I personally feel a bit weird about citing specific real examples in a presentation and would create a fake example for a slide. But if I ever needed to find an example in order to demonstrate the usefulness of RStudio Community, I think I'd have a look at the topics marked Solved on a category like tidyverse, shiny or General.
Solved topics have the 
in the solution-box.
(You can also add in:solved to any search query, but I think this feature currently has a bug that make this not work as intended currently.)
Highlight reprex
My favorite examples make use of a reprex in the question and answer. Here's a recent example of that.
In fact, the reprex package (Get help!) is a great example of some of the awesome work folks in the R community are doing to make R a nice place to learn and do data science. Reproducible Examples have long been the gold standard for how to pose coding questions, and the reprex package is a handy tool to create one in R, with features to include images, error messages, and warning messages, all formatted for github, Stack Overflow or RStudio Community.
For a bit of the backstory, the initial idea came from a tweet from Romain, and then Jenny Bryan ran with it and created the package. Since then, on RStudio Community alone, there's been thousands of solutions to coding programs which were helped by reprex.
RStudio Community just added a question tag, which is optimized around the Question & Answer model popularized by forums like Stack Overflow.
For question askers
I think one suggestion would be to prime students early-on to not just be receptive to the advice they get about solving their specific coding problem, but to be receptive to advice on how they ask their question.
For example the quicker they get on board posing questions as reproducible examples, the quicker they'll have success seeking help from others online.
For helpers
And then I'd also encourage anyone helping answer questions to adopt a positive + reactive approach to answering questions. There exist a lot of documentation out there on how to solve coding problems, but aspiring data scientists, and particularly students, are unlikely to know how to engage well with that material. And so they post poorly formed questions to an online help forum.
I then see too many experienced forum participants getting perturbed by such basic, poorly formed questions, and then reply in a way easily perceived as negative. It sometimes feels like folks answering questions on other help forums have the unrealistic expectation for new R-users will have read docs and help guides they may not have even knew existed.
Instead, it's helpful to have a friendly, welcoming reply on the ready, and direct them the best practices to posing their question in a way most likely to attract help and get a solution.
It can be helpful to offer conditional advice, "Welcome! Sorry about this issue. I'd be happy to answer your question, but first, could you have a look at these docs, and reform your question into a reprex? "