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Resources","sub_category":"Air Quality","monthly_downloads":659,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# **rmweather** \u003ca href='https://github.com/skgrange/rmweather'\u003e\u003cimg src='man/figures/logo.png' align=\"right\" height=\"131.5\" /\u003e\u003c/a\u003e\n\n[![Lifecycle Status](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html)\n[![CRAN status](https://www.r-pkg.org/badges/version/rmweather)](https://cran.r-project.org/package=rmweather)\n[![CRAN log](https://cranlogs.r-pkg.org/badges/last-week/rmweather?color=brightgreen)](https://cran.r-project.org/package=rmweather)\n\n## Introduction\n\n**rmweather** is an R package to conduct meteorological/weather normalisation on air quality so trends and interventions can be investigated in a robust way. For those who are aware of my previous research, **rmweather** is the \"Mk.II\" package of [**normalweatherr**](https://github.com/skgrange/normalweatherr). **rmweather** does less than **normalweatherr**, but it is faster and easier to use. \n\n## Installation\n\n**rmweather** is available from CRAN and can be installed in the normal way: \n\n```\n# Install rmweather from CRAN\ninstall.packages(\"rmweather\")\n```\n\n## Development version\n\nTo install the development version of **rmweather**, the [**remotes**](https://github.com/r-lib/remotes) package will need to be installed first. Then:\n\n```\n# Load helper package\nlibrary(remotes)\n\n# Install rmweather\ninstall_github(\"skgrange/rmweather\")\n```\n\n## Example usage\n\n**rmweather** contains example data from London which can be used to show the meteorological normalisation procedure. The example data are daily means of NO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003ex\u003c/sub\u003e observations at London Marylebone Road. The accompanying surface meteorological data are from London Heathrow, a major airport located 23 km west of Central London. \n\nMost of **rmweather**'s functions begin with `rmw_` so are easy to track and find help for. In this example, we have used **dplyr** and the pipe (`%\u003e%` and pronounced as \"then\") for clarity. The example takes about 30 seconds on my (laptop) system and the model has an *R\u003csup\u003e2\u003c/sup\u003e* value of 76 %. \n\n```\n# Load packages\nlibrary(dplyr)\nlibrary(rmweather)\nlibrary(ranger)\n\n# Have a look at rmweather's example data, from london\nhead(data_london)\n\n# Prepare data for modelling\n# Only use data with valid wind speeds, no2 will become the dependent variable\ndata_london_prepared \u003c- data_london %\u003e% \n  filter(variable == \"no2\",\n         !is.na(ws)) %\u003e% \n  rmw_prepare_data(na.rm = TRUE)\n\n# Grow/train a random forest model and then create a meteorological normalised trend \nlist_normalised \u003c- rmw_do_all(\n  data_london_prepared,\n  variables = c(\n    \"date_unix\", \"day_julian\", \"weekday\", \"air_temp\", \"rh\", \"wd\", \"ws\",\n    \"atmospheric_pressure\"\n  ),\n  n_trees = 300,\n  n_samples = 300,\n  verbose = TRUE\n)\n\n# What units are in the list? \nnames(list_normalised)\n\n# Check model object's performance\nrmw_model_statistics(list_normalised$model)\n\n# Plot variable importances\nlist_normalised$model %\u003e% \n  rmw_model_importance() %\u003e% \n  rmw_plot_importance()\n\n# Check if model has suffered from overfitting\nrmw_predict_the_test_set(\n  model = list_normalised$model,\n  df = list_normalised$observations\n) %\u003e% \n  rmw_plot_test_prediction()\n\n# How long did the process take? \nlist_normalised$elapsed_times\n\n# Plot normalised trend\nrmw_plot_normalised(list_normalised$normalised)\n\n# Investigate partial dependencies, if variable is NA, predict all\ndata_pd \u003c- rmw_partial_dependencies(\n  model = list_normalised$model, \n  df = list_normalised$observations,\n  variable = NA\n)\n\n# Plot partial dependencies\ndata_pd %\u003e% \n  filter(variable != \"date_unix\") %\u003e% \n  rmw_plot_partial_dependencies()\n```\n\nThe meteorologically normalised trend produced is below.\n\n![](man/figures/normalised_no2_example.png)\n\n## Examples and citations\n\nFor usage examples see: \n\nGrange, S. K., Carslaw, D. C., Lewis, A. C., Boleti, E., and Hueglin, C. (2018). [Random forest meteorological normalisation models for Swiss PM\u003csub\u003e10\u003c/sub\u003e trend analysis](https://acp.copernicus.org/articles/18/6223/2018/). *Atmospheric Chemistry and Physics* 18.9, pp. 6223--6239.\n  \nGrange, S. K. and Carslaw, D. C. (2019). [Using meteorological normalisation to detect interventions in air quality time series](http://www.sciencedirect.com/science/article/pii/S004896971834244X). *Science of The Total Environment* 653, pp. 578--588.\n\n## The use of **rmweather** for prediction or counterfactual/business as usual scenarios\n\nA second usage of **rmweather** became established in 2020 to help researchers quantify the effects of the COVID-19 related restrictions on air quality. Briefly, the approach involves the training of random forest models to explain pollutant concentrations based on meteorological and time variables for a training period, say, between 2018 and 2019. After the training period, the model is used in predictive-mode using the experienced meteorological conditions. The predicted time series can be thought of as a counterfactual or business-as-usual (BAU) scenario which the observed time series can be compared with. Critically, an approach like this accounts for the meteorological conditions observed in 2020, which in many locations was unusual and complicates simple analyses. The meteorological sampling and normalisation step is not required for this analysis, but this has been confused in the literature. \n\n### Examples of counterfactural modelling\n\nGrange, S. K., Lee, J. D., Drysdale, W. S., Lewis, A. C., Hueglin, C., Emmenegger, L., and Carslaw, D. C. (2021). [COVID-19 lockdowns highlight a risk of increasing ozone pollution in European urban areas](https://acp.copernicus.org/articles/21/4169/2021/). *Atmospheric Chemistry and Physics* 21.5, pp. 4169--4185.\n\nWang, Y., Wen, Y., Wang, Y., Zhang, S., Zhang, K. M., Zheng, H., Xing, J., Wu, Y., and Hao, J. (2020). [Four-Month Changes in Air Quality during and after the COVID-19 Lockdown in Six Megacities in China](https://doi.org/10.1021/acs.estlett.0c00605). *Environmental Science and Technology Letters* 7.11, pp. 802--808.\n\nFenech, S., Aquilina, N. J., Ryan, V. (2021) [COVID-19-Related Changes in NO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e Concentrations and Associated Health Effects in Malta](https://www.frontiersin.org/articles/10.3389/frsc.2021.631280/full). *Frontiers in Sustainable Cities* 3.631280, pp. 1--12. \n\nShi, Z., Song, C., Liu, B., Lu, G., Xu, J., Van Vu, T., Elliott, R. J. R., Li, W., Bloss, W. J., and Harrison, R. M. (2021).\n[Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns](https://www.science.org/doi/full/10.1126/sciadv.abd6696). *Science Advances* 7.3, eabd6696.\n\n## See also\n\n  - [**ranger**](https://github.com/imbs-hl/ranger)\n  \n  - [**normalweatherr**](https://github.com/skgrange/normalweatherr)\n  \n  - [**deweather**](https://github.com/davidcarslaw/deweather)\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.1021/acs.estlett.0c00605"],"works":{"https://doi.org/10.1021/acs.estlett.0c00605":{"id":"https://openalex.org/W3085550085","doi":"https://doi.org/10.1021/acs.estlett.0c00605","title":"Four-Month Changes in Air Quality during and after the COVID-19 Lockdown in Six Megacities in China","display_name":"Four-Month Changes in Air Quality during and after the COVID-19 Lockdown in Six Megacities in 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