# Genetic drift simulation

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While preparing for the new teaching semester I have created an implementation of NetLogo GenDrift P local in GNU R.**R snippets**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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 types do.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 state sim.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 side n <- 5 # initial number of agent types if (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:

To

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