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.

This is a rather niche post, but the method can likely be adapted for other use cases. In the lab we have many different cell lines stored in liquid nitrogen. The arrangement is: the vials are in specific positions in a box (10 x 10) there are 13 boxes to a cane we have 5 canes Ideally, to retrieve the correct vial from the cell store requires a map.

2023 has been a great year in running for me. Previous running round-ups are here (2022, 2021). My two main goals for 2023 were to run 3000 km and also to run 50 HM-or-more distance runs. I managed both with a couple of weeks left. I also bagged new PBs for 5K, 10K and half marathon as well as a handful of segments on Strava. I won no races but I did win two little running competitions at work.

I’ve been finishing up a project that uses ordered Beta regression (Kubinec 2022), a neat combination of Beta regression and ordered logistic regression that you can use for modeling continuous outcomes that are bounded on either side (in my project, we’re modeling a variable that can only be between 1 and 32, for instance). It’s possible to use something like zero-one-inflated Beta regression for outcomes like this, but that kind of model

Introduction to Exploratory Data Analysis (EDA) Exploratory Data Analysis (EDA) is the crucial first step in the data analysis process. Before applying complex statistical models or machine learning algorithms, it is essential to understand the structure, trends, and peculiarities of the data you are working with.

library(viridis) # Import the viridis color palette library library(ggplot2) set.seed(123) # Set a seed for reproducibility num_flips <- 50000 flips <- sample(c("Heads", "Tails"), num_flips, replace = TRUE) # Image aspect ratio aspect_ratio <- 1 # You can customize the aspect ratio here n_col <- round(sqrt(num_flips) * aspect_ratio) n_row <- ceiling(num_flips / n_col) # Create a color matrix to represent coin flips colors <-

I recently posted a guide (mostly for future-me) about how to analyze conjoint survey data with R. I explore two different estimands that social scientists are interested in—causal average marginal component effects (AMCEs) and descriptive marginal means—and show how to find them with R, with both frequentist and Bayesian approaches. However, that post is a little wrong. It’s not wrong wrong, but it is a bit oversimplified.

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.

In my research, I study international nongovernmental organizations (INGOs) and look at how lots of different institutional and organizational factors influence INGO behavior. For instance, many authoritarian regimes have passed anti-NGO laws and engaged in other forms of legal crackdown, which has forced NGOs to change their programming strategies and their sources of funding.

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.