# Genetic drift simulation

August 13, 2013
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(This article was first published on R snippets, and kindly contributed to R-bloggers)

While preparing for the new teaching semester I have created an implementation of NetLogo GenDrift P local in GNU R.

The model works as follows. Initially a square grid having side size is randomly populated with n types of agents (denoted by colors).
Each agent randomly changes type (color) to one of its eight neighbors colors. In long run only one agent type remains due to random selection process (although it can take a long time if we set
side to a large value).

Here is the simulation code:
library(lattice)library(animation)# initialize simulation# side - square grid size# n    - number of agent types (from 2 to 6)init <- function(side, n) {    matrix(sample.int(n, side * side, TRUE), side)}# plot simulation state# s - simulation state# n - initial number of agent typesdo.plot <- function(s, n) {    print(levelplot(s, at = (0:n) + 0.5,        col.regions = 1 + (1:n),         scales = list(draw = F), xlab = "", ylab = "",        colorkey = list(tick.number = n)))}# perform one step of simulation# state  - simulation statesim.step <- function(state) {    updated <- FALSE    side <- dim(state)[1]    moves_x <- rep(-1:1, 3)[-5]    moves_y <- rep(-1:1, each = 3)[-5]    sequence <- sample.int(length(state))    for (i in sequence) {        x <- 1 + (i - 1)%%side        y <- 1 + (i - 1)%/%side        ref <- sample.int(8, 1)        ref.x <- 1 + (x + moves_x[ref] - 1)%%side        ref.y <- 1 + (y + moves_y[ref] - 1)%%side        if (state[x, y] != state[ref.x, ref.y]) {            state[x, y] <- state[ref.x, ref.y]            updated <- TRUE        }    }    list(state, updated)}go <- function(size, n) {    s <- init(size, n)    updated <- TRUE    for (i in 1:4) {        do.plot(s, n)    }    i <- 0    while (updated) {        i <- i + 1        do.plot(s, n)        out <- sim.step(s)        s <- out[[1]]        updated <- out[[2]]        do.plot(s, n)    }    print(i)    for (i in 1:4) {        do.plot(s, n)    }}set.seed(1)animate <- FALSE  # do we want to save animation?side <- 15        # simulation grid will be side x siden <- 5            # initial number of agent typesif (animate) {    ani.options(interval = 0.2)    saveGIF(go(side, n))} else {    go(side, n)}
We can select if we want to see the animation on the screen or save it to file via  animate option.
The animation the code generates is presented below: