I was asked to write a R course for a group of innovative companies in the North of the Netherlands. The group of 12 people was a mix of engineers and programmers, and the course aimed at giving them a… See more ›

I was asked to write a R course for a group of innovative companies in the North of the Netherlands. The group of 12 people was a mix of engineers and programmers, and the course aimed at giving them a… See more ›

Time for another Twitter-inspired blog post this week, this time from a tweet by @JonKalodimos: Is there a way to do this in #rstats #ggplot2 https://t.co/kxWQFlYpbB— Jonathan Kalodimos (@JonKalodimos) August 27, 2015 I had seen and appreciated Ann’s post on her makeover of the main graphic in NPR’s story and did a quick mental check

Plotly has a new R API and ggplot2 library for making beautiful graphs. The API lets you produce interactive D3.js graphs with R. This post has five examples. Head to our docs to get a key and you can start making, embedding, and sharing plots. The code below produces our first plot. library(plotly) set.seed(100) d <- diamonds plot_ly(d, x = carat,

This post was motivated by this article that discusses the graphics and statistical analysis for a two treatment, two period, two sequence (2x2x2) crossover drug interaction study of a new drug versus the standard. I wanted to write about implementing those graphics and the statistical analysis in R. This post is devoted to the different

Despite having shown various ways to overcome D3 cartographic envy, there are always more examples that can cause the green monster to rear it’s ugly head. Take the Voronoi Arc Map example. For those in need of a primer, a Voronoi tesslation/diagram is: …a partitioning of a plane into regions based on distance to points

The $DAYJOB doesn’t afford much opportunity to work with cartographic datasets, but I really like maps and tinker with shapefiles and geo-data when I can, plus answer a ton of geo-questions on StackOverflow. R makes it easy—one might even say too easy—to work with maps. All it takes to make a map of the continental

This is one of my favorite ggplot2 plots I’ve ever made, but it makes me sad. Can you deduce what this plot conveys? Explain the sporadically dashed colored horizontal bands. Explain the red vertical bars. Explain the black vertical bars. If you answer all three correctly, and you can explain the rest of the plot, I’ll give you the code.

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