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logolink

Overview

logolink is an R package that simplifies setting up and running NetLogo simulations directly from R. It offers a modern, streamlined interface for running models, following the tidyverse principles and integrating seamlessly with the broader tidyverse ecosystem.

logolink is designed for NetLogo 7 and is not compatible with earlier versions.

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While other R packages connect R and NetLogo, logolink is currently the only one that fully supports the latest NetLogo release (NetLogo 7). It is actively maintained, follows tidyverse conventions, and is designed to be simple and straightforward to use.

For context, RNetLogo works only with older versions (up to version 6.0.0, released in December 2016) and has not been updated since June 2017. nlrx provides a powerful framework for managing experiments and results, but supports only up to NetLogo 6.3.0 (released in September 2022) and has many unresolved issues. logolink complements these packages by focusing on simplicity, full compatibility with NetLogo 7, and seamless integration into modern R workflows.

Installation

You can install the released version of logolink from CRAN with:

install.packages("logolink")

And the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("danielvartan/logolink")

Usage

logolink usage is very straightforward. The main functions are:

Along with this package, you will also need NetLogo 7 or higher installed on your computer. You can download it from the NetLogo website.

Setting the NetLogo Path

The procedure for setting the NetLogo path has changed. If you’re using the CRAN release of logolink (version 0.1.0), you can find the previous instructions here.

logolink requires the path to the NetLogo installation to be set as an environment variable named NETLOGO_HOME when running simulations. The exact path varies depending on your operating system but is usually easy to find. On Windows, for example, it typically looks like C:\Program Files\NetLogo 7.0.0.

You can set this environment variable temporarily in your R session using Sys.setenv("NETLOGO_HOME" = "[PATH]"), or permanently by adding it to your .Renviron file.

Example (Windows):

Sys.setenv("NETLOGO_HOME" = file.path("C:", "Program Files", "NetLogo 7.0.0"))

Sys.getenv("NETLOGO_HOME")
#> [1] "C:\Program Files\NetLogo 7.0.0"

Creating an Experiment

To start running your model from R you first need to setup an experiment. You can do this by setting a BehaviorSpace experiment with the create_experiment function. This function will create a XML file that contains all the information about your experiment, including the parameters to vary, the metrics to collect, and the number of runs to perform.

Alternatively, you can set up your experiment directly in NetLogo and save it as part of your model. In this case, you can skip the create_experiment step and just provide the name of the experiment when running the model with run_experiment.

Example:

library(logolink)

setup_file <- create_experiment(
  name = "Wolf Sheep Simple Model Analysis",
  repetitions = 10,
  sequential_run_order = TRUE,
  run_metrics_every_step = TRUE,
  setup = "setup",
  go = "go",
  time_limit = 1000,
  metrics = c(
    'count wolves',
    'count sheep'
  ),
  run_metrics_condition = NULL,
  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
  )
)
setup_file |> inspect_experiment_file()
#> <experiments>
#>   <experiment name="Wolf Sheep Simple Model Analysis" repetitions="10" sequentialRunOrder="true" runMetricsEveryStep="true">
#>     <setup>setup</setup>
#>     <go>go</go>
#>     <timeLimit steps="1000"></timeLimit>
#>     <metric>count wolves</metric>
#>     <metric>count sheep</metric>
#>     <enumeratedValueSet variable="number-of-sheep">
#>       <value value="500"></value>
#>     </enumeratedValueSet>
#>     <steppedValueSet variable="number-of-wolves" first="5" step="1" last="15"></steppedValueSet>
#>     <enumeratedValueSet variable="movement-cost">
#>       <value value="0.5"></value>
#>     </enumeratedValueSet>
#>     <enumeratedValueSet variable="grass-regrowth-rate">
#>       <value value="0.3"></value>
#>     </enumeratedValueSet>
#>     <enumeratedValueSet variable="energy-gain-from-grass">
#>       <value value="2"></value>
#>     </enumeratedValueSet>
#>     <enumeratedValueSet variable="energy-gain-from-sheep">
#>       <value value="5"></value>
#>     </enumeratedValueSet>
#>   </experiment>
#> </experiments>

Running an Experiment

With the experiment file created, you can now run your model using the run_experiment function. This function will execute the NetLogo model with the specified parameters and return the results as a tidy data frame.

model_path <-
  Sys.getenv("NETLOGO_HOME") |>
  file.path(
    "models", "IABM Textbook", "chapter 4", "Wolf Sheep Simple 5.nlogox"
  )
results <- run_experiment(
  model_path = model_path,
  setup_file = setup_file
)
library(dplyr)

results |> glimpse()
#> Rows: 110,110
#> Columns: 10
#> $ run_number             <dbl> 5, 9, 7, 2, 4, 6, 3, 1, 8, 9, 5, 4, 6, 1, 3,…
#> $ number_of_sheep        <dbl> 500, 500, 500, 500, 500, 500, 500, 500, 500,…
#> $ number_of_wolves       <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,…
#> $ movement_cost          <dbl> 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,…
#> $ grass_regrowth_rate    <dbl> 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3,…
#> $ energy_gain_from_grass <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
#> $ energy_gain_from_sheep <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,…
#> $ step                   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,…
#> $ count_wolves           <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,…
#> $ count_sheep            <dbl> 500, 500, 500, 500, 500, 500, 500, 500, 500,…

Analyzing the Data (Bonus Section)

Below is a simple example of how to visualize the results using ggplot2.

library(dplyr)

data <-
  results |>
  group_by(step, number_of_wolves) |>
  summarise(
    across(everything(), ~ mean(.x, na.rm = TRUE))
  ) |>
  arrange(number_of_wolves, step)
library(ggplot2)

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
    )
  ) +
    labs(
      x = "Time step",
      y = "Average number of sheep",
      color = "Wolves"
    ) +
  geom_line()

Please refer to the BehaviorSpace Guide for complete guidance on how to set and run experiments in NetLogo. To gain a better understand of the mechanics behind R and NetLogo communication, see the Running from the Command Line section.

Click here to see logolink full list of functions.

Citation

If you use this package in your research, please cite it to acknowledge the effort put into its development and maintenance. Your citation helps support its continued improvement.

citation("logolink")
#> To cite logolink in publications use:
#> 
#>   Vartanian, D. (2025). logolink: An interface for running NetLogo
#>   simulations from R [Computer software]. CRAN.
#>   https://doi.org/10.32614/CRAN.package.logolink
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Misc{,
#>     title = {logolink: An interface for running NetLogo simulations from R},
#>     author = {Daniel Vartanian},
#>     year = {2025},
#>     doi = {10.32614/CRAN.package.logolink},
#>     note = {Computer software},
#>   }

License 

Copyright (C) 2025 Daniel Vartanian

logolink is free software: you can redistribute it and/or modify it under the
terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later
version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with
this program. If not, see <https://www.gnu.org/licenses/>.

Contributing

Contributions are always welcome! Whether you want to report bugs, suggest new features, or help improve the code or documentation, your input makes a difference. Before opening a new issue, please take a moment to review our Guidelines for Contributing and check the issues tab to see if your topic has already been reported.

You can also support the development of logolink by becoming a sponsor. Click here to make a donation. Please mention logolink in your donation message.

Acknowledgments

logolink brand identity is based on the NetLogo brand identity.