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.
The package is designed to work with NetLogo 7.0.1 and above. If you need support for earlier NetLogo releases (up to 7.0.0), use an older version of this package.
If you find this project useful, please consider giving it a star!
The continuous development of
logolinkdepends on community support. If you can afford to do so, please consider becoming a sponsor.
While other R packages connect R and NetLogo, logolink is currently
the only one that fully supports the latest NetLogo release. 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 7.0.1 or higher installed on your computer. You can download it from the NetLogo website.
After installing NetLogo and logolink, start by loading the package
with:
library(logolink)logolink will try to find out the path to the NetLogo installation
automatically. This is usually successful, but if it fails, you will
need to set it manually. In the latter case, see the documentation of
the
run_experiment
function for more details.
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:
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
)
)If you want to inspect the created experiment file, you can use the
inspect_experiment_file
function:
setup_file |> inspect_experiment_file()
#> <experiments>
#> <experiment name="Wolf Sheep Simple Model Analysis" repetitions="10" sequentialRunOrder="true" runMetricsEveryStep="true" timeLimit="1000">
#> <setup>setup</setup>
#> <go>go</go>
#> <metrics>
#> <metric>count wolves</metric>
#> <metric>count sheep</metric>
#> </metrics>
#> <constants>
#> <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>
#> </constants>
#> </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.
First you need to specify the path to your NetLogo model. In this example, we will use the Wolf Sheep Simple 5 model that comes with any NetLogo installation.
model_path <-
find_netlogo_home() |>
file.path(
"models",
"IABM Textbook",
"chapter 4",
"Wolf Sheep Simple 5.nlogox"
)After specifying the model path, you can run the experiment with:
results <-
model_path |>
run_experiment(setup_file = setup_file)library(dplyr)
results |> glimpse()
#> Rows: 110,110
#> Columns: 10
#> $ run_number <dbl> 5, 3, 8, 6, 4, 7, 1, 2, 9, 5, 9, 2, 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 |>
summarize(
across(everything(), ~ mean(.x, na.rm = TRUE)),
.by = c(step, number_of_wolves)
) |>
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.
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 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},
#> }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 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.
logolink brand identity is based on the
NetLogo brand identity.
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This work was developed with support from the Sustentarea Research and Extension Center at the University of São Paulo (USP). |
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This work was supported by the Department of Science and Technology of the Secretariat of Science, Technology, and Innovation and of the Health Economic-Industrial Complex (SECTICS) of the Ministry of Health of Brazil, and the National Council for Scientific and Technological Development (CNPq) (grant no. 444588/2023-0). |
