Category: Visualization

The folks at Sharp Sight Labs have a great blog post on mapping geospatial data using R:

If you’ve learned the basics of data visualization in R (namely, ggplot2) and you’re interested in geospatial visualization, use this as a small, narrowly-defined exercize to practice some intermediate skills.

There are at least three things that you can learn and practice with this visualization:

1. Learn about color: Part of what makes this visualization compelling are the colors. Notice that in the area surrounding the US, we’re not using pure black, but a dark grey. For the title, we’re not using white, but a medium grey. Also, notice that for the rivers, we’re not using “blue” but a very specific hexadecimal color. These are all deliberate choices. As an exercise, I highly recommend modifying the colors. Play around a bit and see how changing the colors changes the “feel” of the visualization.

2. Learn to build visualizations in layers: I’ve emphasized this several times recently, but layering is an important principle of data visualization. Notice that we’re layering the river data over the USA country map. As an exercise, you could also layer in the state boundaries between the country map and the rivers. To do this, you can use map_data().

3. Learn about ‘Spatial’ data: R has several classes for dealing with ‘geospatial’ data, such as ‘SpatialLines‘, ‘SpatialPoints‘, and others. Spatial data is a whole different animal, so you’ll have to learn its structure. This example will give you a little experience dealing with it.

I also like the iterative approach they discuss.  You’ll almost never get it right the first go-around, but one of the nice things about ggplot2 is that it’s designed to be iterative:  you layer your changes on, making it a bit easier to fiddle with them to get the visualization just right.

David Smith shows off a very cool heatmap package called superheat:

While the superheat pacakge uses the ggplot2 package internally, it doesn’t itself follow the grammar of graphics paradigm: the function is more like a traditional base R graphics function with a couple of dozen options, and it creates a plot directly rather than returning a ggplot2 object that can be further customized. But as long as the options cover your heatmap needs (and that’s likely), you should find it a useful tool next time you need to represent data on a grid.

The superheat package apparently works with any R version after 3.1 (and I can confirm it works on the most recent, R 3.3.2). This arXiv paper provides some details and several case studies, and you can find more examples here. Check out the vignette for detailed usage instructions, and download it from its GitHub repository linked below.

Click through for some great-looking examples.

Tomaz Kastrun shows how to create animated charts in R using ggplot2:

In addition to R code, the ImageMagic program needs to be installed on your machine, as well. Also the speed, quality and many other parameters can be set, when creating animated gif.

Animated gif can be also included into your SSRS report, your Sharepoint site or any other site – like my blog 🙂 and it will stay interactive. In Power BI, importing animated gif as a picture, unfortunately will not work.

Be very careful with this, as not everything supports animated GIFs and you can make some really painful graphs if you try hard enough…

Dikesh Jariwala provides sample R code for seven common visualizations:

In your day-to-day activities, you’ll come across the below listed 7 charts most of the time.

1. Scatter Plot
2. Histogram
3. Bar & Stack Bar Chart
4. Box Plot
5. Area Chart
6. Heat Map
7. Correlogram

We’ll use ‘Big Mart data’ example as shown below to understand how to create visualizations in R. You can download the full dataset from here.

That’s a nice set of visuals, covering a broad swath of potential visualization scenarios.