A few questions to understand a random forest blog
I had a look at your questions and a brief look at the blog you posted. I will not go into detail and answer all your questions since it will take time and there should be one question. Nonetheless, I would like to give you some heads up on this and help you where possible.


When doing a confusion matrix is usually on a test set, you can see also where is used confusionMatrix(data = rparty.test, reference = testing$Sex). What is the usual is to have a training set, cross-validated to pick hyper-parameters and so forth and then test it on the test set. There is no rule just following a logical procedure. If these are new I would recommend diving into what are cross validations vs training set, test set and validation set.
Probably is talking about SMOTE upsampling (aka oversampling). This theory is for creating new artificial variables by creating synthetic ones (by combining features of the unprecedented class). Again I would recommend having a look there are many ways of dealing with an unbalanced dataset. Let's name a few: undersampling, oversampling, ensemble sampling (combing the previous) or you can tune certainly your model in order not to be fair towards a class and more.
Will not go into much detail about the ROC curve but I would recommend checking the AUC graphical plot instead of in this example and try to understand. Hint, the training dataset was balanced hence is reasonable a model would expect a balanced dataset for a test set instead is unbalanced.


All in all, I had a quick overview didn't go into depth of your blog and I didn't the find it very comprehensive plus uses confusing terms (as I remember), for instance, oversampling instead of upsampling or precision instead of specificity and recall for sensitivity. I wrote the answer as I empathise you as new in the area but next time please post only one question and make these sort of questions at the blog if applicable instead of here where the author directly can answer. Hope I helped you little bit, but you should also understand you opened a lot of topics.