What are you trying to achieve?
If you need to encode it to some integer use hash table. If you are using something like linear regression or neural network it would be better to use dummy features (one-hot encoding). So for your dictionary of 5 words ("America", "Brazil", "Chile", "Denmark", "Estonia") you get 5 features (x1, x2, x3, x4, x5) which indicate if some word is equal to one in dictionary. So "Brazil" is represented by (0,1,0,0,0), "Germany" is (0,0,0,0,0). Number of features grows with number of words in dictionary making some features practically useless.
If you are using decision trees you don't need to convert string to integer unless specific algorithm asks you to do so. Again, use hash table to do it. In R you can use factor() function.
If you convert your string to integers and use it as single feature ("America" - 123, "Brazil" - 245), algorithm will try to find patterns in it by comparing numbers but may fail to recognize specific countries.