Plotting training metric after benchmark experiment - r

Question

I want to access and plot both the training accuracy and the test accuracy after a benchmark experiment.
I am using accuracy as a metric.
If I set the aggregation of the accuracy to train.acc and create a list of both test.acc and train.acc, then the benchmark result cannot be plotted because there are two columns of class "acc" in the data frame, which are incidentally identical. However, I can see that the benchmark results contains the training accuracy even if the aggregation is not specified, as I have set the learners' predict.type to "both".
I thought of a workaround, which would be to extract the train.acc from the benchmark object and aggregate it and plot it myself.
How do I do that?
Is there a simpler way?
Thank you!
#Learners
learner_GLM <- makeLearner(cl = "classif.glmnet")
learner_SVM <- makeLearner(cl = "classif.ksvm")
learner_PCA <- cpoPca(rank=2) %>>% learner_GLM
#Data
dataA = datasets::iris
dataB = datasets::iris
#Task
task.A = makeClassifTask(data = dataA,target = "Species" )
task.B = makeClassifTask(data = dataB,target = "Species" )
task = list(task.A, task.B )
#Resample
inner = makeResampleDesc("CV", iters = 2, predict = "both")
outer = makeResampleDesc("CV", iters = 2, predict = "both")
#Tune wrappers
##Ctrl
ctrl = makeTuneControlRandom(maxit = 3L)
#1
numeric_ps = makeParamSet(
makeNumericParam("s", lower = -2, upper = 2, trafo = function(x) 2^x))
learner_GLM = makeTuneWrapper(learner_GLM, resampling =inner, par.set = numeric_ps, control = ctrl, show.info = FALSE)
#2
learner_PCA <- makeTuneWrapper(learner_PCA, resampling =inner, par.set = numeric_ps, control = ctrl, show.info = FALSE)
#3
numeric_ps = makeParamSet(
makeNumericParam("C", lower = -2, upper = 2, trafo = function(x) 2^x),
makeNumericParam("sigma", lower = -2, upper = 2, trafo = function(x) 2^x)
)
learner_SVM = makeTuneWrapper(learner_SVM, resampling = inner, par.set = numeric_ps, control = ctrl)
#Measures
trainaccuracy = setAggregation(acc, train.mean)
measures = list(acc, trainaccuracy)
#BMR
learners = list(learner_GLM,learner_SVM, learner_PCA)
bmr = benchmark(learners, task, outer, measures = measures, show.info = FALSE)
#Plot
plotBMRBoxplots(bmr, acc, style = "violin")
bmr$results$dataA$classif.glmnet.tuned$measures.train
bmr$results$dataA$classif.glmnet.tuned$measures.test