Optimized slide functions for common cases

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"),
  .ref_time_values = NULL,
  .all_rows = FALSE
)

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

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

Arguments

.x

An epi_df object. If ungrouped, we 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.

.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.

Examples

# Compute a 7-day trailing average on cases.
cases_deaths_subset %>%
  group_by(geo_value) %>%
  epi_slide_opt(cases, .f = data.table::frollmean, .window_size = 7) %>%
  dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases)
#> 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_7dav
#>  * <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       11.6
#>  8 ca        2020-03-08    19       13.4
#>  9 ca        2020-03-09    23       16.1
#> 10 ca        2020-03-10    22       18.4
#> # ℹ 4,016 more rows

# Same as above, but adjust `frollmean` settings for speed, accuracy, and
# to allow partially-missing windows.
cases_deaths_subset %>%
  group_by(geo_value) %>%
  epi_slide_opt(
    cases,
    .f = data.table::frollmean, .window_size = 7,
    algo = "exact", hasNA = TRUE, na.rm = TRUE
  ) %>%
  dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases)
#> 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_7dav
#>  * <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
#>  7 ca        2020-03-07    26      11.6 
#>  8 ca        2020-03-08    19      13.4 
#>  9 ca        2020-03-09    23      16.1 
#> 10 ca        2020-03-10    22      18.4 
#> # ℹ 4,016 more rows
# Compute a 7-day trailing average on cases.
cases_deaths_subset %>%
  group_by(geo_value) %>%
  epi_slide_mean(cases, .window_size = 7) %>%
  dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases)
#> 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_7dav
#>  * <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       11.6
#>  8 ca        2020-03-08    19       13.4
#>  9 ca        2020-03-09    23       16.1
#> 10 ca        2020-03-10    22       18.4
#> # ℹ 4,016 more rows

# Same as above, but adjust `frollmean` settings for speed, accuracy, and
# to allow partially-missing windows.
cases_deaths_subset %>%
  group_by(geo_value) %>%
  epi_slide_mean(
    cases,
    .window_size = 7,
    na.rm = TRUE, algo = "exact", hasNA = TRUE
  ) %>%
  dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases)
#> 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_7dav
#>  * <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
#>  7 ca        2020-03-07    26      11.6 
#>  8 ca        2020-03-08    19      13.4 
#>  9 ca        2020-03-09    23      16.1 
#> 10 ca        2020-03-10    22      18.4 
#> # ℹ 4,016 more rows
# Compute a 7-day trailing sum on cases.
cases_deaths_subset %>%
  group_by(geo_value) %>%
  epi_slide_sum(cases, .window_size = 7) %>%
  dplyr::select(geo_value, time_value, cases, cases_7dsum = slide_value_cases)
#> 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_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

Usage

Arguments

Value

See also

Examples