diff --git a/R/Dataset.R b/R/Dataset.R
index a915214..bd3a8b5 100644
--- a/R/Dataset.R
+++ b/R/Dataset.R
@@ -52,7 +52,7 @@ Dataset <- torch::dataset(
     dt <- data.table::data.table(rows=dataCat$rowId, cols=dataCat$columnId)
     maxFeatures <- max(dt[, .N, by=rows][,N])
     start <- Sys.time()
-    tensorList <- lapply(1:max(data %>% pull(rowId)), function(x) {
+    tensorList <- lapply(1:max(data %>% dplyr::pull(rowId)), function(x) {
       torch::torch_tensor(dt[rows==x, cols])
       })
     self$lengths <- lengths
diff --git a/tests/testthat/test-DeepNNTorch.R b/tests/testthat/test-DeepNNTorch.R
index 3c31541..af6492a 100644
--- a/tests/testthat/test-DeepNNTorch.R
+++ b/tests/testthat/test-DeepNNTorch.R
@@ -52,7 +52,7 @@ test_that("setDeepNNTorch with runPlp working checks", {
   testthat::expect_true('performanceEvaluation' %in% names(res))
   
   # check prediction same size as pop
-  testthat::expect_equal(nrow(res$prediction %>% filter(evaluationType %in% c('Train', 'Test'))), 
+  testthat::expect_equal(nrow(res$prediction %>% dplyr::filter(evaluationType %in% c('Train', 'Test'))), 
                          nrow(population))
   
   # check prediction between 0 and 1
@@ -67,7 +67,7 @@ test_that("Triple layer-nn works", {
                             epochs= c(5),  seed=NULL)
   
   sink(nullfile())
-  results <- fitDeepNNTorch(trainData$Train, deepset$param, analysisId=1)
+  results <- fitDeepNNTorch(trainData$Train, deepset, analysisId=1)
   sink()
   
   expect_equal(class(results), 'plpModel')
diff --git a/tests/testthat/test-ResNet.R b/tests/testthat/test-ResNet.R
index f05b4af..41e54d9 100644
--- a/tests/testthat/test-ResNet.R
+++ b/tests/testthat/test-ResNet.R
@@ -66,7 +66,8 @@ test_that("ResNet with runPlp working checks", {
   testthat::expect_true('performanceEvaluation' %in% names(res2))
   
   # check prediction same size as pop
-  testthat::expect_equal(nrow(res2$prediction %>% filter(evaluationType %in% c('Train', 'Test'))), nrow(population))
+  testthat::expect_equal(nrow(res2$prediction %>% 
+                                dplyr::filter(evaluationType %in% c('Train', 'Test'))), nrow(population))
   
   # check prediction between 0 and 1
   testthat::expect_gte(min(res2$prediction$value), 0)
diff --git a/tests/testthat/test-dataset.R b/tests/testthat/test-dataset.R
index 259d40e..e692ec8 100644
--- a/tests/testthat/test-dataset.R
+++ b/tests/testthat/test-dataset.R
@@ -6,7 +6,7 @@ test_that("length of index correct", {
   
 testthat::expect_equal(
 length(dataset$getNumericalIndex()),
-n_distinct(mappedData$covariates %>% pull(covariateId)))
+dplyr::n_distinct(mappedData$covariates %>% dplyr::pull(covariateId)))
 
 })
 
@@ -14,7 +14,7 @@ test_that("number of num and cat features sum correctly", {
   
 testthat::expect_equal(
   dataset$numNumFeatures()+dataset$numCatFeatures(), 
-  n_distinct(mappedData$covariates %>% pull(covariateId))
+  dplyr::n_distinct(mappedData$covariates %>% dplyr::pull(covariateId))
 )
 
 })
@@ -26,7 +26,7 @@ test_that("length of dataset correct", {
   expect_equal(length(dataset), dataset$num$shape[1])
   expect_equal(
     dataset$.length(), 
-    n_distinct(mappedData$covariates %>% pull(rowId))
+    dplyr::n_distinct(mappedData$covariates %>% dplyr::pull(rowId))
   )
   
 })