This repository contains data and R code which are supplements to:
Viral maintenance and excretion dynamics of coronaviruses within an Egyptian rousette fruit bat maternal colony – considerations for spillover, by Marike Geldenhuys , Noam Ross , Muriel Dietrich , John L. de Vries , Marinda Mortlock , Jonathan H. Epstein , Jacqueline Weyer , Janusz T. Pawęska, and Wanda Markotter . Scientific Reports 13, 15829 (2023). https://doi.org/10.1038/s41598-023-42938-w
If referring to the study or methods, please cite the publication. If you re-use these data and/or code in a publication please be sure to cite the Zenodo reference, as well (https://doi.org/10.5281/zenodo.7709716).
data/
contains data from the study and a data dictionary describing all variables.R/
contains functions used in this analysis.reports/
contains literate code for R Markdown reports generated in the analysisoutputs/
contains compiled reports and figures.- This project uses the
{targets} framework to
organize build steps for analysis pipeline. The steps are defined in
the
_targets.R
file and the workflow can be executed by runningrun.R
viasource("run.R")
in your R terminal orRscript run.R
in your system shell. The schematic figure below summarizes the steps. (The figure is generated usingmermaid.js
syntax and should display as a graph on GitHub. It can also be viewed by pasting the code into https://mermaid.live.)
graph LR
subgraph Project Workflow
direction LR
x798304d337dd8ea1(["allplots"]):::queued --> x0b04ba823e0b5eec["png_plots"]:::queued
x023bc1d70802c4e1(["multinomial_model"]):::queued --> xa8aa782838af3774(["partial_effect_plots"]):::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> xe3a4a17736576ba6(["fig_time_series"]):::queued
xf22c729b71100575(["time_series"]):::queued --> xe3a4a17736576ba6(["fig_time_series"]):::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> x90dc8aa96b6ba82f(["repro_effects"]):::queued
x89c8b8fe66c39f8c(["gam_posterior"]):::queued --> x90dc8aa96b6ba82f(["repro_effects"]):::queued
x023bc1d70802c4e1(["multinomial_model"]):::queued --> x90dc8aa96b6ba82f(["repro_effects"]):::queued
x89c8b8fe66c39f8c(["gam_posterior"]):::queued --> x48dd96190e644d8f(["posterior_stats"]):::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> x758ab9da0ef7a894(["day_age_repro_effects"]):::queued
x89c8b8fe66c39f8c(["gam_posterior"]):::queued --> x758ab9da0ef7a894(["day_age_repro_effects"]):::queued
x023bc1d70802c4e1(["multinomial_model"]):::queued --> x758ab9da0ef7a894(["day_age_repro_effects"]):::queued
xb354ea9be719f8bb(["captures_xls"]):::queued --> xe05634441492d438(["dat_captures"]):::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> x36718e65b0da26db(["fig_day_age_repro_effects"]):::queued
x758ab9da0ef7a894(["day_age_repro_effects"]):::queued --> x36718e65b0da26db(["fig_day_age_repro_effects"]):::queued
x798304d337dd8ea1(["allplots"]):::queued --> xbbcceea2cdf8d3f7["svg_plots"]:::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> x26018ab47272d583(["multinomial_model_alt"]):::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> xdf15caacba83d075(["raw_prev"]):::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> x23873f9d63594e23(["fig_fmi_demo"]):::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> x4ee8fa2dac97ae88(["fig_fmi_demo_effects"]):::queued
xc2117931d245afce(["dat_xls"]):::queued --> x733041ef94e8d4a9(["dat_fec"]):::queued
xc2117931d245afce(["dat_xls"]):::queued --> x6cf5d6dc2e05a667(["dat_bat"]):::queued
x023bc1d70802c4e1(["multinomial_model"]):::queued --> x89c8b8fe66c39f8c(["gam_posterior"]):::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> x50a3b768b5746682(["fig_pos_demo_timeseries"]):::queued
xe415b5066c557652(["peak_dates"]):::queued --> x0905a3e9b9cae3b2(["fig_peak_dates"]):::queued
x6cf5d6dc2e05a667(["dat_bat"]):::queued --> xba784c3a136c631a(["dat_cleaned"]):::queued
x733041ef94e8d4a9(["dat_fec"]):::queued --> xba784c3a136c631a(["dat_cleaned"]):::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> xf268c53ef67145e5(["table_fmi_demo"]):::queued
x2d7bbbb9a4c3fe9d(["flextable_gam_summary"]):::queued --> x7998bc630ab92bda(["gam_summary_docx"]):::queued
x7a7f43da56388c67(["fig_bat_demographics"]):::queued --> x798304d337dd8ea1(["allplots"]):::queued
x36718e65b0da26db(["fig_day_age_repro_effects"]):::queued --> x798304d337dd8ea1(["allplots"]):::queued
x90f3a96af7e5a0fc(["fig_fa_cutoffs"]):::queued --> x798304d337dd8ea1(["allplots"]):::queued
x23873f9d63594e23(["fig_fmi_demo"]):::queued --> x798304d337dd8ea1(["allplots"]):::queued
x4ee8fa2dac97ae88(["fig_fmi_demo_effects"]):::queued --> x798304d337dd8ea1(["allplots"]):::queued
x80ade130098abb0f(["fig_fmi_demo_timeseries"]):::queued --> x798304d337dd8ea1(["allplots"]):::queued
x0905a3e9b9cae3b2(["fig_peak_dates"]):::queued --> x798304d337dd8ea1(["allplots"]):::queued
x50a3b768b5746682(["fig_pos_demo_timeseries"]):::queued --> x798304d337dd8ea1(["allplots"]):::queued
x9f1bab82f5a7def2(["fig_repro_effects"]):::queued --> x798304d337dd8ea1(["allplots"]):::queued
xe3a4a17736576ba6(["fig_time_series"]):::queued --> x798304d337dd8ea1(["allplots"]):::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> xe415b5066c557652(["peak_dates"]):::queued
x89c8b8fe66c39f8c(["gam_posterior"]):::queued --> xe415b5066c557652(["peak_dates"]):::queued
x023bc1d70802c4e1(["multinomial_model"]):::queued --> xe415b5066c557652(["peak_dates"]):::queued
xba784c3a136c631a(["dat_cleaned"]):::queued --> x4a5cfaffa1d0e789(["dat_prepped"]):::queued
xe2bed16893714ed7(["model_terms_table_file"]):::queued --> x5ad18f0d36408418(["model_terms_table"]):::queued
xa8aa782838af3774(["partial_effect_plots"]):::queued --> x4426e7f0707b13f1(["partial_effect_plots_png"]):::queued
xa8aa782838af3774(["partial_effect_plots"]):::queued --> x62025a0def5a4707(["partial_effect_plots_svg"]):::queued
x90dc8aa96b6ba82f(["repro_effects"]):::queued --> x9f1bab82f5a7def2(["fig_repro_effects"]):::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> x7a7f43da56388c67(["fig_bat_demographics"]):::queued
x2d7bbbb9a4c3fe9d(["flextable_gam_summary"]):::queued --> xbda0fd67e19f73bc(["outputs_readme"]):::queued
x67d4d2e6dfa8bf90(["model_prev"]):::queued --> xbda0fd67e19f73bc(["outputs_readme"]):::queued
x4426e7f0707b13f1(["partial_effect_plots_png"]):::queued --> xbda0fd67e19f73bc(["outputs_readme"]):::queued
x62025a0def5a4707(["partial_effect_plots_svg"]):::queued --> xbda0fd67e19f73bc(["outputs_readme"]):::queued
x0b04ba823e0b5eec["png_plots"]:::queued --> xbda0fd67e19f73bc(["outputs_readme"]):::queued
xdf15caacba83d075(["raw_prev"]):::queued --> xbda0fd67e19f73bc(["outputs_readme"]):::queued
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xbbcceea2cdf8d3f7["svg_plots"]:::queued --> xbda0fd67e19f73bc(["outputs_readme"]):::queued
xf268c53ef67145e5(["table_fmi_demo"]):::queued --> xbda0fd67e19f73bc(["outputs_readme"]):::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> x5f01ca798133c2d4(["table_gam_summary"]):::queued
x89c8b8fe66c39f8c(["gam_posterior"]):::queued --> x5f01ca798133c2d4(["table_gam_summary"]):::queued
x5ad18f0d36408418(["model_terms_table"]):::queued --> x5f01ca798133c2d4(["table_gam_summary"]):::queued
x023bc1d70802c4e1(["multinomial_model"]):::queued --> x5f01ca798133c2d4(["table_gam_summary"]):::queued
xba784c3a136c631a(["dat_cleaned"]):::queued --> x90f3a96af7e5a0fc(["fig_fa_cutoffs"]):::queued
xe05634441492d438(["dat_captures"]):::queued --> x370906d20a0ac4c5(["captures_cleaned"]):::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> x023bc1d70802c4e1(["multinomial_model"]):::queued
xba784c3a136c631a(["dat_cleaned"]):::queued --> xdb44d218f76593df(["dat_cleaned_csv"]):::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> x67d4d2e6dfa8bf90(["model_prev"]):::queued
x89c8b8fe66c39f8c(["gam_posterior"]):::queued --> x67d4d2e6dfa8bf90(["model_prev"]):::queued
x023bc1d70802c4e1(["multinomial_model"]):::queued --> x67d4d2e6dfa8bf90(["model_prev"]):::queued
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x89c8b8fe66c39f8c(["gam_posterior"]):::queued --> x1e7dc8900c0252af(["model_diagnostics"]):::queued
x023bc1d70802c4e1(["multinomial_model"]):::queued --> x1e7dc8900c0252af(["model_diagnostics"]):::queued
x26018ab47272d583(["multinomial_model_alt"]):::queued --> x1e7dc8900c0252af(["model_diagnostics"]):::queued
x48dd96190e644d8f(["posterior_stats"]):::queued --> x1e7dc8900c0252af(["model_diagnostics"]):::queued
xba784c3a136c631a(["dat_cleaned"]):::queued --> xf22c729b71100575(["time_series"]):::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> xf22c729b71100575(["time_series"]):::queued
x89c8b8fe66c39f8c(["gam_posterior"]):::queued --> xf22c729b71100575(["time_series"]):::queued
x023bc1d70802c4e1(["multinomial_model"]):::queued --> xf22c729b71100575(["time_series"]):::queued
x4a5cfaffa1d0e789(["dat_prepped"]):::queued --> x80ade130098abb0f(["fig_fmi_demo_timeseries"]):::queued
end
linkStyle 0 stroke-width:0px;
- This project requires R version 4.3.1 (2023-06-16). This project uses
the {renv} framework to record R
package dependencies and versions. Packages and versions used are
recorded in
renv.lock
and code used to manage dependencies is inrenv/
and other files in the root project directory. On starting an R session in the working directory, runrenv::restore()
to install R package dependencies. - The package also requires
cmdstan
to be installed. (Version 2.32.0 was used). If not already installed, runcmdstanr::install_cmdstan(version = "2.32.0")
afterrenv::restore()