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covidHubUtils

Utility functions for the COVID-19 forecast hub

Installation

The covidHubUtils package relies on a small number of packages, including many from the tidyverse and, importantly, the zoltr package that is used to access the Zoltar API for downloading forecasts. Please install zoltr from GitHub, as this development version often has important features not yet on the CRAN version:

devtools::install_github("reichlab/zoltr")

The covidHubUtils package currently is only available on GitHub, and it may be installed using the devtools package:

devtools::install_github("reichlab/covidHubUtils")

Getting Started

For those starting out we recommend you begin with the Getting Started vignette.

Features

Currently available:

Reading Forecast Data

  • get_model_designations(models, source, hub_repo_path, as_of): Assemble a data frame with columns model and designation. Note: Currently only support versioned model designations in a local clone of the covid19-forecast-hub repository.
  • load_latest_forecasts(models, last_forecast_date, forecast_date_window_size, locations, types, targets, source, hub_repo_path, as_of, verbose, hub): Load the most recent forecasts in a specified time window either from a local clone of the covid19-forecast-hub repository or Zoltar.
  • load_forecasts(models, forecast_dates, locations, types, targets, source, hub_repo_path, as_of, verbose, hub): Load all available forecasts either from a local clone of the covid19-forecast-hub repository or Zoltar.

Reading Observed "Truth" Data

  • load_truth(truth_source, target_variable, truth_end_date, temporal_resolution, locations, data_location, local_repo_path, hub): Load truth data for specified target variable and locations from covid19-forecast-hub repository. Note: Only support national level and state level truth data for "inc hosp" from "HealthData" source.

Plotting Forecasts

  • plot_forecasts(forecast_data, truth_data, hub, models, target_variable, locations, facet, facet_scales, forecast_dates, intervals, horizon, truth_source, use_median_as_point, plot_truth, plot, fill_by_model, truth_as_of, title, subtitle, show_caption): Plot forecasts with optional truth data for multiple models, locations and forecast dates. To see more example plots, please to go vignettes/demo.

Scoring Forecasts

  • score_forecasts(forecasts, truth, desired_score_types = c(...), return_format = c("long", "wide")) Calculate specified scores for each combination of model, forecast_date, location, horizon, temporal_resolution, target_variable, and target_end_date in the forecasts data frame. Please see this reference for valid scores in the desired_score_types vector.

Download and pre-process "Truth" Data

  • download_raw_nytimes(save_location): Download raw truth data from NYTimes and write to CSV files.
  • download_raw_usafacts(save_location): Download raw truth data from USAFacts and write to CSV files.
  • preprocess_nytimes(save_location): Preprocess raw truth data from NYTimes into Cumulative/Incident - Deaths/Cases and write to CSVs
  • preprocess_usafacts(save_location): Preprocess raw truth data from USAFacts into Cumulative/Incident - Deaths/Cases and write to CSVs
  • preprocess_jhu(save_location): Preprocess raw truth data from JHU CSSE into Cumulative/Incident - Deaths/Cases and write to CSVs. Note: To use this method, the covidData package needs to be installed.
  • preprocess_hospitalization(save_location): Preprocess raw hospitalization data into Cumulative/Incident hospitalizations and write to CSVs. Note: To use this method, the covidData package needs to be installed.
  • preprocess_truth_for_zoltar(target, issue_date): Preprocess raw truth data from JHU CSSE into Cumulative/Incident - Deaths/Cases for Zoltar. Note: To use this method, the covidData package needs to be installed.
  • save_truth_for_zoltar(save_location): Write results from preprocess_truth_for_zoltar() to CSVs. Note: To use this method, the covidData package needs to be installed.

Calculating Forecast Similarities

  • calc_cramers_dist_equal_space(q_F, tau_F, q_G, tau_G, approx_rule): Calculating approximated Cramer's distance between a pair of distributions F and G that are represented by a collection of equally-spaced quantiles.
  • calc_cramers_dist_unequal_space(q_F, tau_F, q_G, tau_G, approx_rule): Calculating approximated Cramer's distance between a pair of distributions F and G that are represented by a collection of unequally-spaced quantiles. *calc_cramers_dist_one_model_pair(q_F, tau_F, q_G, tau_G, approx_rule): A wrapper function for calc_cramers_dist_equal_space() and calc_cramers_dist_unequal_space().

Contributing Guidelines

If you would like to contribute your work, please follow this list to create a pull request:

  • New functions should come with unit tests, or a promise of a new unit test in the form of an issue if getting the functionality merged in is urgent.
  • If you added a new .R file with unit tests, add the tests to .github/workflows/pr_unittest.yaml.
  • Small/quick fixes don't need to be tested, necessarily.
  • Update NEWS.md by adding a short summary of your changes under “Changes since last release.”
  • Update README.md if you created a new function or add a new parameter to existing functions.
  • Update DESCRIPTION when you are using a new dependency in your script.