Running ‘testthat.R’ [35s/35s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(xgboost)
  > 
  > test_check("xgboost")
  [1]	train-error:0.016275 
  [1]	train-merror:0.046667 
  [2]	train-merror:0.033333 
  [3]	train-merror:0.040000 
  [4]	train-merror:0.033333 
  [5]	train-merror:0.020000 
  [6]	train-merror:0.026667 
  [7]	train-merror:0.013333 
  [8]	train-merror:0.006667 
  [9]	train-merror:0.013333 
  [10]	train-merror:0.020000 
  [11]	train-merror:0.013333 
  [12]	train-merror:0.013333 
  [13]	train-merror:0.013333 
  [14]	train-merror:0.013333 
  [15]	train-merror:0.013333 
  [1]	train-error:0.046522 
  [2]	train-error:0.022263 
  [3]	train-error:0.007063 
  [4]	train-error:0.015200 
  [1]	train-error:0.046522 
  [2]	train-error:0.022263 
  [3]	train-error:0.007063 
  [4]	train-error:0.015200 
  [3]	train-error:0.007063 
  [4]	train-error:0.015200 
  [3]	train-error:0.007063 
  [4]	train-error:0.015200 
  [1]	train-error:0.046522+0.001465	test-error:0.046523+0.005863 
  [2]	train-error:0.022263+0.000787	test-error:0.022263+0.003150 
  [1]	train-auc:0.900000	test-auc:0.800000 
  [1]	train-error:0.203746	test-error:0.204221 
  [2]	train-error:0.200368	test-error:0.200497 
  [1]	train-error:0.203746	test-error:0.204221 
  [2]	train-error:0.200368	test-error:0.200497 
  [1]	train-error:0.203746	test-error:0.204221 
  [2]	train-error:0.200368	test-error:0.200497 
  [1]	train-error:0.203746	test-error:0.204221 
  [2]	train-error:0.201136	test-error:0.201117 
  [1]	train-error:0.203746	test-error:0.204221 
  [2]	train-error:0.201750	test-error:0.201117 
  [1]	train-error:0.201136+0.002733	test-error:0.206817+0.000185 
  [2]	train-error:0.201136+0.002733	test-error:0.206817+0.000185 
  [1]	train-error:0.496238	test-error:0.484171 
  [2]	train-error:0.496238	test-error:0.484171 
  [1]	train-error:0.203746	test-error:0.204221 
  [2]	train-error:0.200368	test-error:0.200497 
  [1]	train-error:0.203746	test-error:0.204221 
  [2]	train-error:0.200368	test-error:0.200497 
  [1]	train-error:0.204481+0.096101	test-error:0.207983+0.390638 
  [2]	train-error:0.200259+0.099363	test-error:0.203377+0.398311 
  [3]	train-error:0.200259+0.099363	test-error:0.203533+0.398241 
  [4]	train-error:0.200182+0.099709	test-error:0.203686+0.398162 
  [1]	train-error:0.204481+0.096101	test-error:0.207983+0.390638 
  [2]	train-error:0.200259+0.099363	test-error:0.203377+0.398311 
  [3]	train-error:0.200259+0.099363	test-error:0.203533+0.398241 
  [4]	train-error:0.200182+0.099709	test-error:0.203686+0.398162 
  [1]	train-rmse:0.390075+0.096278	test-rmse:0.410845+0.212731 
  [2]	train-rmse:0.369592+0.128371	test-rmse:0.403459+0.260862 
  [1]	train-merror:0.037770+0.015780	test-merror:0.039501+0.028845 
  [2]	train-merror:0.037770+0.013065	test-merror:0.039501+0.028845 
  [3]	train-merror:0.039983+0.015912	test-merror:0.039501+0.028845 
  [4]	train-merror:0.031133+0.009969	test-merror:0.032744+0.027791 
  [5]	train-merror:0.035558+0.010842	test-merror:0.039501+0.028845 
  [1]	eval-error:0.042831	train-error:0.046522 
  [2]	eval-error:0.021726	train-error:0.022263 
  [1]	eval-error:0.042831	train-error:0.046522 
  [2]	eval-error:0.021726	train-error:0.022263 
  [20:11:02] 100x126 matrix with 2200 entries loaded from /tmp/RtmpLoVIcC/xgb.DMatrix_4f1c1d056f57
  [1]	train-error:0.046522 
  [2]	train-error:0.022263 
  [1]	eval-error:0.025450	train-error:0.029633 
  [2]	eval-error:0.003724	train-error:0.005988 
  [1]	train-error:0.178571 
  [2]	train-error:0.178571 
  [3]	train-error:0.166667 
  [4]	train-error:0.166667 
  [5]	train-error:0.166667 
  [6]	train-error:0.190476 
  [7]	train-error:0.166667 
  [8]	train-error:0.166667 
  [9]	train-error:0.154762 
  [10]	train-error:0.166667 
  [1]	train-error:0.273810 
  [2]	train-error:0.285714 
  [3]	train-error:0.285714 
  [4]	train-error:0.285714 
  [5]	train-error:0.285714 
  [6]	train-error:0.285714 
  [7]	train-error:0.285714 
  [8]	train-error:0.285714 
  [9]	train-error:0.285714 
  [10]	train-error:0.285714 
  [1]	train-error:0.261905 
  ── 1. Error: xgb.importance works with and without feature names (@test_helpers.
  ggplot2 package is required
  1: xgb.ggplot.importance(importance_matrix = importance.Tree) at testthat/test_helpers.R:126
  2: stop("ggplot2 package is required", call. = FALSE)
  
  ── 2. Error: xgb.importance works with GLM model (@test_helpers.R#136)  ────────
  ggplot2 package is required
  1: xgb.ggplot.importance(importance.GLM) at testthat/test_helpers.R:136
  2: stop("ggplot2 package is required", call. = FALSE)
  
  ── 3. Error: xgb.plot.tree works with and without feature names (@test_helpers.R
  DiagrammeR package is required for xgb.plot.tree
  1: xgb.plot.tree(feature_names = feature.names, model = bst.Tree) at testthat/test_helpers.R:140
  2: stop("DiagrammeR package is required for xgb.plot.tree", call. = FALSE)
  
  ── 4. Error: xgb.plot.multi.trees works with and without feature names (@test_he
  there is no package called 'DiagrammeR'
  1: xgb.plot.multi.trees(model = bst.Tree, feature_names = feature.names, features_keep = 3) at testthat/test_helpers.R:145
  2: DiagrammeR::create_node_df
  3: getExportedValue(pkg, name)
  4: asNamespace(ns)
  5: getNamespace(ns)
  6: tryCatch(loadNamespace(name), error = function(e) stop(e))
  7: tryCatchList(expr, classes, parentenv, handlers)
  8: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  9: value[[3L]](cond)
  
  ── 5. Error: xgb.plot.deepness works (@test_helpers.R#153)  ────────────────────
  ggplot2 package is required for plotting the graph deepness.
  1: xgb.ggplot.deepness(model = bst.Tree) at testthat/test_helpers.R:153
  2: stop("ggplot2 package is required for plotting the graph deepness.", call. = FALSE)
  
  [1]	train-rmse:10.747972 
  [2]	train-rmse:9.690369 
  [3]	train-rmse:8.739426 
  [4]	train-rmse:7.883958 
  [5]	train-rmse:7.114398 
  [6]	train-rmse:6.423699 
  [7]	train-rmse:5.802751 
  [8]	train-rmse:5.245382 
  [9]	train-rmse:4.745181 
  [10]	train-rmse:4.296904 
  [11]	train-rmse:3.895390 
  [12]	train-rmse:3.536258 
  [13]	train-rmse:3.215397 
  [14]	train-rmse:2.929422 
  [15]	train-rmse:2.674589 
  [16]	train-rmse:2.448668 
  [17]	train-rmse:2.248347 
  [18]	train-rmse:2.071898 
  [19]	train-rmse:1.916581 
  [20]	train-rmse:1.780638 
  [21]	train-rmse:1.662167 
  [22]	train-rmse:1.559175 
  [23]	train-rmse:1.470356 
  [24]	train-rmse:1.394075 
  [25]	train-rmse:1.328962 
  [26]	train-rmse:1.273625 
  [27]	train-rmse:1.226807 
  [28]	train-rmse:1.187454 
  [29]	train-rmse:1.154489 
  [30]	train-rmse:1.127020 
  [31]	train-rmse:1.104215 
  [32]	train-rmse:1.085353 
  [33]	train-rmse:1.069678 
  [34]	train-rmse:1.056795 
  [35]	train-rmse:1.046242 
  [36]	train-rmse:1.037560 
  [37]	train-rmse:1.030459 
  [38]	train-rmse:1.024645 
  [39]	train-rmse:1.019900 
  [40]	train-rmse:1.016019 
  [41]	train-rmse:1.012858 
  [42]	train-rmse:1.010275 
  [43]	train-rmse:1.008168 
  [44]	train-rmse:1.006454 
  [45]	train-rmse:1.005056 
  [46]	train-rmse:1.003913 
  [47]	train-rmse:1.002987 
  [48]	train-rmse:1.002230 
  [49]	train-rmse:1.001613 
  [50]	train-rmse:1.001111 
  [51]	train-rmse:1.000702 
  [52]	train-rmse:1.000368 
  [53]	train-rmse:1.000097 
  [54]	train-rmse:0.999874 
  [55]	train-rmse:0.999694 
  [56]	train-rmse:0.999546 
  [57]	train-rmse:0.999426 
  [58]	train-rmse:0.999328 
  [59]	train-rmse:0.999248 
  [60]	train-rmse:0.999183 
  [61]	train-rmse:0.999130 
  [62]	train-rmse:0.999086 
  [63]	train-rmse:0.999050 
  [64]	train-rmse:0.999022 
  [65]	train-rmse:0.998998 
  [66]	train-rmse:0.998979 
  [67]	train-rmse:0.998963 
  [68]	train-rmse:0.998950 
  [69]	train-rmse:0.998939 
  [70]	train-rmse:0.998931 
  [71]	train-rmse:0.998924 
  [72]	train-rmse:0.998918 
  [73]	train-rmse:0.998913 
  [74]	train-rmse:0.998909 
  [75]	train-rmse:0.998906 
  [76]	train-rmse:0.998903 
  [77]	train-rmse:0.998901 
  [78]	train-rmse:0.998899 
  [79]	train-rmse:0.998898 
  [80]	train-rmse:0.998897 
  [81]	train-rmse:0.998896 
  [82]	train-rmse:0.998895 
  [83]	train-rmse:0.998894 
  [84]	train-rmse:0.998894 
  [85]	train-rmse:0.998893 
  [86]	train-rmse:0.998893 
  [87]	train-rmse:0.998893 
  [88]	train-rmse:0.998893 
  [89]	train-rmse:0.998892 
  [90]	train-rmse:0.998892 
  [91]	train-rmse:0.998892 
  [92]	train-rmse:0.998892 
  [93]	train-rmse:0.998892 
  [94]	train-rmse:0.998892 
  [95]	train-rmse:0.998892 
  [96]	train-rmse:0.998892 
  [97]	train-rmse:0.998892 
  [98]	train-rmse:0.998892 
  [99]	train-rmse:0.998892 
  [100]	train-rmse:0.998892 
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 451 SKIPPED: 2 FAILED: 5
  1. Error: xgb.importance works with and without feature names (@test_helpers.R#126) 
  2. Error: xgb.importance works with GLM model (@test_helpers.R#136) 
  3. Error: xgb.plot.tree works with and without feature names (@test_helpers.R#140) 
  4. Error: xgb.plot.multi.trees works with and without feature names (@test_helpers.R#145) 
  5. Error: xgb.plot.deepness works (@test_helpers.R#153) 
  
  Error: testthat unit tests failed
  Execution halted