--- title: "Finding data sources and signals of interest" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Finding data sources and signals of interest} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- ```{r, echo = FALSE, message = FALSE} knitr::opts_chunk$set(collapse = TRUE, comment = "#>") options(tibble.print_min = 4L, tibble.print_max = 4L, max.print = 4L) library(epidatr) library(dplyr) ``` The Epidata API includes numerous data streams -- medical claims data, cases and deaths, mobility, and many others -- covering different geographic regions. This can make it a challenge to find the data stream that you are most interested in. Example queries with all the endpoint functions available in this package are given [below](#example-queries). ## Using the documentation The Epidata documentation lists all the data sources and signals available through the API for [COVID-19](https://cmu-delphi.github.io/delphi-epidata/api/covidcast_signals.html) and for [other diseases](https://cmu-delphi.github.io/delphi-epidata/api/README.html#source-specific-parameters). The site also includes a search tool if you have a keyword (e.g. "Taiwan") in mind. ## Signal metadata Some endpoints have partner metadata available that provides information about the signals that are available, for example, what time ranges they are available for, and when they have been updated. ```{r, echo = FALSE} suppressMessages(invisible(capture.output(endpts <- avail_endpoints()))) filter(endpts, endsWith(Endpoint, "_meta()")) %>% knitr::kable() ``` ## Interactive tooling We provide a couple `epidatr` functions to help find data sources and signals. The `avail_endpoints()` function lists endpoints, each of which, except for COVIDcast, corresponds to a single data source. `avail_endpoints()` outputs a `tibble` of endpoints and brief descriptions, which explicitly state when they cover non-US locations: ```{r, eval = FALSE} avail_endpoints() ``` ```{r, echo = FALSE} suppressMessages(invisible(capture.output(endpts <- avail_endpoints()))) knitr::kable(endpts) ``` The `covidcast_epidata()` function lets you look more in-depth at the data sources available through the COVIDcast endpoint. The function describes all available data sources and signals: ```{r} covid_sources <- covidcast_epidata() head(covid_sources$sources, n = 2) ``` Each source is included as an entry in the `covid_sources$sources` list, associated with a `tibble` describing included signals. If you use an editor that supports tab completion, such as RStudio, type `covid_sources$source$` and wait for the tab completion popup. You will be able to browse the list of data sources. ```{r} covid_sources$signals ``` If you use an editor that supports tab completion, type `covid_sources$signals$` and wait for the tab completion popup. You will be able to type the name of signals and have the autocomplete feature select them from the list for you. In the tab-completion popup, signal names are prefixed with the name of the data source for filtering convenience. _Note_ that some signal names have dashes in them, so to access them we rely on the backtick operator: ```{r} covid_sources$signals$`fb-survey:smoothed_cli` ``` These signal objects can be used directly to fetch data, without requiring us to use the `pub_covidcast()` function. Simply use the `$call` attribute of the object: ```{r} epidata <- covid_sources$signals$`fb-survey:smoothed_cli`$call( "state", "pa", epirange(20210405, 20210410) ) knitr::kable(epidata) ``` ## Example Queries ### COVIDcast Main Endpoint API docs: County geo_values are [FIPS codes](https://en.wikipedia.org/wiki/List_of_United_States_FIPS_codes_by_county) and are discussed in the API docs [here](https://cmu-delphi.github.io/delphi-epidata/api/covidcast_geography.html). The example below is for Orange County, California. ```{r} pub_covidcast( source = "fb-survey", signals = "smoothed_accept_covid_vaccine", geo_type = "county", time_type = "day", time_values = epirange(20201221, 20201225), geo_values = "06059" ) ``` The `covidcast` endpoint supports `*` in its time and geo fields: ```{r} pub_covidcast( source = "fb-survey", signals = "smoothed_accept_covid_vaccine", geo_type = "county", time_type = "day", time_values = epirange(20201221, 20201225), geo_values = "*" ) ``` ### Other Covid Endpoints #### COVID-19 Hospitalization: Facility Lookup API docs: ```{r, eval = FALSE} pub_covid_hosp_facility_lookup(city = "southlake") pub_covid_hosp_facility_lookup(state = "WY") # A non-example (there is no city called New York in Wyoming) pub_covid_hosp_facility_lookup(state = "WY", city = "New York") ``` #### COVID-19 Hospitalization by Facility API docs: ```{r, eval = FALSE} pub_covid_hosp_facility( hospital_pks = "100075", collection_weeks = epirange(20200101, 20200501) ) ``` #### COVID-19 Hospitalization by State API docs: ```{r, eval = FALSE} pub_covid_hosp_state_timeseries(states = "MA", dates = "20200510") ``` ### Flu Endpoints #### Delphi's ILINet forecasts API docs: ```{r, eval = FALSE} del <- pub_delphi(system = "ec", epiweek = 201501) names(del[[1L]]$forecast) ``` #### FluSurv hospitalization data API docs: ```{r, eval = FALSE} pub_flusurv(locations = "ca", epiweeks = 202001) ``` #### Fluview data API docs: ```{r, eval = FALSE} pub_fluview(regions = "nat", epiweeks = epirange(201201, 202001)) ``` #### Fluview virological data from clinical labs API docs: ```{r, eval = FALSE} pub_fluview_clinical(regions = "nat", epiweeks = epirange(201601, 201701)) ``` #### Fluview metadata API docs: ```{r, eval = FALSE} pub_fluview_meta() ``` #### Google Flu Trends data API docs: ```{r, eval = FALSE} pub_gft(locations = "hhs1", epiweeks = epirange(201201, 202001)) ``` #### ECDC ILI API docs: ```{r, eval = FALSE} pub_ecdc_ili(regions = "Armenia", epiweeks = 201840) ``` #### KCDC ILI API docs: ```{r, eval = FALSE} pub_kcdc_ili(regions = "ROK", epiweeks = 200436) ``` #### NIDSS Flu API docs: ```{r, eval = FALSE} pub_nidss_flu(regions = "taipei", epiweeks = epirange(200901, 201301)) ``` #### ILI Nearby Nowcast API docs: ```{r, eval = FALSE} pub_nowcast(locations = "ca", epiweeks = epirange(202201, 202319)) ``` ### Dengue Endpoints #### Delphi's Dengue Nowcast API docs: ```{r, eval = FALSE} pub_dengue_nowcast(locations = "pr", epiweeks = epirange(201401, 202301)) ``` #### NIDSS dengue API docs: ```{r, eval = FALSE} pub_nidss_dengue(locations = "taipei", epiweeks = epirange(200301, 201301)) ``` ### PAHO Dengue API docs: ```{r, eval=FALSE} pub_paho_dengue(regions = "ca", epiweeks = epirange(200201, 202319)) ``` ### Other Endpoints #### Wikipedia Access API docs: ```{r, eval = FALSE} pub_wiki( language = "en", articles = "influenza", time_type = "week", time_values = epirange(202001, 202319) ) ``` ### Private methods These require private access keys to use (separate from the Delphi Epidata API key). To actually run these locally, you will need to store these secrets in your `.Reviron` file, or set them as environmental variables.
Usage of private endpoints #### CDC API docs: ```{r, eval=FALSE} pvt_cdc(auth = Sys.getenv("SECRET_API_AUTH_CDC"), epiweeks = epirange(202003, 202304), locations = "ma") ``` #### Dengue Digital Surveillance Sensors API docs: ```{r, eval=FALSE} pvt_dengue_sensors( auth = Sys.getenv("SECRET_API_AUTH_SENSORS"), names = "ght", locations = "ag", epiweeks = epirange(201404, 202004) ) ``` #### Google Health Trends API docs: ```{r, eval=FALSE} pvt_ght( auth = Sys.getenv("SECRET_API_AUTH_GHT"), epiweeks = epirange(199301, 202304), locations = "ma", query = "how to get over the flu" ) ``` #### NoroSTAT metadata API docs: ```{r, eval=FALSE} pvt_meta_norostat(auth = Sys.getenv("SECRET_API_AUTH_NOROSTAT")) ``` #### NoroSTAT data API docs: ```{r, eval=FALSE} pvt_norostat(auth = Sys.getenv("SECRET_API_AUTH_NOROSTAT"), locations = "1", epiweeks = 201233) ``` #### Quidel Influenza testing API docs: ```{r, eval=FALSE} pvt_quidel(auth = Sys.getenv("SECRET_API_AUTH_QUIDEL"), locations = "hhs1", epiweeks = epirange(200301, 202105)) ``` #### Sensors API docs: ```{r, eval=FALSE} pvt_sensors( auth = Sys.getenv("SECRET_API_AUTH_SENSORS"), names = "sar3", locations = "nat", epiweeks = epirange(200301, 202105) ) ``` #### Twitter API docs: ```{r, eval=FALSE} pvt_twitter( auth = Sys.getenv("SECRET_API_AUTH_TWITTER"), locations = "nat", time_type = "week", time_values = epirange(200301, 202105) ) ```