Fast looping through a data frame with an if statement

I have a quite simple but relatively large data frame that looks somewhat like this:

vec1 <- rbinom(350000, 1, 0.1)
vec2 <- rep(NA, length(vec1))
df <- data.frame(vec1, vec2)

I need to assign "not available" or "free" in the second column, depending on if there is a 0 or 1 in the first column of the respective line.

This can be done via loop:

for (i in 1:nrow(df)) {
  if (df[i,1] == 0) {
    data1[i,2] <- "not available"
  } else {
    data1[i,2] <- "free"
  }
}

Unfortunately it takes quite a while this way. I am sure there must be quicker ways to achieve this.
Anybody with a hint?

>Solution :

You can use ifelse

transform(df, vec2 = ifelse(vec1==0, "not available", "free"))

or

transform(df, vec2 = c("free","not available")[(vec1==0)+1])