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Getting Started with healthyR.ts

A Quick Introduction

Steven P. Sanderson II, MPH

2024-10-11

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 -0.0315  968.
#> 2     1     2  0.0455 1013.
#> 3     1     3 -0.161   850.
#> 4     1     4  0.0163  864.
#> 5     1     5 -0.0239  843.
#> 6     1     6  0.0662  899.
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> -0.03150204, 0.04552251, -0.16079037, 0.01632092, -0.02386263, 0…
#> $ cum_y <dbl> 968.4980, 1012.5864, 849.7723, 863.6413, 843.0326, 898.8643, 783…

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")

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.