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Getting Started with healthyR.ts

A Quick Introduction

Steven P. Sanderson II, MPH

2023-11-14

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 -0.0732  927.
#> 2     1     2  0.0947 1015.
#> 3     1     3 -0.109   904.
#> 4     1     4 -0.0362  871.
#> 5     1     5  0.0641  927.
#> 6     1     6  0.279  1185.
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> -0.073165367, 0.094720018, -0.109204950, -0.036236448, 0.0640770…
#> $ cum_y <dbl> 926.8346, 1014.6244, 903.8224, 871.0711, 926.8867, 1185.3602, 12…

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.