Fix lint issues

R/data.R

@@ -50,4 +50,3 @@
 #'
 #' @source <https://github.com/Infectious-Disease-Modeling-Hubs/example-complex-forecast-hub/>
 "example_model_output"
-

R/linear_pool.R

@@ -86,15 +86,16 @@ linear_pool <- function(model_outputs, weights = NULL,
                         weights_col_name = "weight",
                         model_id = "hub-ensemble",
                         task_id_cols = NULL,
-                        n_samples=1e4,
+                        n_samples = 1e4,
                         ...) {
 
   # validate_ensemble_inputs
   valid_types <- c("mean", "quantile", "cdf", "pmf")
-  validated_inputs <- validate_ensemble_inputs(model_outputs, weights=weights,
-                                       weights_col_name = weights_col_name,
-                                       task_id_cols = task_id_cols,
-                                       valid_output_types = valid_types)
+  validated_inputs <- model_outputs |>
+    validate_ensemble_inputs(weights = weights,
+                             weights_col_name = weights_col_name,
+                             task_id_cols = task_id_cols,
+                             valid_output_types = valid_types)
 
   model_outputs_validated <- validated_inputs$model_outputs
   weights_validated <- validated_inputs$weights
@@ -107,17 +108,17 @@ linear_pool <- function(model_outputs, weights = NULL,
       type <- split_outputs$output_type[1]
       if (type %in% c("mean", "cdf", "pmf")) {
         simple_ensemble(split_outputs, weights = weights_validated,
-                              weights_col_name = weights_col_name,
-                              agg_fun = "mean", agg_args = list(),
-                              model_id = model_id,
-                              task_id_cols = task_id_cols_validated)
+                        weights_col_name = weights_col_name,
+                        agg_fun = "mean", agg_args = list(),
+                        model_id = model_id,
+                        task_id_cols = task_id_cols_validated)
       } else if (type == "quantile") {
         linear_pool_quantile(split_outputs, weights = weights_validated,
-                              weights_col_name = weights_col_name,
-                              model_id = model_id,
-                              n_samples = n_samples,
-                              task_id_cols = task_id_cols_validated,
-                              ...)
+                             weights_col_name = weights_col_name,
+                             model_id = model_id,
+                             n_samples = n_samples,
+                             task_id_cols = task_id_cols_validated,
+                             ...)
       }
     }) |>
     hubUtils::as_model_out_tbl()

R/linear_pool_quantile.R

@@ -39,11 +39,11 @@
 #' @importFrom rlang .data
 
 linear_pool_quantile <- function(model_outputs, weights = NULL,
-                        weights_col_name = "weight",
-                        model_id = "hub-ensemble",
-                        task_id_cols = NULL,
-                        n_samples = 1e4,
-                        ...) {
+                                 weights_col_name = "weight",
+                                 model_id = "hub-ensemble",
+                                 task_id_cols = NULL,
+                                 n_samples = 1e4,
+                                 ...) {
   quantile_levels <- unique(model_outputs$output_type_id)
 
   if (is.null(weights)) {
@@ -56,27 +56,30 @@ linear_pool_quantile <- function(model_outputs, weights = NULL,
       dplyr::left_join(weights, by = weight_by_cols)
 
     agg_args <- c(list(x = quote(.data[["pred_qs"]]),
-                     weights = quote(.data[[weights_col_name]]),
-                     normwt = TRUE,
-                     probs = as.numeric(quantile_levels)))
+                       weights = quote(.data[[weights_col_name]]),
+                       normwt = TRUE,
+                       probs = as.numeric(quantile_levels)))
 
     group_by_cols <- c(task_id_cols, weights_col_name)
   }
 
+  sample_q_lvls <- seq(from = 0, to = 1, length.out = n_samples + 2)[2:n_samples]
   quantile_outputs <- model_outputs |>
     dplyr::group_by(model_id, dplyr::across(dplyr::all_of(group_by_cols))) |>
     dplyr::summarize(
-      pred_qs = list(distfromq::make_q_fn(
-        ps = as.numeric(.data$output_type_id),
-        qs = .data$value,
-        ...)(seq(from = 0, to = 1, length.out = n_samples + 2)[2:n_samples])),
-      .groups = "drop") |>
+      pred_qs = list(
+        distfromq::make_q_fn(
+          ps = as.numeric(.data$output_type_id),
+          qs = .data$value, ...
+        )(sample_q_lvls)
+      ),
+      .groups = "drop"
+    ) |>
     tidyr::unnest(.data$pred_qs) |>
     dplyr::group_by(dplyr::across(dplyr::all_of(task_id_cols))) |>
-    dplyr::summarize(
-      output_type_id = list(quantile_levels),
-      value = list(do.call(Hmisc::wtd.quantile, args = agg_args)),
-      .groups = "drop") |>
+    dplyr::summarize(output_type_id = list(quantile_levels),
+                     value = list(do.call(Hmisc::wtd.quantile, args = agg_args)),
+                     .groups = "drop") |>
     tidyr::unnest(cols = tidyselect::all_of(c("output_type_id", "value"))) |>
     dplyr::mutate(model_id = model_id, .before = 1) |>
     dplyr::mutate(output_type = "quantile", .before = .data$output_type_id) |>

R/simple_ensemble.R

@@ -48,19 +48,20 @@ simple_ensemble <- function(model_outputs, weights = NULL,
 
   # validate_ensemble_inputs
   valid_types <- c("mean", "median", "quantile", "cdf", "pmf")
-  validated_inputs <- validate_ensemble_inputs(model_outputs, weights = weights,
-                                       weights_col_name = weights_col_name,
-                                       task_id_cols = task_id_cols,
-                                       valid_output_types = valid_types)
-
-    model_outputs_validated <- validated_inputs$model_outputs
-    weights_validated <- validated_inputs$weights
-    task_id_cols_validated <- validated_inputs$task_id_cols
+  validated_inputs <- model_outputs |>
+    validate_ensemble_inputs(weights = weights,
+                             weights_col_name = weights_col_name,
+                             task_id_cols = task_id_cols,
+                             valid_output_types = valid_types)
+
+  model_outputs_validated <- validated_inputs$model_outputs
+  weights_validated <- validated_inputs$weights
+  task_id_cols_validated <- validated_inputs$task_id_cols
 
   if (is.null(weights_validated)) {
     agg_args <- c(agg_args, list(x = quote(.data[["value"]])))
   } else {
-    weight_by_cols <- 
+    weight_by_cols <-
       colnames(weights_validated)[colnames(weights_validated) != weights_col_name]
 
     model_outputs_validated <- model_outputs_validated %>%

R/validate_ensemble_inputs.R

@@ -31,7 +31,7 @@
 #'
 #' @noRd
 
-validate_ensemble_inputs <- function(model_outputs, weights=NULL,
+validate_ensemble_inputs <- function(model_outputs, weights = NULL,
                                      weights_col_name = "weight",
                                      task_id_cols = NULL,
                                      valid_output_types) {
@@ -72,7 +72,7 @@ validate_ensemble_inputs <- function(model_outputs, weights=NULL,
   }
 
   if (!is.null(weights)) {
-      req_weight_cols <- c("model_id", weights_col_name)
+    req_weight_cols <- c("model_id", weights_col_name)
     if (!all(req_weight_cols %in% colnames(weights))) {
       cli::cli_abort(c(
         "x" = "{.arg weights} did not include required columns
@@ -109,5 +109,5 @@ validate_ensemble_inputs <- function(model_outputs, weights=NULL,
   validated_inputs <- list(model_outputs = model_outputs,
                            weights = weights,
                            task_id_cols = task_id_cols)
-  return (validated_inputs)
+  return(validated_inputs)
 }

R/validate_output_type_ids.R

@@ -20,7 +20,7 @@ validate_output_type_ids <- function(model_outputs, task_id_cols) {
   same_output_id <- model_outputs |>
     dplyr::filter(.data$output_type %in% c("cdf", "pmf", "quantile")) |>
     dplyr::group_by(.data$model_id, dplyr::across(dplyr::all_of(task_id_cols)), .data$output_type) |>
-    dplyr::summarize(output_type_id_list=list(sort(.data$output_type_id))) |>
+    dplyr::summarize(output_type_id_list = list(sort(.data$output_type_id))) |>
     dplyr::ungroup() |>
     dplyr::group_split(dplyr::across(dplyr::all_of(task_id_cols)), .data$output_type) |>
     purrr::map(.f = function(split_outputs) {
@@ -29,12 +29,12 @@ validate_output_type_ids <- function(model_outputs, task_id_cols) {
     unlist()
 
   false_counter <- length(same_output_id[same_output_id == FALSE])
-  if (FALSE %in% same_output_id) {
+  if (false_counter != 0) {
     cli::cli_abort(c(
       "x" = "{.arg model_outputs} contains {.val {false_counter}} invalid distributions.",
       "i" = "Within each group defined by a combination of task id variables
              and output type, all models must provide the same set of
              output type ids"
-     ))
+    ))
   }
 }

data-raw/example_model_output.R

@@ -5,8 +5,8 @@
 library(hubData)
 hub_path <- "../example-complex-forecast-hub"
 example_model_output <- hubData::connect_hub(hub_path) |>
-    dplyr::collect() |>
-    dplyr::select(model_id, location, reference_date, horizon, target_end_date, target, output_type, output_type_id, value)
+  dplyr::collect() |>
+  dplyr::select(model_id, location, reference_date, horizon, target_end_date,
+                target, output_type, output_type_id, value)
 
 usethis::use_data(example_model_output, overwrite = TRUE)
-

data-raw/example_target_data.R

@@ -7,7 +7,7 @@ target_data_path <- file.path(hub_path, "target-data",
                               "flu-hospitalization-time-series.csv")
 
 example_target_data <- read.csv(target_data_path) |>
-    dplyr::mutate(date = as.Date(date)) |>
-    dplyr::rename(time_idx = date)
+  dplyr::mutate(date = as.Date(date)) |>
+  dplyr::rename(time_idx = date)
 
 usethis::use_data(example_target_data, overwrite = TRUE)

inst/example-data/example-simple-forecast-hub/README.Rmd

@@ -30,7 +30,7 @@ library(hubUtils)
 library(dplyr)
 
 model_outputs <- hubData::connect_hub(hub_path = ".") %>%
-    dplyr::collect()
+  dplyr::collect()
 head(model_outputs)
 
 target_data <- read.csv("target-data/covid-hospitalizations.csv")