break up create_clean_reported_cases()

NAMESPACE

@@ -33,6 +33,7 @@ export(LogNormal)
 export(NonParametric)
 export(Normal)
 export(R_to_growth)
+export(add_breakpoints)
 export(adjust_infection_to_report)
 export(apply_tolerance)
 export(backcalc_opts)
@@ -64,6 +65,7 @@ export(extract_inits)
 export(extract_samples)
 export(extract_stan_param)
 export(fill_missing)
+export(filter_leading_zeros)
 export(fix_dist)
 export(fix_parameters)
 export(forecast_infections)

NEWS.md

@@ -19,6 +19,10 @@
 - A bug was fixed where an internal function for applying a default cdf cutoff failed due to a difference a vector length issue. By @jamesmbaazam in #858 and reviewed by @sbfnk.
 - All parameters have been changed to the new parameter interface. By @sbfnk in #871 and reviewed by @seabbs.
 
+## Package changes
+
+- The internal functions `create_clean_reported_cases()` has been broken up into several functions, with relevant ones `filter_leading_zeros()`, `add_breakpoints()` and `apply_zero_threshold()` exposed to the user. By @sbfnk in #884 and reviewed by @seabbs.
+
 ## Documentation
 
 - Brought the docs on `alpha_sd` up to date with the code change from prior PR #853. By @zsusswein in #862 and reviewed by @jamesmbaazam.

R/create.R

@@ -1,5 +1,5 @@
 #' Create Clean Reported Cases
-#' @description `r lifecycle::badge("stable")`
+#' @description `r lifecycle::badge("deprecated")`
 #' Filters leading zeros, completes dates, and applies an optional threshold at
 #' which point 0 cases are replaced with a user supplied value (defaults to
 #' `NA`).
@@ -12,16 +12,12 @@
 #' number of cases based on the 7-day average. If the average is above this
 #' threshold then the zero is replaced using `fill`.
 #'
-#' @param fill Numeric, defaults to NA. Value to use to replace NA values or
-#' zeroes that are flagged because the 7-day average is above the
-#' `zero_threshold`. If the default NA is used then dates with NA values or with
-#' 7-day averages above the `zero_threshold` will be skipped in model fitting.
-#' If this is set to 0 then the only effect is to replace NA values with 0.
+#' @param fill Deprecated; zero dates with  7-day averages above the
+#'   `zero_threshold` will be skipped in model fitting.
 #' @param add_breakpoints Logical, defaults to TRUE. Should a breakpoint column
 #' be added to the data frame if it does not exist.
 #'
 #' @inheritParams estimate_infections
-#' @importFrom data.table copy merge.data.table setorder setDT frollsum
 #' @return A cleaned data frame of reported cases
 #' @keywords internal
 #' @examples
@@ -33,55 +29,15 @@ create_clean_reported_cases <- function(data, horizon = 0,
                                         zero_threshold = Inf,
                                         fill = NA_integer_,
                                         add_breakpoints = TRUE) {
-  reported_cases <- data.table::setDT(data)
-  reported_cases_grid <- data.table::copy(reported_cases)[,
-   .(date = seq(min(date), max(date) + horizon, by = "days"))
-  ]
-
-  reported_cases <- data.table::merge.data.table(
-    reported_cases, reported_cases_grid,
-    by = "date", all.y = TRUE
-  )
-
-  if (is.null(reported_cases$breakpoint) && add_breakpoints) {
-    reported_cases$breakpoint <- 0
+  reported_cases <- add_horizon(data, horizon = horizon)
+  if (add_breakpoints) {
+    reported_cases <- add_breakpoints(reported_cases)
   }
-  if (!is.null(reported_cases$breakpoint)) {
-    reported_cases[is.na(breakpoint), breakpoint := 0]
-  }
-  reported_cases <- data.table::setorder(reported_cases, date)
-  ## Filter out 0 reported cases from the beginning of the data
   if (filter_leading_zeros) {
-    reported_cases <- reported_cases[order(date)][
-      date >= min(date[confirm[!is.na(confirm)] > 0])
-    ]
-  }
-  # Calculate `average_7_day` which for rows with `confirm == 0`
-  # (the only instance where this is being used) equates to the 7-day
-  # right-aligned moving average at the previous data point.
-  reported_cases <-
-    reported_cases[
-      ,
-      `:=`(average_7_day = (
-          data.table::frollsum(confirm, n = 8, na.rm = TRUE)
-        ) / 7
-      )
-    ]
-  # Check case counts preceding zero case counts and set to 7 day average if
-  # average over last 7 days is greater than a threshold
-  if (!is.infinite(zero_threshold)) {
-    reported_cases <- reported_cases[
-      confirm == 0 & average_7_day > zero_threshold,
-      confirm := NA_integer_
-    ]
-  }
-  reported_cases[is.na(confirm), confirm := fill]
-  reported_cases[, "average_7_day" := NULL]
-  ## set accumulate to FALSE in added rows
-  if ("accumulate" %in% colnames(reported_cases)) {
-    reported_cases[is.na(accumulate), accumulate := FALSE]
+    reported_cases <- filter_leading_zeros(reported_cases)
   }
-  return(reported_cases)
+  reported_cases <- apply_zero_threshold(reported_cases, zero_threshold)
+  return(reported_cases[])
 }
 
 #' Create complete cases

R/estimate_infections.R

@@ -140,6 +140,20 @@ estimate_infections <- function(data,
       "estimate_infections(data)"
     )
   }
+  if (!missing(filter_leading_zeros)) {
+    lifecycle::deprecate_warn(
+      "1.7.0",
+      "estimate_infections(filter_leading_zeros)",
+      "filter_leading_zeros()"
+    )
+  }
+  if (!missing(zero_threshold)) {
+    lifecycle::deprecate_warn(
+      "1.7.0",
+      "estimate_infections(zero_threshold)",
+      "apply_zero_threshold()"
+    )
+  }
   # Validate inputs
   check_reports_valid(data, model = "estimate_infections")
   assert_class(generation_time, "generation_time_opts")
@@ -184,6 +198,17 @@ estimate_infections <- function(data,
   )
   # Fill missing dates
   reported_cases <- default_fill_missing_obs(data, obs, "confirm")
+  # Check initial zeros to check for deprecated filter zero functionality
+  if (reported_cases[date == min(date), "confirm"] == 0) {
+    cli_warn(c(
+      "!" = "Filtering initial zero observations in the data. This
+      functionality will be removed in future versions of EpiNow2. In order
+      to retain the default behaviour and filter initial zero observations
+      use the {.fn filter_leading_zeros()} function on the data before
+      calling {.fn estimate_infections()}."
+    ))
+  }
+
   # Create clean and complete cases
   reported_cases <- create_clean_reported_cases(
     reported_cases, horizon,

R/estimate_secondary.R

@@ -172,6 +172,20 @@ estimate_secondary <- function(data,
       "estimate_secondary(data)"
     )
   }
+  if (!missing(filter_leading_zeros)) {
+    lifecycle::deprecate_warn(
+      "1.7.0",
+      "estimate_secondary(filter_leading_zeros)",
+      "filter_leading_zeros()"
+    )
+  }
+  if (!missing(zero_threshold)) {
+    lifecycle::deprecate_warn(
+      "1.7.0",
+      "estimate_secondary(zero_threshold)",
+      "apply_zero_threshold()"
+    )
+  }
   # Validate the inputs
   check_reports_valid(data, model = "estimate_secondary")
   assert_class(secondary, "secondary_opts")
@@ -200,6 +214,15 @@ estimate_secondary <- function(data,
 
   secondary_reports_dirty <-
     reports[, list(date, confirm = secondary, accumulate)]
+  if (secondary_reports_dirty[date == min(date), "confirm"] == 0) {
+    cli_warn(
+      "!" = "Filtering initial zero observations in the data. This
+      functionality will be removed in future versions of EpiNow2. In order
+      to retain the default behaviour and filter initial zero observations
+      use the {.fn filter_leading_zeros()} function on the data before
+      calling {.fn estimate_secondary()."
+    )
+  }
   secondary_reports <- create_clean_reported_cases(
     secondary_reports_dirty,
     filter_leading_zeros = filter_leading_zeros,

R/preprocessing.R

@@ -42,7 +42,7 @@
 ##'   using a data set that has multiple columns of hwich one of them
 ##'   corresponds to observations that are to be processed here.
 ##' @param by Character vector. Name(s) of any additional column(s) where
-##'   missing data should be processed separately for each value in the column.
+##'   data processing should be done separately for each value in the column.
 ##'   This is useful when using data representing e.g. multiple geographies. If
 ##'   NULL (default) no such grouping is done.
 ##' @return a data.table with an `accumulate` column that indicates whether
@@ -177,3 +177,153 @@ default_fill_missing_obs <- function(data, obs, obs_column) {
   }
   return(data)
 }
+
+##' Add missing values for future dates
+##'
+##' @param accumulate The number of days to accumulate when generating posterior
+##'   prediction, e.g. 7 for weekly accumulated forecasts.
+##' @inheritParams add_horizon
+##' @inheritParams estimate_infections
+##' @importFrom data.table copy merge.data.table setDT
+##' @return A data.table with missing values for future dates
+##' @keywords internal
+add_horizon <- function(data, horizon, accumulate = 1L,
+                        obs_column = "confirm", by = NULL) {
+  assert_data_frame(data)
+  assert_character(obs_column)
+  assert_character(by, null.ok = TRUE)
+  assert_names(
+    colnames(data),
+    must.include = c("date", by, obs_column)
+  )
+  assert_integerish(horizon, lower = 0)
+  assert_integerish(accumulate, lower = 1)
+  assert_date(data$date, any.missing = FALSE)
+
+  reported_cases <- data.table::setDT(data)
+  if (horizon > 0) {
+    reported_cases_grid <- data.table::copy(reported_cases)[,
+      .(date = seq(max(date) + 1, max(date) + horizon, by = "days")),
+      by = by
+    ]
+    ## if we accumulate add the column
+    if (accumulate > 1 || "accumulate" %in% colnames(data)) {
+      reported_cases_grid[, accumulate := TRUE]
+      ## set accumulation to FALSE where appropriate
+      if (horizon >= accumulate) {
+        reported_cases_grid[
+          as.integer(date - min(date) - 1) %% accumulate == 0,
+          accumulate := FALSE
+        ]
+      }
+    }
+    ## fill any missing columns
+    reported_cases_grid <- data.table::merge.data.table(
+      reported_cases, reported_cases_grid,
+      by = "date", all.y = TRUE
+    )
+  }
+  return(reported_cases[])
+}
+
+##' Add breakpoints to certain dates in a data set.
+##'
+##' @param dates A vector of dates to use as breakpoints.
+##' @inheritParams estimate_infections
+##' @return A data.table with `breakpoint` set to 1 on each of the specified
+##'   dates.
+##' @export
+##' @importFrom data.table setDT
+##' @examples
+##' reported_cases <- add_breakpoints(example_confirmed, as.Date("2020-03-26"))
+add_breakpoints <- function(data, dates = as.Date(character(0))) {
+  assert_data_frame(data)
+  assert_names(colnames(data), must.include = "date")
+  assert_date(dates)
+  assert_date(data$date, any.missing = FALSE)
+  reported_cases <- data.table::setDT(data)
+  if (is.null(reported_cases$breakpoint)) {
+    reported_cases$breakpoint <- 0
+  }
+  missing_dates <- setdiff(dates, data$date)
+  if (length(missing_dates) > 0) {
+    cli_abort("Breakpoint date{?s} not found in data: {.var {missing_dates}}")
+  }
+  reported_cases[date %in% dates, breakpoint := 1]
+  return(reported_cases)
+}
+
+##' Filter leading zeros from a data set.
+##'
+##' @inheritParams estimate_infections
+##' @inheritParams fill_missing
+##' @return A data.table with leading zeros removed.
+##' @export
+##' @importFrom data.table setDT
+##' @examples
+##' cases <- data.frame(
+##'   date = as.Date("2020-01-01") + 0:10,
+##'   confirm = c(0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
+##' )
+##' filter_leading_zeros(cases)
+filter_leading_zeros <- function(data, obs_column = "confirm", by = NULL) {
+  assert_data_frame(data)
+  assert_character(obs_column)
+  assert_character(by, null.ok = TRUE)
+  assert_names(
+    colnames(data),
+    must.include = c("date", by, obs_column)
+  )
+  reported_cases <- data.table::setDT(data)
+  reported_cases <- reported_cases[order(date)][
+    date >= min(date[get(obs_column)[!is.na(get(obs_column))] > 0])
+  ]
+  return(reported_cases[])
+}
+
+##' Converts zero case counts to NA (missing) if the 7-day average is above a
+##' threshold.
+##'
+##' This function aims to detect spurious zeroes by comparing the 7-day average
+##' of the case counts to a threshold. If the 7-day average is above the
+##' threshold, the zero case count is replaced with NA.
+##'
+##' @param threshold Numeric, defaults to Inf. Indicates if detected zero cases
+##'   are meaningful by using a threshold number of cases based on the 7-day
+##'   average. If the average is above this threshold at the time of a zero
+##'   observation count then the zero is replaced with a missing (`NA`) count
+##'   and thus ignored in the likelihood.
+##'
+##' @inheritParams estimate_infections
+##' @inheritParams fill_missing
+##' @importFrom data.table setDT frollsum
+##' @return A data.table with the zero threshold applied.
+apply_zero_threshold <- function(data, threshold = Inf,
+                                 obs_column = "confirm") {
+  assert_data_frame(data)
+  assert_numeric(threshold)
+  reported_cases <- data.table::setDT(data)
+
+  # Calculate `average_7_day` which for rows with `confirm == 0`
+  # (the only instance where this is being used) equates to the 7-day
+  # right-aligned moving average at the previous data point.
+  reported_cases <-
+    reported_cases[
+      ,
+      `:=`(average_7_day = (
+          data.table::frollsum(get(obs_column), n = 8, na.rm = TRUE)
+        ) / 7
+      )
+    ]
+  # Check case counts preceding zero case counts and set to 7 day average if
+  # average over last 7 days is greater than a threshold
+  if (!is.infinite(threshold)) {
+    reported_cases <- reported_cases[
+      get(obs_column) == 0 & average_7_day > threshold,
+      paste(obs_column) := NA_integer_
+    ]
+  }
+  reported_cases[is.na(get(obs_column)), paste(obs_column) := NA_integer_]
+  reported_cases[, "average_7_day" := NULL]
+  return(reported_cases[])
+}

_pkgdown.yml

@@ -78,6 +78,13 @@ reference:
     contents:
       - contains("_opts")
       - opts_list
+  - title: Preprocess data
+    desc: Functions used for prepropcessing data
+    contents:
+      - fill_missing
+      - add_breakpoints
+      - filter_leading_zeros
+      - apply_zero_threshold
   - title: Summarise Across Regions
     desc: Functions used for summarising across regions (designed for use with regional_epinow)
     contents: