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epi_df object

Source: R/epi_df.R
epi_df.Rd

One of the two main data structures for storing time series in epiprocess. It is simply tibble with at least two columns, geo_value and time_value, that provide the keys for the time series. It can have any other columns, which can be seen as measured variables at each key. In brief, an epi_df represents a snapshot of an epidemiological data set at a point in time.

Usage

as_epi_df(x, ...)

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

# S3 method for class 'tbl_df'
as_epi_df(
  x,
  geo_type = deprecated(),
  time_type = deprecated(),
  as_of,
  other_keys = character(),
  ...
)

# S3 method for class 'data.frame'
as_epi_df(x, as_of, other_keys = character(), ...)

# S3 method for class 'tbl_ts'
as_epi_df(x, as_of, other_keys = character(), ...)

new_epi_df(
  x = tibble::tibble(geo_value = character(), time_value = as.Date(integer())),
  geo_type,
  time_type,
  as_of,
  other_keys = character(),
  ...
)

is_epi_df(x)

Arguments

x

An object.

...

Additional arguments passed to methods.

geo_type

[Deprecated] in as_epi_df(), has no effect; the geo value type is inferred from the location column and set to "custom" if not recognized. In new_epi_df(), should be set to the same value that would be inferred.

time_type

[Deprecated] in as_epi_df(), has no effect: the time value type inferred from the time column and set to "custom" if not recognized. Unpredictable behavior may result if the time type is not recognized. In new_epi_df(), should be set to the same value that would be inferred.

as_of

Time value representing the time at which the given data were available. For example, if as_of is January 31, 2022, then the epi_df object that is created would represent the most up-to-date version of the data available as of January 31, 2022. If the as_of argument is missing, then the current day-time will be used.

other_keys

If your tibble has additional keys, be sure to specify them as a character vector here (typical examples are "age" or sub-geographies).

Value

An epi_df object.

TRUE if the object inherits from epi_df.

Details

An epi_df is a tibble with (at least) the following columns:

  • geo_value: A character vector representing the geographical unit of observation. This could be a country code, a state name, a county code, etc.

  • time_value: A date or integer vector representing the time of observation.

Other columns can be considered as measured variables, which we also refer to as signal variables. An epi_df object also has metadata with (at least) the following fields:

  • geo_type: the type for the geo values.

  • as_of: the time value at which the given data were available.

Most users should use as_epi_df. The input tibble x to the constructor must contain the columns geo_value and time_value. All other columns will be preserved as is, and treated as measured variables. If as_of is missing, then the function will try to guess it from an as_of, issue, or version column of x (if any of these are present), or from as an as_of field in its metadata (stored in its attributes); if this fails, then the current day-time will be used. The new_epi_df constructor assumes its arguments have already been validated, so it should mainly be used by advanced users.

Metadata for an epi_df object x can be accessed (and altered) via attributes(x)$metadata. The first field in the above list, geo_type, can usually be inferred from the geo_value columns. They are not currently used by any downstream functions in the epiprocess package, and serve only as useful bits of information to convey about the data set at hand. More information on their coding is given below.

The last field in the above list, as_of, is one of the most unique aspects of an epi_df object. In brief, we can think of an epi_df object as a single snapshot of a data set that contains the most up-to-date values of the signals variables, as of the time specified in the as_of field.

If an epi_df ever loses its geo_value or time_value columns, it will decay into a regular tibble.

A companion object is the epi_archive object, which contains the full version history of a given data set. Revisions are common in many types of epidemiological data streams, and paying attention to data revisions can be important for all sorts of downstream data analysis and modeling tasks. See the documentation for epi_archive for more details on how data versioning works in the epiprocess package (including how to generate epi_df objects, as data snapshots, from an epi_archive object).

Geo Types

The following geo types are recognized in an epi_df.

  • "county": each observation corresponds to a U.S. county; coded by 5-digit FIPS code.

  • "hrr": each observation corresponds to a U.S. hospital referral region (designed to represent regional healthcare markets); there are 306 HRRs in the U.S; coded by number (nonconsecutive, between 1 and 457).

  • "state": each observation corresponds to a U.S. state; coded by 2-digit postal abbreviation (lowercase); note that Puerto Rico is "pr" and Washington D.C. is "dc".

  • "hhs": each observation corresponds to a U.S. HHS region; coded by number (consecutive, between 1 and 10).

  • "nation": each observation corresponds to a country; coded by ISO 31661- alpha-2 country codes (lowercase).

An unrecognizable geo type is labeled "custom".

Time Types

The following time types are recognized in an epi_df.

  • "day": each observation corresponds to a day; coded as a Date object, as in as.Date("2022-01-31").

  • "week": each observation corresponds to a week; the alignment can be arbitrary (as to whether a week starts on a Monday, Tuesday); coded as a Date object, representing the start date of week.

  • "yearmonth": each observation corresponds to a month; coded as a tsibble::yearmonth object.

  • "integer": a generic integer index (e.g. years or something else).

An unrecognizable time type is labeled "custom".

Functions

  • as_epi_df(): The preferred way of constructing epi_dfs

  • new_epi_df(): Lower-level constructor for epi_df object

Examples

# Convert a `tsibble` that has county code as an extra key
# Notice that county code should be a character string to preserve any leading zeroes
ex1_input <- tibble::tibble(
  geo_value = c(
    "06059", "06061", "06067",
    "12111", "12113", "12117",
    "42101", "42103", "42105"
  ),
  state_name = rep(c("ca", "fl", "pa"), each = 3),
  time_value = rep(seq(as.Date("2020-06-01"), as.Date("2020-06-03"),
    by = "day"
  ), length.out = length(geo_value)),
  value = 1:length(geo_value) + 0.01 * rnorm(length(geo_value))
) %>%
  tsibble::as_tsibble(index = time_value, key = c(geo_value, state_name))

# The `other_keys` metadata (`"state_name"` in this case) is automatically
# inferred from the `tsibble`'s `key`:
ex1 <- as_epi_df(x = ex1_input, as_of = "2020-06-03")
attr(ex1, "metadata")[["other_keys"]]
#> [1] "state_name"

# Dealing with misspecified column names:
# Geographical and temporal information must be provided in columns named
# `geo_value` and `time_value`; if we start from a data frame with a
# different format, it must be converted to use `geo_value` and `time_value`
# before calling `as_epi_df`.
ex2_input <- tibble::tibble(
  state = rep(c("ca", "fl", "pa"), each = 3), # misnamed
  pol = rep(c("blue", "swing", "swing"), each = 3), # extra key
  reported_date = rep(seq(as.Date("2020-06-01"), as.Date("2020-06-03"),
    by = "day"
  ), length.out = length(state)), # misnamed
  value = 1:length(state) + 0.01 * rnorm(length(state))
)
print(ex2_input)
#> # A tibble: 9 × 4
#>   state pol   reported_date value
#>   <chr> <chr> <date>        <dbl>
#> 1 ca    blue  2020-06-01    0.991
#> 2 ca    blue  2020-06-02    2.00 
#> 3 ca    blue  2020-06-03    3.00 
#> 4 fl    swing 2020-06-01    3.99 
#> 5 fl    swing 2020-06-02    5.01 
#> 6 fl    swing 2020-06-03    6.02 
#> 7 pa    swing 2020-06-01    7.00 
#> 8 pa    swing 2020-06-02    7.99 
#> 9 pa    swing 2020-06-03    9.00 

ex2 <- ex2_input %>%
  dplyr::rename(geo_value = state, time_value = reported_date) %>%
  as_epi_df(
    as_of = "2020-06-03",
    other_keys = "pol"
  )
attr(ex2, "metadata")
#> $geo_type
#> [1] "state"
#> 
#> $time_type
#> [1] "day"
#> 
#> $as_of
#> [1] "2020-06-03"
#> 
#> $other_keys
#> [1] "pol"
#> 

# Adding additional keys to an `epi_df` object
ex3_input <- covid_incidence_county_subset %>%
  dplyr::filter(time_value > "2021-12-01", state_name == "Massachusetts") %>%
  dplyr::slice_tail(n = 6)

ex3 <- ex3_input %>%
  tsibble::as_tsibble() %>% # needed to add the additional metadata
  # add 2 extra keys
  dplyr::mutate(
    state = rep("MA", 6),
    pol = rep(c("blue", "swing", "swing"), each = 2)
  ) %>%
  as_epi_df(other_keys = c("state", "pol"))

attr(ex3, "metadata")
#> $geo_type
#> [1] "county"
#> 
#> $time_type
#> [1] "day"
#> 
#> $as_of
#> [1] "2024-10-22 20:05:58 UTC"
#> 
#> $other_keys
#> [1] "state" "pol"  
#> 

# Decays to a tibble
covid_incidence_county_subset %>%
  dplyr::select(-geo_value)
#> # A tibble: 16,212 × 4
#>    time_value cases county_name       state_name   
#>  * <date>     <dbl> <chr>             <chr>        
#>  1 2020-06-01     4 Barnstable County Massachusetts
#>  2 2020-06-01     0 Berkshire County  Massachusetts
#>  3 2020-06-01    78 Bristol County    Massachusetts
#>  4 2020-06-01     0 Dukes County      Massachusetts
#>  5 2020-06-01    92 Essex County      Massachusetts
#>  6 2020-06-01     0 Franklin County   Massachusetts
#>  7 2020-06-01    35 Hampden County    Massachusetts
#>  8 2020-06-01     4 Hampshire County  Massachusetts
#>  9 2020-06-01    98 Middlesex County  Massachusetts
#> 10 2020-06-01     0 Nantucket County  Massachusetts
#> # ℹ 16,202 more rows