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There are a number of ways you might speed up your code. A good way to get specific advice on this is to provide a reproducible example (what folks usually refer to as a reprex) of your problem, data and code. (And I'd suggest offering a smaller, but representative dataset to start with).
In terms of general strategies to speed up your R-code (or any code for that matter), I would suggest checking out @csgillespie's "Efficient optimisation" chapter from "Efficient R programming". If you can overlook his (British) misspellings of optimisation, there's a wealth of good advice there.
Given the limited info about the issue so far, and just some ideas; I might work through
- data loading, can you load your data faster? perhaps via a database?
- are there computationally costly operations that can be avoided or optimized?
- can you benefit from
parallelisation? - can you reduce how much data your visualization needs to plot (eg smoothers or densities)?