logolink is an R package that makes it
easy to set up and run NetLogo simulations
directly from R. It is built for NetLogo 7 and does not support older
versions.
The package takes a modern, streamlined approach to running NetLogo models. It follows the tidyverse principles and integrates naturally with the tidyverse ecosystem.
If you find this project useful, please consider giving it a star!
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.
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")logolink usage is very straightforward. The main functions are:
create_experiment: Create a NetLogo BehaviorSpace experiment XML file.run_experiment: Run a NetLogo BehaviorSpace experiment.
Along with this package, you will also need NetLogo 7 or higher installed on your computer. You can download it from the NetLogo website.
logolink requires the path to the NetLogo executable when running
simulations with the
run_experiment
function. This path is OS-independent, but easy to locate. On Windows,
for example, it is typically something like
C:\Program Files\NetLogo 7.0.0\NetLogo.exe.
Example (Linux):
netlogo_path <- file.path("", "opt", "netlogo-7-0-0", "bin", "NetLogo")
netlogo_path
#> [1] "/opt/netlogo-7-0-0/bin/NetLogo"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>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 <- file.path(
"", "opt", "netlogo-7-0-0", "models", "IABM Textbook", "chapter 4",
"Wolf Sheep Simple 5.nlogox"
)results <- run_experiment(
netlogo_path = netlogo_path,
model_path = model_path,
setup_file = setup_file
)library(dplyr)
results |> glimpse()
#> Rows: 110,110
#> Columns: 10
#> $ run_number <dbl> 6, 7, 4, 3, 5, 2, 8, 1, 9, 2, 1, 6, 4, 7, 8,…
#> $ 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,…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.
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 [Computer software].
#> https://github.com/danielvartan/logolink
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Misc{,
#> title = {logolink: An interface for running NetLogo simulations},
#> author = {Daniel Vartanian},
#> year = {2025},
#> url = {https://github.com/danielvartan/logolink},
#> note = {R package},
#> }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/>.
Contributions are welcome! Whether you want to report bugs, suggest features, or improve the code or documentation, your input is highly valued. Please check the issues tab for existing issues or to open a new one.
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.
logolink brand identity is based on the
NetLogo brand identity.