You don't have to do it all by hand anymore. Note: I don't work with this kind of data.
Here's what happens more often than it should when I sit down to make some figures:
- I start with R and remember seeing an example similar to what I'm trying to make
- Searching for the example leads me back to the helpful but limited ggplot2 docs or a Stackoverflow question
- I piece together what I need from what I've found, knowing all the while that a better example was out there somewhere
This kind of thing drives me crazy, so here's a short list of places where decent R graph examples can be found. Hopefully these sites help others find what they need. One caveat: just because R can spit out a particular figure doesn't mean that figure is appropriate or represents the data well. Your mileage may vary, etc.
- R Graph Catalog. Intended to complement a guidebook, this set of examples covers a wide variety of presentations and audiences. It filters graph types based on whether they're recommended or not (but hey, you can still use the examples). All code is included right on the site and on Github.
- Quick-R Graphs. This site covers the basics and includes some useful figures like a plotting symbol chart.
- Cookbook for R Graphs. Another book accompaniment.
- Plotly R Library. Here's where things start to get exotic. Plotly isn't R specific but supposedly plays nice with ggplot2. It could be worth using for interactive charts.
- Gvis cookbook. A ggplot2 alternative. It also allows for interactive graphs.
- Wikibooks R Programming Graphics. A few more examples, including some in 3D (those should probably be avoided, honestly).
- R-bloggers. Not a list of examples as much as a source for examples, especially those of the bleeding-edge kind.
If you're getting tired of finding example graphs, there's also GrapheR, a GUI for producing R graphics.
Edit: wanted to add a postscript about some hacky ways to make ggplot2 display patterns.
Edit 2: I found one more resource in the ZevRoss ggplot2 cheatsheet. It's primary a guide to themes in plots, one of the more fiddly aspects of plotting in R.