# Fun With Random Walks

2018-02-12

Let’s consider a game where a gambler is likely to win \$1 with a probability of p and lose \$1 with a probability of 1-p.

Now, let’s consider a game where a gambler is likely to win \$1 and lose \$1 with a probability of 1. The player starts the game with X dollars in hand. The player plays the game until the money in his hand reaches N (N> X) or he has no money left. What is the probability that the player will reach the target value? (We know that the player will not leave the game until he reaches the N value he wants to win.)

The problem of the story above is known in literature as Gambler’s Ruin or Random Walk. In this article, I will simulate this problem with R with different settings and examine how the game results change with different settings.

Click through for the script and analysis.  There’s a reason they call this game the gambler’s ruin.

## Loops Versus Apply: Speed Comparison

2018-02-19

Mike Spencer compares lapply (single core and its multi-core version) versus a for loop in R: But how fast were they? Can we get faster? Thankfully R provides `system.time()` for timing code execution. In order to get faster, it makes sense to use all the processing power our machines have. The ‘parallel’ library has some […]

## Quoted Concatenation In R

2018-02-19

John Mount has a quick tip for R users: Here is an R tip. Need to quote a lot of names at once? Use qc(). This function is part of wrapr.