Get started with epidatr

The epidatr package provides access to all the endpoints of the Delphi Epidata API, and can be used to make requests for specific signals on specific dates and in select geographic regions.

Setup

Installation

You can install the stable version of this package from CRAN:

Code

install.packages("epidatr")
pak::pkg_install("epidatr")
renv::install("epidatr")

Or if you want the development version, install from GitHub:

Code

# Install the dev version using `pak` or `remotes`
pak::pkg_install("cmu-delphi/epidatr@dev")
remotes::install_github("cmu-delphi/epidatr", ref = "dev")
renv::install("cmu-delphi/epidatr@dev")

API Keys

The Delphi API requires a (free) API key for full functionality. While most endpoints are available without one, there are limits on API usage for anonymous users, including a rate limit.

To generate your key, register for a pseudo-anonymous account. See the save_api_key() function documentation for details on how to set up epidatr to use your API key.

Note that private endpoints (i.e. those prefixed with pvt_) require a separate key that needs to be passed as an argument. These endpoints require specific data use agreements to access.

Basic Usage

Fetching data from the Delphi Epidata API is simple. Suppose we are interested in the covidcast endpoint, which provides access to a wide range of data on COVID-19. Reviewing the endpoint documentation, we see that we need to specify a data source name, a signal name, a geographic level, a time resolution, and the location and times of interest.

The pub_covidcast() function lets us access the covidcast endpoint:

Code

library(epidatr)
#> ! epidatr cache is being used (set env var EPIDATR_USE_CACHE=FALSE if not
#>   intended).
#> ℹ The cache directory is /fasterHome/workingDataDir/epidatr.
#> ℹ The cache will be cleared after 1 day and will be pruned if it exceeds 1024
#>   MB.
#> ℹ The log of cache transactions is stored at
#>   /fasterHome/workingDataDir/epidatr/logfile.txt.
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> 
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> 
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

# Obtain the most up-to-date version of the smoothed covid-like illness (CLI)
# signal from the COVID-19 Trends and Impact survey for the US
epidata <- pub_covidcast(
  source = "fb-survey",
  signals = "smoothed_cli",
  geo_type = "nation",
  time_type = "day",
  geo_values = "us",
  time_values = epirange(20210105, 20210410)
)
knitr::kable(head(epidata))

geo_value	signal	source	geo_type	time_type	time_value	direction	issue	lag	value	stderr	sample_size
us	smoothed_cli	fb-survey	nation	day	2021-01-05	NA	2021-01-10	5	1.184132	0.0162137	360654
us	smoothed_cli	fb-survey	nation	day	2021-01-06	NA	2021-01-29	23	1.179046	0.0162516	356720
us	smoothed_cli	fb-survey	nation	day	2021-01-07	NA	2021-01-29	22	1.197495	0.0165100	351906
us	smoothed_cli	fb-survey	nation	day	2021-01-08	NA	2021-01-29	21	1.218064	0.0167278	348471
us	smoothed_cli	fb-survey	nation	day	2021-01-09	NA	2021-01-29	20	1.219899	0.0168694	342855
us	smoothed_cli	fb-survey	nation	day	2021-01-10	NA	2021-01-29	19	1.231889	0.0171074	336455

pub_covidcast() returns a tibble. (Here we’re using knitr::kable() to make it more readable.) Each row represents one observation in Pennsylvania on one day. The state abbreviation is given in the geo_value column, the date in the time_value column. Here value is the requested signal – in this case, the smoothed estimate of the percentage of people with COVID-like illness, based on the symptom surveys, and stderr is its standard error.

The Epidata API makes signals available at different geographic levels, depending on the endpoint. To request signals for all states instead of the entire US, we use the geo_type argument paired with * for the geo_values argument. (Only some endpoints allow for the use of * to access data at all locations. Check the help for a given endpoint to see if it supports *.)

Code

# Obtain the most up-to-date version of the smoothed covid-like illness (CLI)
# signal from the COVID-19 Trends and Impact survey for all states
pub_covidcast(
  source = "fb-survey",
  signals = "smoothed_cli",
  geo_type = "state",
  time_type = "day",
  geo_values = "*",
  time_values = epirange(20210105, 20210410)
)

Alternatively, we can fetch the full time series for a subset of states by listing out the desired locations in the geo_value argument and using * in the time_values argument:

Code

# Obtain the most up-to-date version of the smoothed covid-like illness (CLI)
# signal from the COVID-19 Trends and Impact survey for Pennsylvania
pub_covidcast(
  source = "fb-survey",
  signals = "smoothed_cli",
  geo_type = "state",
  time_type = "day",
  geo_values = c("pa", "ca", "fl"),
  time_values = "*"
)

Getting versioned data

The Epidata API stores a historical record of all data, including corrections and updates, which is particularly useful for accurately backtesting forecasting models. To fetch versioned data, we can use the as_of argument.

Code

# Obtain the smoothed covid-like illness (CLI) signal from the COVID-19
# Trends and Impact survey for Pennsylvania as it was on 2021-06-01
pub_covidcast(
  source = "fb-survey",
  signals = "smoothed_cli",
  geo_type = "state",
  time_type = "day",
  geo_values = "pa",
  time_values = epirange(20210105, 20210410),
  as_of = "2021-06-01"
)

See vignette("versioned-data") for details and more ways to specify versioned data.

Plotting

Because the output data is in a standard tibble format, we can easily plot it using ggplot2:

Code

library(ggplot2)
ggplot(epidata, aes(x = time_value, y = value)) +
  geom_line() +
  labs(
    title = "Smoothed CLI from Facebook Survey",
    subtitle = "PA, 2021",
    x = "Date",
    y = "CLI"
  )

ggplot2 can also be used to create choropleths.

Code

library(maps)

# Obtain the most up-to-date version of the smoothed covid-like illness (CLI)
# signal from the COVID-19 Trends and Impact survey for all states on a single day
cli_states <- pub_covidcast(
  source = "fb-survey",
  signals = "smoothed_cli",
  geo_type = "state",
  time_type = "day",
  geo_values = "*",
  time_values = 20210410
)

# Get a mapping of states to longitude/latitude coordinates
states_map <- map_data("state")

# Convert state abbreviations into state names
cli_states <- mutate(
  cli_states,
  state = ifelse(
    geo_value == "dc",
    "district of columbia",
    state.name[match(geo_value, tolower(state.abb))] %>% tolower()
  )
)

# Add coordinates for each state
cli_states <- left_join(states_map, cli_states, by = c("region" = "state"))

# Plot
ggplot(cli_states, aes(x = long, y = lat, group = group, fill = value)) +
  geom_polygon(colour = "black", linewidth = 0.2) +
  coord_map("polyconic") +
  labs(
    title = "Smoothed CLI from Facebook Survey",
    subtitle = "All states, 2021-04-10",
    x = "Longitude",
    y = "Latitude"
  )

Finding locations of interest

Most data is only available for the US. Select endpoints report other countries at the national and/or regional levels. Endpoint descriptions explicitly state when they cover non-US locations.

For endpoints that report US data, see the geographic coding documentation for available geographic levels.

International data

International data is available via

pub_dengue_nowcast (North and South America)
pub_ecdc_ili (Europe)
pub_kcdc_ili (Korea)
pub_nidss_dengue (Taiwan)
pub_nidss_flu (Taiwan)
pub_paho_dengue (North and South America)
pvt_dengue_sensors (North and South America)

Finding data sources and signals of interest

Above we used data from Delphi’s symptom surveys, but the Epidata API includes numerous data streams: medical claims data, cases and deaths, mobility, and many others. This can make it a challenge to find the data stream that you are most interested in.

The Epidata documentation lists all the data sources and signals available through the API for COVID-19 and for other diseases.

You can also use the avail_endpoints() function to get a table of endpoint functions:

Code

avail_endpoints()

#> ℹ Data is available for the US only, unless otherwise specified

Endpoint	Description
pub_covid_hosp_facility()	COVID hospitalizations by facility
pub_covid_hosp_facility_lookup()	Helper for finding COVID hospitalization facilities
pub_covid_hosp_state_timeseries()	COVID hospitalizations by state
pub_covidcast()	Various COVID and flu signals via the COVIDcast endpoint
pub_covidcast_meta()	Metadata for the COVIDcast endpoint
pub_delphi()	Delphi’s ILINet outpatient doctor visits forecasts
pub_dengue_nowcast()	Delphi’s PAHO dengue nowcasts (North and South America)
pub_ecdc_ili()	ECDC ILI incidence (Europe)
pub_flusurv()	CDC FluSurv flu hospitalizations
pub_fluview()	CDC FluView ILINet outpatient doctor visits
pub_fluview_clinical()	CDC FluView flu tests from clinical labs
pub_fluview_meta()	Metadata for the FluView endpoint
pub_gft()	Google Flu Trends flu search volume
pub_kcdc_ili()	KCDC ILI incidence (Korea)
pub_meta()	Metadata for the Delphi Epidata API
pub_nidss_dengue()	NIDSS dengue cases (Taiwan)
pub_nidss_flu()	NIDSS flu doctor visits (Taiwan)
pub_nowcast()	Delphi’s ILI Nearby nowcasts
pub_paho_dengue()	PAHO dengue data (North and South America)
pub_wiki()	Wikipedia webpage counts by article
pvt_cdc()	CDC total and by topic webpage visits
pvt_dengue_sensors()	PAHO dengue digital surveillance sensors (North and South America)
pvt_ght()	Google Health Trends health topics search volume
pvt_meta_norostat()	Metadata for the NoroSTAT endpoint
pvt_norostat()	CDC NoroSTAT norovirus outbreaks
pvt_quidel()	Quidel COVID-19 and influenza testing data
pvt_sensors()	Influenza and dengue digital surveillance sensors
pvt_twitter()	HealthTweets total and influenza-related tweets

See vignette("signal-discovery") for more information.