1. Polishing a map
You now have all the data together for your plot, but we aren't quite finished yet. There are a few issues with our map and a few things we might adjust to make it even better.
2. Polishing a map
Making good plots is always an iterative process. You try something, take a look, keep the things that work, and try something else for the things that don't work. Then repeat.
Here are some suggestions we have for what you should think about when you are critiquing your plots.
First, the whole point of making your plot was to display some data. Make sure it is the data that gets the most attention, this means removing or toning down distracting non-data items.
When working with maps, it's important that the spatial context you add is useful and not distracting. For example, adding roads might help someone orient themselves, but don't make the roads the most salient visual object.
Remember that most viewers of the plot won't be as familiar with the data as you are. Make sure your title, legend and labels are clear, for example spell out variable meanings rather than relying on the variable name used in R.
Annotations are non-data items that help your viewers. If there is something particular your viewers should see or take away, point it out! And make sure you clearly attribute data sources.
3. Critiquing our map
Let's take a look at some improvements you might make to your plot.
Our neighborhood shape file had neighborhoods from all over the New York area, but the ones outside of Manhattan are just distracting, you should remove them.
4. Critiquing our map
What neighborhood is the really high income next to Central Park? Unless you know New York that's hard to answer. The neighborhood data should be adding this context but it doesn't right now because there are no labels. You should add some.
5. Critiquing our map
The legend says "Estimate", estimate of what? And where did this data come from?
6. Critiquing our map
Finally, you should always experiment with colors and line weights of all your objects. You might not think it matters, but take a look at this example.
7. The effect of line weights and color
Here's a choropleth of the Netherlands where both provinces and municipalities have the same weight black lines.
Here's the same plot where all lines are now grey and the muncipalities are much thinner and lighter than the provinces. Notice how much less busy the plot seems, and how you focus more on the data, rather than the mass of black lines.
8. Let's practice!
OK, your turn to fix these things and really make this map shine.