namespace slice() (#213)

Author: simonpcouch

tests/testthat/test-parsnip-survival-censoring-weights.R

@@ -150,7 +150,7 @@ test_that('compute Graf weights', {
       predict(mod_fit, df, type = "time"),
       df
     ) %>%
-    slice(5)
+    dplyr::slice(5)
 
   wt_times <-
     parsnip:::graf_weight_time_vec(pred_surv$surv,

tests/testthat/test-survival-tune-grid.R

@@ -124,7 +124,7 @@ test_that("grid tuning survival models with static metric", {
     concordance_survival = numeric(0)
   )
 
-  expect_equal(metric_all %>% slice(), exp_metric_all)
+  expect_equal(metric_all %>% dplyr::slice(), exp_metric_all)
 })
 
 test_that("grid tuning survival models with integrated metric", {
@@ -268,7 +268,7 @@ test_that("grid tuning survival models with integrated metric", {
     brier_survival_integrated = numeric(0)
   )
 
-  expect_equal(metric_all %>% slice(), exp_metric_all)
+  expect_equal(metric_all %>% dplyr::slice(), exp_metric_all)
 })
 
 test_that("grid tuning survival models with dynamic metric", {
@@ -422,7 +422,7 @@ test_that("grid tuning survival models with dynamic metric", {
     brier_survival = numeric(0)
   )
 
-  expect_equal(metric_all %>% slice(), exp_metric_all)
+  expect_equal(metric_all %>% dplyr::slice(), exp_metric_all)
 })
 
 test_that("grid tuning survival models mixture of metric types", {
@@ -593,5 +593,5 @@ test_that("grid tuning survival models mixture of metric types", {
     brier_survival = numeric(0)
   )
 
-  expect_equal(metric_all %>% slice(), exp_metric_all)
+  expect_equal(metric_all %>% dplyr::slice(), exp_metric_all)
 })

tests/testthat/test-survival-tune-show-best.R

@@ -70,7 +70,7 @@ test_that("show_best with censored data - dynamic metric - bayes", {
     bayes_dyn_res %>%
       collect_metrics() %>%
       arrange(mean) %>%
-      slice(1:2)
+      dplyr::slice(1:2)
   )
   expect_snapshot(
     show_best(bayes_dyn_res)
@@ -119,7 +119,7 @@ test_that("show_best with censored data - static metric - anova racing", {
     count(.config) %>%
     filter(n == num_rs) %>%
     arrange(.config) %>%
-    slice(1) %>%
+    dplyr::slice(1) %>%
     pluck(".config")
 
   expect_equal(
@@ -178,7 +178,7 @@ test_that("show_best with censored data - static metric (+dyn) - W/L racing", {
     collect_metrics() %>%
     filter(.metric == "concordance_survival" & n == num_rs) %>%
     arrange(desc(mean)) %>%
-    slice(1:5) %>%
+    dplyr::slice(1:5) %>%
     pluck(".config")
 
   expect_equal(
@@ -241,7 +241,7 @@ test_that("show_best with censored data - dyn metric (+stc) - W/L racing", {
     collect_metrics() %>%
     filter(.metric == "brier_survival" & n == num_rs) %>%
     arrange(mean) %>%
-    slice(1:5) %>%
+    dplyr::slice(1:5) %>%
     pluck(".config")
 
   expect_equal(

tests/testthat/test-survival-tune_race_anova.R

@@ -205,7 +205,7 @@ test_that("race tuning (anova) survival models with static metric", {
     concordance_survival = numeric(0)
   )
 
-  expect_equal(metric_all %>% slice(), exp_metric_all)
+  expect_equal(metric_all %>% dplyr::slice(), exp_metric_all)
 })
 
 test_that("race tuning (anova) survival models with integrated metric", {
@@ -435,7 +435,7 @@ test_that("race tuning (anova) survival models with integrated metric", {
     brier_survival_integrated = numeric(0)
   )
 
-  expect_equal(metric_all %>% slice(), exp_metric_all)
+  expect_equal(metric_all %>% dplyr::slice(), exp_metric_all)
 })
 
 test_that("race tuning (anova) survival models with dynamic metrics", {
@@ -682,7 +682,7 @@ test_that("race tuning (anova) survival models with dynamic metrics", {
     brier_survival = numeric(0)
   )
 
-  expect_equal(metric_all %>% slice(), exp_metric_all)
+  expect_equal(metric_all %>% dplyr::slice(), exp_metric_all)
 })
 
 test_that("race tuning (anova) survival models with mixture of metric types", {
@@ -1036,7 +1036,7 @@ test_that("race tuning (anova) survival models with mixture of metric types", {
     concordance_survival = numeric(0)
   )
 
-  expect_equal(metric_all %>% slice(), exp_metric_all)
+  expect_equal(metric_all %>% dplyr::slice(), exp_metric_all)
 })
 
 test_that("race tuning (anova) - unneeded eval_time", {

tests/testthat/test-survival-tune_race_win_loss.R

@@ -203,7 +203,7 @@ test_that("race tuning (win_loss) survival models with static metric", {
     concordance_survival = numeric(0)
   )
 
-  expect_equal(metric_all %>% slice(), exp_metric_all)
+  expect_equal(metric_all %>% dplyr::slice(), exp_metric_all)
 
 })
 
@@ -433,7 +433,7 @@ test_that("race tuning (win_loss) survival models with integrated metric", {
     brier_survival_integrated = numeric(0)
   )
 
-  expect_equal(metric_all %>% slice(), exp_metric_all)
+  expect_equal(metric_all %>% dplyr::slice(), exp_metric_all)
 })
 
 test_that("race tuning (win_loss) survival models with dynamic metrics", {
@@ -666,7 +666,7 @@ test_that("race tuning (win_loss) survival models with dynamic metrics", {
     brier_survival = numeric(0)
   )
 
-  expect_equal(metric_all %>% slice(), exp_metric_all)
+  expect_equal(metric_all %>% dplyr::slice(), exp_metric_all)
 })
 
 test_that("race tuning (win_loss) survival models with mixture of metric types", {
@@ -1017,7 +1017,7 @@ test_that("race tuning (win_loss) survival models with mixture of metric types",
     concordance_survival = numeric(0)
   )
 
-  expect_equal(metric_all %>% slice(), exp_metric_all)
+  expect_equal(metric_all %>% dplyr::slice(), exp_metric_all)
 })