diff --git a/R/get_fcast.R b/R/get_fcast.R index 2783d39..58670db 100644 --- a/R/get_fcast.R +++ b/R/get_fcast.R @@ -14,7 +14,7 @@ #' models using the full dataset. #' #' @param df Data frame of weekly observations containing -#' `target_end_date` (Date) and `observation` (numeric). +#' `target_end_date` (Date), `location` (character), `target` (character), and `observation` (numeric). #' @param eval_start_date Date or string coercible to Date. First date at #' which forecasts are evaluated. At least 52 weeks of data must precede #' this date. @@ -77,7 +77,9 @@ get_fcast <- function( eval_start_date <- as.Date(eval_start_date) stopifnot( is.data.frame(df), - all(c("target_end_date", "observation") %in% names(df)), + all(c("target_end_date", "observation", "target", "location") %in% names(df)), + length(unique(df$target)) == 1, + length(unique(df$location)) == 1, is.numeric(h), length(h) == 1, h > 0, diff --git a/R/globals.R b/R/globals.R index 7dbc396..3d31271 100644 --- a/R/globals.R +++ b/R/globals.R @@ -30,7 +30,6 @@ utils::globalVariables(c( # forecasts_key / to_respilens / metadata_key "target", # metadata_key (package dataset) - "loc_data", # get_fcast plot "q95", "q95_lower", diff --git a/R/loc_data.R b/R/loc_data.R deleted file mode 100644 index d4794d7..0000000 --- a/R/loc_data.R +++ /dev/null @@ -1,21 +0,0 @@ -#' Location Dataset -#' -#' US states census data. Includes state abbreviations, full state names, ???? -#' -#' @format A data frame with 53 rows and 12 variables: -#' \describe{ -#' \item{abbreviation}{State abbreviation code} -#' \item{location}{State abbreviation code 2} -#' \item{location_name}{Full state name} -#' \item{population}{State population} -#' \item{count_rate0p3}{??} -#' \item{count_rate0p5}{??} -#' \item{count_rate0p7}{??} -#' \item{count_rate1}{??} -#' \item{count_rate1p7}{??} -#' \item{count_rate3}{??} -#' \item{count_rate4}{??} -#' \item{count_rate5}{??} -#' } -#' @source Where the data came from ?? -"loc_data" diff --git a/R/to_respilens.R b/R/to_respilens.R index dc85eb9..42c6d13 100644 --- a/R/to_respilens.R +++ b/R/to_respilens.R @@ -1,25 +1,18 @@ #' Build RespiLens metadata key from `model_out_tbl` data #' #' @param model_out_tbl Forecast tibble from `get_fcast()`. +#' @param loc Location string. #' @return Named list for RespiLens metadata key -metadata_key <- function(model_out_tbl) { +metadata_key <- function(model_out_tbl, loc) { # remove peak targets df <- model_out_tbl |> dplyr::filter(!grepl("peak", target, ignore.case = TRUE)) - abbr <- df$location[[1]] - loc_row <- loc_data[loc_data$abbreviation == abbr, ] - - # safety check - if (nrow(loc_row) == 0 || any(is.na(loc_row$location_name))) { - stop("Location not found in loc_data.") - } - list( - location = loc_row$location, - abbreviation = loc_row$abbreviation, - location_name = loc_row$location_name, - population = loc_row$population, + location = loc, + abbreviation = loc, + location_name = loc, + population = 0, dataset = "ACCIDDA Suite", series_type = "projection", hubverse_keys = list( @@ -124,6 +117,7 @@ forecasts_key <- function(model_out_tbl) { #' Convert accida_cast to RespiLens format #' @param accida_cast An object of class `accida_cast`, the output of `get_fcast()`. +#' @param loc A character string that describes the location of the data provided. #' @return A named list with a single metadata JSON structure and one JSON structure per location. #' @noRd to_respilens <- function(accida_cast) { @@ -148,15 +142,16 @@ to_respilens <- function(accida_cast) { model_out_tbl <- model_out_tbl |> dplyr::filter(output_type != "sample") - loc <- unique(model_out_tbl$location) + model_loc <- unique(model_out_tbl$location) + gt_loc <-unique(oracle_output$location) - if (length(loc) != 1) { - stop("Expected exactly one location.") + if (length(model_loc) != 1 || length(gt_loc) != 1) { + stop("Expected exactly one location in input data.") } return( list( - metadata = metadata_key(model_out_tbl), + metadata = metadata_key(model_out_tbl, loc), ground_truth = ground_truth_key(oracle_output), forecasts = forecasts_key(model_out_tbl) ) diff --git a/TODO b/TODO index 5133311..3ce992b 100644 --- a/TODO +++ b/TODO @@ -6,6 +6,4 @@ Rscript external_to_projections.R \ --target-data-path \ --locations-data-path -https://github.com/ACCIDDA/RespiLens/blob/main/scripts/external_to_projections.R - -Describe the loc_data \ No newline at end of file +https://github.com/ACCIDDA/RespiLens/blob/main/scripts/external_to_projections.R \ No newline at end of file diff --git a/data/loc_data.rda b/data/loc_data.rda deleted file mode 100644 index 492bf68..0000000 Binary files a/data/loc_data.rda and /dev/null differ diff --git a/docs/articles/acciddasuite.html b/docs/articles/acciddasuite.html index 4768f26..3ddd507 100644 --- a/docs/articles/acciddasuite.html +++ b/docs/articles/acciddasuite.html @@ -39,7 +39,8 @@
    • @@ -95,8 +96,11 @@

    Statistical Modelling get_data

    -

    Ideally, you would load your own data here.

    -

    For demonstration purposes, we will load data from the CDC +

    If you would like to load your own ground truth data, you can +follow these steps for +formatting.

    +

    For demonstration purposes, we will load ground truth data from the +CDC National Health Safety Network. The data dictionary is available here.

     library(dplyr)
@@ -106,19 +110,19 @@ 
    
 df <- get_data(pathogen = "covid", geo_values = "ny")
 summary(df)
 #>      as_of              location            target         
-#>  Min.   :2026-01-25   Length:286         Length:286        
-#>  1st Qu.:2026-01-25   Class :character   Class :character  
-#>  Median :2026-01-25   Mode  :character   Mode  :character  
-#>  Mean   :2026-01-25                                        
-#>  3rd Qu.:2026-01-25                                        
-#>  Max.   :2026-01-25                                        
+#>  Min.   :2026-03-01   Length:291         Length:291        
+#>  1st Qu.:2026-03-01   Class :character   Class :character  
+#>  Median :2026-03-01   Mode  :character   Mode  :character  
+#>  Mean   :2026-03-01                                        
+#>  3rd Qu.:2026-03-01                                        
+#>  Max.   :2026-03-01                                        
 #>  target_end_date       observation     
 #>  Min.   :2020-08-08   Min.   :   60.0  
-#>  1st Qu.:2021-12-19   1st Qu.:  461.2  
-#>  Median :2023-05-02   Median :  995.0  
-#>  Mean   :2023-05-02   Mean   : 1644.0  
-#>  3rd Qu.:2024-09-12   3rd Qu.: 2241.8  
-#>  Max.   :2026-01-24   Max.   :11833.0
    +#> 1st Qu.:2021-12-28 1st Qu.: 442.5 +#> Median :2023-05-20 Median : 982.0 +#> Mean :2023-05-20 Mean : 1621.8 +#> 3rd Qu.:2024-10-08 3rd Qu.: 2213.0 +#> Max. :2026-02-28 Max. :11833.0

    Time Series Cross-Validation @@ -174,15 +178,15 @@

    Time Series Cross-Validation#> Models evaluated: #> model_id wis #> <char> <num> -#> ARIMA 39.46377 -#> ETS 39.50977 -#> THETA 43.41059 -#> ENSEMBLE 63.82600 -#> SNAIVE 272.68963 +#> THETA 27.20068 +#> ARIMA 35.22355 +#> ETS 41.93945 +#> ENSEMBLE 53.91791 +#> SNAIVE 262.42959 #> #> Forecast horizon: -#> From: 2025-12-27 -#> To: 2026-02-21 +#> From: 2026-01-31 +#> To: 2026-03-28 #> #> Contents: #> $hubcast hub forecast object @@ -196,21 +200,21 @@

    Time Series Cross-Validation#> # A tibble: 200 × 9 #> model_id reference_date target horizon location target_end_date output_type #> <chr> <date> <chr> <int> <chr> <date> <chr> -#> 1 ARIMA 2025-12-20 wk inc … 1 NY 2025-12-27 quantile -#> 2 ARIMA 2025-12-20 wk inc … 1 NY 2025-12-27 quantile -#> 3 ARIMA 2025-12-20 wk inc … 1 NY 2025-12-27 quantile -#> 4 ARIMA 2025-12-20 wk inc … 1 NY 2025-12-27 quantile -#> 5 ARIMA 2025-12-20 wk inc … 1 NY 2025-12-27 quantile -#> 6 ARIMA 2025-12-20 wk inc … 2 NY 2026-01-03 quantile -#> 7 ARIMA 2025-12-20 wk inc … 2 NY 2026-01-03 quantile -#> 8 ARIMA 2025-12-20 wk inc … 2 NY 2026-01-03 quantile -#> 9 ARIMA 2025-12-20 wk inc … 2 NY 2026-01-03 quantile -#> 10 ARIMA 2025-12-20 wk inc … 2 NY 2026-01-03 quantile +#> 1 ARIMA 2026-01-24 wk inc … 1 NY 2026-01-31 quantile +#> 2 ARIMA 2026-01-24 wk inc … 1 NY 2026-01-31 quantile +#> 3 ARIMA 2026-01-24 wk inc … 1 NY 2026-01-31 quantile +#> 4 ARIMA 2026-01-24 wk inc … 1 NY 2026-01-31 quantile +#> 5 ARIMA 2026-01-24 wk inc … 1 NY 2026-01-31 quantile +#> 6 ARIMA 2026-01-24 wk inc … 2 NY 2026-02-07 quantile +#> 7 ARIMA 2026-01-24 wk inc … 2 NY 2026-02-07 quantile +#> 8 ARIMA 2026-01-24 wk inc … 2 NY 2026-02-07 quantile +#> 9 ARIMA 2026-01-24 wk inc … 2 NY 2026-02-07 quantile +#> 10 ARIMA 2026-01-24 wk inc … 2 NY 2026-02-07 quantile #> # ℹ 190 more rows #> # ℹ 2 more variables: output_type_id <chr>, value <dbl> #> #> $oracle_output -#> # A tibble: 286 × 6 +#> # A tibble: 291 × 6 #> location target_end_date target output_type output_type_id oracle_value #> <chr> <date> <chr> <chr> <lgl> <dbl> #> 1 NY 2020-08-08 wk inc covi… quantile NA 517 @@ -223,23 +227,23 @@

    Time Series Cross-Validation#> 8 NY 2020-09-26 wk inc covi… quantile NA 656 #> 9 NY 2020-10-03 wk inc covi… quantile NA 851 #> 10 NY 2020-10-10 wk inc covi… quantile NA 840 -#> # ℹ 276 more rows +#> # ℹ 281 more rows fcast$score #> Key: <model_id> #> model_id wis interval_coverage_50 interval_coverage_95 #> <char> <num> <num> <num> -#> 1: ARIMA 39.46377 0.75 1 -#> 2: ETS 39.50977 1.00 1 -#> 3: THETA 43.41059 0.50 1 -#> 4: ENSEMBLE 63.82600 1.00 1 -#> 5: SNAIVE 272.68963 0.25 1 +#> 1: THETA 27.20068 1.00 1 +#> 2: ARIMA 35.22355 0.75 1 +#> 3: ETS 41.93945 0.75 1 +#> 4: ENSEMBLE 53.91791 1.00 1 +#> 5: SNAIVE 262.42959 0.25 1 #> wis_relative_skill #> <num> -#> 1: 0.6052929 -#> 2: 0.6059985 -#> 3: 0.6658289 -#> 4: 0.9789593 -#> 5: 4.1824967

    +#> 1: 0.4826402 +#> 2: 0.6249954 +#> 3: 0.7441602 +#> 4: 0.9567021 +#> 5: 4.6564669

    Adding extra_models @@ -272,20 +276,19 @@

    Adding extra_models#> <accida_cast> #> #> Models evaluated: -#> model_id wis -#> <char> <num> -#> EPIESTIM 30.78158 -#> ETS 32.52275 -#> ENSEMBLE 35.58608 -#> ARIMA 37.37021 -#> THETA 47.36624 -#> CUSTOM_ARIMA 126.93577 -#> SNAIVE 268.02628 -#> PROPHET 1664.29545 +#> model_id wis +#> <char> <num> +#> THETA 21.70325 +#> ARIMA 26.70864 +#> ETS 29.52682 +#> ENSEMBLE 36.52850 +#> CUSTOM_ARIMA 140.41996 +#> SNAIVE 284.57923 +#> PROPHET 425.66068 #> #> Forecast horizon: -#> From: 2025-12-27 -#> To: 2026-02-14 +#> From: 2026-01-31 +#> To: 2026-03-21 #> #> Contents: #> $hubcast hub forecast object diff --git a/docs/articles/acciddasuite_files/figure-html/models-1.png b/docs/articles/acciddasuite_files/figure-html/models-1.png index 0289971..d791214 100644 Binary files a/docs/articles/acciddasuite_files/figure-html/models-1.png and b/docs/articles/acciddasuite_files/figure-html/models-1.png differ diff --git a/docs/articles/acciddasuite_files/figure-html/tscv-setup-1.png b/docs/articles/acciddasuite_files/figure-html/tscv-setup-1.png index 5538be7..05770a3 100644 Binary files a/docs/articles/acciddasuite_files/figure-html/tscv-setup-1.png and b/docs/articles/acciddasuite_files/figure-html/tscv-setup-1.png differ diff --git a/docs/articles/index.html b/docs/articles/index.html index 87302d5..aca61ec 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -19,7 +19,8 @@
  • Reference
  • -
      • @@ -39,6 +40,8 @@

      All vignettes

      acciddasuite
      +
      +
      Preparing External Data
      Disease Forecast Planning for Public Health
      diff --git a/man/loc_data.Rd b/man/loc_data.Rd deleted file mode 100644 index dd66ae3..0000000 --- a/man/loc_data.Rd +++ /dev/null @@ -1,33 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/loc_data.R -\docType{data} -\name{loc_data} -\alias{loc_data} -\title{Location Dataset} -\format{ -A data frame with 53 rows and 12 variables: -\describe{ -\item{abbreviation}{State abbreviation code} -\item{location}{State abbreviation code 2} -\item{location_name}{Full state name} -\item{population}{State population} -\item{count_rate0p3}{??} -\item{count_rate0p5}{??} -\item{count_rate0p7}{??} -\item{count_rate1}{??} -\item{count_rate1p7}{??} -\item{count_rate3}{??} -\item{count_rate4}{??} -\item{count_rate5}{??} -} -} -\source{ -Where the data came from ?? -} -\usage{ -loc_data -} -\description{ -US states census data. Includes state abbreviations, full state names, ???? -} -\keyword{datasets} diff --git a/respi.json b/respi.json new file mode 100644 index 0000000..af92336 --- /dev/null +++ b/respi.json @@ -0,0 +1 @@ +{"metadata":{"location":"NY","abbreviation":"NY","location_name":"NY","population":0,"dataset":"ACCIDDA Suite","series_type":"projection","hubverse_keys":{"models":["ARIMA","ENSEMBLE","EPIESTIM","ETS","SNAIVE","THETA"],"targets":"wk inc rsv hosp","horizons":["1","2","3"],"output_types":"quantile"}},"ground_truth":{"dates":["2023-12-02","2023-12-09","2023-12-16","2023-12-23","2023-12-30","2024-01-06","2024-01-13","2024-01-20","2024-01-27","2024-02-03","2024-02-10","2024-02-17","2024-02-24","2024-03-02","2024-03-09","2024-03-16","2024-03-23","2024-03-30","2024-04-06","2024-04-13","2024-04-20","2024-04-27","2024-05-04","2024-05-11","2024-05-18","2024-05-25","2024-06-01","2024-06-08","2024-06-15","2024-06-22","2024-06-29","2024-07-06","2024-07-13","2024-07-20","2024-07-27","2024-08-03","2024-08-10","2024-08-17","2024-08-24","2024-08-31","2024-09-07","2024-09-14","2024-09-21","2024-09-28","2024-10-05","2024-10-12","2024-10-19","2024-10-26","2024-11-02","2024-11-09","2024-11-16","2024-11-23","2024-11-30","2024-12-07","2024-12-14","2024-12-21","2024-12-28","2025-01-04","2025-01-11","2025-01-18","2025-01-25","2025-02-01","2025-02-08","2025-02-15","2025-02-22","2025-03-01","2025-03-08","2025-03-15","2025-03-22","2025-03-29","2025-04-05","2025-04-12","2025-04-19","2025-04-26","2025-05-03","2025-05-10","2025-05-17","2025-05-24","2025-05-31","2025-06-07","2025-06-14","2025-06-21","2025-06-28","2025-07-05","2025-07-12","2025-07-19","2025-07-26","2025-08-02","2025-08-09","2025-08-16","2025-08-23","2025-08-30","2025-09-06","2025-09-13","2025-09-20","2025-09-27","2025-10-04","2025-10-11","2025-10-18","2025-10-25","2025-11-01","2025-11-08","2025-11-15","2025-11-22","2025-11-29","2025-12-06","2025-12-13","2025-12-20","2025-12-27","2026-01-03","2026-01-10","2026-01-17","2026-01-24","2026-01-31","2026-02-07","2026-02-14","2026-02-21"],"wk inc rsv hosp":[18,21,42,38,37,22,33,18,17,9,15,19,13,16,7,10,9,5,5,2,5,5,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,16,45,179,265,404,563,725,874,990,1041,1230,889,716,625,611,522,426,392,295,328,256,240,195,171,120,109,95,60,58,44,38,19,26,23,12,13,16,14,14,16,10,17,12,13,19,27,31,30,45,30,47,37,48,57,66,104,134,189,281,364,390,468,552,549,631,593,635,736,711,728]},"forecasts":{"2025-08-30":{"wk inc rsv 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diff --git a/tests/testthat/test-loc_data.R b/tests/testthat/test-loc_data.R deleted file mode 100644 index 8dd8d47..0000000 --- a/tests/testthat/test-loc_data.R +++ /dev/null @@ -1,10 +0,0 @@ -test_that("loc_data dataset exists and has expected structure", { - expect_true(exists("loc_data", envir = asNamespace("acciddasuite"))) - - data("loc_data", package = "acciddasuite") - - expect_s3_class(loc_data, "data.frame") - expect_true("abbreviation" %in% names(loc_data)) - expect_true("location" %in% names(loc_data)) - expect_true("location_name" %in% names(loc_data)) -}) diff --git a/vignettes/acciddasuite.Rmd b/vignettes/acciddasuite.Rmd index 6355a8f..5a73d20 100644 --- a/vignettes/acciddasuite.Rmd +++ b/vignettes/acciddasuite.Rmd @@ -32,9 +32,9 @@ We will aim to demonstrate the basic steps in a forecasting task, as defined by ## `get_data` -**Ideally, you would load your own data here**. +**If you would like to load your own ground truth data, you can follow [these](external_data.html) steps for formatting.** -For demonstration purposes, we will load data from the [CDC National Health Safety Network](https://data.cdc.gov/Public-Health-Surveillance/Weekly-Hospital-Respiratory-Data-HRD-Metrics-by-Ju/mpgq-jmmr/about_data). The data dictionary is available [here](https://dev.socrata.com/foundry/data.cdc.gov/mpgq-jmmr). +For demonstration purposes, we will load ground truth data from the [CDC National Health Safety Network](https://data.cdc.gov/Public-Health-Surveillance/Weekly-Hospital-Respiratory-Data-HRD-Metrics-by-Ju/mpgq-jmmr/about_data). The data dictionary is available [here](https://dev.socrata.com/foundry/data.cdc.gov/mpgq-jmmr). ```{r, get_data} library(dplyr) diff --git a/vignettes/external_data.Rmd b/vignettes/external_data.Rmd new file mode 100644 index 0000000..118d037 --- /dev/null +++ b/vignettes/external_data.Rmd @@ -0,0 +1,95 @@ +--- +title: "Preparing External Data" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Preparing External Data} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +# Utilizing external ground truth data + +The `acciddasuite` supports forecasting of hospitalization incidence due to COVID-19, RSV, or influenza based on given ground truth data. While you are able to quickly pull state-level `inc hosp` data using `get_data()`, you may desire to use your own hospitalization ground truth data for forecasting (i.e., as the `df` parameter for modeling in `get_fcast()`). To do so, you must esnure your data adheres to the format described below. Please note that `get_fcast()` only processes one location at a time; so if you have multiple locations' ground truth data, you should separate your ground truth dataset into one data frame per location and then initiate a `get_fcast()` run for each. + + + +## Columns and data types + +Your ground truth data must contain 4 distinct columns: + +| column name | data type | description | +|-------------|-----------|-------------| +|`target_end_date`|Date|The date for which an observation is recorded| +|`observation`|numeric|The observed value for a given date| +|`location`|character|The location for which the data describes. You may use any location identifier you like, as long as it is consistent and represented as a character.| +|`target`|character|The data stream being observed (e.g, "inc hosp influenza" or "inc hosp rsv")| + + + +## Data content restrictions + +Once your data has `target_end_date`, `observation`, `location` and `target` columns, you must ensure the following for successful processing in `get_fcast()`: + +* `target_end_date` values should be unique and have no NAs (i.e., there should be no duplicate or missing values in the `target_end_date` column) +* `target_end_date` values should be +* Your data must be on a **weekly** cadence (i.e., `target_end_date` values should be exactly one week apart, **and be continuous from week 1 to end**) +* The `target` column must contain only one unique value (i.e, the same target is recorded for all of your data) +* Your dataset should contain ≥ 52 entries (weeks of observations; rows) where the `target_end_date` is < the `eval_start_date` parameter you pass into `get_fcast()`. Please note that NA entries in the `observation` column will be filtered out silently, and may result in the reduction of number of observations in your dataset. It is optimal to ensure that NA values are resolved prior to use in `get_fcast()`. + +After ensuring compliance with all of the stipulations above, you can read your dataset in (either as `data.frame` or `tibble`) and pass it as the `df` parameter in `get_fcast()`. + +## Final form data + +Properly converted data, for example, your `df` may resemble this: + +```{r, include=FALSE} +df <- data.frame( + target_end_date = as.Date("2024-01-01") + seq(0, by = 7, length.out = 52), + observation = rpois(52, lambda = 20), + location = "NY", + target = "inc hosp influenza" +) +``` + +```{r, echo=FALSE} +head(df) +``` + +Data type compliance: + +```{r} +class(df$target_end_date) +class(df$observation) +class(df$location) +class(df$target) +``` + +Which can now be used as the `df` parameter in `get_fcast()`: + +```{r, eval=FALSE} +x = get_fcast( + df, + eval_start_date = "2025-01-01", + h = 3, + top_n = 5 +) +``` + +where `eval_start_date` (`2025-01-01`) begins after 52 consecutive weeks of `target_end_date` entries: + +```{r, include=FALSE} +eval_start_date <- "2025-01-01" +``` + + +```{r} +sum(df$target_end_date < eval_start_date, na.rm = TRUE) +``` + +You can have more than 52 entries preceding your `eval_start_date`, but 52 is the minimum. + + + + + + diff --git a/vignettes/respi.json b/vignettes/respi.json new file mode 100644 index 0000000..af92336 --- /dev/null +++ b/vignettes/respi.json @@ -0,0 +1 @@ +{"metadata":{"location":"NY","abbreviation":"NY","location_name":"NY","population":0,"dataset":"ACCIDDA Suite","series_type":"projection","hubverse_keys":{"models":["ARIMA","ENSEMBLE","EPIESTIM","ETS","SNAIVE","THETA"],"targets":"wk inc rsv 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