rud.is

There were enough eye-catching glitches in the experimental javascript support and the ugly large-number display in the spam example post that I felt compelled to make a couple formatting tweaks in the code. I also didn’t have time to do “real” work on the codebase this weekend.

So, along with spacing adjustments, there’s now an “add_commas” non-mandatory option that will toss commas in large numbers so they’re easy to read. Here’s an example of the new output (both the Raphaël display and commas):

As usual, it’s up on github

One of the last items for the 1.0 release is support for multiple columns of data. That will require some additional refactoring, so I’ve been procrastinating by exploring the recent “fudging” discovery. Despite claims to the contrary on other sites, there are more folks playing with slopegraphs than you might imagine. The inspiration for today’s installment comes from Jon Custer (@stuffisthings). He has a two part “Telling Stories with Data” series that does some exploration of export data with slopegraphs. In his “Slopegraph Strikes Back” post, Jon does a spiffy job discussing data visualization fundamentals and walks the reader through his re-design of a chart on commodities ranking, including a commentary on an aspect of slopegraphs that I’ve been noticing as I’ve been doing my exploring: the ‘scale’ problem (which I began to point out in the aforementioned “fudging” post).

The data set Jon is working with allows for a great exploration as to what works best when trying to convey a message with slopegraphs. I took the values from one of the tables he extracted:

and made a “raw” slopegraph from them (focusing on the “top 10”). The graphic won’t even come close to fitting in this post but you can grab the PDF of it and see how scale is the primary enemy of slopegraphs. It does show how gold and precious metal ores have skyrocketed from 1998 to 2007, but it’s hardly an engaging and easy to read visualization (unless you really like using your scroll wheel).

Jon grok’d this point, too, and decided to focus on the power law ranking and use the slopegraph to present the rate of change of each commodity:

While he didn’t “pull a Tufte” and just include values without caveat (see left & right 90° side labels), I still believe that there needs to be either increased annotation or the inclusion of base tabular data. Using my PySlopegraph code (forgot to mention the name change), I worked up a version of Jon’s visualization that I believe provides a clean, honest view of the data (click for larger view):

Because the chart is still based on the percentages that are fairly precise:

I finally added an option to the PySlopegraph configuration file for rounding (NOTE: rounding != true binning). If you add the “round_precision” option with a value that supports Python’s round function’s little-known second parameter (arbitrary positional rounding), you can have the values round to decimal or tens/hundreds/etc places which will help with scaling issues, but will also group items (in ways that you may not have originally intended). For this chart, if we use a value of “1” (first decimal rounding precision…use negative values for rounding on the whole integer side of the decimal) it’s still unreadable due to the scale it imposes by that precision, so I ended up using the nearest whole integer rounding option (value of “0”) and also included the table of actual values, along with annotating the “rate of change” nature of the slopes.

This (again) defeats the “no wasted ink (pixels?)” component of Tufte’s original creation, but I believe it’s necessary for some types of slopegraphs to ensure the chart can stand on it’s own. I’m definitely becoming more convinced that many slopegraphs are more suited for an interactive visualization where you can encode more information in rollovers/popups/etc plus allow for switching of view from percentage, power-law ranking or raw numeric comparison.

For those interested in playing with this particular data set, it’ll be included in the next github code push, which will also include the rounding feature.

As the codebase gets closer to the 1.0 stretch we now have the addition of slope colors for when values go up/down or remain constant between points. The code still only handles two columns of data, but the intent is for each segment to also be colored appropriately (up/down/same) in a multi-column layout.

I was scanning for ‘slopegraph’ again via a few search engines and came across Chris Conley’s (@ResearchChat) Education and Health Care – Using Slopegraphs to Understand Complex Systems. I really like what Chris has done with the slopegraph formatting and copied the LINH data example to the project. As you can see, Chris came up with a pretty neat way to handle the overlapping data/label issue (and one which I may “borrow” when expanding my slopegraph generator):

Since Chris used colors, it seemed like a fitting example to use to show off the newest feature of the slopegraph code. Here’s the output for the same data with my implementation.

Both “slope_up_color” & “slope_down_color” (Lines 23-24) control the slope color.

This example also showed that the ‘header’ processing needs some tweaking. The code currently assumes the header label width will be less than or equal to the width of the data labels. We’ll need to do some bounding box fitting and/or canvas expansion to enable more robust header text formatting.

Given the minor tweak, no code inclusion here but yet-another handy link to github.