evolved

This educational R package involves both simple and complex functions for simulating and analyzing biological data. It is adequate for inquiries that assume different levels of student independence (e.g., as categorized by Banchi & Bell, 2008), and add up to other options of software where students can handle, organize, and visualize biological data. evolved is heavily oriented towards providing tools for inquiry-based learning - where students follow scientific practices to actively construct knowledge (Pedaste et al, 2015) - and thus most of its computer functions rely either on (A) simulating data from simple models that can usually be derived from first principles (see Table 1) or in (B) analyzing (measuring, testing, visualizing) datasets with characteristics that are common to many fields related to evolutionary biology.

Installation

You can install the development version of evolved from GitHub with:

# install.packages("devtools")
devtools::install_github("Auler-J/evolved")

Usage

Following we show all the functions designed to be handled directly by users. Functions use examples are provided in the vignettes.

Function name	Biology field mostly associated with function	Function purpose	Designed to be opened?
calcFossilDivTT()	Paleobiology	Estimates fossil Diversity through time	yes
countSeqDiffs()	Phylogenetics	Calculates the number of different sites between two protein sequences	yes
NatSelSim()	Population genetics	Simulate natural selection in a bi-allelic population	yes
OneGenHW()	Population genetics	Simulates stochastic allelic frequencies in a population that follows Hardy-Weinberg Equilibrium	yes
WFDriftSim()	Population genetics	Simulates allele frequency change through generations under genetic drift	yes
simulateBirthDeath()	Macroevolution	Simulates number of species following a birth-death model	no
SimulateTree()	Macroevolution	Simulates a phylogenetic tree following a birth-death model	no

Vignettes

To view the vignettes, run the following code:

vignette("vignette_name", package = "evolved")

Specifically, the following topics are addressed in every vignette (ordered from basic to advanced):

intro_r
- Learn R basic syntax
- Learn R basic coding: objects, vector calculations, etc
- Learn basic plotting and annotation functions
- Learn the basic object classes vignettes will use
popgen_intro
- Simple math notation
- Probability of independent events
- Random number generators and density/mass probability functions
- Malthusian growth
- Mendelian genetics and Hardy-Weinberg Equilibrium (HWE) at a single locus
- Heterozygosity
- HWE, deleterious alleles, and mutation
- Mendelian genetics at multiple loci
- Describing genetic variation in DNA segments
popgen_drift
- Genetic drift: qualitative expectation
- Genetic drift: building intuition
- Dissecting the variability in outcomes
- Genetic drift and heterozygosity decay
- Effective population size
- Historical note
popgen_selection
- Breeding effective population size
- Mutation-drift equilibrium
- Selection
- The case of the peppered moths
deeptime_clocks
- Exploring sequence data
- A simple measure of genetic distance
- The Poisson correction
- Building a molecular clock
- Molecular clocks and inferences about deep time
- Jukes-Cantor correction
- The uncertainty of your molecular clock
deeptime_rocks
- Exploring fossil occurrences
- Diversity patterns in deep time
- The spatial distribution of the record
- Drawing conclusions from the fossil record
- Technical note: Dating fossils in absolute time
birthdeath_deeptime
- The birth-death (BD) model
- Deterministic expectations of the birth-death model
- Estimation under the simple birth-death
- Effects of variation on the net diversification rate
- The birth-death model is a stochastic process
- Adding in extinction
- Age-richness models and empirical richness through time
birthdeath_phylogenies
- Estimating diversification rates under pure birth
- Estimating diversification rates under birth-death
- What affects speciation and extinction?

References

Banchi, H., & Bell, R. (2008). The many levels of inquiry. Science and children, 46(2), 26.

Pedaste, M., Mäeots, M., Siiman, L. A., De Jong, T., Van Riesen, S. A., Kamp, E. T., … & Tsourlidaki, E. (2015). Phases of inquiry-based learning: Definitions and the inquiry cycle. Educational research review, 14, 47-61.