The goal of
healthyR.ts is to provide a consistent verb framework for performing time series analysis and forecasting on both administrative and clinical hospital data.
You can install the released version of healthyR.ts from CRAN with:
And the development version from GitHub with:
# install.packages("devtools") devtools::install_github("spsanderson/healthyR.ts")
This is a basic example which shows you how to generate random walk data.
library(healthyR.ts) library(ggplot2) df <- ts_random_walk() head(df) #> # A tibble: 6 × 4 #> run x y cum_y #> <dbl> <dbl> <dbl> <dbl> #> 1 1 1 0.175 1175. #> 2 1 2 0.0481 1231. #> 3 1 3 -0.195 991. #> 4 1 4 -0.235 759. #> 5 1 5 -0.157 640. #> 6 1 6 0.108 708.
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)