Chapter 10 Appendix C: Installation and Quick Start Guide

10.1 Purpose of This Appendix

This appendix helps new users install and begin using lifesimulatoR.

No prior experience with R is assumed.

By the end of this appendix you should be able to:

Install R and RStudio
Install lifesimulatoR
Load the package
Run your first simulation
Access documentation and tutorials

10.2 Step 1: Install R

R is the programming language used by lifesimulatoR.

Download the latest version from:

https://cran.r-project.org

Follow the installation instructions for your operating system.

10.3 Step 2: Install RStudio

RStudio provides a convenient interface for working with R.

Download RStudio Desktop from:

https://posit.co/download/rstudio-desktop/

Install using the default settings.

10.4 Step 3: Install Required Packages

Open RStudio and run:

install.packages("remotes")

This package allows installation directly from GitHub.

10.5 Step 4: Install lifesimulatoR

Install the package from GitHub:

remotes::install_github(
  "NoushinN/lifesimulatoR"
)

Replace the repository name if it changes in the future.

10.6 Step 5: Load the Package

library(lifesimulatoR)

If no error appears, installation was successful.

10.7 Your First Simulation

Run a simple molecular evolution simulation:

sim <- simulate_abiogenesis(
  n_molecules = 100,
  generations = 100,
  mutation_rate = 0.02,
  selection_strength = 1,
  seed = 123
)

head(sim)

You should see a data frame describing how the population changed through time.

10.8 Create Your First Plot

plot_simulation(
  sim,
  x = "generation",
  y = "mean_fitness"
)

This plot shows how mean fitness changes throughout the simulation.

10.9 Exploring Diversity

Create a molecular population:

pool <- create_prebiotic_pool(
  n_molecules = 100,
  seed = 123
)

Summarize the population:

summarize_molecules(pool)

Calculate entropy:

counts <- c(10, 5, 1)

shannon_entropy(counts)

10.10 Exploring Protocells

cells <- protocell_simulation(
  n_cells = 20,
  steps = 100,
  seed = 123
)

head(cells)

Plot the results:

plot_simulation(
  cells,
  x = "step",
  y = "n_cells"
)

10.11 Exploring Autocatalytic Networks

network <- autocatalytic_network(
  n_types = 8,
  steps = 50,
  catalysis_probability = 0.2,
  seed = 123
)

head(network$time_series)

View the network structure:

network$catalysis_matrix

10.12 Accessing Help

View package documentation:

help(package = "lifesimulatoR")

View help for individual functions:

?simulate_abiogenesis
?create_prebiotic_pool
?protocell_simulation
?autocatalytic_network

View tutorials:

browseVignettes("lifesimulatoR")

10.13 Common Problems and Solutions

10.13.1 Package Not Found

Error:

there is no package called 'lifesimulatoR'

Solution:

Reinstall using:

remotes::install_github(
  "NoushinN/lifesimulatoR"
)

10.13.2 Function Not Found

Error:

could not find function

Solution:

Load the package first:

library(lifesimulatoR)

10.13.3 Plotting Error

Error:

argument "x" is missing

Solution:

Always specify both axes:

plot_simulation(
  sim,
  x = "generation",
  y = "mean_fitness"
)

10.13.4 Shannon Entropy Error

Error:

invalid 'type' (character)

Solution:

shannon_entropy() expects counts, not character sequences:

counts <- c(10,5,1)

shannon_entropy(counts)

10.14 Suggested Learning Path

New users are encouraged to follow this sequence:

Getting Started
Origin of Life: Big Questions
Prebiotic Chemistry
Molecular Evolution
Diversity and Complexity
Protocells
Autocatalytic Networks
Comparing Origin-of-Life Frameworks
Classroom Labs and Activities

10.15 Next Steps

After becoming comfortable with the package you can:

Explore parameter sensitivity
Design new simulations
Extend existing functions
Develop classroom activities
Contribute to package development

10.16 Key Takeaways

lifesimulatoR is designed to be easy to install and use.
The package provides educational simulations inspired by origin-of-life research.
Simulations can be explored with only a few lines of code.
Documentation, vignettes, and labs provide guided learning opportunities.
The package can support teaching, self-study, workshops, and exploratory research.