModel based upon AIC score

Question

I am trying to find the best GLModel (based just opon AIC score at the moment). The data I work with are accessible here:

https://drive.google.com/open?id=0B5IgiR_svnKcY25TQ29ZMGN3NVE

Here below is the piece of code that fails to return the best model:

rm(list = ls())

mod_data <- read.csv("mod_data.csv", header = T)

mod_headers <- names(mod_data[3:ncol(mod_data)-1])

f <- function(mod_headers){

  null_model <- glm(newcol ~ 1, data=mod_data, family = binomial(link = "logit"), control = list(maxit = 50))
  best_model <- null_model
  best_aic <- AIC(null_model)

  for(i in 1:length(mod_headers)){
    tab <- combn(mod_headers,i)
    for(j in 1:ncol(tab)){
      tab_new <- c(tab[,j])
      mod_tab_new <- c(tab_new, "newcol")
      model <- glm(newcol ~., data=mod_data[c(mod_tab_new)], family = binomial(link = "logit"), control = list(maxit = 50000))
      if(AIC(model) < best_aic){
        best_model <- model
        best_aic <- AIC(model)
      }
    }
    return(best_model)
  }
}

f(mod_headers)

For some reason, it always gets stuck at model with just one independent variable ("var5" with AIC score 678.8) althought I know at least one better model ("var1", "var5" and "var8" with AIC score 677.6) returned by this piece of code:

rm(list = ls())

mod_data <- read.csv("mod_data.csv", header = T)

mod_headers <- names(mod_data[3:ncol(mod_data)-1])

library(tidyverse)
library(iterators)

whichcols <- Reduce("c", map(1:length(mod_headers), ~lapply(iter(combn(mod_headers,.x), by="col"),function(y) c(y))))
models <- map(1:length(whichcols), ~glm(newcol ~., data=mod_data[c(whichcols[[.x]], "newcol")], family = binomial(link = "logit")))
print(models[[which.min(sapply(1:length(models),function(x)AIC(models[[x]])))]])

It is unknown to me why the first piece of code fails to return desired outcome.

Thanks for tips!