Populating a data frame in R in a loop

It is often preferable to avoid loops and use vectorized functions. If that is not possible there are two approaches:

1. Preallocate your data.frame. This is not recommended because indexing is slow for data.frames.
2. Use another data structure in the loop and transform into a data.frame afterwards. A list is very useful here.

Example to illustrate the general approach:

mylist <- list() #create an empty list

for (i in 1:5) {
  vec <- numeric(5) #preallocate a numeric vector
  for (j in 1:5) { #fill the vector
    vec[j] <- i^j 
  }
  mylist[[i]] <- vec #put all vectors in the list
}
df <- do.call("rbind",mylist) #combine all vectors into a matrix

In this example it is not necessary to use a list, you could preallocate a matrix. However, if you do not know how many iterations your loop will need, you should use a list.

Finally here is a vectorized alternative to the example loop:

outer(1:5,1:5,function(i,j) i^j)

As you see it's simpler and also more efficient.

You could do it like this:

 iterations = 10
 variables = 2

 output <- matrix(ncol=variables, nrow=iterations)

 for(i in 1:iterations){
  output[i,] <- runif(2)

 }

 output

and then turn it into a data.frame

 output <- data.frame(output)
 class(output)

what this does:

1. create a matrix with rows and columns according to the expected growth
2. insert 2 random numbers into the matrix
3. convert this into a dataframe after the loop has finished.

this works too.

df = NULL
for (k in 1:10)
    {
       x = 1
       y = 2
       z = 3
       df = rbind(df, data.frame(x,y,z))
     }

output will look like this

df #enter

x y z #col names
1 2 3