Improve no visible binding issue fix (#97)

DESCRIPTION

@@ -46,6 +46,7 @@ Imports:
     magrittr,
     matrixStats,
     purrr,
+    rlang,
     tidyr,
     tidyselect
 Remotes:

NAMESPACE

@@ -4,3 +4,4 @@ export("%>%")
 export(linear_pool)
 export(simple_ensemble)
 importFrom(magrittr,"%>%")
+importFrom(rlang,.data)

R/hubEnsembles-package.R

@@ -1 +0,0 @@
-utils::globalVariables(c("model_id", "output_type", "output_type_id", "value", "pred_qs"))

R/linear_pool.R

@@ -80,6 +80,8 @@
 #' all.equal(lp_from_component_qs$value, lp_qs, tolerance = 1e-3,
 #'           check.attributes=FALSE)
 #'
+#' @importFrom rlang .data
+
 linear_pool <- function(model_outputs, weights = NULL,
                         weights_col_name = "weight",
                         model_id = "hub-ensemble",
@@ -100,7 +102,7 @@ linear_pool <- function(model_outputs, weights = NULL,
 
   # calculate linear opinion pool for different types
   ensemble_model_outputs <- model_outputs_validated |>
-    dplyr::group_split(output_type) |>
+    dplyr::group_split(.data$output_type) |>
     purrr::map_dfr(.f = function(split_outputs) {
       type <- split_outputs$output_type[1]
       if (type %in% c("mean", "cdf", "pmf")) {

R/linear_pool_quantile.R

@@ -36,6 +36,7 @@
 #'     3. Collect the samples from all component models and extract the desired quantiles.
 #'   Steps 1 and 2 in this process are performed by `distfromq::make_q_fun`.
 #' @return a `model_out_tbl` object of ensemble predictions for the `quantile` output type.
+#' @importFrom rlang .data
 
 linear_pool_quantile <- function(model_outputs, weights = NULL,
                         weights_col_name = "weight",
@@ -66,19 +67,19 @@ linear_pool_quantile <- function(model_outputs, weights = NULL,
     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(output_type_id),
-        qs = value,
+        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") |>
-    tidyr::unnest(pred_qs) |>
+    tidyr::unnest(.data$pred_qs) |>
     dplyr::group_by(dplyr::across(dplyr::all_of(task_id_cols))) |>
     dplyr::summarize(
       output_type_id = list(quantile_levels),
       value = list(do.call(Hmisc::wtd.quantile, args = agg_args)),
       .groups = "drop") |>
     tidyr::unnest(cols = tidyselect::all_of(c("output_type_id", "value"))) |>
     dplyr::mutate(model_id = model_id, .before = 1) |>
-    dplyr::mutate(output_type = "quantile", .before = output_type_id) |>
+    dplyr::mutate(output_type = "quantile", .before = .data$output_type_id) |>
     dplyr::ungroup()
 
   return(quantile_outputs)

R/validate_output_type_ids.R

@@ -14,15 +14,15 @@
 #' @return no return value
 #'
 #' @noRd
-#'
+#' @importFrom rlang .data
 
 validate_output_type_ids <- function(model_outputs, task_id_cols) {
   same_output_id <- model_outputs |>
-    dplyr::filter(output_type %in% c("cdf", "pmf", "quantile")) |>
-    dplyr::group_by(model_id, dplyr::across(dplyr::all_of(task_id_cols)), output_type) |>
-    dplyr::summarize(output_type_id_list=list(sort(output_type_id))) |>
+    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::ungroup() |>
-    dplyr::group_split(dplyr::across(dplyr::all_of(task_id_cols)), output_type) |>
+    dplyr::group_split(dplyr::across(dplyr::all_of(task_id_cols)), .data$output_type) |>
     purrr::map(.f = function(split_outputs) {
       length(unique(split_outputs$output_type_id_list)) == 1
     }) |>