Working with epi_df objects and time series data

The epi_df data structure provided by epiprocess provides convenient ways to perform common processing tasks. In this vignette, we will:

construct an epi_df from a data frame
perform rolling time-window computations using epi_slide()
perform group-level aggregation using sum_groups_epi_df()
detect and fill time gaps using complete.epi_df() and tsibble
perform geographic aggregation (not yet implemented)

Getting data into `epi_df` format

As in vignette("epiprocess"), we will fetch daily reported COVID-19 cases from CA, FL, NY, and TX (note: here we’re using new, not cumulative cases) using the epidatr package, and then convert this to epi_df format.

library(epiprocess)
library(dplyr)

The data is included in this package (via the epidatasets package) and can be loaded with:

edf <- cases_deaths_subset %>%
  select(geo_value, time_value, cases) %>%
  arrange(geo_value, time_value)

The data can also be fetched from the Delphi Epidata API with the following query:

library(epidatr)

d <- as.Date("2024-03-20")

edf <- pub_covidcast(
  source = "jhu-csse",
  signals = "confirmed_incidence_num",
  geo_type = "state",
  time_type = "day",
  geo_values = "ca,fl,ny,tx,ga,pa",
  time_values = epirange(20200301, 20211231),
  as_of = d
) %>%
  select(geo_value, time_value, cases = value) %>%
  arrange(geo_value, time_value) %>%
  as_epi_df(as_of = d)

The data has 2,684 rows and 3 columns.

Rolling computations using `epi_slide`

A very common operation in time series processing is aggregating the values of the time series by applying some function on a rolling time window of data points. The key tool that allows this is epi_slide(). The function always first makes sure to group the data by the grouping variables of the epi_df object, which includes the geo_value and possibly other_keys columns. It then applies the rolling slide computation inside each group.

The epi_slide() function has three ways to specify the computation to be performed:

by using a tidy evaluation approach
by passing a formula
by passing a function

Slide the tidy way

Usually, the most convenient way to setup a computation in epi_slide() is to pass in an expression for tidy evaluation. In this case, we can simply define the name of the new column directly as part of the expression, setting it equal to a computation in which we can access any columns of .x by name, just as we would in a call to, say, dplyr::mutate(). For example:

slide_output <- edf %>%
  epi_slide(cases_7sd = sd(cases, na.rm = TRUE), .window_size = 7)

As a simple sanity check, we visualize the 7-day trailing averages computed on top of the original counts:

library(ggplot2)

ggplot(slide_output, aes(x = time_value)) +
  geom_col(aes(y = cases, fill = geo_value), alpha = 0.5, show.legend = FALSE) +
  geom_line(aes(y = cases_7sd, col = geo_value), show.legend = FALSE) +
  facet_wrap(~geo_value, scales = "free_y") +
  scale_x_date(minor_breaks = "month", date_labels = "%b %y") +
  labs(x = "Date", y = "Reported COVID-19 cases")

As we can see from the Texas plot, the state moved to weekly reporting of COVID-19 cases in summer of 2021.

Note that without epi_slide(), the computation is much less convenient. For instance, a rough equivalent of the above computation would be the following, which is easy to get wrong:

edf %>%
  complete(geo_value, time_value = seq.Date(min(time_value), max(time_value), by = "day")) %>%
  arrange_canonical() %>%
  group_by(geo_value) %>%
  mutate(cases_7sd = slider::slide_dbl(cases, .f = sd, na.rm = TRUE, .before = 7, .after = 0))
#> An `epi_df` object, 4,026 x 4 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2024-03-20
#> 
#> # A tibble: 4,026 × 4
#> # Groups:   geo_value [6]
#>   geo_value time_value cases cases_7sd
#> * <chr>     <date>     <dbl>     <dbl>
#> 1 ca        2020-03-01     6     NA   
#> 2 ca        2020-03-02     4      1.41
#> 3 ca        2020-03-03     6      1.15
#> 4 ca        2020-03-04    11      2.99
#> 5 ca        2020-03-05    10      2.97
#> 6 ca        2020-03-06    18      5.08
#> # ℹ 4,020 more rows

Furthermore epi_slide() allows for selecting .ref_time_value, which the latter recipe does not support.

Slide with a function

We can also pass a function to the second argument in epi_slide(). In this case, the passed function .f must have the form function(x, g, t, ...), where

x is an epi_df with the same column names as the input epi_df
g is a one-row tibble containing the values of the grouping variables for the associated group, for instance g$geo_value
t is the ref_time_value for the current window
... are additional arguments

The same computation as above can be done with a function:

edf %>%
  epi_slide(.f = function(x, g, t) sd(x$cases, na.rm = TRUE), .window_size = 7)
#> An `epi_df` object, 4,026 x 4 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2024-03-20
#> 
#> # A tibble: 4,026 × 4
#> # Groups:   geo_value [6]
#>   geo_value time_value cases slide_value
#> * <chr>     <date>     <dbl>       <dbl>
#> 1 ca        2020-03-01     6       NA   
#> 2 ca        2020-03-02     4        1.41
#> 3 ca        2020-03-03     6        1.15
#> 4 ca        2020-03-04    11        2.99
#> 5 ca        2020-03-05    10        2.97
#> 6 ca        2020-03-06    18        5.08
#> # ℹ 4,020 more rows

`epi_slide()` with a formula

The same computation as above can be done with a formula, where all references to the columns must be made with the prefix .x$..., for instance:

edf %>%
  epi_slide(~ sd(.x$cases, na.rm = TRUE), .window_size = 7)
#> An `epi_df` object, 4,026 x 4 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2024-03-20
#> 
#> # A tibble: 4,026 × 4
#> # Groups:   geo_value [6]
#>   geo_value time_value cases slide_value
#> * <chr>     <date>     <dbl>       <dbl>
#> 1 ca        2020-03-01     6       NA   
#> 2 ca        2020-03-02     4        1.41
#> 3 ca        2020-03-03     6        1.15
#> 4 ca        2020-03-04    11        2.99
#> 5 ca        2020-03-05    10        2.97
#> 6 ca        2020-03-06    18        5.08
#> # ℹ 4,020 more rows

Note that the name of the column defaults to slide_value in the unnamed formula or function case. This can be adjusted with .new_col_name.

Rolling computations with multiple column outputs

If your formula (or function) returns a data.frame, then the columns of the data.frame will be unpacked into the resulting epi_df (in the sense of tidyr::unpack()). For example, the following computes the 7-day trailing average of daily cases as well as the the 7-day trailing standard deviation of daily cases:

edf %>%
  epi_slide(
    ~ data.frame(cases_mean = mean(.x$cases, na.rm = TRUE), cases_sd = sd(.x$cases, na.rm = TRUE)),
    .window_size = 7
  )
#> An `epi_df` object, 4,026 x 5 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2024-03-20
#> 
#> # A tibble: 4,026 × 5
#> # Groups:   geo_value [6]
#>   geo_value time_value cases cases_mean cases_sd
#> * <chr>     <date>     <dbl>      <dbl>    <dbl>
#> 1 ca        2020-03-01     6       6       NA   
#> 2 ca        2020-03-02     4       5        1.41
#> 3 ca        2020-03-03     6       5.33     1.15
#> 4 ca        2020-03-04    11       6.75     2.99
#> 5 ca        2020-03-05    10       7.4      2.97
#> 6 ca        2020-03-06    18       9.17     5.08
#> # ℹ 4,020 more rows

Optimized rolling mean and sums

For the two most common sliding operations, we offer two optimized versions: epi_slide_mean() and epi_slide_sum(). These are much faster than epi_slide(), so we recommend using them when you are only interested in the mean or sum of a column. The following computes the 7-day trailing mean of daily cases:

edf %>%
  group_by(geo_value) %>%
  epi_slide_mean("cases", .window_size = 7, na.rm = TRUE)
#> An `epi_df` object, 4,026 x 4 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2024-03-20
#> 
#> # A tibble: 4,026 × 4
#> # Groups:   geo_value [6]
#>   geo_value time_value cases slide_value_cases
#> * <chr>     <date>     <dbl>             <dbl>
#> 1 ca        2020-03-01     6              6   
#> 2 ca        2020-03-02     4              5   
#> 3 ca        2020-03-03     6              5.33
#> 4 ca        2020-03-04    11              6.75
#> 5 ca        2020-03-05    10              7.4 
#> 6 ca        2020-03-06    18              9.17
#> # ℹ 4,020 more rows
edf %>%
  group_by(geo_value) %>%
  epi_slide_sum("cases", .window_size = 7, na.rm = TRUE)
#> An `epi_df` object, 4,026 x 4 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2024-03-20
#> 
#> # A tibble: 4,026 × 4
#> # Groups:   geo_value [6]
#>   geo_value time_value cases slide_value_cases
#> * <chr>     <date>     <dbl>             <dbl>
#> 1 ca        2020-03-01     6                 6
#> 2 ca        2020-03-02     4                10
#> 3 ca        2020-03-03     6                16
#> 4 ca        2020-03-04    11                27
#> 5 ca        2020-03-05    10                37
#> 6 ca        2020-03-06    18                55
#> # ℹ 4,020 more rows

Running a forecaster on a sliding window of data

The natural next step is to use the sliding window to forecast future values. However to do this correctly, we should make sure that our data is historically accurate. The data structure we use for that is the epi_archive and the analogous slide function is epix_slide(). To read further along this train of thought, see vignette("epi_archive").

Adding more keys to an `epi_df` and aggregating groups with `sum_groups_epi_df`

An epi_df object can have more key columns than just geo_value and time_value. For example, if we have demographic attributes like age group, we can add this as a key column. We can then aggregate the data by these key columns using sum_groups_epi_df(). Let’s use influenza hospitalization rate data from the CDC system FluSurv as an example. We can get it from the Delphi Epidata API

library(epidatr)
flu_data <- pub_flusurv(
  locations = "ca",
  epiweeks = epirange(201801, 202001),
) %>%
  select(location, epiweek, issue, rate_age_0, rate_age_1, rate_age_2, rate_age_3, rate_age_4) %>%
  tidyr::pivot_longer(cols = starts_with("rate_age_"), names_to = "age_group", values_to = "rate")
flu_data
#> # A tibble: 305 × 5
#>   location epiweek    issue      age_group   rate
#>   <chr>    <date>     <date>     <chr>      <dbl>
#> 1 CA       2017-12-31 2018-12-30 rate_age_0   4.4
#> 2 CA       2017-12-31 2018-12-30 rate_age_1   1.7
#> 3 CA       2017-12-31 2018-12-30 rate_age_2   2.7
#> 4 CA       2017-12-31 2018-12-30 rate_age_3  18.7
#> 5 CA       2017-12-31 2018-12-30 rate_age_4  99.7
#> 6 CA       2018-01-07 2019-01-06 rate_age_0   4.4
#> # ℹ 299 more rows

We can now convert this data to an epi_df object and set the age_group column as an additional group key:

flu_data <- flu_data %>% as_epi_df(other_keys = "age_group", as_of = as.Date("2024-03-20"))
#> inferring time_value column.
#> inferring geo_value column.
flu_data
#> An `epi_df` object, 305 x 5 with metadata:
#> * geo_type  = state
#> * time_type = week
#> * other_keys = age_group
#> * as_of     = 2024-03-20
#> 
#> # A tibble: 305 × 5
#>   geo_value age_group  time_value issue       rate
#> * <chr>     <chr>      <date>     <date>     <dbl>
#> 1 CA        rate_age_0 2017-12-31 2018-12-30   4.4
#> 2 CA        rate_age_1 2017-12-31 2018-12-30   1.7
#> 3 CA        rate_age_2 2017-12-31 2018-12-30   2.7
#> 4 CA        rate_age_3 2017-12-31 2018-12-30  18.7
#> 5 CA        rate_age_4 2017-12-31 2018-12-30  99.7
#> 6 CA        rate_age_0 2018-01-07 2019-01-06   4.4
#> # ℹ 299 more rows

Note that the epi_df object now has an additional key column age_group. This means that there should only be one row for each combination of geo_value, time_value, and age_group in the dataset (this is enforced at construction time).

Now we can aggregate the data by age_group, if we want to compute the total:

group_cols <- key_colnames(exclude = "age_group")
flu_data %>%
  sum_groups_epi_df("rate", group_cols = group_cols)
#> An `epi_df` object, 61 x 3 with metadata:
#> * geo_type  = custom
#> * time_type = week
#> * as_of     = 2024-03-20
#> 
#> # A tibble: 61 × 3
#>   geo_value time_value  rate
#> * <chr>     <date>     <dbl>
#> 1 total     2017-12-31 127. 
#> 2 total     2018-01-07  76.7
#> 3 total     2018-01-14  57.8
#> 4 total     2018-01-21  37.9
#> 5 total     2018-01-28  30  
#> 6 total     2018-02-04  22.8
#> # ℹ 55 more rows

Detecting and filling time gaps with `complete.epi_df`

Sometimes you may have missing data in your time series. This can be due to actual missing data, or it can be due to the fact that the data is only reported on certain days. In the latter case, it is often useful to fill in the missing data with explicit zeros. This can be done with the complete.epi_df() function.

First, let’s create a data set with some missing data. We will reuse the dataset edf from above, but modify it slightly.

edf_missing <- edf %>%
  filter(geo_value %in% c("ca", "tx")) %>%
  group_by(geo_value) %>%
  slice(1:3, 5:6)

edf_missing %>%
  print(n = 10)
#> An `epi_df` object, 10 x 3 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2024-03-20
#> 
#> # A tibble: 10 × 3
#> # Groups:   geo_value [2]
#>    geo_value time_value cases
#>  * <chr>     <date>     <dbl>
#>  1 ca        2020-03-01     6
#>  2 ca        2020-03-02     4
#>  3 ca        2020-03-03     6
#>  4 ca        2020-03-05    10
#>  5 ca        2020-03-06    18
#>  6 tx        2020-03-01     0
#>  7 tx        2020-03-02     0
#>  8 tx        2020-03-03     0
#>  9 tx        2020-03-05     3
#> 10 tx        2020-03-06     1

Now let’s fill in the missing data with explicit zeros:

edf_missing %>%
  complete(
    time_value = seq.Date(min(time_value), max(time_value), by = "day"),
    fill = list(cases = 0)
  ) %>%
  print(n = 12)
#> An `epi_df` object, 12 x 3 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2024-03-20
#> 
#> # A tibble: 12 × 3
#> # Groups:   geo_value [2]
#>    geo_value time_value cases
#>  * <chr>     <date>     <dbl>
#>  1 ca        2020-03-01     6
#>  2 ca        2020-03-02     4
#>  3 ca        2020-03-03     6
#>  4 ca        2020-03-04     0
#>  5 ca        2020-03-05    10
#>  6 ca        2020-03-06    18
#>  7 tx        2020-03-01     0
#>  8 tx        2020-03-02     0
#>  9 tx        2020-03-03     0
#> 10 tx        2020-03-04     0
#> 11 tx        2020-03-05     3
#> 12 tx        2020-03-06     1

Detecting and filling time gaps with `tsibble`

We can also use the tsibble package to detect and fill time gaps. We’ll work with county-level reported COVID-19 cases in MA and VT.

The data is included in this package (via the epidatasets package) and can be loaded with:

library(epiprocess)
library(dplyr)
library(readr)

x <- covid_incidence_county_subset

The data can also be fetched from the Delphi Epidata API with the following query:

library(epidatr)

d <- as.Date("2024-03-20")

# Get mapping between FIPS codes and county&state names:
y <- read_csv("https://github.com/cmu-delphi/covidcast/raw/c89e4d295550ba1540d64d2cc991badf63ad04e5/Python-packages/covidcast-py/covidcast/geo_mappings/county_census.csv", # nolint: line_length_linter
  col_types = c(
    FIPS = col_character(),
    CTYNAME = col_character(),
    STNAME = col_character()
  )
) %>%
  filter(STNAME %in% c("Massachusetts", "Vermont"), STNAME != CTYNAME) %>%
  select(geo_value = FIPS, county_name = CTYNAME, state_name = STNAME)

# Fetch only counties from Massachusetts and Vermont, then append names columns as well
x <- pub_covidcast(
  source = "jhu-csse",
  signals = "confirmed_incidence_num",
  geo_type = "county",
  time_type = "day",
  geo_values = paste(y$geo_value, collapse = ","),
  time_values = epirange(20200601, 20211231),
  as_of = d
) %>%
  select(geo_value, time_value, cases = value) %>%
  inner_join(y, by = "geo_value", relationship = "many-to-one", unmatched = c("error", "drop")) %>%
  as_epi_df(as_of = d)

The data contains 16,212 rows and 5 columns.

Converting to `tsibble` format

For manipulating and wrangling time series data, the tsibble already provides a host of useful tools. A tsibble object (formerly, of class tbl_ts) is basically a tibble (data frame) but with two specially-marked columns: an index column representing the time variable (defining an order from past to present), and a key column identifying a unique observational unit for each time point. In fact, the key can be made up of any number of columns, not just a single one.

In an epi_df object, the index variable is time_value, and the key variable is typically geo_value (though this need not always be the case: for example, if we have an age group variable as another column, then this could serve as a second key variable). The epiprocess package thus provides an implementation of as_tsibble() for epi_df objects, which sets these variables according to these defaults.

library(tsibble)

xt <- as_tsibble(x)
head(xt)
#> # A tsibble: 6 x 5 [1D]
#> # Key:       geo_value [1]
#>   geo_value time_value cases county_name       state_name   
#>   <chr>     <date>     <dbl> <chr>             <chr>        
#> 1 25001     2020-06-01     4 Barnstable County Massachusetts
#> 2 25001     2020-06-02     2 Barnstable County Massachusetts
#> 3 25001     2020-06-03     6 Barnstable County Massachusetts
#> 4 25001     2020-06-04     4 Barnstable County Massachusetts
#> 5 25001     2020-06-05     2 Barnstable County Massachusetts
#> 6 25001     2020-06-06     2 Barnstable County Massachusetts
key(xt)
#> [[1]]
#> geo_value
index(xt)
#> time_value
interval(xt)
#> <interval[1]>
#> [1] 1D

We can also set the key variable(s) directly in a call to as_tsibble(). Similar to SQL keys, if the key does not uniquely identify each time point (that is, the key and index together do not not uniquely identify each row), then as_tsibble() throws an error:

head(as_tsibble(x, key = "county_name"))
#> Error in `validate_tsibble()`:
#> ! A valid tsibble must have distinct rows identified by key and index.
#> ℹ Please use `duplicates()` to check the duplicated rows.

As we can see, there are duplicate county names between Massachusetts and Vermont, which caused the error.

head(duplicates(x, key = "county_name"))
#> # A tibble: 6 × 5
#>   geo_value time_value cases county_name     state_name   
#>   <chr>     <date>     <dbl> <chr>           <chr>        
#> 1 25009     2020-06-01    92 Essex County    Massachusetts
#> 2 25011     2020-06-01     0 Franklin County Massachusetts
#> 3 50009     2020-06-01     0 Essex County    Vermont      
#> 4 50011     2020-06-01     0 Franklin County Vermont      
#> 5 25009     2020-06-02    90 Essex County    Massachusetts
#> 6 25011     2020-06-02     0 Franklin County Massachusetts

Keying by both county name and state name, however, does work:

head(as_tsibble(x, key = c("county_name", "state_name")))
#> # A tsibble: 6 x 5 [1D]
#> # Key:       county_name, state_name [1]
#>   geo_value time_value cases county_name    state_name
#>   <chr>     <date>     <dbl> <chr>          <chr>     
#> 1 50001     2020-06-01     0 Addison County Vermont   
#> 2 50001     2020-06-02     0 Addison County Vermont   
#> 3 50001     2020-06-03     0 Addison County Vermont   
#> 4 50001     2020-06-04     0 Addison County Vermont   
#> 5 50001     2020-06-05     0 Addison County Vermont   
#> 6 50001     2020-06-06     1 Addison County Vermont

One of the major advantages of the tsibble package is its ability to handle implicit gaps in time series data. In other words, it can infer what time scale we’re interested in (say, daily data), and detect apparent gaps (say, when values are reported on January 1 and 3 but not January 2). We can subsequently use functionality to make these missing entries explicit, which will generally help avoid bugs in further downstream data processing tasks.

Let’s first remove certain dates from our data set to create gaps:

state_naming <- read_csv("https://github.com/cmu-delphi/covidcast/raw/c89e4d295550ba1540d64d2cc991badf63ad04e5/Python-packages/covidcast-py/covidcast/geo_mappings/state_census.csv", # nolint: line_length_linter
  col_types = c(NAME = col_character(), ABBR = col_character())
) %>%
  transmute(state_name = NAME, abbr = tolower(ABBR)) %>%
  as_tibble()

# First make geo value more readable for tables, plots, etc.
x <- x %>%
  inner_join(state_naming, by = "state_name", relationship = "many-to-one", unmatched = c("error", "drop")) %>%
  mutate(geo_value = paste(substr(county_name, 1, nchar(county_name) - 7), state_name, sep = ", ")) %>%
  select(geo_value, time_value, cases)

xt <- as_tsibble(x) %>% filter(cases >= 3)

The functions has_gaps(), scan_gaps(), count_gaps() in the tsibble package each provide useful summaries, in slightly different formats.

head(has_gaps(xt))
#> # A tibble: 6 × 2
#>   geo_value                 .gaps
#>   <chr>                     <lgl>
#> 1 Addison, Vermont          TRUE 
#> 2 Barnstable, Massachusetts TRUE 
#> 3 Bennington, Vermont       TRUE 
#> 4 Berkshire, Massachusetts  TRUE 
#> 5 Bristol, Massachusetts    TRUE 
#> 6 Caledonia, Vermont        TRUE
head(scan_gaps(xt))
#> # A tsibble: 6 x 2 [1D]
#> # Key:       geo_value [1]
#>   geo_value        time_value
#>   <chr>            <date>    
#> 1 Addison, Vermont 2020-08-28
#> 2 Addison, Vermont 2020-08-29
#> 3 Addison, Vermont 2020-08-30
#> 4 Addison, Vermont 2020-08-31
#> 5 Addison, Vermont 2020-09-01
#> 6 Addison, Vermont 2020-09-02
head(count_gaps(xt))
#> # A tibble: 6 × 4
#>   geo_value        .from      .to           .n
#>   <chr>            <date>     <date>     <int>
#> 1 Addison, Vermont 2020-08-28 2020-10-04    38
#> 2 Addison, Vermont 2020-10-06 2020-10-23    18
#> 3 Addison, Vermont 2020-10-25 2020-11-04    11
#> 4 Addison, Vermont 2020-11-06 2020-11-10     5
#> 5 Addison, Vermont 2020-11-14 2020-11-18     5
#> 6 Addison, Vermont 2020-11-20 2020-11-20     1

We can also visualize the patterns of missingness:

library(ggplot2)

ggplot(
  count_gaps(xt),
  aes(
    x = reorder(geo_value, desc(geo_value)),
    color = geo_value
  )
) +
  geom_linerange(aes(ymin = .from, ymax = .to)) +
  geom_point(aes(y = .from)) +
  geom_point(aes(y = .to)) +
  coord_flip() +
  labs(x = "County", y = "Date") +
  theme(legend.position = "none")

Using the fill_gaps() function from tsibble, we can replace all gaps by an explicit value. The default is NA, but in the current case, where missingness is not at random but rather represents a small value that was censored (only a hypothetical with COVID-19 reports, but certainly a real phenomenon that occurs in other signals), it is better to replace it by zero, which is what we do here. (Other approaches, such as LOCF: last observation carried forward in time, could be accomplished by first filling with NA values and then following up with a second call to tidyr::fill().)

fill_gaps(xt, cases = 0) %>%
  head()
#> # A tsibble: 6 x 3 [1D]
#> # Key:       geo_value [1]
#>   geo_value        time_value cases
#>   <chr>            <date>     <dbl>
#> 1 Addison, Vermont 2020-08-27     3
#> 2 Addison, Vermont 2020-08-28     0
#> 3 Addison, Vermont 2020-08-29     0
#> 4 Addison, Vermont 2020-08-30     0
#> 5 Addison, Vermont 2020-08-31     0
#> 6 Addison, Vermont 2020-09-01     0

Note that the time series for Addison, VT only starts on August 27, 2020, even though the original (uncensored) data set itself was drawn from a period that went back to June 6, 2020. By setting .full = TRUE, we can at zero-fill over the entire span of the observed (censored) data.

xt_filled <- fill_gaps(xt, cases = 0, .full = TRUE)

head(xt_filled)
#> # A tsibble: 6 x 3 [1D]
#> # Key:       geo_value [1]
#>   geo_value        time_value cases
#>   <chr>            <date>     <dbl>
#> 1 Addison, Vermont 2020-06-01     0
#> 2 Addison, Vermont 2020-06-02     0
#> 3 Addison, Vermont 2020-06-03     0
#> 4 Addison, Vermont 2020-06-04     0
#> 5 Addison, Vermont 2020-06-05     0
#> 6 Addison, Vermont 2020-06-06     0

Explicit imputation for missingness (zero-filling in our case) can be important for protecting against bugs in all sorts of downstream tasks. For example, even something as simple as a 7-day trailing average is complicated by missingness. The function epi_slide() looks for all rows within a window of 7 days anchored on the right at the reference time point (when .window_size = 7). But when some days in a given week are missing because they were censored because they had small case counts, taking an average of the observed case counts can be misleading and is unintentionally biased upwards. Meanwhile, running epi_slide() on the zero-filled data brings these trailing averages (appropriately) downwards, as we can see inspecting Plymouth, MA around July 1, 2021.

xt %>%
  as_epi_df(as_of = as.Date("2024-03-20")) %>%
  group_by(geo_value) %>%
  epi_slide(cases_7dav = mean(cases), .window_size = 7) %>%
  ungroup() %>%
  filter(
    geo_value == "Plymouth, MA",
    abs(time_value - as.Date("2021-07-01")) <= 3
  ) %>%
  print(n = 7)
#> An `epi_df` object, 0 x 4 with metadata:
#> * geo_type  = custom
#> * time_type = day
#> * as_of     = 2024-03-20
#> 
#> # A tibble: 0 × 4
#> # ℹ 4 variables: geo_value <chr>, time_value <date>, cases <dbl>,
#> #   cases_7dav <dbl>

xt_filled %>%
  as_epi_df(as_of = as.Date("2024-03-20")) %>%
  group_by(geo_value) %>%
  epi_slide(cases_7dav = mean(cases), .window_size = 7) %>%
  ungroup() %>%
  filter(
    geo_value == "Plymouth, MA",
    abs(time_value - as.Date("2021-07-01")) <= 3
  ) %>%
  print(n = 7)
#> An `epi_df` object, 0 x 4 with metadata:
#> * geo_type  = custom
#> * time_type = day
#> * as_of     = 2024-03-20
#> 
#> # A tibble: 0 × 4
#> # ℹ 4 variables: geo_value <chr>, time_value <date>, cases <dbl>,
#> #   cases_7dav <dbl>

Geographic aggregation

We do not yet provide tools for geographic aggregation in epiprocess. However, we have some Python geocoding utilities available. Reach out to us if this is functionality you would like to see us add to epiprocess.

Attribution

The percent_cli data is a modified part of the COVIDcast Epidata API Doctor Visits data. This dataset is licensed under the terms of the Creative Commons Attribution 4.0 International license. Copyright Delphi Research Group at Carnegie Mellon University 2020.

This document contains a dataset that is a modified part of the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University as republished in the COVIDcast Epidata API. This data set is licensed under the terms of the Creative Commons Attribution 4.0 International license by the Johns Hopkins University on behalf of its Center for Systems Science in Engineering. Copyright Johns Hopkins University 2020.

From the COVIDcast Epidata API: These signals are taken directly from the JHU CSSE COVID-19 GitHub repository without changes.

Getting data into epi_df format

Rolling computations using epi_slide