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Base S3 methods for an epi_df object

Source: R/methods-epi_df.R
print.epi_df.Rd

The print and summary methods provide informative displays of an epi_df object. Both show the data's dimensions and core metadata such as geo_type, time_type, other_keys, and as_of.

summary() provides detailed statistics about the epi_df object, including the time range included, gap analysis, and per-signal latency. See the print method documentation for a comprehensive description of the output. See Details: section for what specifically is included.

Usage

# S3 method for class 'epi_df'
print(x, ...)

# S3 method for class 'epi_df'
summary(object, ...)

# S3 method for class 'epi_df'
group_by(.data, ...)

# S3 method for class 'epi_df'
ungroup(x, ...)

# S3 method for class 'epi_df'
group_modify(.data, .f, ..., .keep = FALSE)

# S3 method for class 'epi_df'
unnest(data, ...)

Arguments

x

an epi_df

...

Additional arguments; unused in summary(); forwarded to underlying base or {dplyr} methods in the rest.

object

an epi_df

.data

an epi_df

.f

function or formula; see dplyr::group_modify

.keep

Boolean; see dplyr::group_modify

data

an epi_df

Details

This method also includes a brief latency summary, representing the difference between the as_of date and the most recent observation found in the data.

summary method

This method provides a more detailed look at the data's structure:

  • The time range section reports the global minimum and maximum time values found across all time series. It notes whether every series covers this full span or if some start later or end earlier, providing insight into staggered data availability across different geographic units.

  • The gap analysis section identifies missing data patterns by distinguishing between missing rows (implicit gaps where time steps are skipped) and missing values (explicit NAs within the observed range of a series). It also reports the average number of rows per time value to help detect potential coverage issues.

  • The latency section details the time difference between the most recent observation and the as_of date of the epi_df. This breakdown is provided per signal to highlight specific key combinations that are lagging or entirely empty.