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Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\",\n  message = FALSE,\n  warning = FALSE,\n  fig.retina = 2,\n  fig.align = 'center'\n)\n```\n\n# USAID Flood Response – Post Intervention Survey (Mulanje, 2019–2020)\n\n\u003c!-- badges: start --\u003e\n\n[![License: CC BY\n4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)\n\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15837461.svg)](https://doi.org/10.5281/zenodo.15837461)\n\u003c!-- badges: end --\u003e\n\nThis dataset contains detailed post-intervention monitoring data for\nrural water points in the Mulanje district of Malawi, collected as part\nof the USAID Flood Response program during 2019 and 2020. Using the\nmWater mobile data collection platform, enumerators conducted on-site\nassessments of water point conditions following flood recovery efforts.\n\nThe data captures a comprehensive range of water point characteristics,\nincluding physical condition through photographs, operational\nperformance of pumps, and hydraulic measurements such as time and effort\nrequired to pump a standard volume of water. Additionally, water quality\nparameters were rigorously tested—covering chemical contaminants like\narsenic, ammonia, fluoride, nitrate, free chlorine, and total dissolved\nsolids, as well as physical indicators such as pH, temperature, and\nturbidity.\n\nMicrobiological quality was assessed via E. coli concentrations,\nincluding counts per 100 milliliters, confidence intervals, and risk\nclassifications, supported by photographic documentation of test\nresults. These indicators provide critical insight into the safety and\nusability of water sources after flood-related disruptions.\n\n### Use Cases\n\nThis dataset serves multiple practical purposes for water management and\npublic health:\n\n-    **Evaluating the effectiveness** of flood recovery interventions on\n    water infrastructure.\n\n-    **Monitoring water quality trends** to identify ongoing or emerging\n    contamination risks.\n\n-    **Informing maintenance and rehabilitation priorities** based on\n    pump performance and structural assessments.\n\n-    **Supporting public health risk assessments** through microbial\n    contamination data.\n\n-    **Providing evidence for community-level decision making** and\n    donor reporting.\n\n-    **Guiding future emergency preparedness and response planning** for\n    water systems in flood-prone areas.\n\n### Potential Users\n\nThe dataset is highly valuable to a range of stakeholders including:\n\n1.   Government agencies responsible for water supply and sanitation,\n    particularly at the district and national levels.\n\n2.   International donor organizations and development partners managing\n    WASH and disaster recovery programs.\n\n3.   Field engineers and technical teams engaged in infrastructure\n    repair and monitoring.\n\n4.   Public health officials tracking waterborne disease risks.\n\n5.   Researchers studying environmental health, water security, and\n    climate resilience.\n\n6.   NGOs and civil society organizations supporting community water\n    management and advocacy.\n\n## Installation\n\nYou can install the development version of postfloodintervention from\n[GitHub](https://github.com/) with:\n\n``` r\n# install.packages(\"devtools\")\ndevtools::install_github(\"openwashdata/postfloodintervention\")\n```\n\n```{r}\n## Run the following code in console if you don't have the packages\n## install.packages(c(\"dplyr\", \"knitr\", \"readr\", \"stringr\", \"gt\", \"kableExtra\"))\nlibrary(dplyr)\nlibrary(knitr)\nlibrary(readr)\nlibrary(stringr)\nlibrary(gt)\nlibrary(kableExtra)\nlibrary(postfloodintervention)\ndata(postfloodintervention)\n```\n\nAlternatively, you can download the individual datasets as a CSV or XLSX\nfile from the table below.\n\n1.  Click Download CSV. A window opens that displays the CSV in your\n    browser.\n2.  Right-click anywhere inside the window and select \"Save Page As...\".\n3.  Save the file in a folder of your choice.\n\n```{r, echo=FALSE, message=FALSE, warning=FALSE}\n\nextdata_path \u003c- \"https://github.com/openwashdata/postfloodintervention/raw/main/inst/extdata/\"\n\nread_csv(\"data-raw/dictionary.csv\") |\u003e \n  distinct(file_name) |\u003e \n  dplyr::mutate(file_name = str_remove(file_name, \".rda\")) |\u003e \n  dplyr::rename(dataset = file_name) |\u003e \n  mutate(\n    CSV = paste0(\"[Download CSV](\", extdata_path, dataset, \".csv)\"),\n    XLSX = paste0(\"[Download XLSX](\", extdata_path, dataset, \".xlsx)\")\n  ) |\u003e \n  knitr::kable()\n\n```\n\n## Data\n\nThe package provides access to  post-intervention monitoring data for rural water points in the Mulanje district of Malawi, collected as part of the USAID Flood Response program during 2019 and 2020.\n\n### postfloodintervention\n\nThe dataset `postfloodintervention` contains \n`r nrow(postfloodintervention)` observations and\n`r ncol(postfloodintervention)` variables\n\n```{r}\npostfloodintervention |\u003e \n  head(3) |\u003e \n  gt::gt() |\u003e\n  gt::as_raw_html()\n```\n\nFor an overview of the variable names, see the following table.\n\n```{r echo=FALSE, message=FALSE, warning=FALSE}\nreadr::read_csv(\"data-raw/dictionary.csv\") |\u003e\n  dplyr::filter(file_name == \"postfloodintervention.rda\") |\u003e\n  dplyr::select(variable_name:description) |\u003e \n  knitr::kable() |\u003e \n  kableExtra::kable_styling(\"striped\") |\u003e \n  kableExtra::scroll_box(height = \"200px\")\n```\n\n## Example\n\n```{r}\n## Run the following code in console if you don't have the packages\n## install.packages(c(\"postfloodintervention\", \"tidyverse\"))\nlibrary(postfloodintervention)\n\n# Water Quality Parameters\n# Purpose: Multi-panel boxplots for chemical indicators (arsenic, fluoride, nitrate, ammonia, free chlorine, pH) to detect outliers or contamination patterns.\n\n# Load libraries\nlibrary(tidyverse)\n\n# Select relevant chemical columns and pivot longer for plotting\nchemicals_long \u003c- postfloodintervention %\u003e%\n  select(arsenic_magnitude, fluoride_ppm, nitrate_mg_per_l, ammonia_mg_per_l, free_chlorine_mg_per_l, ph) %\u003e%\n  pivot_longer(\n    cols = everything(),\n    names_to = \"chemical\",\n    values_to = \"value\"\n  ) %\u003e%\n  filter(!is.na(value))  # Remove missing values\n\n# Plot multi-panel boxplots\nggplot(chemicals_long, aes(x = chemical, y = value)) +\n  geom_boxplot(fill = \"#4a90e2\", outlier.color = \"red\") +\n  facet_wrap(~ chemical, scales = \"free\") +   # Free y-scale per chemical\n  labs(\n    title = \"Water Quality Parameters: Chemical Indicators\",\n    x = NULL,\n    y = \"Concentration\"\n  ) +\n  theme_minimal() +\n  theme(axis.text.x = element_blank(),   # Hide x labels since facets show names\n        axis.ticks.x = element_blank())\n\n```\n\n## License\n\nData are available as\n[CC-BY](https://github.com/openwashdata/postfloodintervention/blob/main/LICENSE.md).\n\n## Citation\n\nPlease cite this package using:\n\n```{r}\ncitation(\"postfloodintervention\")\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopenwashdata%2Fpostfloodintervention","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fopenwashdata%2Fpostfloodintervention","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopenwashdata%2Fpostfloodintervention/lists"}