slide_mean and slide_sum

NAMESPACE

@@ -23,6 +23,8 @@ S3method(bake,step_adjust_latency)
 S3method(bake,step_epi_ahead)
 S3method(bake,step_epi_lag)
 S3method(bake,step_epi_slide)
+S3method(bake,step_epi_slide_mean)
+S3method(bake,step_epi_slide_sum)
 S3method(bake,step_growth_rate)
 S3method(bake,step_lag_difference)
 S3method(bake,step_population_scaling)
@@ -62,6 +64,8 @@ S3method(prep,step_adjust_latency)
 S3method(prep,step_epi_ahead)
 S3method(prep,step_epi_lag)
 S3method(prep,step_epi_slide)
+S3method(prep,step_epi_slide_mean)
+S3method(prep,step_epi_slide_sum)
 S3method(prep,step_growth_rate)
 S3method(prep,step_lag_difference)
 S3method(prep,step_population_scaling)
@@ -92,6 +96,8 @@ S3method(print,step_adjust_latency)
 S3method(print,step_epi_ahead)
 S3method(print,step_epi_lag)
 S3method(print,step_epi_slide)
+S3method(print,step_epi_slide_mean)
+S3method(print,step_epi_slide_sum)
 S3method(print,step_growth_rate)
 S3method(print,step_lag_difference)
 S3method(print,step_naomit)
@@ -202,6 +208,8 @@ export(step_epi_ahead)
 export(step_epi_lag)
 export(step_epi_naomit)
 export(step_epi_slide)
+export(step_epi_slide_mean)
+export(step_epi_slide_sum)
 export(step_growth_rate)
 export(step_lag_difference)
 export(step_population_scaling)
@@ -272,6 +280,8 @@ importFrom(glue,glue)
 importFrom(hardhat,extract_recipe)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
+importFrom(lubridate,is.period)
+importFrom(lubridate,time_length)
 importFrom(magrittr,"%>%")
 importFrom(magrittr,extract2)
 importFrom(recipes,bake)

R/step_epi_slide_mean.R

@@ -0,0 +1,188 @@
+#' Calculate a rolling mean
+#'
+#' `step_epi_slide_mean()` creates a *specification* of a recipe step that will
+#'   generate one or more new columns of derived data by computing a sliding
+#'   mean along existing data.
+#'
+#'
+#' @inheritParams step_epi_lag
+#' @param before,after non-negative integers.
+#'   How far `before` and `after` each `time_value` should
+#'   the sliding window extend? Any value provided for either
+#'   argument must be a single, non-`NA`, non-negative,
+#'   [integer-compatible][vctrs::vec_cast] number of time steps. Endpoints of
+#'   the window are inclusive. Common settings:
+#'   * For trailing/right-aligned windows from `time_value - time_step(k)` to
+#'   `time_value`, use `before=k, after=0`. This is the most likely use case
+#'   for the purposes of forecasting.
+#'   * For center-aligned windows from `time_value - time_step(k)` to
+#'   `time_value + time_step(k)`, use `before=k, after=k`.
+#'   * For leading/left-aligned windows from `time_value` to
+#'   `time_value + time_step(k)`, use `after=k, after=0`.
+#'
+#'   You may also pass a [lubridate::period], like `lubridate::weeks(1)` or a
+#'   character string that is coercible to a [lubridate::period], like
+#'   `"2 weeks"`.
+#' @template step-return
+#'
+#' @export
+#' @examples
+#' library(dplyr)
+#' jhu <- case_death_rate_subset %>%
+#'   filter(time_value >= as.Date("2021-01-01"), geo_value %in% c("ca", "ny"))
+#' rec <- epi_recipe(jhu) %>%
+#'   step_epi_slide(case_rate, death_rate,
+#'     .f = \(x) mean(x, na.rm = TRUE),
+#'     before = 6L
+#'   )
+#' bake(prep(rec, jhu), new_data = NULL)
+step_epi_slide_mean <-
+  function(recipe,
+           ...,
+           before = 0L,
+           after = 0L,
+           role = "predictor",
+           prefix = "epi_slide_mean_",
+           skip = FALSE,
+           id = rand_id("epi_slide_mean")) {
+    if (!is_epi_recipe(recipe)) {
+      rlang::abort("This recipe step can only operate on an `epi_recipe`.")
+    }
+    arg_is_scalar(before, after)
+    before <- try_period(before)
+    after <- try_period(after)
+    arg_is_chr_scalar(role, prefix, id)
+    arg_is_lgl_scalar(skip)
+    add_step(
+      recipe,
+      step_epi_slide_mean_new(
+        terms = enquos(...),
+        before = before,
+        after = after,
+        role = role,
+        trained = FALSE,
+        prefix = prefix,
+        keys = epi_keys(recipe),
+        columns = NULL,
+        skip = skip,
+        id = id
+      )
+    )
+  }
+
+
+step_epi_slide_mean_new <-
+  function(terms,
+           before,
+           after,
+           role,
+           trained,
+           prefix,
+           keys,
+           columns,
+           skip,
+           id) {
+    step(
+      subclass = "epi_slide_mean",
+      terms = terms,
+      before = before,
+      after = after,
+      role = role,
+      trained = trained,
+      prefix = prefix,
+      keys = keys,
+      columns = columns,
+      skip = skip,
+      id = id
+    )
+  }
+
+
+
+#' @export
+prep.step_epi_slide_mean <- function(x, training, info = NULL, ...) {
+  col_names <- recipes::recipes_eval_select(x$terms, data = training, info = info)
+
+  check_type(training[, col_names], types = c("double", "integer"))
+  time_type <- attributes(training)$metadata$time_type
+  before <- lubridate_period_to_integer(x$before, time_type)
+  after <- lubridate_period_to_integer(x$after, time_type)
+  step_epi_slide_mean_new(
+    terms = x$terms,
+    before = before,
+    after = after,
+    role = x$role,
+    trained = TRUE,
+    prefix = x$prefix,
+    keys = x$keys,
+    columns = col_names,
+    skip = x$skip,
+    id = x$id
+  )
+}
+
+#' lubridate converts to seconds by default, and as.integer doesn't throw errors if it isn't actually an integer
+#' @importFrom lubridate time_length is.period
+#' @keywords internal
+lubridate_period_to_integer <- function(value, time_type) {
+  if (is.period(value)) {
+    if (time_type == "day") {
+      value <- time_length(value, unit = "day")
+    } else if (time_type == "week") {
+      value <- time_length(value, unit = "week")
+    } else {
+      cli_abort(
+        "unsupported time type of {time_type}. Use integer instead of lubridate period.",
+        class = "epipredict__step_epi_slide_mean__unsupported_error"
+      )
+    }
+    if (value %% 1 !=0) {
+      cli_abort(
+        "Converted `before` value of {before} is not an integer.",
+        class = "epipredict__step_epi_slide_mean__unsupported_error"
+      )
+    }
+    value <- as.integer(value)
+  }
+  return(value)
+}
+
+
+#' @export
+bake.step_epi_slide_mean <- function(object, new_data, ...) {
+  recipes::check_new_data(names(object$columns), object, new_data)
+  col_names <- as.vector(object$columns)
+  name_prefix <- object$prefix
+  new_names <- glue::glue("{name_prefix}{col_names}")
+  ## ensure no name clashes
+  new_data_names <- colnames(new_data)
+  intersection <- new_data_names %in% new_names
+  if (any(intersection)) {
+    nms <- new_data_names[intersection]
+    cli_abort(
+      c("In `step_epi_slide_mean()` a name collision occurred. The following variable names already exist:",
+        `*` = "{.var {nms}}"
+      ),
+      call = caller_env(),
+      class = "epipredict__step__name_collision_error"
+    )
+  }
+  renaming <- glue::glue("slide_value_{col_names}")
+  names(renaming) <- new_names
+  names(new_names) <- glue::glue("slide_value_{col_names}")
+  new_data %>%
+    group_by(across(all_of(object$keys[-1]))) %>%
+    epi_slide_mean(col_names, before = object$before, after = object$after) %>%
+    rename(renaming)
+}
+
+
+#' @export
+print.step_epi_slide_mean <- function(x, width = max(20, options()$width - 30), ...) {
+  print_epi_step(
+    x$columns, x$terms, x$trained,
+    title = "Calculating epi_slide for ",
+    conjunction = "with", extra_text = x$f_name
+  )
+  invisible(x)
+}

R/step_epi_slide_sum.R

@@ -0,0 +1,160 @@
+#' Calculate a rolling sum
+#'
+#' `step_epi_slide_sum()` creates a *specification* of a recipe step that will
+#'   generate one or more new columns of derived data by computing a sliding
+#'   sum along existing data.
+#'
+#'
+#' @inheritParams step_epi_lag
+#' @param before,after non-negative integers.
+#'   How far `before` and `after` each `time_value` should
+#'   the sliding window extend? Any value provided for either
+#'   argument must be a single, non-`NA`, non-negative,
+#'   [integer-compatible][vctrs::vec_cast] number of time steps. Endpoints of
+#'   the window are inclusive. Common settings:
+#'   * For trailing/right-aligned windows from `time_value - time_step(k)` to
+#'   `time_value`, use `before=k, after=0`. This is the most likely use case
+#'   for the purposes of forecasting.
+#'   * For center-aligned windows from `time_value - time_step(k)` to
+#'   `time_value + time_step(k)`, use `before=k, after=k`.
+#'   * For leading/left-aligned windows from `time_value` to
+#'   `time_value + time_step(k)`, use `after=k, after=0`.
+#'
+#'   You may also pass a [lubridate::period], like `lubridate::weeks(1)` or a
+#'   character string that is coercible to a [lubridate::period], like
+#'   `"2 weeks"`.
+#' @template step-return
+#'
+#' @export
+#' @examples
+#' library(dplyr)
+#' jhu <- case_death_rate_subset %>%
+#'   filter(time_value >= as.Date("2021-01-01"), geo_value %in% c("ca", "ny"))
+#' rec <- epi_recipe(jhu) %>%
+#'   step_epi_slide_sum(case_rate, death_rate,
+#'     before = 6L
+#'   )
+#' bake(prep(rec, jhu), new_data = NULL)
+step_epi_slide_sum <-
+  function(recipe,
+           ...,
+           before = 0L,
+           after = 0L,
+           role = "predictor",
+           prefix = "epi_slide_sum_",
+           skip = FALSE,
+           id = rand_id("epi_slide_sum")) {
+    if (!is_epi_recipe(recipe)) {
+      rlang::abort("This recipe step can only operate on an `epi_recipe`.")
+    }
+    arg_is_scalar(before, after)
+    before <- try_period(before)
+    after <- try_period(after)
+    arg_is_chr_scalar(role, prefix, id)
+    arg_is_lgl_scalar(skip)
+    add_step(
+      recipe,
+      step_epi_slide_sum_new(
+        terms = enquos(...),
+        before = before,
+        after = after,
+        role = role,
+        trained = FALSE,
+        prefix = prefix,
+        keys = epi_keys(recipe),
+        columns = NULL,
+        skip = skip,
+        id = id
+      )
+    )
+  }
+
+
+step_epi_slide_sum_new <-
+  function(terms,
+           before,
+           after,
+           role,
+           trained,
+           prefix,
+           keys,
+           columns,
+           skip,
+           id) {
+    step(
+      subclass = "epi_slide_sum",
+      terms = terms,
+      before = before,
+      after = after,
+      role = role,
+      trained = trained,
+      prefix = prefix,
+      keys = keys,
+      columns = columns,
+      skip = skip,
+      id = id
+    )
+  }
+
+
+
+#' @export
+prep.step_epi_slide_sum <- function(x, training, info = NULL, ...) {
+  col_names <- recipes::recipes_eval_select(x$terms, data = training, info = info)
+
+  check_type(training[, col_names], types = c("double", "integer"))
+  time_type <- attributes(training)$metadata$time_type
+  before <- lubridate_period_to_integer(x$before, time_type)
+  after <- lubridate_period_to_integer(x$after, time_type)
+  step_epi_slide_sum_new(
+    terms = x$terms,
+    before = before,
+    after = after,
+    role = x$role,
+    trained = TRUE,
+    prefix = x$prefix,
+    keys = x$keys,
+    columns = col_names,
+    skip = x$skip,
+    id = x$id
+  )
+}
+
+#' @export
+bake.step_epi_slide_sum <- function(object, new_data, ...) {
+  recipes::check_new_data(names(object$columns), object, new_data)
+  col_names <- as.vector(object$columns)
+  name_prefix <- object$prefix
+  new_names <- glue::glue("{name_prefix}{col_names}")
+  ## ensure no name clashes
+  new_data_names <- colnames(new_data)
+  intersection <- new_data_names %in% new_names
+  if (any(intersection)) {
+    nms <- new_data_names[intersection]
+    cli_abort(
+      c("In `step_epi_slide_sum()` a name collision occurred. The following variable names already exist:",
+        `*` = "{.var {nms}}"
+      ),
+      call = caller_env(),
+      class = "epipredict__step__name_collision_error"
+    )
+  }
+  renaming <- glue::glue("slide_value_{col_names}")
+  names(renaming) <- new_names
+  names(new_names) <- glue::glue("slide_value_{col_names}")
+  new_data %>%
+    group_by(across(all_of(object$keys[-1]))) %>%
+    epi_slide_sum(col_names, before = object$before, after = object$after) %>%
+    rename(all_of(renaming))
+}
+
+
+#' @export
+print.step_epi_slide_sum <- function(x, width = max(20, options()$width - 30), ...) {
+  print_epi_step(
+    x$columns, x$terms, x$trained,
+    title = "Calculating epi_slide for ",
+    conjunction = "with", extra_text = x$f_name
+  )
+  invisible(x)
+}