Get started with covidcast

API keys

By default, this package submits queries to the API anonymously. All the examples in the package documentation are compatible with anonymous use of the API, but there are some limits on anonymous queries, including rate limits on the number of queries that can be submitted per hour. To lift these limits, see the “API keys” section of the covidcast_signal() documentation for information on how to register for and use an API key.

Plotting and mapping

This package provides convenient functions for plotting and mapping these signals. For example, simple line charts are easy to construct:

plot(cli, plot_type = "line",
     title = "Survey results in Allegheny County, PA")

For more details and examples, including choropleth and bubble maps, see vignette("plotting-signals").

Finding signals of interest

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

The COVIDcast Data Sources and Signals documentation lists all data sources and signals available through COVIDcast. When you find a signal of interest, get the data source name (such as jhu-csse or fb-survey) and the signal name (such as confirmed_incidence_num or smoothed_wcli). These are provided as arguments to covidcast_signal() to request the data you want.

Finding counties and metro areas

The COVIDcast API identifies counties by their 5-digit FIPS code and metropolitan areas by their CBSA ID number. (See the geographic coding documentation for details.) This means that to query a specific county or metropolitan area, we must have some way to quickly find its identifier.

This package includes several utilities intended to make the process easier. For example, if we look at ?county_census, we find that the package provides census data (such as population) on every county in the United States, including its FIPS code. Similarly, by looking at ?msa_census we can find data about metropolitan statistical areas, their corresponding CBSA IDs, and recent census data.

(Note: the msa_census data includes types of area beyond metropolitan statistical areas, including micropolitan statistical areas. The LSAD column identifies the type of each area. The COVIDcast API only provides estimates for metropolitan statistical areas, not for their divisions or for micropolitan areas.)

Building on these datasets, the convenience functions name_to_fips() and name_to_cbsa() conduct grep()-based searching of county or metropolitan area names to find FIPS or CBSA codes, respectively:

name_to_fips("Allegheny")

## Allegheny County 
##          "42003"

name_to_cbsa("Pittsburgh")

## Pittsburgh, PA 
##        "38300"

Since these functions return vectors of IDs, we can use them to construct the geo_values argument to covidcast_signal() to select specific regions to query.

You may also want to convert FIPS codes or CBSA IDs back to well-known names, for instance to report in tables or graphics. The package provides inverse mappings county_fips_to_name() and cbsa_to_name() that work in the analogous way:

county_fips_to_name("42003")

##              42003 
## "Allegheny County"

cbsa_to_name("38300")

##            38300 
## "Pittsburgh, PA"

See their documentation for more details (for example, the options for handling matches when counties have the same name).

Data known “as of” a specific date

By default, covidcast_signal() fetches the most recent issue available. This is the best option for users who simply want to graph the latest data or construct dashboards. But if we are interested in knowing when data was reported, we can request specific data versions using the as_of, issues, or lag arguments. (Note these are mutually exclusive; only one can be specified at a time.)

First, we can request the data that was available as of a specific date, using the as_of argument:

covidcast_signal(data_source = "doctor-visits", signal = "smoothed_adj_cli",
                 start_day = "2020-05-01", end_day = "2020-05-01",
                 geo_type = "state", geo_values = "pa", as_of = "2020-05-07")

## A `covidcast_signal` dataframe with 1 rows and 15 columns.
## 
## data_source : doctor-visits
## signal      : smoothed_adj_cli
## geo_type    : state
## 
##     data_source           signal geo_value time_value        source geo_type
## 1 doctor-visits smoothed_adj_cli        pa 2020-05-01 doctor-visits    state
##   time_type      issue lag missing_value missing_stderr missing_sample_size
## 1       day 2020-05-07   6             0              5                   5
##      value stderr sample_size
## 1 2.581509     NA          NA

This shows that an estimate of about 2.3% was issued on May 7. If we don’t specify as_of, we get the most recent estimate available:

covidcast_signal(data_source = "doctor-visits", signal = "smoothed_adj_cli",
                 start_day = "2020-05-01", end_day = "2020-05-01",
                 geo_type = "state", geo_values = "pa")

## A `covidcast_signal` dataframe with 1 rows and 15 columns.
## 
## data_source : doctor-visits
## signal      : smoothed_adj_cli
## geo_type    : state
## 
##     data_source           signal geo_value time_value        source geo_type
## 1 doctor-visits smoothed_adj_cli        pa 2020-05-01 doctor-visits    state
##   time_type      issue lag missing_value missing_stderr missing_sample_size
## 1       day 2020-07-04  64             0              5                   5
##      value stderr sample_size
## 1 5.973572     NA          NA

Note the substantial change in the estimate, to over 5%, reflecting new data that became available after May 7 about visits occurring on May 1. This illustrates the importance of issue date tracking, particularly for forecasting tasks. To backtest a forecasting model on past data, it is important to use the data that would have been available at the time, not data that arrived much later.

Multiple issues of observations

By using the issues argument, we can request all issues in a certain time period:

covidcast_signal(data_source = "doctor-visits", signal = "smoothed_adj_cli",
                 start_day = "2020-05-01", end_day = "2020-05-01",
                 geo_type = "state", geo_values = "pa",
                 issues = c("2020-05-01", "2020-05-15")) %>%
  knitr::kable()

This estimate was clearly updated many times as new data for May 1st arrived. Note that these results include only data issued or updated between 2020-05-01 and 2020-05-15. If a value was first reported on 2020-04-15, and never updated, a query for issues between 2020-05-01 and 2020-05-15 will not include that value among its results.

After fetching multiple issues of data, we can use the latest_issue() or earliest_issue() functions to subset the data frame to view only the latest or earliest issue of each observation.

Observations issued with a specific lag

Finally, we can use the lag argument to request only data reported with a certain lag. For example, requesting a lag of 7 days means to request only issues 7 days after the corresponding time_value:

covidcast_signal(data_source = "doctor-visits", signal = "smoothed_adj_cli",
                 start_day = "2020-05-01", end_day = "2020-05-07",
                 geo_type = "state", geo_values = "pa", lag = 7) %>%
  knitr::kable()

## Warning: Data not fetched for the following days: 2020-05-01

Note that though this query requested all values between 2020-05-01 and 2020-05-07, May 3rd and May 4th were not included in the results set. This is because the query will only include a result for May 3rd if a value were issued on May 10th (a 7-day lag), but in fact the value was not updated on that day:

covidcast_signal(data_source = "doctor-visits", signal = "smoothed_adj_cli",
                 start_day = "2020-05-03", end_day = "2020-05-03",
                 geo_type = "state", geo_values = "pa",
                 issues = c("2020-05-09", "2020-05-15")) %>%
  knitr::kable()