Katie Jolly wanted to surprise a friend with a nice geeky gift: a custom-made map cutout. Using R and some visual finetuning in Inkscape, she was able to made the below.
A detailed write-up of how Katie got to this product is posted here.
Basically, the R’s tigris package included all data on roads, and the ArcGIS Open Data Hub provided the neighborhood boundaries. Fortunately, the sf package is great for transforming and manipulating geospatial data, and includes some functions to retrieve a subset of roads based on their distance to a centroid. With this subset, Katie could then build these wonderful plots in no time with ggplot2.
Lisa Charlotte Rost of DataWrapper often writes about data visualization and lately she has focused on the (im)proper use of color in visualization. In this recent blog, she gives a bunch of great tips and best practices, some of which I copied below:
Xeno.graphics is the collection of unusual charts and maps Maarten Lambrechts maintains. It’s a repository of novel, innovative, and experimental visualizations to inspire you, to fight xenographphobia, and popularize new chart types.
For instance, have you ever before heard of a time curve? These are very useful to visualize the development of a relationship over time.
The upset plot is another example of an upcoming visualization. It can demonstrate the overlap or insection in a dataset. For instance, in the social network of #rstats twitter heroes, as the below example from the Xenographics website does.
The below necklace map is new to me too. What it does precisely is unclear to me as well.
There are hundreds of other interestingcharts, maps, figures, and plots, so do have a look yourself. Moreover, the xenographics collection is still growing. If you know of one that isn’t here already, please submit it. You can also expect some posts about certain topics around xenographics.
Joel Simon is the genius behind an experimental project exploring optimized school blueprints. Joel used graph-contraction and ant-colony pathing algorithms as growth processes, which could generate elementary school designs optimized for all kinds of characteristics: walking time, hallway usage, outdoor views, and escape routes just to name a few.
Aleszu started his analysis on only the French wines, with a simple word count per region:
Next, he applied TF-IDF to surface the words that are most characteristic for specific French wine regions — words used often in combination with that specific region, but not in relation to other regions.
The data also contained some price information, which Aleszu mapped France with ggplot2 and the maps package to demonstrate which French wine regions are generally more costly.
On the full dataset, Alezsu also demonstrated that there is a strong relationship between price and points, meaning that, in general, more expensive wines seem to get better reviews:
The full script and more details you can find in the orginal blog.