Introduction

Generating random walk data for timesieries analysis does not have to be difficult, and in fact is not. It can be generated for multiple simulations and have a tidy output. How? ts_random_walk() from the {healthyR.ts} package. Let’s take a look at the function.

Function

Here is the full function call.

ts_random_walk(
  .mean = 0,
  .sd = 0.1,
  .num_walks = 100,
  .periods = 100,
  .initial_value = 1000
)

Now let’s look at the arguments to the parameters.

.mean - The desired mean of the random walks
.sd - The standard deviation of the random walks
.num_walks - The number of random walks you want generated
.periods - The length of the random walk(s) you want generated
.initial_value - The initial value where the random walks should start

The underlying data of this function is generated by rnorm()

Example

Let’s take a look at an example and see some visuals.

library(healthyR.ts)
library(ggplot2)

df <- ts_random_walk(.num_walks = 100)

df

# A tibble: 10,000 × 4
     run     x        y cum_y
   <dbl> <dbl>    <dbl> <dbl>
 1     1     1 -0.144    856.
 2     1     2  0.00648  862.
 3     1     3  0.0726   924.
 4     1     4 -0.152    784.
 5     1     5  0.0228   802.
 6     1     6 -0.0455   765.
 7     1     7  0.0972   840.
 8     1     8 -0.234    643.
 9     1     9 -0.0501   611.
10     1    10 -0.0358   589.
# … with 9,990 more rows

There are attributes attached to the output of this function, let’s see what they are.

atb <- attributes(df)

names_to_print <- names(atb)[which(names(atb) != "row.names")]

atb[names_to_print]

$class
[1] "tbl_df"     "tbl"        "data.frame"

$names
[1] "run"   "x"     "y"     "cum_y"

$.mean
[1] 0

$.sd
[1] 0.1

$.num_walks
[1] 100

$.periods
[1] 100

$.initial_value
[1] 1000

Now lets visualize.

df %>%
   ggplot(
       mapping = aes(
           x = x
           , y = cum_y
           , color = factor(run)
           , group = factor(run)
        )
    ) +
    geom_line(alpha = 0.8) +
    ts_random_walk_ggplot_layers(df)

Voila!