Calculate the column means of returns (using colMeans()), and call this stockmu.
Create a sequence (seq()) of length 50, which begins at a risk-free rate of 1% and ends at the maximum value of stockmu called grid.
Initialize two empty vectors with the same length as grid using rep(), where you will store the portfolio means and standard deviations. Call these vpm and vpsd.
Initialize an empty matrix of 50 rows and 30 columns. Call this mweights. You can use the matrix() function to do so.
Create a for loop that starts on the first value of grid and ends on the last. The for-loop should create a portfolio called opt using returns and with a target return of grid.
With each iteration, the for loop should fill the vectors vpm ($pm), vpsd ($ps) with their respective values from opt.
Store the portfolio weights line by line in mweights ($pw).

In-sample vs. out-of-sample evaluation