LogoActions: GitHub Actions for the NetLogo Community 🚀

Author

Daniel Vartanian

Published

January 11, 2026

Overview

This document presents the output of a GitHub Action workflow that sets up NetLogo and runs experiments using Quarto and the logolink R package.

See the repository README for more details.

Set the Environment

Load Packages

library(logolink) # github.com/danielvartan/logolink

library(dplyr)
library(ggplot2)
library(magrittr)

Set `ggplot2` Theme

theme_set(
  theme_bw() +
    theme(
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank(),
      legend.frame = element_blank(),
      legend.ticks = element_line(color = "white")
    )
)

Wolf Sheep Predation Model

Set Model Path

model_path <-
  find_netlogo_home() |>
  file.path(
    "models",
    "IABM Textbook",
    "chapter 4",
    "Wolf Sheep Simple 5.nlogox"
  )

Create Experiment

setup_file <- create_experiment(
  name = "Wolf Sheep Simple Model Analysis",
  repetitions = 10,
  sequential_run_order = TRUE,
  run_metrics_every_step = TRUE,
  time_limit = 1000,
  setup = 'setup',
  go = 'go',
  metrics = c(
    'count wolves',
    'count sheep'
  ),
  constants = list(
    "number-of-sheep" = 500,
    "number-of-wolves" = list(
      first = 5,
      step = 1,
      last = 15
    ),
    "movement-cost" = 0.5,
    "grass-regrowth-rate" = 0.3,
    "energy-gain-from-grass" = 2,
    "energy-gain-from-sheep" = 5
  )
)

Run Experiment

results <-
  model_path |>
  run_experiment(setup_file = setup_file)

results |> glimpse()

List of 2
 $ metadata:List of 6
  ..$ timestamp       : POSIXct[1:1], format: "2026-01-11 21:17:22"
  ..$ netlogo_version : chr "7.0.3"
  ..$ output_version  : chr "2.0"
  ..$ model_file      : chr "Wolf Sheep Simple 5.nlogox"
  ..$ experiment_name : chr "Wolf Sheep Simple Model Analysis"
  ..$ world_dimensions: Named int [1:4] -17 17 -17 17
  .. ..- attr(*, "names")= chr [1:4] "min-pxcor" "max-pxcor" "min-pycor" "max-pycor"
 $ table   : tibble [110,110 × 10] (S3: tbl_df/tbl/data.frame)
  ..$ run_number            : num [1:110110] 1 1 1 1 1 1 1 1 1 1 ...
  ..$ number_of_sheep       : num [1:110110] 500 500 500 500 500 500 500 500 500 500 ...
  ..$ number_of_wolves      : num [1:110110] 5 5 5 5 5 5 5 5 5 5 ...
  ..$ movement_cost         : num [1:110110] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...
  ..$ grass_regrowth_rate   : num [1:110110] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 ...
  ..$ energy_gain_from_grass: num [1:110110] 2 2 2 2 2 2 2 2 2 2 ...
  ..$ energy_gain_from_sheep: num [1:110110] 5 5 5 5 5 5 5 5 5 5 ...
  ..$ step                  : num [1:110110] 0 1 2 3 4 5 6 7 8 9 ...
  ..$ count_wolves          : num [1:110110] 5 5 5 5 5 5 5 5 5 5 ...
  ..$ count_sheep           : num [1:110110] 500 498 497 495 493 491 491 490 489 487 ...

results

$metadata
$metadata$timestamp
[1] "2026-01-11 21:17:22 GMT"

$metadata$netlogo_version
[1] "7.0.3"

$metadata$output_version
[1] "2.0"

$metadata$model_file
[1] "Wolf Sheep Simple 5.nlogox"

$metadata$experiment_name
[1] "Wolf Sheep Simple Model Analysis"

$metadata$world_dimensions
min-pxcor max-pxcor min-pycor max-pycor 
      -17        17       -17        17 


$table
# A tibble: 110,110 × 10
   run_number number_of_sheep number_of_wolves movement_cost grass_regrowth_rate
        <dbl>           <dbl>            <dbl>         <dbl>               <dbl>
 1          1             500                5           0.5                 0.3
 2          1             500                5           0.5                 0.3
 3          1             500                5           0.5                 0.3
 4          1             500                5           0.5                 0.3
 5          1             500                5           0.5                 0.3
 6          1             500                5           0.5                 0.3
 7          1             500                5           0.5                 0.3
 8          1             500                5           0.5                 0.3
 9          1             500                5           0.5                 0.3
10          1             500                5           0.5                 0.3
# ℹ 110,100 more rows
# ℹ 5 more variables: energy_gain_from_grass <dbl>,
#   energy_gain_from_sheep <dbl>, step <dbl>, count_wolves <dbl>,
#   count_sheep <dbl>

Plot Results

plot_data <-
  results |>
  extract2("table") |>
  summarize(
    across(everything(), ~ mean(.x, na.rm = TRUE)),
    .by = c(step, number_of_wolves)
  ) |>
  arrange(number_of_wolves, step)

plot_data |>
  mutate(
    number_of_wolves = as.factor(number_of_wolves)
  ) |>
  ggplot(
    aes(
      x = step,
      y = count_sheep,
      group = number_of_wolves,
      color = number_of_wolves
    )
  ) +
  geom_line() +
  labs(
    x = "Time Step",
    y = "Average Number of Sheep",
    color = "Wolves"
  )

Spread of Disease Model

Set Model Path

model_path <-
  find_netlogo_home() |>
  file.path(
    "models",
    "IABM Textbook",
    "chapter 6",
    "Spread of Disease.nlogox"
  )

Create Experiment

setup_file <- create_experiment(
  name = "Population Density (Runtime)",
  repetitions = 10,
  sequential_run_order = TRUE,
  run_metrics_every_step = TRUE,
  time_limit = 1000,
  setup = 'setup',
  go = 'go',
  metrics = c(
    'count turtles with [infected?]'
  ),
  constants = list(
    "variant" = "mobile",
    "num-people" = list(
      first = 50,
      step = 50,
      last = 200
    ),
    "connections-per-node" = 4.1,
    "num-infected" = 1,
    "disease-decay" = 0
  )
)

Run Experiment

results <-
  model_path |>
  run_experiment(setup_file = setup_file)

results |> glimpse()

List of 2
 $ metadata:List of 6
  ..$ timestamp       : POSIXct[1:1], format: "2026-01-11 21:17:46"
  ..$ netlogo_version : chr "7.0.3"
  ..$ output_version  : chr "2.0"
  ..$ model_file      : chr "Spread of Disease.nlogox"
  ..$ experiment_name : chr "Population Density (Runtime)"
  ..$ world_dimensions: Named int [1:4] -20 20 -20 20
  .. ..- attr(*, "names")= chr [1:4] "min-pxcor" "max-pxcor" "min-pycor" "max-pycor"
 $ table   : tibble [8,465 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ run_number                 : num [1:8465] 1 1 1 1 1 1 1 1 1 1 ...
  ..$ variant                    : chr [1:8465] "mobile" "mobile" "mobile" "mobile" ...
  ..$ num_people                 : num [1:8465] 50 50 50 50 50 50 50 50 50 50 ...
  ..$ connections_per_node       : num [1:8465] 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1 ...
  ..$ num_infected               : num [1:8465] 1 1 1 1 1 1 1 1 1 1 ...
  ..$ disease_decay              : num [1:8465] 0 0 0 0 0 0 0 0 0 0 ...
  ..$ step                       : num [1:8465] 0 1 2 3 4 5 6 7 8 9 ...
  ..$ count_turtles_with_infected: num [1:8465] 1 1 1 1 1 1 1 1 1 1 ...

results

$metadata
$metadata$timestamp
[1] "2026-01-11 21:17:46 GMT"

$metadata$netlogo_version
[1] "7.0.3"

$metadata$output_version
[1] "2.0"

$metadata$model_file
[1] "Spread of Disease.nlogox"

$metadata$experiment_name
[1] "Population Density (Runtime)"

$metadata$world_dimensions
min-pxcor max-pxcor min-pycor max-pycor 
      -20        20       -20        20 


$table
# A tibble: 8,465 × 8
   run_number variant num_people connections_per_node num_infected disease_decay
        <dbl> <chr>        <dbl>                <dbl>        <dbl>         <dbl>
 1          1 mobile          50                  4.1            1             0
 2          1 mobile          50                  4.1            1             0
 3          1 mobile          50                  4.1            1             0
 4          1 mobile          50                  4.1            1             0
 5          1 mobile          50                  4.1            1             0
 6          1 mobile          50                  4.1            1             0
 7          1 mobile          50                  4.1            1             0
 8          1 mobile          50                  4.1            1             0
 9          1 mobile          50                  4.1            1             0
10          1 mobile          50                  4.1            1             0
# ℹ 8,455 more rows
# ℹ 2 more variables: step <dbl>, count_turtles_with_infected <dbl>

Tidy Data

data <-
  results |>
  extract2("table") |>
  rename(infected = count_turtles_with_infected) |>
  mutate(
    variant = as.factor(variant),
    frac_infected = infected / num_people
  ) |>
  arrange(run_number, num_infected, step)

data |> glimpse()

Rows: 8,465
Columns: 9
$ run_number           <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ variant              <fct> mobile, mobile, mobile, mobile, mobile, mobile, m…
$ num_people           <dbl> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5…
$ connections_per_node <dbl> 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1,…
$ num_infected         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ disease_decay        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ step                 <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,…
$ infected             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ frac_infected        <dbl> 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0…

data

Plot Data

data |>
  mutate(
    num_people = as.factor(num_people)
  ) |>
  ggplot(
    aes(
      x = step,
      y = frac_infected,
      color = num_people
    )
  ) +
  geom_point(
    aes(shape = num_people),
    size = 1,
    alpha = 0.75
  ) +
  scale_x_continuous(breaks = seq(0, max(data$step), 100)) +
  labs(
    x = "Steps",
    y = "Fraction of Infected",
    title = "Infected Over Time",
    color = "People",
    shape = "People"
  )

plot <-
  data |>
  ggplot(
    aes(
      x = step,
      y = frac_infected, # infected
      color = as.factor(num_people)
    )
  ) +
  scale_x_continuous(
    breaks = seq(0, max(data$step), 100)
  ) +
  labs(
    x = "Steps",
    y = "Fraction of Infected",
    title = "Infected Over Time",
    color = "People"
  )

data_i <-
  data |>
  group_by(num_people, run_number) |>
  group_split()

for (i in data_i) {
  plot <-
    plot +
    geom_line(
      data = i,
      aes(x = step, y = frac_infected),
      linewidth = 1,
      alpha = 0.75
    )
}

plot

Summarize Data

summarized_data <-
  data |>
  select(num_people, step, infected, frac_infected) |>
  summarize(
    mean = mean(frac_infected, na.rm = TRUE),
    sd = sd(frac_infected, na.rm = TRUE),
    .by = c(num_people, step)
  ) |>
  arrange(num_people, step)

summarized_data |> glimpse()

Rows: 1,080
Columns: 4
$ num_people <dbl> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,…
$ step       <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1…
$ mean       <dbl> 0.020, 0.022, 0.022, 0.026, 0.026, 0.026, 0.026, 0.026, 0.0…
$ sd         <dbl> 0.000000000, 0.006324555, 0.006324555, 0.009660918, 0.00966…

summarized_data

Plot Summarized Data with SD Error Bars

summarized_data |>
  filter(step %% 10 == 0) |>
  ggplot(
    aes(
      x = step,
      y = mean,
      color = as.factor(num_people)
    )
  ) +
  geom_point(
    aes(shape = as.factor(num_people)),
    size = 1,
    alpha = 0.75
  ) +
  geom_errorbar(
    aes(
      ymin = mean + sd,
      ymax = mean - sd
    ),
    width = 5
  ) +
  geom_line() +
  scale_x_continuous(
    breaks = seq(0, max(summarized_data$step), 100)
  ) +
  labs(
    title = "Infected Over Time",
    x = "Steps",
    y = "Fraction of Infected",
    color = "People",
    shape = "People"
  )

Plot Summarized Data with SE Error Bars

summarized_data |>
  filter(step %% 10 == 0) |>
  mutate(se = sd / sqrt(10)) |>
  ggplot(aes(
    x = step,
    y = mean,
    color = as.factor(num_people)
  )) +
  geom_point(
    aes(shape = as.factor(num_people)),
    size = 1,
    alpha = 0.75
  ) +
  geom_errorbar(
    aes(
      ymin = mean + se,
      ymax = mean - se
    ),
    width = 5
  ) +
  geom_line() +
  scale_x_continuous(
    breaks = seq(0, max(summarized_data$step), 100)
  ) +
  labs(
    title = "Infected Over Time",
    x = "Steps",
    y = "Fraction of Infected",
    color = "People",
    shape = "People"
  )

Overview

Set the Environment

Load Packages

Set ggplot2 Theme

Wolf Sheep Predation Model

Set Model Path

Create Experiment

Run Experiment

Plot Results

Spread of Disease Model

Set Model Path

Create Experiment

Run Experiment

Tidy Data

Plot Data

Summarize Data

Plot Summarized Data with SD Error Bars

Plot Summarized Data with SE Error Bars

Set `ggplot2` Theme