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---
output: github_document
always_allow_html: true
editor_options:
markdown:
wrap: 72
chunk_output_type: console
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
message = FALSE,
warning = FALSE,
fig.retina = 2,
fig.align = 'center'
)
```
# Borehole Drilling and Construction Data – Malawi, 2018 - 2024
<!-- badges: start -->
[](https://creativecommons.org/licenses/by/4.0/)
[](https://doi.org/10.5281/zenodo.17543305)
<!-- badges: end -->
This dataset captures detailed information on borehole drilling and
construction activities conducted across selected sites in Malawi between
2018 and 2024. Data were collected using the mWater digital platform, documenting
each stage of groundwater development, from site selection and
hydrogeological studies to drilling operations, borehole design, and
post-construction assessments.
The dataset includes variables on location and site suitability, survey
and geophysical methods, drilling parameters, equipment and safety
compliance, geological formations encountered, casing and gravel pack
details, and initial water quality measurements. Together, these data
provide a comprehensive technical record of Malawi’s borehole
development process.
**Potential Use Cases**
This dataset is valuable to a wide range of stakeholders, including:
1. Government Water Departments and District Councils: for monitoring
drilling quality, verifying contractor performance, and maintaining
an updated inventory of functional water points.
2. Development Partners and NGOs: for planning new water supply
interventions, assessing borehole performance, and improving
accountability in donor-funded projects.
3. Hydrogeologists and WASH Engineers: for analyzing aquifer
characteristics, drilling efficiency, and the relationship between
geology and borehole productivity.
4. Researchers and Academic Institutions: for studying groundwater
development practices, environmental sustainability, and data-driven
approaches to rural water supply.
5. Policy Makers and Regulators: for strengthening national standards,
promoting data transparency, and improving decision-making in the
water resources sector.
## Installation
You can install the development version of drillingdata from
[GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("openwashdata/drillingdata")
```
```{r}
## Run the following code in console if you don't have the packages
## install.packages(c("dplyr", "knitr", "readr", "stringr", "gt", "kableExtra"))
library(dplyr)
library(knitr)
library(readr)
library(stringr)
library(gt)
library(kableExtra)
```
Alternatively, you can download the individual datasets as a CSV or XLSX
file from the table below.
1. Click Download CSV. A window opens that displays the CSV in your
browser.
2. Right-click anywhere inside the window and select "Save Page As...".
3. Save the file in a folder of your choice.
```{r, echo=FALSE, message=FALSE, warning=FALSE}
extdata_path <- "https://github.com/openwashdata/drillingdata/raw/main/inst/extdata/"
read_csv("data-raw/dictionary.csv") |>
distinct(file_name) |>
dplyr::mutate(file_name = str_remove(file_name, ".rda")) |>
dplyr::rename(dataset = file_name) |>
mutate(
CSV = paste0("[Download CSV](", extdata_path, dataset, ".csv)"),
XLSX = paste0("[Download XLSX](", extdata_path, dataset, ".xlsx)")
) |>
knitr::kable()
```
## Data
The package provides access to the variables on location and site suitability, survey and geophysical methods, drilling parameters, equipment and safety compliance, geological formations encountered, casing and gravel pack details, and initial water quality measurements
```{r}
library(drillingdata)
```
### drillingdata
The dataset `drillingdata` contains data `r nrow(drillingdata)` observations and `r ncol(drillingdata)` variables
```{r}
drillingdata |>
head(3) |>
gt::gt() |>
gt::as_raw_html()
```
For an overview of the variable names, see the following table.
```{r echo=FALSE, message=FALSE, warning=FALSE}
readr::read_csv("data-raw/dictionary.csv") |>
dplyr::filter(file_name == "drillingdata.rda") |>
dplyr::select(variable_name:description) |>
knitr::kable() |>
kableExtra::kable_styling("striped") |>
kableExtra::scroll_box(height = "200px")
```
## Example
```{r}
library(drillingdata)
# Histogram of Borehole Depths
# Load required packages
library(ggplot2)
library(dplyr)
# prepare data
boreholes_clean <- drillingdata %>%
mutate(depth_m = as.numeric(borehole_depth_miters)) %>%
filter(!is.na(depth_m)) # remove missing values
# Plot graph
ggplot(boreholes_clean, aes(x = depth_m)) +
geom_histogram(binwidth = 10, fill = "skyblue", color = "black", alpha = 0.8) +
labs(
title = "Distribution of Borehole Depths",
subtitle = "Number of boreholes in different depth ranges",
x = "Borehole Depth (meters)",
y = "Number of Boreholes"
) +
theme_minimal(base_size = 14)
```
## License
Data are available as
[CC-BY](https://github.com/openwashdata/%7B%7B%7Bpackagename%7D%7D%7D/blob/main/LICENSE.md).
## Citation
Please cite this package using:
```{r}
citation("drillingdata")
```