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sdtaylor / phenology_estimators

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new readme file #9

Closed sdtaylor closed 5 years ago

sdtaylor commented 5 years ago

Estimating transition dates from status-based phenology observations: a test of methods

The is the code reposity for the following manuscript:

Taylor SD. 2019. Estimating transition dates from status-based phenology observations: a test of methods. PeerJ Preprints 7:e27629v1 https://doi.org/10.7287/peerj.preprints.27629v1

File structure

data/: This holds the original data from the Waananen et. al. 2018 study, obtained from https://doi.org/10.5061/dryad.487db24.

derived_data/: This holds the phenology data prepared for the analysis, and the output from the estimators. Due to size this data is not in the GitHub repo, but is in the repository (https://doi.org/10.5281/zenodo.2616771)

manuscript/: The manuscript Rmarkdown files and figures.

tests/: Tests to ensure the code in the estimators.R script are functioning correctly, as well as some small example data to run the tests with.

estimators.R 'weibull.R' These 2 scripts contain the functions for all estimators.

initial_data_formatting_individual.R initial_data_formatting_population.R These 2 scripts generate the phenology data used by the estimators. Taking the raw data from data and generating the random Monte Carlo samples with the parameters specified in the text. They generate the following files:

- derived_data/population_flowering_data_for_estimators.csv
- derived_data/individual_flowering_data_for_estimators.csv
- derived_data/population_true_flowering_dates.csv
- derived_data/individual_true_flowering_dates.csv

run_estimators_individual.R run_estimators_population.R These 2 scripts apply the estimators to the derived data, and write the following result files:

- population_results_from_estimators.csv
- individual_results_from_estimators.csv

analysis_and_figures.R analysis_proportion_of_obs_kept.R 'analysis_gam_logistic_supplement.R' These 3 scripts generate all the figures and statistics in the analysis, including supplements.

install_packages.R This script installs the packages used throughout the analysis.

config.R The configuration file specifies the parameters used the Monte Carlo analysis as well as specifying all file names.

Required packages

The following R packages are used in this analysis and can be installed by running the install_packages.R script.

Running the analysis

To run the analysis from scratch, run the scripts in the following order:

initial_data_formatting_individual.R initial_data_formatting_population.R run_estimators_individual.R run_estimators_population.R analysis_and_figures.R analysis_proportion_of_obs_kept.R

This will take from 12-24 hours in total to run depending on the system. To decrease this time set the bootstrap amounts in config.R to something smaller, such as 15 for the population and 3 for the individual.

Alternatively, you can obtain the results from the computationally intensive steps from the Zenodo repository (https://doi.org/10.5281/zenodo.2616771) in the derived_data folder. With these in place run analysis_and_figures.R and analysis_proportion_of_obs_kept.R to generate the figures.

sdtaylor commented 5 years ago

required packages

c('tidyverse','mgcv','survival','testthat','ggridges','progress')