Getting Started with healthyR.ts

Lets load in the libraries

library(healthyR.ts)
library(ggplot2)
library(dplyr)

Lets generage data and take a look

df <- ts_random_walk()
head(df)
#> # A tibble: 6 × 4
#>     run     x         y cum_y
#>   <dbl> <dbl>     <dbl> <dbl>
#> 1     1     1 -0.140     860.
#> 2     1     2  0.0255    882.
#> 3     1     3 -0.244     667.
#> 4     1     4 -0.000557  667.
#> 5     1     5  0.0622    708.
#> 6     1     6  0.115     789.
glimpse(df)
#> Rows: 10,000
#> Columns: 4
#> $ run   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ x     <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 1…
#> $ y     <dbl> -0.1400043517, 0.0255317055, -0.2437263611, -0.0005571287, 0.062…
#> $ cum_y <dbl> 859.9956, 881.9528, 666.9977, 666.6261, 708.0604, 789.3749, 645.…

Now that the data has been generated, lets take a look at it.

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)

That is still pretty noisy, so lets see this in a different way. Lets clear this up a bit to make it easier to see the full range of the possible volatility of the random walks.

library(dplyr)
library(ggplot2)

df %>%
    group_by(x) %>%
    summarise(
        min_y = min(cum_y),
        max_y = max(cum_y)
    ) %>%
    ggplot(
        aes(x = x)
    ) +
    geom_line(aes(y = max_y), color = "steelblue") +
    geom_line(aes(y = min_y), color = "firebrick") +
    geom_ribbon(aes(ymin = min_y, ymax = max_y), alpha = 0.2) +
    ts_random_walk_ggplot_layers(df)

Lets look at volatility from several different percentages.


# Random Walk for volatility range 1-15%
df1 <- ts_random_walk(.sd = 0.01)
df2 <- ts_random_walk(.sd = 0.05)
df3 <- ts_random_walk(.sd = 0.10)
df4 <- ts_random_walk(.sd = 0.15)

# Merge data frames into one
df_merged <- dplyr::bind_rows(
    df1 %>% mutate(ver = "A) Vol 1%"),
    df2 %>% mutate(ver = "B) Vol 5%"),
    df3 %>% mutate(ver = "C) Vol 10%"),
    df4 %>% mutate(ver = "D) Vol 15%")
)

# Plot range between minimum and maximum values
df_merged %>%
    ggplot(aes(
        x = x, y = cum_y,
        color = factor(run), group = factor(run)
    )) +
    geom_line(alpha = 0.8) +
    labs(title = "", x = "", y = "") +
    facet_wrap(~ver, scales = "free") +
    scale_y_continuous(labels = scales::comma) +
    theme(legend.position = "none")

A Quick Introduction

Steven P. Sanderson II, MPH

2024-10-13