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.
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.
Here's where to go next once you've covered the basics of visualization. When it's time to actually start making things.
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.
Tips on making it through, what I would tell my previous self going in, and advice on taking advantage of the unique opportunity that is graduate school.
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.
You get a lot of bang for the buck with R, charting-wise, but it can be confusing at first, especially if you've never written code. Here are some examples to get started.
When presented with a static graphic, it can be useful to see specific values after you see overall patterns. This tutorial shows you how to add simple interactions to a choropleth map so you can get specifics for regions.
You saw how to make basic heat maps a while back, but you might want more flexibility for a specific data set. Once you understand the components of a heat map, the rest is straightforward.
Sometimes these cartograms can distort areas beyond recognition, but they can also provide a better visual representation for a region with a wide range of subregions. At the least, they're fun to look at.
From the basic area chart, to the stacked version, to the streamgraph, the geometry is similar. Once you know how to do one, you can do them all.
When base graphics and existing packages don't do it for you, turn to low-level graphics functions to make what you want.
Single data points from a large dataset can make it more relatable, but those individual numbers don't mean much without something to compare to. That's where distributions come in.
These tend to be made ad hoc and are usually pieced together manually, which takes a lot of time. Here's a way to lay the framework in R, so you don't have to do all the work yourself.
Time series charts can easily turn to spaghetti when you have multiple categories. By highlighting the ones of interest, you can direct focus and allow comparisons.
The familiar but underused layout is a good way to look at patterns over time. This tutorial gives you an easy way to make them and guides you through the code so you can adapt it to your needs.
You can control graph elements with code as you output things from R, but sometimes it is easier to do it manually. Inkscape, an Open Source alternative to Adobe Illustrator, might be what you are looking for.
Filled contour plots are useful for looking at density across two dimensions and are often used to visualize geographic data. It's straightforward to make them in R — once you get your data in the right format, that is.
Color can drastically change how a chart reads and what you see in your data, so don't leave it up to chance with defaults.
When you have several time series over many categories, it can be useful to show them separately rather than put it all in one graph. This is one way to do it interactively with categorical filters.
There are various ways to visualize connections, but one of the most intuitive and straightforward ways is to actually connect entities or objects with lines. And when it comes to geographic connections, great circles are a nice way to do this.
Ever since Hans Rosling presented a motion chart to tell his story of the wealth and health of nations, there has been an affinity for proportional bubbles on an x-y axis. This tutorial is for the static version of the motion chart: the bubble chart.
The goal of Chernoff faces is to show a bunch of variables at once via facial features like lips, eyes, and nose size. Most of the time there are better solutions, but the faces can be interesting to work with.