Replication Code for the Paper Titled "Air quality improvements can strengthen China's food security" published in Nature Food
This repository comprises three main directories:
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/data: This directory houses both the processed and original source data. Please note that we have excluded its contents from this presentation as it currently remains empty. -
/figures: Within this directory, you can find the visualizations generated by our R scripts. Each figure is named specifically. -
/figures_paper: In this directory, you will find the figures indexed for use in the paper. -
/script: This directory contains the source R code necessary for replicating the paper's results. This includes data processing, preparation, and figure generation.
Further insights into the script directory:
-
Files prefixed with
loadare responsible for loading packages, functions, and formulas. -
Files with prefixes
cal_andmakeare utilized for preprocessing the source data. For instance, they illustrate how to convert gridded data into tabular data. -
Files with prefixes
results_are used for generating and plotting the results presented in the main text and supporting information. -
publish_figs.Rserves the purpose of renaming the figures in/figuresand moving them to/figures_paper. -
tidy_shp.Ris responsible for generating smaller shapefiles, suitable for plotting. -
tidy_all.Rcombines the data generated by thecal_scripts into a single, tidy dataset.
Additional information in the data directory:
The /data directory contains the outputs generated by the results_ scripts.
The /data/outputs directory contains the outputs produced by the cal_ scripts.
The /data/inputs directory holds the source data acquired from various sources. It is important to note that, due to varying data policies, you will need to download the source data yourself.
Please be aware that we have omitted the data files from this presentation due to their substantial size. If you require access to these files, please reach out to the authors at [email protected].
We recommend downloading the source data and using the provided scripts for processing. However, you can also directly access the processed file, tidied.qs (1.57 GB), via the following link: Download tidied.qs
sessioninfo::session_info()─ Session info ────────────────────────────────────────────────────────────
setting value
version R version 4.2.3 (2023-03-15 ucrt)
os Windows 10 x64 (build 19045)
system x86_64, mingw32
ui RStudio
language (EN)
collate Chinese (Simplified)_China.utf8
ctype Chinese (Simplified)_China.utf8
tz Asia/Taipei
date 2023-09-19
rstudio 2022.12.0+353 Elsbeth Geranium (desktop)
pandoc 2.19.2 @ D:/Software/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
─ Packages ────────────────────────────────────────────────────────────────
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