Replacing the magrittr pipe

DESCRIPTION

@@ -15,7 +15,7 @@ License: MIT + file LICENSE
 URL: https://tune.tidymodels.org/, https://github.com/tidymodels/tune
 BugReports: https://github.com/tidymodels/tune/issues
 Depends:
-    R (>= 4.0)
+    R (>= 4.1)
 Imports:
     cli (>= 3.3.0),
     dials (>= 1.3.0.9000),
@@ -50,22 +50,22 @@ Suggests:
     kknn,
     knitr,
     modeldata,
-    probably,
+    probably (>= 1.0.3.9001),
     scales,
     spelling,
     splines2,
     testthat (>= 3.0.0),
     xgboost,
     xml2
 Remotes:
+    tidymodels/dials,
     tidymodels/hardhat,
     tidymodels/parsnip,
+    tidymodels/probably,
     tidymodels/recipes,
     tidymodels/rsample,
     tidymodels/tailor,
-    tidymodels/workflows,
-    tidymodels/dials,
-    tidymodels/probably
+    tidymodels/workflows
 Config/Needs/website: pkgdown, tidymodels, kknn, doParallel, doFuture,
     tidyverse/tidytemplate
 Config/testthat/edition: 3

NAMESPACE

@@ -262,7 +262,6 @@ importFrom(cli,qty)
 importFrom(dials,encode_unit)
 importFrom(dials,is_unknown)
 importFrom(dials,parameters)
-importFrom(dplyr,"%>%")
 importFrom(dplyr,all_of)
 importFrom(dplyr,anti_join)
 importFrom(dplyr,arrange)

R/0_imports.R

@@ -1,4 +1,4 @@
-#' @importFrom dplyr filter select %>% full_join mutate bind_rows case_when vars
+#' @importFrom dplyr filter select full_join mutate bind_rows case_when vars
 #' @importFrom dplyr all_of ungroup slice bind_cols pull sample_n desc anti_join
 #' @importFrom dplyr distinct arrange rename mutate_if starts_with inner_join
 #' @importFrom dplyr last

R/acquisition.R

@@ -84,20 +84,20 @@ predict.prob_improve <-
     }
 
     new_data <-
-      new_data %>%
+      new_data |>
       mutate(.sd = ifelse(.sd <= object$eps, object$eps, .sd))
 
     if (maximize) {
       new_data <-
-        new_data %>%
+        new_data |>
         mutate(delta = ((.mean - best - trade_off) / .sd))
     } else {
       new_data <-
-        new_data %>%
+        new_data |>
         mutate(delta = ((trade_off + best - .mean) / .sd))
     }
-    new_data %>%
-      dplyr::mutate(objective = pnorm(delta)) %>%
+    new_data |>
+      dplyr::mutate(objective = pnorm(delta)) |>
       dplyr::select(objective)
   }
 
@@ -137,23 +137,23 @@ predict.exp_improve <- function(object, new_data, maximize, iter, best, ...) {
   }
 
   new_data <-
-    new_data %>%
+    new_data |>
     mutate(sd_trunc = ifelse(.sd <= object$eps, object$eps, .sd))
 
   if (maximize) {
-    new_data <- new_data %>% mutate(delta = .mean - best - trade_off)
+    new_data <- new_data |> mutate(delta = .mean - best - trade_off)
   } else {
-    new_data <- new_data %>% mutate(delta = trade_off + best - .mean)
+    new_data <- new_data |> mutate(delta = trade_off + best - .mean)
   }
   new_data <-
-    new_data %>%
+    new_data |>
     mutate(
       snr = delta / sd_trunc,
       z = ifelse(.sd <= object$eps, 0, snr),
       objective = (delta * pnorm(z)) + (sd_trunc * dnorm(z))
     )
 
-  new_data %>% dplyr::select(objective)
+  new_data |> dplyr::select(objective)
 }
 
 
@@ -188,9 +188,9 @@ predict.conf_bound <- function(object, new_data, maximize, iter, ...) {
 
   # `tune` is setup to always maximize the objective function
   if (maximize) {
-    new_data <- new_data %>% mutate(objective = .mean + kappa * .sd)
+    new_data <- new_data |> mutate(objective = .mean + kappa * .sd)
   } else {
-    new_data <- new_data %>% mutate(objective = -(.mean + kappa * .sd))
+    new_data <- new_data |> mutate(objective = -(.mean + kappa * .sd))
   }
-  new_data %>% dplyr::select(objective)
+  new_data |> dplyr::select(objective)
 }

R/case_weights.R

@@ -14,10 +14,10 @@
 #' library(parsnip)
 #' library(dplyr)
 #'
-#' frequency_weights(1:10) %>%
+#' frequency_weights(1:10) |>
 #'   .use_case_weights_with_yardstick()
 #'
-#' importance_weights(seq(1, 10, by = .1))%>%
+#' importance_weights(seq(1, 10, by = .1))|>
 #'   .use_case_weights_with_yardstick()
 .use_case_weights_with_yardstick <- function(x) {
   UseMethod(".use_case_weights_with_yardstick")

R/checks.R

@@ -451,8 +451,8 @@ check_initial <- function(x,
     param_nms <- .get_tune_parameter_names(x)
     if (inherits(x, "tune_race")) {
       num_resamples <-
-        x %>%
-        collect_metrics(summarize = FALSE) %>%
+        x |>
+        collect_metrics(summarize = FALSE) |>
         dplyr::count(.config)
       max_resamples <- max(num_resamples$n)
       configs <- num_resamples$.config[num_resamples$n == max_resamples]
@@ -461,8 +461,8 @@ check_initial <- function(x,
       x$.order <- NULL
     } else {
       num_grid <-
-        collect_metrics(x) %>%
-        dplyr::distinct(!!!rlang::syms(param_nms)) %>%
+        collect_metrics(x) |>
+        dplyr::distinct(!!!rlang::syms(param_nms)) |>
         nrow()
     }
     if (any(checks == "bayes")) {
@@ -472,7 +472,7 @@ check_initial <- function(x,
     }
   }
   if (!any(names(x) == ".iter")) {
-    x <- x %>% dplyr::mutate(.iter = 0L)
+    x <- x |> dplyr::mutate(.iter = 0L)
   }
   x
 }