Utility functions for the COVID-19 forecast hub
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")For those starting out we recommend you begin with the Getting Started vignette.
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.get_model_designations(models, source, hub_repo_path): Assemble a data frame with columns model and designation.load_latest_forecasts(models, last_forecast_date, forecast_date_window_size, locations, types, targets, source, hub_repo_path, as_of, verbose): 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, as_of, verbose): Load all available forecasts from Zoltar.
Reading Observed "Truth" Data
load_truth(truth_source, target_variable, truth_end_date, temporal_resolution, locations, data_location, local_repo_path): 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_forecast(forecast_data, truth_data, 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 ofmodel,forecast_date,location,horizon,temporal_resolution,target_variable, andtarget_end_datein theforecastsdata frame. Please see this reference for valid scores in thedesired_score_typesvector.
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 CSVspreprocess_usafacts(save_location): Preprocess raw truth data from USAFacts into Cumulative/Incident - Deaths/Cases and write to CSVspreprocess_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 frompreprocess_truth_for_zoltar()to CSVs. Note: To use this method, the covidData package needs to be installed.