tidy interface to single species population models
The goal of tidypop is twofold. First, to facilitate teaching simple single species population models in a tidy context. Second, to provide a ‘tidy’ interface based on data frames to existing population packages.
In a ‘tidy’ context, the main functions should take inputs and produce
outputs that are data frames ready to use with dplyr and ggplot2.
Using these packages revolutionized the use of R in my classroom.
Unfortunately, I found myself having to push students much farther in
the use of programming than I would like in order to use existing
demographic packages. There was a lot of extra work to convert the
return values into dataframes for plotting purposes. In addition,
population models require iteration, and that also increased the
pressure on their understanding.
I started thinking about this a couple of years ago and this package is the result of that plus many suggestions received on twitter, especially from @pixxpih. The key idea is to separate the processing of arguments, iteration of the model, etc, from the one step transition model. Students can easily write a one-line function specifying the transition – it is often just a direct translation from a discrete time equation in their notes.
I also want it to be easy to implement parameters that change with time. Management changes the parameters of the model, e.g. increasing the per capita birth rate. The simplest way to do this is to create a dataframe with one row per time step.
tidypop is not available from CRAN
but you can install from GitHub with:
# install.packages("devtools")
devtools::install_github("atyre2/tidypop")This is a basic example which shows you how to solve a common problem:
library(tidypop)
# a simple exponential growth model
anexppop <- function(N0, b, d){
N1 <- N0 * (1 + b - d)
return(N1)
}
# make the input dataframe
inputs <- data.frame(Year = 2018:2025, b = 0.2, d = 0.15)
iterate(inputs, 23, anexppop)
#> Year b d N
#> 1 2018 0.2 0.15 23.00000
#> 2 2019 0.2 0.15 24.15000
#> 3 2020 0.2 0.15 25.35750
#> 4 2021 0.2 0.15 26.62538
#> 5 2022 0.2 0.15 27.95664
#> 6 2023 0.2 0.15 29.35448
#> 7 2024 0.2 0.15 30.82220
#> 8 2025 0.2 0.15 32.36331
# time dependent model
inputs <- data.frame(Year = 2018:2025, b = seq(0.2, 0.1, length = 8), d = 0.15)
iterate(inputs, 23, anexppop)
#> Year b d N
#> 1 2018 0.2000000 0.15 23.00000
#> 2 2019 0.1857143 0.15 24.15000
#> 3 2020 0.1714286 0.15 25.01250
#> 4 2021 0.1571429 0.15 25.54848
#> 5 2022 0.1428571 0.15 25.73097
#> 6 2023 0.1285714 0.15 25.54718
#> 7 2024 0.1142857 0.15 24.99974
#> 8 2025 0.1000000 0.15 24.10689Please note that the ‘tidypop’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.