The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
This package contains functions for working with the Zoltar forecast repository’s API, including projects, models, forecasts, and truth. Read more about this package at the zoltr pkgdown site. Documentation on Zolar itself is at docs.zoltardata.com.
You can install the released version of zoltr from CRAN with:
install.packages("zoltr")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("reichlab/zoltr")Note: Due to the rapid pace of zoltr development, CRAN version lags behind development. We highly suggest you install the development version to get the latest features.
For those starting out we recommend you begin with the “Getting Started” vignette.
Read more at the zoltr pkgdown
site, but briefly you use the new_connection() function
to create a connection to Zoltar and then pass that
connection along with the URL of the resource of interest
(e.g., a project, model, or forecast) to this package’s various
functions like projects() or
project_info().
library(zoltr)
zoltar_connection <- new_connection()
zoltar_authenticate(zoltar_connection, Sys.getenv("Z_USERNAME"), Sys.getenv("Z_PASSWORD"))
zoltar_connection
#> ZoltarConnection 'https://zoltardata.com' authenticated (exp=2024-06-27 15:55:21 UTC)
the_projects <- projects(zoltar_connection)
project_url <- the_projects[the_projects$name == "Docs Example Project", "url"]
the_project_info <- project_info(zoltar_connection, project_url)
names(the_project_info)
#> [1] "id" "url" "owner" "is_public" "name"
#> [6] "description" "home_url" "logo_url" "core_data" "truth"
#> [11] "model_owners" "models" "units" "targets" "timezeros"
the_project_info$name
#> [1] "Docs Example Project"The native forecast data format supported by the Zoltar API is a
list. See docs.zoltardata.com for format
details. You can find an example at vignettes/docs-predictions.json . By
convention this package referred to this as forecast_data.
This package supports conversion to this format (which is used
throughout the package) from the CDC’s CSV file format [1] via the
forecast_data_from_cdc_csv_file() function. Future versions
will support bidirectional conversion, as well as support for a more
general CSV format.
[1] Details about the CDC CSV format were formerly found in a Word document (“flu_challenge_2016-17_update.docx”) that’s since been deleted. From that document’s “Objectives” section:
For each week during the season, participants will be asked to provide national and regional probabilistic forecasts for the entire influenza season (seasonal targets) and for the next four weeks (four-week ahead targets). The seasonal targets are the onset week, the peak week, and the peak intensity of the 2016-2017 influenza season. The four-week ahead targets are the percent of outpatient visits experiencing influenza-like illness (ILI) one week, two weeks, three weeks, and four weeks ahead from date of the forecast.
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.