Visualize your data like an expert with hundreds of practical how-tos for presentation, analysis, and understanding.
Stacked area charts let you see categorical data over time. Interaction allows you to focus on specific categories without losing sight of the big picture.
For presentation purposes, it can be useful to adjust the style of your axes and reference lines for readability. It's all about the details.
Provide a slider for the standard bar chart so that users can shift focus to a point of interest.
Make a bunch of charts, string them together like a flip book, and there's your animation. Sometimes good for showing changes over time. Always fun to play with.
Quickly compare two time series variables with this line-area chart hybrid that originated in the 1700s. Also known as: difference chart.
The conterminous United States always gets the attention, while Alaska and Hawaii are often left out. It is time to bring them back into view.
It's like working with a bunch of tiny dots, and oh look, all of sudden patterns emerge.
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.