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This vignette outlines the codebase and functionality of the portalcasting package (v0.60.5), which underlies the automated iterative forecasting within the Portal Predictions or forecasts production pipeline. portalcasting has utilities for setting up local versions of the pipeline for developing and testing new models, which are covered in detail in other vignettes.


To install the most recent version of portalcasting from GitHub:


Directory Structure

The package uses a directory tree with two levels to organize the project:

  • main: project folder encompassing all content
  • subdirectories: specific subfolders that organize the project files

structured as

│   <stable version of resources used to populate other folders>
│   <model controls list>
│   <model scripts>
│   <dataset control list>
│   <rodent datasets>
│   <covariates, newmoons, and metadata data files>
│   <previous and current model forecasts>
│   <casts metadata file>
│   <previous and current model fits>
│   <ui, server, and application files>

The main argument controls the location of the directory and defaults to ".", the present working location. To group the project subfolders into a multi-leveled folder, simply add structure to the main input, such as main = "~/project_folder".

Instantiating a Directory

Setting up a fully functional directory for a production or sandbox pipeline consists of two steps: creating (instantiating folders that are missing) and filling (adding files to the folders). These steps can be executed separately or in combination via a general setup_dir() function or via specialized versions of setup_dir(): setup_sandbox() (for creating a pipeline with defaults to facilitate sandboxing) and setup_production() (for creating a production pipeline).

These functions are general and flexible, but are designed to work well under default settings. To alter the directory configurations in setup_<> and create_dir(), use the settings argument, which takes a list of inputs, condensed and detailed in directory_settings().


The directory is established using create_dir(), which takes main as an argument and in sequence creates each of the levels’ folders if they do not already exist. A typical user is likely to want to change the main input (to locate the forecasting directory where they would like it), but general users should not alter the subdirectories structure, and so that option is not directly available. If needed, the subdirectories can be altered via the directory_settings() controls.

create_dir() also initializes the directory_configuration.yaml file, which is held within main and contains metadata about the directory setting up process.


The directory is filled (loaded with files for forecasting) using a series of subdirectory-specific functions that are combined in the overall fill_dir() function:

  • fill_resources() downloads each of the resources for the directory, which presently include the source data (rodents), covariate data (weather, NDVI), and previous forecasts’ archive. Upon completion of the downloads, fill_resources() updates directory_configuration.yaml with downloaded versions.
  • fill_forecasts() moves the existing model forecast output files from the resources subdirectory to the forecasts subdirectory.
  • fill_fits() moves the existing model fit files from the resource subdirectory to the fits subdirectory.
  • fill_models() writes the model controls list and scripts into the models subdirectory.
  • fill_data() prepares the forecasting data files from the resources downloaded data files and moves them into the data subdirectory.
  • fill_app() moves the app-building files into the directory and renders components based on local content.

Each of these components can be run individually, as well. For example, fill_data() can be used to set up the complete set of data for a given model run.


The directory is updated (loaded with any out of date resources and re-filling data) using the update_dir function, which provides an update-flavored implementation of the core functions.

Running models

Models are run using a function pipeline similar to the creation and filling function pipelines, with flexible controls through a variety of arguments, but robust operation under default settings.

  • portalcast() is the overarching function that controls forecasting of the Portal data
    • make_model_combinations() takes the input arguments and available components and produces a data frame of model run combinations (model - dataset - species).
    • cast() runs (“casts”) each of the model combinations using the fit and cast functions described in the model controls list.

Data IO

portalcasting has a generalized read_data() function that allows for toggling among read_rodents(), read_rodents_dataset(), read_covariates(), read_newmoons(), and read_metadata(), which each have specific loading procedures in place. Similar to the read_data() functions, read_forecasts() provides a simple user interface for reading the forecast files into the R session.

For saving out, write_data() provides a simple means for interfacing with potentially pre-existing data files, with logical inputs for saving generally and overwriting a pre-existing file specifically, and flexible file naming. The type of data saved out is currently restricted to .csv .json, and .yaml, which is extracted from the filename given.

The directory configuration file is a special file, and has its own IO functions separate from the rest: write_directory_configuration() creates the file (from within create_dir(), update_directory_configuration() adds downloads information from inside fill_resources()) and read_directory_configuration() brings the information from the file into the R session. Reading the configuration file into R is also the means by which directory settings are passed among functions (to limit clashing arguments and reduce verbosity).


To facilitate tidy and easy-to-follow code, we introduce a few important utility functions, which are put to use throughout the codebase.

(Rodent) data interpolating

round_na.interp() combines the round, na.interp, and pmax functions to provide a single-function for interpolating to biologically reasonable values.

File paths

file_ext() determines the file extension, based on the separating character (sep_char), which facilitates use with generalized URL APIs.


messageq() provides a simple wrapper on message that also has a logical input for quieting. This helps switch messaging off as desired while localizing the actual boolean operator code to one spot. break_line() makes a single horizontal breaking line, break_lines makes multiple break_lines, and castle makes a castle character element, all for use in messageq.


foy() calculates the fraction of year of a date.