In dymium-org/dymium: Easy Install and Load Stable Dymium Packages

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(dymium)
knitr::opts_knit$set(root.dir = here::here())

here::here()

What is a module?

There are many ways to store a group of functions in R. The simplest way is to save them as a script, whereas putting them into a package should be the last resort since it can pose new challenges for non-experienced R users. The ‘modules’ package allows source code to be organised in a way that mimics a package, while being much simpler to develop, and gives some of the features of a package with less overhead.

Using existing modules

Customise existing modules

change model parameters

Authoring your own modules

requiments

Understand data.table and R6 packages

create a module template

create_new_module(name = "demography", 
                  event = c("birth", "death"), 
                  path = "modules")

# It is recommended to assign this module to a variable called: event_demography_birth
# for example: event_demography_birth <- modules::use('/Users/amarin/Google Drive/PhD/projects/dymium/packages/dymium/modules/demography/birth.R')
# default setup, you may edit the below import statments to match your requirements.
modules::import('dymiumCore')
modules::expose(here::here('/Users/amarin/Google Drive/PhD/projects/dymium/packages/dymium/modules/demography/logger.R')) # import lgr's logger. To use the logger use 'lg' (default logger's name).
constants <- modules::use(here::here('/Users/amarin/Google Drive/PhD/projects/dymium/packages/dymium/modules/demography/constants.R'))
helpers <- modules::use(here::here('/Users/amarin/Google Drive/PhD/projects/dymium/packages/dymium/modules/demography/helpers.R'))

modules::export('^^run|^util|^test') # default exported functions

#' Birth
#'
#' @param object a dymium agent class object
#' @param model a model object or a list of model objects
#' @param target a positive integers or a list of positive integers
#' @param time_steps positive integer()
#'
#' @return object
run <- function(object, model = NULL, target = NULL, time_steps = NULL) {

  # early return if `time_steps` is not the current time
  if (!dymiumCore::is_scheduled(time_steps)) {
    return(invisible(object))
  }

  lg$info('Running Birth')

  # uncomment the line belows if the event doesn't require `model`
  # eg. If the event is deterministic like ageing.
  # if (!is.null(model)) {
  #   lg$warn('`model` will not be used.')
  # }

  # uncomment the line belows if the event doesn't require `target`
  # eg. If the event is to be applied to all agents.
  # if (!is.null(target)) {
  #   lg$warn('`target` will not be used.')
  # }

  # (Recommended)
  # create a reference to the main agent object for easy access eg:
  # PopObj <- assign_reference(object, Pop)

  # (Recommended)
  # create a reference to ModelContainer for easy access eg:
  # ModObj <- assign_reference(object, ModelContainer)

  # TODO: Target object
  # create a reference to TargetContainer (Not yet implemented) for easy access
  # TargetObj <- assign_reference(object, TargetContainer)

  # return the first argument (`object`) to make event functions pipe-able.
  invisible(object)
}

# private utility functions (.util_*) -------------------------------------
.util_function <- function(x) {}

TransitionEventname <-
  R6::R6Class(classname = 'TransitionEventname',
              inherit = dymiumCore::Transition,
              public = list(

              ))

# exported utility functions (util_*) -------------------------------------
util_function <- function(x) {}

create a pipeline

# run for 20 time steps
for (i in c(1:20)) {
  world %>%
    event_1$run() %>%
    ... %>%
    event_n$run()
}

dymium-org/dymium documentation built on Jan. 13, 2020, 2:04 p.m.