Thanks. So the glm model object is based on about 200,000 observations, which might explain why the graphs are slow to show up (ie it's not RStudio which is slow, it's R)...? I suppose I might be able to modify my code to just plot an x% (1% perhaps) random sample of the data instead, which might capture the essence of what's going on without taking so much time.
An example of the code I've written (where glm_B is the model object created by glm():
res <- residuals(glm_B, type = "deviance")
predicted_values <- exp(predict(glm_B))
plot(predicted_values, res, ylim = c(-10000, 10000))
abline(h = 0, lty = 2)