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A time-series companion package to healthyR

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spsanderson/healthyR.ts

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CRAN_Status_BadgeLifecycle: experimentalPRs Welcome

The Time Series Modeling Companion to healthyR

To view the full wiki, click here:Full healthyR.tsWiki

healthyR.ts is a comprehensive R package designed specifically fortime series analysis and forecasting of hospital administrative andclinical data. Built on the powerfultidymodels ecosystem, it provides aconsistent, user-friendly framework that simplifies complex time seriesworkflows.

Why healthyR.ts?

Hospital data analysis often requires handling time series for metricslike: - Average Length of Stay (ALOS) - Readmission rates - Patientvolumes and admissions - Bed occupancy rates - Clinical outcomes overtime

healthyR.ts takes the guesswork out of time series analysis byproviding:

✅Automated Workflows - One-function solutions for completemodeling pipelines
✅Visual Analytics - Rich plotting functions for data exploration
✅Data Generators - Simulate realistic time series for testing andvalidation
✅Statistical Tools - Comprehensive suite of time seriesstatistics
✅Clustering - Feature-based time series clustering capabilities
✅Forecasting - 15 automated model workflows (ARIMA, Prophet,XGBoost, and more)

Key Features

🤖 Automatic Modeling Workflows

Complete end-to-end modeling pipelines in a single function call:

  • ts_auto_arima() - Automatic ARIMA modeling
  • ts_auto_prophet_reg() - Facebook’s Prophet algorithm
  • ts_auto_xgboost() - Gradient boosting for time series
  • ts_auto_nnetar() - Neural network autoregression
  • Plus 11 more specialized workflows!

Each function handles recipe creation, model specification, workflowsetup, model fitting, tuning, and calibration automatically.

📊 Visualization Suite

  • Calendar heatmaps for temporal patterns
  • Time series clustering plots
  • Velocity, acceleration, and growth visualizations
  • QQ plots and scedasticity analysis
  • Moving average and SMA plots
  • Event analysis visualizations

🎲 Data Generation

Generate synthetic time series data for testing: - Random walks andBrownian motion - Geometric Brownian motion - ARIMA simulations - Customparameter configurations

📈 Statistical Analysis

  • ADF stationarity tests
  • Fast Fourier Transform (FFT) analysis
  • Confidence intervals
  • Lag correlation analysis
  • Time series feature extraction

Installation

Stable Release (CRAN)

Install the latest stable version fromCRAN:

install.packages("healthyR.ts")

Development Version

Get the latest features and bug fixes fromGitHub:

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

Quick Start

Basic Example: Random Walk Simulation

Generate and visualize random walk data to understand market volatilityor patient flow variations:

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  0.0698 1070.#> 2     1     2  0.0626 1137.#> 3     1     3  0.124  1277.#> 4     1     4  0.0504 1342.#> 5     1     5  0.0143 1361.#> 6     1     6 -0.0639 1274.

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. Letsclear this up a bit to make it easier to see the full range of thepossible 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)

Calendar Heatmap Visualization

Visualize temporal patterns in your data with calendar heatmaps -perfect for identifying seasonal trends or unusual patterns in hospitalmetrics:

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

Discover patterns by clustering time series based on their statisticalfeatures:

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       2        1     0.741       1.55       -0.0995          0.474#>  2       2        2     0.730       1.50       -0.0155          0.654#>  3       1        3     0.766       1.62       -0.471           0.562#>  4       1        4     0.715       1.46       -0.253           0.457#>  5       1        5     0.730       1.48       -0.372           0.417#>  6       2        6     0.751       1.61        0.122           0.646#>  7       2        7     0.745       1.58        0.260           0.236#>  8       2        8     0.761       1.60        0.319           0.419#>  9       1        9     0.747       1.59       -0.235           0.191#> 10       1       10     0.732       1.50       -0.0371          0.269#> 11       1       11     0.746       1.54       -0.310           0.357#> 12       1       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 7, 5#> #> Cluster means:#>   ts_x_acf1 ts_x_acf10 ts_diff1_acf1 ts_diff1_acf10 ts_diff2_acf1 ts_seas_acf1#> 1 0.7387865   1.528308    -0.2909349      0.3638392    -0.5916245    0.2930543#> 2 0.7456468   1.568532     0.1172685      0.4858013    -0.1799728    0.2876449#>   ts_entropy#> 1  0.6438176#> 2  0.4918321#> #> Clustering vector:#>  [1] 2 2 1 1 1 2 2 2 1 1 1 1#> #> Within cluster sum of squares by cluster:#> [1] 0.3660630 0.3704304#>  (between_SS / total_SS =  59.8 %)#> #> Available components:#> #> [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"#> [6] "betweenss"    "size"         "iter"         "ifault"

Event Analysis

Analyze time series behavior before and after significant events (e.g.,policy changes, new treatments):

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 Simulation

Generate realistic ARIMA time series for testing and validation:

output<- ts_arima_simulator()output$plots$static_plot

Available Models

Automated Workflow Functions

Each function creates a complete modeling pipeline including recipe,model specification, workflow, fitting, and calibration:

FunctionModel TypeDescription
ts_auto_arima()ARIMAAutomatic ARIMA with auto-tuning
ts_auto_arima_xgboost()HybridARIMA errors with XGBoost
ts_auto_prophet_reg()ProphetFacebook’s Prophet algorithm
ts_auto_prophet_boost()HybridProphet with XGBoost
ts_auto_xgboost()MLGradient boosting
ts_auto_nnetar()Neural NetNeural network autoregression
ts_auto_exp_smoothing()ETSExponential smoothing
ts_auto_smooth_es()SmoothSmooth package ETS
ts_auto_theta()ThetaTheta method
ts_auto_croston()CrostonFor intermittent demand
ts_auto_lm()LinearLinear regression with time features
ts_auto_mars()MARSMultivariate adaptive regression splines
ts_auto_glmnet()GLMElastic net regression
ts_auto_svm_poly()SVMSupport vector machine (polynomial)
ts_auto_svm_rbf()SVMSupport vector machine (radial)

Function Categories

healthyR.ts includes 90+ functions organized into these categories:

  • 📊 Data Generators: Create synthetic time series data (randomwalks, Brownian motion, ARIMA)
  • 📈 Plotting Functions: Comprehensive visualization suite for timeseries
  • 🔍 Clustering: Feature-based time series clustering and analysis
  • 🤖 Forecasting: Automated modeling workflows and model comparison
  • 📐 Statistical Functions: Tests, transformations, and time seriesstatistics
  • 🔧 Utilities: Helper functions for data manipulation andtransformation
  • 📉 Augment Functions: Add features like velocity, acceleration,and growth rates
  • 🧮 Vector Functions: Vectorized operations for time series
  • 🔬 Recipe Steps: Custom tidymodels recipe steps for time series

Documentation

Learning Resources

Vignettes

Example Use Cases

  1. Hospital Admissions Forecasting - Predict daily/weeklyadmissions using multiple models
  2. Length of Stay Analysis - Analyze and forecast ALOS trends
  3. Readmission Rate Monitoring - Track and predict readmissionpatterns
  4. Resource Planning - Forecast bed occupancy and staffing needs
  5. Seasonal Pattern Detection - Identify and visualize seasonaltrends in clinical data

Contributing

Contributions are welcome! Here’s how you can help:

  • 🐛Report bugs viaGitHubIssues
  • 💡Suggest features through issue requests
  • 🔧Submit pull requests for bug fixes or new features
  • 📖Improve documentation by suggesting clarifications or additions

Please follow thetidyverse style guidefor code contributions.

Related Packages

  • healthyR - Hospital dataanalysis companion package
  • healthyR.ai - Machinelearning companion for healthcare
  • healthyverse -Meta-package loading all healthyR packages

Citation

If you usehealthyR.ts in your research or publications, please cite:

citation("healthyR.ts")

Support

License

MIT License - seeLICENSE for details

Author: Steven P. Sanderson II, MPH
Maintainer: Steven P. Sanderson II, MPH (spsanderson@gmail.com)
Copyright: © 2020-2025 Steven P. Sanderson II, MPH

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