Learn to visualize your data like an expert with these practical how-tos for presentation, analysis, and understanding.
No need to settle for the mapping defaults in R. Apply map projections to show geographic data in a way most suitable for your work.
Before you can do anything with data, you have to get it into the application. Working with an Arduino is no different. Although the process is changes, if you're used to working with desktop software.
Geographic data is often available as a shapefile, and there's plenty of heavy software to get that data in a map. R is an open source option, and as a bonus, much of the work can be done in a few lines of code.
Customizing your charts doesn't have to be a time-intensive process. With just a teeny bit more effort, you can get something that fits your needs.
Small multiples are great, and the right interactions can make them even better. A primer and a how-to.
You have a list of things that can be ordered by different values. Let them sort themselves out.
Choropleth maps are useful to show values for areas on a map, but they can be limited. In contrast, dot density maps are sometimes better for showing distributions within regions.
Treemaps are useful to view and explore hierarchical data. Interaction can help you look at the data in greater detail.
For when your geographic data is evenly spread rather than aggregated by government boundaries.
Email provides a window into who we interact with and what we do. This tutorial describes how to get that data in the format you want.
Change detection for a time series can be tricky, but guess what, there's an R package for that. Then show the results in a custom plot.
Too many points to plot often means obscured patterns in the clutter. Density maps offer a smooth alternative.
The chart type often goes overlooked because people don't understand them. Maybe this will help.
As people and things move through a place, it can be useful to see their connected paths instead of just individual points.
A big part of statistics is comparisons, and perhaps more importantly, to figure out what to compare things to. Perspective changes with the baseline.
Text can provide much needed context to traditional visual cues and can be used as a visual cue itself in some cases.
A frequent challenge of visualization is behind the scenes, to get the data and to mold it into the format you need. Do that. Then map.
The combination of a time series chart and a scatter plot lets you compare two variables along with temporal changes.
Maybe you want to make spatial comparisons over time or across categories. Organized small maps might do the trick.
Also known as specialized or custom line charts. Figure out how to draw lines with the right spacing and pointed in the right direction, and you've got your slopegraphs.
Make a lot of charts at once, line them up in a grid, and you can make quick comparisons across several categories.
Although time series plots and small multiples can go a long way, animation can make your data feel more real and relatable. Here is how to do it in R via the animated GIF route.
When you plot a lot of data at once, points and lines can obscure others and hide patterns. Transparency can help reveal what is really there.