Getting a legend in ggplot2 when the aesthetic value is set to be constant instead of a variable can be tricky. I go through an example of a situation where this might be useful and show how to first get a legend and then how to "prettify" that legend.

Extending my simulation examples into the world of generalized linear models, I simulate Poisson data to explore what a quadratic relationship looks like on the scale of the data when fitting a generalized linear model with a log link.

Checking for autocorrelation must be done carefully when some observations are missing from a time series or the time series is measured for independent groups. I show an approach where I pad the dataset with NA via tidyr::complete() to fill in any missed sampling times and make sure groups are considered independent prior to calculating the autocorrelation function.

In this post I delve into the details of the R functions I've been using in my simulation examples, focusing on the replicate() function and the map family of functions from the purrr package. I spend a little time showing the parallels between the replicate() function and a for() loop.

In my second simulation example I show how to simulate data from a basic two-level hierarchical design. I go on to explore how well the random effects variance component is estimated for different sample sizes.

Unstandardizing coefficients in order to interpret them on the original scale is often necessary when explanatory variables were standardized to help with model convergence when fitting generalized linear mixed models. Here I show one automated approach to unstandardize coefficients from a generalized linear mixed model fit with lme4.

In this post I show one approach for making added variable plots from a model with many continuous explanatory variables. Since this is done for every variable in the model, I show how to automate the process via functions from package purrr.

A quick example of reversing the legend order in a ggplot2 plot, which is done via guide_legend() instead of by changing the order of the levels in the dataset.

Where I discuss simulations, why I love them, and get started on a simulation series with a simple two-group linear model simulation.

Since January is the month for analyzing last year's data, I take a quick look at my 2017 gas mileage in my commuter car (Fiesta! 🎆). I use package googlesheets for reading data, skimr for a quick summary of the dataset, and ggplot2 for plotting.

I talk about why I'm seeing more students who have many datasets to read in and combine and then go through an example to show one way to approach the problem.

Checking for model fit from generalized linear mixed models (GLMM) can be challenging. The DHARMa package helps with this by giving simulated residuals but doesn't work with all model types. I show how to use tools in DHARMa to extend it for use with unsupported models fit with glmmTMB() and zeroinfl().