Add sample data script and corresponding .rda files

DESCRIPTION

@@ -15,3 +15,6 @@ Config/testthat/edition: 3
 URL: https://github.com/Infectious-Disease-Modeling-Hubs/hubExamples
 BugReports: https://github.com/Infectious-Disease-Modeling-Hubs/hubExamples/issues
 Config/Needs/website: Infectious-Disease-Modeling-Hubs/hubStyle
+Depends: 
+    R (>= 2.10)
+LazyData: true

R/data.R

@@ -0,0 +1,65 @@
+#' Forecast outputs
+#'
+#' Example forecast data that represents model outputs from a hub (in this case, forecast data
+#' represents three influenza-related targets (wk inc flu hosp, wk flu hops rate category,
+#' and wk flu hosp tate) for two reference dates in 2022.
+#'
+#' @format ## `forecast_outputs`
+#' A data frame with 5,424 rows and 9 columns:
+#' \describe{
+#'   \item{location}{FIPS code identifying a location}
+#'   \item{reference_date}{the starting point of the forecast in yyyy-mm-dd format}
+#'   \item{horizon}{number of units ahead being forecasted (weeks, in this case)}
+#'   \item{target_end_date}{the date of occurrence of the outcome of interest in yyyy-mm-dd format;
+#'          this can be calculated directly from the `reference_date` and `horizon`
+#'          as follows: `target_end_date = reference_date + 7*horizon`}
+#'   \item{target}{a unique identifier for the target}
+#'   \item{output_type}{the type of representation of the prediction}
+#'   \item{output_type_id}{more identifying information specific to the output type;
+#'        output_type_id is not relevant for every kind of output_type (for example,
+#'        hubs will not expect output_type_id values when the output_type is mean or median}
+#'   \item{value}{the model’s prediction}
+#'   \item{model_id}{the name of the model}
+#'   ...
+#' }
+#' @source <https://github.com/Infectious-Disease-Modeling-Hubs/example-complex-forecast-hub/>
+"forecast_outputs"
+
+#' Forecast target time series
+#'
+#' Example time series target data from a hub that predicts influenza-related targets.
+#'
+#' @format ## `forecast_target_ts`
+#' A data frame with 10,255 rows and 3 columns:
+#' \describe{
+#'   \item{date}{the date of the target observation in yyyy-mm-dd format}
+#'   \item{location}{FIPS code identifying a location}
+#'   \item{value}{the value of the target's observations}
+#'   ...
+#' }
+#' @source <https://github.com/Infectious-Disease-Modeling-Hubs/example-complex-forecast-hub/>
+"forecast_target_ts"
+
+#' Forecast target values
+#'
+#' Example target data that represents the source of "truth" that model output data
+#' will be scored against. This example represents influenza-related targets.
+#'
+#' @format ## `forecast_target_values`
+#' A data frame with 198,485 rows and 6 columns:
+#' \describe{
+#'   \item{location}{FIPS code identifying a location}
+#'   \item{target_end_date}{the target's obversation date in yyyy-mm-dd format;
+#'          this is used to match on the `target_end_date` field in model output data
+#'          submitted to the hub}
+#'   \item{target}{a unique identifier for the target}
+#'   \item{output_type}{the type of representation of the prediction}
+#'   \item{output_type_id}{more identifying information specific to the output type;
+#'         as in the model output data, output_type_id is not relevant for output_type
+#'         of mean and median; target data that represents quantile output_type will
+#'         not have an output_type_id.}
+#'   \item{value}{the value of the target's observations}
+#'   ...
+#' }
+#' @source <https://github.com/Infectious-Disease-Modeling-Hubs/example-complex-forecast-hub/>
+"forecast_target_values"

data-raw/generate_example_forecast_data.R

@@ -0,0 +1,35 @@
+## code to prepare `forecast_outputss` dataset
+
+
+library(distfromq)
+library(dplyr)
+library(ggplot2)
+library(hubData)
+library(readr)
+
+hub_path <- "~/code/example-complex-forecast-hub"
+forecast_outputs <- hubData::connect_hub(hub_path) |>
+  dplyr::collect()
+
+q_lvls_keep <- c("0.05", "0.1", "0.25", "0.5", "0.75", "0.9", "0.95")
+d_keep <- c("2022-11-19", "2022-12-17")
+forecast_outputs <- forecast_outputs |>
+  dplyr::filter(
+    location %in% c("25", "48"),
+    (output_type != "quantile" |
+        (output_type == "quantile" & output_type_id %in% q_lvls_keep)
+    ),
+    reference_date %in% d_keep
+  )
+
+target_ts_data_path <- file.path(hub_path, "target-data", "time-series.csv")
+forecast_target_ts <- read_csv(target_ts_data_path) |>
+  as.data.frame()
+
+target_values_data_path <- file.path(hub_path, "target-data", "target-values.csv")
+forecast_target_values <- read_csv(target_values_data_path) |>
+  as.data.frame()
+
+usethis::use_data(forecast_outputs, overwrite = TRUE)
+usethis::use_data(forecast_target_ts, overwrite = TRUE)
+usethis::use_data(forecast_target_values, overwrite = TRUE)