(This article was first published on

# I am interested in how small bits of individualized instructions can create collective action.**Econometrics by Simulation**, and kindly contributed to R-bloggers)# In this simulation I will give a single instruction to each individual in the swarm.

# Choose another individual who is not too close, then accelerate towards that individual.

# I also control momentum causing the previous movement and direction to only decay at a small rate.

# TO SEE Original Script

# Critters are initially distributed randomly on a 1 x 1 grid.

ncritters = 40

xypos = matrix(runif(ncritters*2),ncol=2)

plot(xypos, main="Critters are Initially Distributed Randomly"

, xlab="X", ylab="Y")

# Now let's imagine that each critter has an ideal safe distance from each other critter.

safe.dist = .3

critter.speed = .001

# If another critter is not at that safe distance than the critter will move towards the closest nearby critter.

# Let's see how this works.

# First let's check how close each critter is to each other critter.

# We will accomplish this by going through each critter and checking how far away each other critter is.

distances = NULL

for (i in 1:ncritters) distances = rbind(distances, apply((xypos[i,]-t(xypos))^2,2,sum))

# In order to prevent critters from always chasing whatever is closest to them (and themselves) we drop anything which is closer than the safe.distance.

distances[abs(distances)

# The apply command will apply the order command to each row whiel the [1,] selects only the critter that is closes.

# Plot the

plot(xypos, xlab = "X", ylab = "Y")

for (i in 1:ncritters) arrows(x0=xypos[i,1], y0=xypos[i,2],

x1=xypos[closest,][i,1],

y1=xypos[closest,][i,2],

length=.1)

# This calculates the difference between the current position of each critter and that of the closest critter.

ab = xypos-xypos[closest,]

# To see how this is calculated, see my previous post simulating a werewolf attack.

# Now calculate the difference in the horizontal and vertical axes that the critters will move as a projection into the direction of the closest critter outside of the safe zone.

a.prime = critter.speed/(1 + (ab[,2]^2)/(ab[,1]^2))^.5

b.prime = (critter.speed^2-a.prime^2)^.5

# This corrects the movement to ensure that the critters are flying at each other rather than away from each other.

movement = cbind(a.prime * sign(ab[,2]), b.prime * sign(ab[,1]))

between = function(xy1,xy2,point) (point>xy1&point

# Set the new xypos

xypos1 = xypos+movement

points(xypos1, col="red")

# ------------------------------------------------------

# Let's turn this into an animation.

library(animation)

# loopnum = 100; ncritters=40; inertia = .5; show.grid=T; ani.pause=F; plot.fixed=F; plot.centered=F; brownian = F; arrow = T

flocking <- ani.pause="F," arrow="T)" brownian="F," function="" inertia=".5," loopnum="100," ncritters="40," p="" plot.centered="F," plot.fixed="F," show.grid="T,">

# Generate xy initial positions.

# xypos will hold the current critter position while

# xypos0 will hold the position of the critters the previous time.

xypos = xypos0 = matrix(runif(ncritters*2),ncol=2)-.5

movement0 = 0

# Loop though all of the loops.

for (i in 1:loopnum) {

# This specifies the range to be graphed.

if (plot.fixed) rangex=rangey = -.5:.5

if (!plot.fixed) {

rangex = c(min(xypos[,1]), max(xypos[,1]))

rangey = c(min(xypos[,2]), max(xypos[,2]))

}

# This handles the grid size when

if (plot.centered&!plot.fixed) {

rangex=c(-max(abs(xypos[,1])), max(abs(xypos[,1])))

rangey=c(-max(abs(xypos[,2])), max(abs(xypos[,2])))

}

# This centers the plot at the middle (0,0) if the plot width is also set to be fixed.

if (plot.centered&plot.fixed) rangex=rangex-mean(rangex)

if (plot.centered&plot.fixed) rangey=rangey-mean(rangey)

# Draw critters

plot(xypos, main="Swarming Animation", xlab="X", ylab="Y", axes=F, ylim=rangey, xlim=rangex, type="p")

# Draw arrows. The start of the arrows is the previous periods location.

if (arrow&i>1) arrows(x0=xypos0[,1],y0=xypos0[,2],x1=xypos[,1],y1=xypos[,2], length = .1)

# Show the grid in the background.

if (show.grid) {

abline(v=seq(-10,10,.1))

abline(h=seq(-10,10,.1))

}

# Show the origin

text(0,0, "(0,0)")

# Calculate each critters distance from each other

distances = NULL

for (i in 1:ncritters) distances = rbind(distances, apply((xypos[i,]-t(xypos))^2,2,sum))

# Drop those within the safe zone.

distances[abs(distances)

# This selects the critter closest to the selected critter.

closest = matrix(1:ncritters, ncol=ncritters, nrow=ncritters)[apply(abs(distances), 1, order)[1,]]

# distances[as.apply(!apply(distances, 1, is.na),1,sum)==0,]=0

# As done above

ab = xypos-xypos[closest,]

a.prime = critter.speed/(1 + (ab[,2]^2)/(ab[,1]^2))^.5

b.prime = (critter.speed^2-a.prime^2)^.5

movement = cbind(a.prime * sign(ab[,2]), b.prime * sign(ab[,1]))

between = function(xy1,xy2,point) (point>xy1&point

movement = movement*(-1)^between(xypos,xypos[closest,], xypos-movement)

movement[is.na(movement)]=0

movement0 = movement0*inertia + movement

# This fancy dodad allows half of the change in movement to be due to random variation.

if (brownian) movement0=movement0+matrix(rnorm(ncritters*2),ncol=2)*critter.speed/2

# Set the previous round's xy position to be equal to the current round's.

xypos0 = xypos

# Update the current round's.

xypos = xypos+movement0

# This is only used in the event that the animate package is in use.

if (ani.pause) ani.pause()

}

}

# This generates a GIF animation demonstrating smoothly how these GIFs can be incorper

ani.options(ani.width=400, ani.height=400, interval=.1)

# You must have imagemagick installed for this to work.

saveGIF(flocking(300,100,.999, ani.pause=T), movie.name = "Swarming.gif", replace=T)

# Here are two different graphs generated by the previous command(though the one on the bottom uses 200 frames while the one on the top uses 300)

# Let's see how this works.

flocking()

flocking(400,100,.99)

flocking(400,100,.99, plot.fixed=T)

To

**leave a comment**for the author, please follow the link and comment on his blog:**Econometrics by Simulation**.R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...