Understanding and accessing versioned data

Data known “as of” a specific date

By default, endpoint functions fetch 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 that these are mutually exclusive; only one can be specified at a time. Also, not all endpoints support all three parameters, so please check the documentation for that specific endpoint.

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

epidata <- pub_covidcast(
  source = "doctor-visits",
  signals = "smoothed_adj_cli",
  time_type = "day",
  time_values = epirange("2020-05-01", "2020-05-01"),
  geo_type = "state",
  geo_values = "pa",
  as_of = "2020-05-07"
)
#> Warning: Loading from the cache at /fasterHome/workingDataDir/epidatr; see
#> /fasterHome/workingDataDir/epidatr/logfile.txt for more details.
#> This warning is displayed once every 8 hours.
knitr::kable(epidata)

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:

epidata <- pub_covidcast(
  source = "doctor-visits",
  signals = "smoothed_adj_cli",
  time_type = "day",
  time_values = epirange("2020-05-01", "2020-05-01"),
  geo_type = "state",
  geo_values = "pa"
)
knitr::kable(epidata)

Note the substantial change in the estimate, from less than 3% to almost 6%, 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 the model was or would have been fit, 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:

epidata <- pub_covidcast(
  source = "doctor-visits",
  signals = "smoothed_adj_cli",
  time_type = "day",
  time_values = epirange("2020-05-01", "2020-05-01"),
  geo_type = "state",
  geo_values = "pa",
  issues = epirange("2020-05-01", "2020-05-15")
)
knitr::kable(epidata)

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 (inclusive) 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.

The issues parameter also accepts a list of dates.

pub_covidcast(
  source = "doctor-visits",
  signals = "smoothed_adj_cli",
  time_type = "day",
  time_values = epirange("2020-05-01", "2020-05-01"),
  geo_type = "state",
  geo_values = "pa",
  issues = c("2020-05-07", "2020-05-09", "2020-05-15")
)

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 fetches only data issued exactly 7 days after the corresponding time_value:

epidata <- pub_covidcast(
  source = "doctor-visits",
  signals = "smoothed_adj_cli",
  time_type = "day",
  time_values = epirange("2020-05-01", "2020-05-07"),
  geo_type = "state",
  geo_values = "pa",
  lag = 7
)
knitr::kable(epidata)

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:

epidata <- pub_covidcast(
  source = "doctor-visits",
  signals = "smoothed_adj_cli",
  time_type = "day",
  time_values = epirange("2020-05-03", "2020-05-03"),
  geo_type = "state",
  geo_values = "pa",
  issues = epirange("2020-05-09", "2020-05-15")
)
knitr::kable(epidata)