Optimized slide functions for common cases — epi_slide

epi_slide_opt allows sliding an n-timestep data.table::froll or slider::summary-slide function over variables in an epi_df object. These functions tend to be much faster than epi_slide(). See vignette("epi_df") for more examples.

epi_slide_mean is a wrapper around epi_slide_opt with .f = datatable::frollmean.

epi_slide_sum is a wrapper around epi_slide_opt with .f = datatable::frollsum.

Usage

epi_slide_opt(
  .x,
  .col_names,
  .f,
  ...,
  .window_size = NULL,
  .align = c("right", "center", "left"),
  .prefix = NULL,
  .suffix = NULL,
  .new_col_names = NULL,
  .ref_time_values = NULL,
  .all_rows = FALSE
)

epi_slide_mean(
  .x,
  .col_names,
  ...,
  .window_size = NULL,
  .align = c("right", "center", "left"),
  .prefix = NULL,
  .suffix = NULL,
  .new_col_names = NULL,
  .ref_time_values = NULL,
  .all_rows = FALSE
)

epi_slide_sum(
  .x,
  .col_names,
  ...,
  .window_size = NULL,
  .align = c("right", "center", "left"),
  .prefix = NULL,
  .suffix = NULL,
  .new_col_names = NULL,
  .ref_time_values = NULL,
  .all_rows = FALSE
)

Arguments

.x

An epi_df object. If ungrouped, we temporarily group by geo_value and any columns in other_keys. If grouped, we make sure the grouping is by geo_value and other_keys.

.col_names

<tidy-select> An unquoted column name (e.g., cases), multiple column names (e.g., c(cases, deaths)), other tidy-select expression, or a vector of characters (e.g. c("cases", "deaths")). Variable names can be used as if they were positions in the data frame, so expressions like x:y can be used to select a range of variables.

The tidy-selection renaming interface is not supported, and cannot be used to provide output column names; if you want to customize the output column names, use dplyr::rename after the slide.

.f

Function; together with ... specifies the computation to slide. .f must be one of data.table's rolling functions (frollmean, frollsum, frollapply. See data.table::roll) or one of slider's specialized sliding functions (slide_mean, slide_sum, etc. See slider::summary-slide).

The optimized data.table and slider functions can't be directly passed as the computation function in epi_slide without careful handling to make sure each computation group is made up of the .window_size dates rather than .window_size points. epi_slide_opt (and wrapper functions epi_slide_mean and epi_slide_sum) take care of window completion automatically to prevent associated errors.

...

Additional arguments to pass to the slide computation .f, for example, algo or na.rm in data.table functions. You don't need to specify .x, .window_size, or .align (or before/after for slider functions).

.window_size

The size of the sliding window. The accepted values depend on the type of the time_value column in .x:

if time type is Date and the cadence is daily, then .window_size can be an integer (which will be interpreted in units of days) or a difftime with units "days"
if time type is Date and the cadence is weekly, then .window_size must be a difftime with units "weeks"
if time type is a yearmonth or an integer, then .window_size must be an integer

.align

The alignment of the sliding window.

If "right" (default), then the window has its end at the reference time. This is likely the most common use case, e.g. .window_size=7 and .align="right" slides over the past week of data.
If "left", then the window has its start at the reference time.
If "center", then the window is centered at the reference time. If the window size is odd, then the window will have floor(window_size/2) points before and after the reference time; if the window size is even, then the window will be asymmetric and have one more value before the reference time than after.

.prefix

Optional glue::glue format string; name the slide result column(s) by attaching this prefix to the corresponding input column(s). Some shorthand is supported for basing the output names on .window_size or other arguments; see "Prefix and suffix shorthand" below.

.suffix

Optional glue::glue format string; like .prefix. The default naming behavior is equivalent to .suffix = "_{.n}{.time_unit_abbr}{.align_abbr}{.f_abbr}". Can be used in combination with .prefix.

.new_col_names

Optional character vector with length matching the number of input columns from .col_names; name the slide result column(s) with these names. Cannot be used in combination with .prefix and/or .suffix.

.ref_time_values

The time values at which to compute the slides values. By default, this is all the unique time values in .x.

.all_rows

If .all_rows = FALSE, the default, then the output epi_df will have only the rows that had a time_value in .ref_time_values. Otherwise, all the rows from .x are included by with a missing value marker (typically NA, but more technically the result of vctrs::vec_cast-ing NA to the type of the slide computation output).

Value

An epi_df object with one or more new slide computation columns added. It will be ungrouped if .x was ungrouped, and have the same groups as .x if .x was grouped.

Prefix and suffix shorthand

glue::glue format strings specially interpret content within curly braces. E.g., glue::glue("ABC{2 + 2}") evaluates to "ABC4". For .prefix and .suffix, we provide glue with some additional variable bindings:

{.n} will be the number of time steps in the computation corresponding to the .window_size.
{.time_unit_abbr} will be a lower-case letter corresponding to the time_type of .x
{.align_abbr} will be "" if .align is the default of "right"; otherwise, it will be the first letter of .align
{.f_abbr} will be a character vector containing a short abbreviation for .f factoring in the input column type(s) for .col_names

Examples

library(dplyr)

# Add a column (`cases_7dsum`) containing a 7-day trailing sum on `cases`:
cases_deaths_subset %>%
  select(geo_value, time_value, cases) %>%
  epi_slide_sum(cases, .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
#>    geo_value time_value cases cases_7dsum
#>  * <chr>     <date>     <dbl>       <dbl>
#>  1 ca        2020-03-01     6          NA
#>  2 ca        2020-03-02     4          NA
#>  3 ca        2020-03-03     6          NA
#>  4 ca        2020-03-04    11          NA
#>  5 ca        2020-03-05    10          NA
#>  6 ca        2020-03-06    18          NA
#>  7 ca        2020-03-07    26          81
#>  8 ca        2020-03-08    19          94
#>  9 ca        2020-03-09    23         113
#> 10 ca        2020-03-10    22         129
#> # ℹ 4,016 more rows

# Add a column (`cases_rate_7dav`) containing a 7-day trailing average on `case_rate`:
covid_case_death_rates_extended %>%
  epi_slide_mean(case_rate, .window_size = 7)
#> An `epi_df` object, 37,576 x 5 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2022-05-31
#> 
#> # A tibble: 37,576 × 5
#>    geo_value time_value case_rate death_rate case_rate_7dav
#>  * <chr>     <date>         <dbl>      <dbl>          <dbl>
#>  1 ak        2020-03-01         0          0             NA
#>  2 ak        2020-03-02         0          0             NA
#>  3 ak        2020-03-03         0          0             NA
#>  4 ak        2020-03-04         0          0             NA
#>  5 ak        2020-03-05         0          0             NA
#>  6 ak        2020-03-06         0          0             NA
#>  7 ak        2020-03-07         0          0              0
#>  8 ak        2020-03-08         0          0              0
#>  9 ak        2020-03-09         0          0              0
#> 10 ak        2020-03-10         0          0              0
#> # ℹ 37,566 more rows

# Use a less common specialized slide function:
cases_deaths_subset %>%
  epi_slide_opt(cases, slider::slide_min, .window_size = 7)
#> An `epi_df` object, 4,026 x 7 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2024-03-20
#> 
#> # A tibble: 4,026 × 7
#>    geo_value time_value case_rate_7d_av death_rate_7d_av cases cases_7d_av
#>  * <chr>     <date>               <dbl>            <dbl> <dbl>       <dbl>
#>  1 ca        2020-03-01         0.00327         0            6        1.29
#>  2 ca        2020-03-02         0.00435         0            4        1.71
#>  3 ca        2020-03-03         0.00617         0            6        2.43
#>  4 ca        2020-03-04         0.00980         0.000363    11        3.86
#>  5 ca        2020-03-05         0.0134          0.000363    10        5.29
#>  6 ca        2020-03-06         0.0200          0.000363    18        7.86
#>  7 ca        2020-03-07         0.0294          0.000363    26       11.6 
#>  8 ca        2020-03-08         0.0341          0.000363    19       13.4 
#>  9 ca        2020-03-09         0.0410          0.000726    23       16.1 
#> 10 ca        2020-03-10         0.0468          0.000726    22       18.4 
#> # ℹ 4,016 more rows
#> # ℹ 1 more variable: cases_7dmin <dbl>

# Specify output column names and/or a naming scheme:
cases_deaths_subset %>%
  select(geo_value, time_value, cases) %>%
  group_by(geo_value) %>%
  epi_slide_sum(cases, .window_size = 7, .new_col_names = "case_sum") %>%
  ungroup()
#> 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
#>    geo_value time_value cases case_sum
#>  * <chr>     <date>     <dbl>    <dbl>
#>  1 ca        2020-03-01     6       NA
#>  2 ca        2020-03-02     4       NA
#>  3 ca        2020-03-03     6       NA
#>  4 ca        2020-03-04    11       NA
#>  5 ca        2020-03-05    10       NA
#>  6 ca        2020-03-06    18       NA
#>  7 ca        2020-03-07    26       81
#>  8 ca        2020-03-08    19       94
#>  9 ca        2020-03-09    23      113
#> 10 ca        2020-03-10    22      129
#> # ℹ 4,016 more rows
cases_deaths_subset %>%
  select(geo_value, time_value, cases) %>%
  group_by(geo_value) %>%
  epi_slide_sum(cases, .window_size = 7, .prefix = "sum_") %>%
  ungroup()
#> 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
#>    geo_value time_value cases sum_cases
#>  * <chr>     <date>     <dbl>     <dbl>
#>  1 ca        2020-03-01     6        NA
#>  2 ca        2020-03-02     4        NA
#>  3 ca        2020-03-03     6        NA
#>  4 ca        2020-03-04    11        NA
#>  5 ca        2020-03-05    10        NA
#>  6 ca        2020-03-06    18        NA
#>  7 ca        2020-03-07    26        81
#>  8 ca        2020-03-08    19        94
#>  9 ca        2020-03-09    23       113
#> 10 ca        2020-03-10    22       129
#> # ℹ 4,016 more rows

# Additional settings can be sent to the {data.table} and {slider} functions
# via `...`. This example passes some arguments to `frollmean` settings for
# speed, accuracy, and to allow partially-missing windows:
covid_case_death_rates_extended %>%
  epi_slide_mean(
    case_rate,
    .window_size = 7,
    na.rm = TRUE, algo = "exact", hasNA = TRUE
  )
#> An `epi_df` object, 37,576 x 5 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2022-05-31
#> 
#> # A tibble: 37,576 × 5
#>    geo_value time_value case_rate death_rate case_rate_7dav
#>  * <chr>     <date>         <dbl>      <dbl>          <dbl>
#>  1 ak        2020-03-01         0          0              0
#>  2 ak        2020-03-02         0          0              0
#>  3 ak        2020-03-03         0          0              0
#>  4 ak        2020-03-04         0          0              0
#>  5 ak        2020-03-05         0          0              0
#>  6 ak        2020-03-06         0          0              0
#>  7 ak        2020-03-07         0          0              0
#>  8 ak        2020-03-08         0          0              0
#>  9 ak        2020-03-09         0          0              0
#> 10 ak        2020-03-10         0          0              0
#> # ℹ 37,566 more rows

# If the more specialized possibilities for `.f` don't cover your needs, you
# can use `epi_slide_opt` with `.f = data.table::frollapply` to apply a
# custom function at the cost of more computation time. See also `epi_slide`
# if you need something even more general.
cases_deaths_subset %>%
  select(geo_value, time_value, case_rate_7d_av, death_rate_7d_av) %>%
  epi_slide_opt(c(case_rate_7d_av, death_rate_7d_av),
    data.table::frollapply,
    FUN = median, .window_size = 28,
    .suffix = "_{.n}{.time_unit_abbr}_median"
  ) %>%
  print(n = 40)
#> An `epi_df` object, 4,026 x 6 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2024-03-20
#> 
#> # A tibble: 4,026 × 6
#>    geo_value time_value case_rate_7d_av death_rate_7d_av case_rate_7d_av_28d_m…¹
#>  * <chr>     <date>               <dbl>            <dbl>                   <dbl>
#>  1 ca        2020-03-01         0.00327         0                        NA     
#>  2 ca        2020-03-02         0.00435         0                        NA     
#>  3 ca        2020-03-03         0.00617         0                        NA     
#>  4 ca        2020-03-04         0.00980         0.000363                 NA     
#>  5 ca        2020-03-05         0.0134          0.000363                 NA     
#>  6 ca        2020-03-06         0.0200          0.000363                 NA     
#>  7 ca        2020-03-07         0.0294          0.000363                 NA     
#>  8 ca        2020-03-08         0.0341          0.000363                 NA     
#>  9 ca        2020-03-09         0.0410          0.000726                 NA     
#> 10 ca        2020-03-10         0.0468          0.000726                 NA     
#> 11 ca        2020-03-11         0.0519          0.00109                  NA     
#> 12 ca        2020-03-12         0.0639          0.00145                  NA     
#> 13 ca        2020-03-13         0.0766          0.00109                  NA     
#> 14 ca        2020-03-14         0.0875          0.00145                  NA     
#> 15 ca        2020-03-15         0.0947          0.00181                  NA     
#> 16 ca        2020-03-16         0.144           0.00145                  NA     
#> 17 ca        2020-03-17         0.167           0.00218                  NA     
#> 18 ca        2020-03-18         0.221           0.00435                  NA     
#> 19 ca        2020-03-19         0.275           0.00544                  NA     
#> 20 ca        2020-03-20         0.350           0.00689                  NA     
#> 21 ca        2020-03-21         0.385           0.00762                  NA     
#> 22 ca        2020-03-22         0.480           0.0109                   NA     
#> 23 ca        2020-03-23         0.559           0.0123                   NA     
#> 24 ca        2020-03-24         0.684           0.0156                   NA     
#> 25 ca        2020-03-25         0.806           0.0181                   NA     
#> 26 ca        2020-03-26         1.05            0.0218                   NA     
#> 27 ca        2020-03-27         1.20            0.0279                   NA     
#> 28 ca        2020-03-28         2.22            0.0588                    0.0911
#> 29 ca        2020-03-29         1.38            0.0352                    0.119 
#> 30 ca        2020-03-30         1.74            0.0396                    0.155 
#> 31 ca        2020-03-31         2.00            0.0432                    0.194 
#> 32 ca        2020-04-01         2.27            0.0483                    0.248 
#> 33 ca        2020-04-02         2.50            0.0566                    0.312 
#> 34 ca        2020-04-03         2.74            0.0639                    0.368 
#> 35 ca        2020-04-04         1.93            0.0381                    0.433 
#> 36 ca        2020-04-05         3.26            0.0762                    0.519 
#> 37 ca        2020-04-06         3.31            0.0806                    0.621 
#> 38 ca        2020-04-07         3.30            0.0922                    0.745 
#> 39 ca        2020-04-08         3.38            0.105                     0.928 
#> 40 ca        2020-04-09         3.18            0.110                     1.13  
#> # ℹ 3,986 more rows
#> # ℹ abbreviated name: ¹case_rate_7d_av_28d_median
#> # ℹ 1 more variable: death_rate_7d_av_28d_median <dbl>