One of the best features of Strava is the battle to be King (or Queen) of the Mountain. Originally, in cycling, segments were typically climbs or difficult sections of road, and the simple idea, is who can complete the segment in the quickest time. Hence they would be KOM/QOM, King or Queen of the Mountain. Segments quickly expanded to pretty much any section of a course and to include running segments, to separate them from cycling.

A few days ago, my wife, a bunch of my kids, and I were huddled around a big wall map of the United States, joking about the relative unimportance of Rhode Island, the smallest state in the US. It’s one of the states I never ever think about: …and it’s just so small . Amid the joking, my wife came to Rhode Island’s defense by declaring that even though it’s so small, it has one of the highest proportions of coastline to land borders.

The students in my summer data visualization class are finishing up their final projects this week and I’ve been answering a bunch of questions on our class Slack. Often these are relatively standard reminders of how to tinker with specific ggplot layers (chaning the colors of a legend, adding line breaks in labels, etc.), but today one student had a fascinating and tricky question that led me down a realy fun dataviz rabbit hole.

As I explained in my previous blog post, in June 2023 I drove my family across the country in a 5,000-mile roundtrip odyssey from Atlanta, Georgia to just-south-of-Salt Lake City, Utah, and back again.

In a couple days, I’m going to drive across the country to Utah, my home state. I haven’t been out west with my whole family in four years—not since 2019 when we moved from Spanish Fork, Utah to Atlanta, Georgia. According to Google Maps, it’s a mere 1,900 miles (3,000 kilometers) through the middle of the United States and should take 28 hours, assuming no stops.

I’ve taught a course on data visualization with R since 2017, and it’s become one of my more popular classes, especially since it’s all available asynchronously online with hours of Creative Commons-licensed videos and materials.

mapscanner It is sometimes easy in the midst of the cutting-edge world of uniquesoftware development that is rOpenSci to forget that even though oursoftware might be freely available from anywhere in the world, access toadequate hardware is often restricted.

Some notes to self about future directions for the "million DNA barcodes map" http://iphylo.org/~rpage/bold-map/. At the moment we have an interactive map that we can pan and zoom, and click on a marker to get a list of one or more barcodes at the location. We can also filter by major taxonomic group. Here are some ideas on what could be next. Search At the moment search is simply browsing the map.

Editor’s note: This is a guest post by Matt Sundquist from Plot.ly. Ggplotly and Plotly’s R API let you make ggplot2 plots, add py$ggplotly(), and make your plots interactive, online, and drawn with D3. Let’s make some.1. Getting Started and Examples Here is Fisher’s iris data.library("ggplot2")ggiris <- qplot(Petal.Width, Sepal.Length, data = iris, color = Species)print(ggiris) Let’s make it in Plotly.

We have previously written about creating interactive maps on the web from R, with the interactive maps on Github. See here, here, here, and here. A different approach is to use CartoDB, a freemium service with sql interface to your data tables that provides a map to visualize data in those tables.