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healthyR.ts

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.

Installation

You can install the released version of healthyR.ts from CRAN with:

install.packages("healthyR.ts")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("spsanderson/healthyR.ts")

Example

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 0.0521   1052.
#> 2     1     2 0.000486 1053.
#> 3     1     3 0.0567   1112.
#> 4     1     4 0.125    1252.
#> 5     1     5 0.0825   1355.
#> 6     1     6 0.00340  1360.

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)

This package comes with a wide variety of functions from Data Generators to Statistics functions. The function ts_random_walk() in the above example is a Data Generator.

Let’s take a look at a plotting function.

data_tbl <- data.frame(
  date_col = seq.Date(
    from = as.Date("2020-01-01"),
    to   = as.Date("2022-06-01"),
    length.out = 365*2 + 180
    ),
  value = rnorm(365*2+180, mean = 100)
)

ts_calendar_heatmap_plot(
  .data          = data_tbl
  , .date_col    = date_col
  , .value_col   = value
  , .interactive = FALSE
)

Time Series Clustering via Features:

data_tbl <- ts_to_tbl(AirPassengers) %>%
  mutate(group_id = rep(1:12, 12))

output <- ts_feature_cluster(
  .data = data_tbl,
  .date_col = date_col,
  .value_col = value,
  group_id,
  .features = c("acf_features","entropy"),
  .scale = TRUE,
  .prefix = "ts_",
  .centers = 3
)

ts_feature_cluster_plot(
  .data = output,
  .date_col = date_col,
  .value_col = value,
  .center = 2,
  group_id
)

#> $plot
#> $plot$static_plot

#> 
#> $plot$plotly_plot
#> 
#> 
#> $data
#> $data$original_data
#> # A tibble: 144 × 4
#>    index     date_col   value group_id
#>    <yearmon> <date>     <dbl>    <int>
#>  1 Jan 1949  1949-01-01   112        1
#>  2 Feb 1949  1949-02-01   118        2
#>  3 Mar 1949  1949-03-01   132        3
#>  4 Apr 1949  1949-04-01   129        4
#>  5 May 1949  1949-05-01   121        5
#>  6 Jun 1949  1949-06-01   135        6
#>  7 Jul 1949  1949-07-01   148        7
#>  8 Aug 1949  1949-08-01   148        8
#>  9 Sep 1949  1949-09-01   136        9
#> 10 Oct 1949  1949-10-01   119       10
#> # ℹ 134 more rows
#> 
#> $data$kmm_data_tbl
#> # A tibble: 3 × 3
#>   centers k_means  glance          
#>     <int> <list>   <list>          
#> 1       1 <kmeans> <tibble [1 × 4]>
#> 2       2 <kmeans> <tibble [1 × 4]>
#> 3       3 <kmeans> <tibble [1 × 4]>
#> 
#> $data$user_item_tbl
#> # A tibble: 12 × 8
#>    group_id ts_x_acf1 ts_x_acf10 ts_diff1_acf1 ts_diff1_acf10 ts_diff2_acf1
#>       <int>     <dbl>      <dbl>         <dbl>          <dbl>         <dbl>
#>  1        1     0.741       1.55       -0.0995          0.474       -0.182 
#>  2        2     0.730       1.50       -0.0155          0.654       -0.147 
#>  3        3     0.766       1.62       -0.471           0.562       -0.620 
#>  4        4     0.715       1.46       -0.253           0.457       -0.555 
#>  5        5     0.730       1.48       -0.372           0.417       -0.649 
#>  6        6     0.751       1.61        0.122           0.646        0.0506
#>  7        7     0.745       1.58        0.260           0.236       -0.303 
#>  8        8     0.761       1.60        0.319           0.419       -0.319 
#>  9        9     0.747       1.59       -0.235           0.191       -0.650 
#> 10       10     0.732       1.50       -0.0371          0.269       -0.510 
#> 11       11     0.746       1.54       -0.310           0.357       -0.556 
#> 12       12     0.735       1.51       -0.360           0.294       -0.601 
#> # ℹ 2 more variables: ts_seas_acf1 <dbl>, ts_entropy <dbl>
#> 
#> $data$cluster_tbl
#> # A tibble: 12 × 9
#>    cluster group_id ts_x_acf1 ts_x_acf10 ts_diff1_acf1 ts_diff1_acf10
#>      <int>    <int>     <dbl>      <dbl>         <dbl>          <dbl>
#>  1       1        1     0.741       1.55       -0.0995          0.474
#>  2       1        2     0.730       1.50       -0.0155          0.654
#>  3       2        3     0.766       1.62       -0.471           0.562
#>  4       2        4     0.715       1.46       -0.253           0.457
#>  5       2        5     0.730       1.48       -0.372           0.417
#>  6       1        6     0.751       1.61        0.122           0.646
#>  7       1        7     0.745       1.58        0.260           0.236
#>  8       1        8     0.761       1.60        0.319           0.419
#>  9       2        9     0.747       1.59       -0.235           0.191
#> 10       2       10     0.732       1.50       -0.0371          0.269
#> 11       2       11     0.746       1.54       -0.310           0.357
#> 12       2       12     0.735       1.51       -0.360           0.294
#> # ℹ 3 more variables: ts_diff2_acf1 <dbl>, ts_seas_acf1 <dbl>, ts_entropy <dbl>
#> 
#> 
#> $kmeans_object
#> $kmeans_object[[1]]
#> K-means clustering with 2 clusters of sizes 5, 7
#> 
#> Cluster means:
#>   ts_x_acf1 ts_x_acf10 ts_diff1_acf1 ts_diff1_acf10 ts_diff2_acf1 ts_seas_acf1
#> 1 0.7456468   1.568532     0.1172685      0.4858013    -0.1799728    0.2876449
#> 2 0.7387865   1.528308    -0.2909349      0.3638392    -0.5916245    0.2930543
#>   ts_entropy
#> 1  0.4918321
#> 2  0.6438176
#> 
#> Clustering vector:
#>  [1] 1 1 2 2 2 1 1 1 2 2 2 2
#> 
#> Within cluster sum of squares by cluster:
#> [1] 0.3704304 0.3660630
#>  (between_SS / total_SS =  59.8 %)
#> 
#> Available components:
#> 
#> [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
#> [6] "betweenss"    "size"         "iter"         "ifault"

Time to/from Event Analysis

library(dplyr)
df <- ts_to_tbl(AirPassengers) %>% select(-index)

ts_time_event_analysis_tbl(
  .data = df,
  .horizon = 6,
  .date_col = date_col,
  .value_col = value,
  .direction = "both"
) %>%
  ts_event_analysis_plot()

ts_time_event_analysis_tbl(
  .data = df,
  .horizon = 6,
  .date_col = date_col,
  .value_col = value,
  .direction = "both"
) %>%
  ts_event_analysis_plot(.plot_type = "individual")

ARIMA Simulators

output <- ts_arima_simulator()
output$plots$static_plot

Automatic Workflows which can be thought of as Boiler Plate Time Series modeling. This is in it’s infancy in this package.

This is just a start of what is in this package!

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.