Some random walk fun. I use 400 steps in each example.

One-Dimensional Random Walk

A random walk using a recursive equation.

# Empty vector to store the walk

rw_1 <- numeric(400)

# Initial value

rw_1 <- 7

# The Random Walk equation in a for-loop

for(i in 2:400){

rw_1[i] <- 1*rw_1[i - 1] + rnorm(1,0,2)

}

plot(rw_1) A random walk using R’s “cumsum” command. Here, I will generate a vector of randomly selected 1’s and -1’s. “Cumsum” then compiles those values.

# A vector of 1's and -1's

rw_2 <- sample(c(1, -1), 400, replace = T)

rw_2 <- cumsum(rw_2)

plot(rw_2) Two-Dimensional Random Walk

Now for the real fun. Here, the walk can move forward (1) or backward (-1) along either dimension 1 or 2. So, if the walk moves forward (1) in dimension 1, dimension 2 receives a value of 0 for that step. If the walk moves backward (-1) in dimension 2, dimension 1 receives a 0 for that step.

# A matrix to store our walk

# Column 1 is dimension 1, column 2 is dimension 2

rw_3 <- matrix(0, ncol = 2, nrow = 400)

index <- cbind(
1:400, sample(c(1, 2),
400,
replace = T)
)

The “index” merits some explaining. The walk will randomly choose to move in dimension 1 (column 1 in “rw_3”) or 2 (column 2 in “rw_3”). This index establishes a way of assigning which choice the walk makes. Here is what “index” looks like:

head(index)
##      [,1] [,2]
## [1,]    1    2
## [2,]    2    2
## [3,]    3    2
## [4,]    4    2
## [5,]    5    2
## [6,]    6    2

The first column values tell the random walk which step its on (i.e., which row in “rw_3”), and the second column values tell the random walk which dimension it will step through (i.e., which column in “rw_3”).

So the “index” represents a random selection of dimension 1 or 2 at each step. Now I can apply that random choice to the random choice of stepping forward or backward (1 or -1).

# At each step, select a dimension (specified by the index; column 1 or 2 of rw_3)

# Then randomly select forward or backward

rw_3[index] <- sample(c(-1, 1),
400,
replace = T)

# Now sum each column (dimension) just like our 1-dimensional walks

rw_3[,1] <- cumsum(rw_3[,1])
rw_3[,2] <- cumsum(rw_3[,2])

Here is a visualization of the walk:

library(plotly)

rw_3 <- data.frame(rw_3)
rw_3\$step <- c(1:400)

names(rw_3)[1:2] <- c("Dim_1", "Dim_2")

plot_ly(rw_3, x = ~step, y = ~Dim_1, z = ~Dim_2, type = 'scatter3d', mode = 'lines',
line = list(color = '#1f77b4', width = 1))

Bo$$^2$$m =)