SORTEE Code Club

Open & reproducible coding for ecologists and evolutionary biologists

📣 Announcements

NoteNext Session: June 23, 2026 · 09:00 UTC

Topic: TADA-SORTEE code guidelines with Ed Ivimey-Cook

Interested? Become a SORTEE Member!

  1. Join SORTEE to get session links
  2. Check out the resources page for beginner-friendly guides
  3. No previous coding experience needed — all skill levels welcome!

Who We Are

SORTEE Code Club is part of the Society for Open, Reliable, and Transparent Ecology and Evolutionary Biology (SORTEE).

We meet every month to learn, share, and improve our coding skills. Whether you’re debugging your first R script or building a Shiny app, there’s a place for you here.

We cover:

  • 🐍 R and Python for ecological data analysis
  • 📊 Data visualisation with ggplot2 and beyond
  • 🔁 Reproducible workflows with Quarto and RMarkdown
  • 🌐 Open science tools: OSF, GitHub, Zenodo

A Little Reproducible Science 🔬

The plot below is generated from R code embedded directly in this page — this is what makes Quarto powerful!

library(ggplot2)

set.seed(42)
n <- 80
data <- data.frame(precipitation_mm = runif(n, 200, 1800),
                   temperature_c    = runif(n, 5, 30))

data$species_richness <- round(0.05 * data$precipitation_mm - 0.8  * data$temperature_c +
                                 rnorm(n, 0, 8) + 20)

ggplot(data, aes(x = precipitation_mm, y = species_richness, colour = temperature_c)) +
  geom_point(alpha = 0.7, size = 2.5) +
  geom_smooth(method = "lm", se = TRUE) +
  scale_colour_gradient(low = "#2C9B8A", high = "#E07B39",
                        name = "Temperature (°C)") +
  labs(x = "Annual precipitation (mm)",
       y = "Species richness",
       caption = "Simulated data for demonstration") +
  theme_bw(base_size = 12)

Scatter plot showing a positive relationship between precipitation and species richness

Simulated species richness across a precipitation gradient

Get Involved

We’re always looking for people to share their coding knowledge — beginners welcome! You could run a:

  • Tutorial (30–45 min): teach a skill you’ve recently learned
  • Code review (20 min): share a workflow for group feedback
  • Demo (10 min): show a package or tool you like

Express interest →

Missing something from our schedule? Tell us what you’d like to learn.

Current wishlist:

  1. Bayesian statistics in R (Stan / brms)
  2. Spatial data with sf and terra
  3. Machine learning for ecologists
  4. Python for R users

Open an issue on GitHub →

SORTEE Code Club is a volunteer-run community committed to a welcoming, inclusive environment for all skill levels. Read our Code of Conduct.