Learn to visualize your data like an expert with these practical how-tos for presentation, analysis, and understanding.
The relatively new and lesser known time series visualization can be useful if you know what you're looking at, and they take up a lot less space.
The code to create these bar chart variations is almost the same as if you were to make a standard bar chart. But make sure you get the math right.
Where to start? What to learn next? Here's a course to help take you from beginner to advanced.
It's easy to draw dots. The challenge is to make them meaningful and readable.
Learn to draw lines wherever and however you want, and you've got yourself some flexibility.
You can customize graphics in R with
par(), but the docs are mostly text and just organized alphabetically. Here is a more visual reference, categorized by what you can change. Plus, a one-page printout.
The chart type seems simple enough, but there sure are a lot of bad ones out there. Get yourself out of default mode.
Let readers focus on the regions they care about to make their own comparisons and conclusions.
It might not be sexy, but you have to load your data and get it in the right format before you can visualize it. Here are the basics, which might be all you need.
Fill those empty polygons with color, based on shapefile or external data.
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.