https://github.com/openwashdata/ugabore
Borehole repair data from central Uganda associated with a project report completed by Joseph Lwere for the “data science for openwashdata” course
https://github.com/openwashdata/ugabore
analysis borehole data open-data r uganda wash water
Last synced: 4 months ago
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Borehole repair data from central Uganda associated with a project report completed by Joseph Lwere for the “data science for openwashdata” course
- Host: GitHub
- URL: https://github.com/openwashdata/ugabore
- Owner: openwashdata
- Created: 2024-04-30T13:43:49.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-20T13:09:52.000Z (almost 2 years ago)
- Last Synced: 2025-09-04T21:36:42.222Z (9 months ago)
- Topics: analysis, borehole, data, open-data, r, uganda, wash, water
- Language: R
- Homepage: https://openwashdata.github.io/ugabore/
- Size: 942 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.Rmd
- Citation: CITATION.cff
Awesome Lists containing this project
README
---
output: github_document
always_allow_html: true
editor_options:
markdown:
wrap: 72
chunk_output_type: console
bibliography: references.bib
---
```{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'
)
```
# ugabore
[](https://creativecommons.org/licenses/by/4.0/)
[](https://github.com/openwashdata/ugabore/actions/workflows/R-CMD-check.yaml)
[](https://zenodo.org/doi/10.5281/zenodo.12188179)
The goal of `ugabore` is to provide users with documentation on borehole repair data collected from two districts in central Uganda where a borehole operation and maintenance program is run. The dataset is associated with the following [project report](https://ds4owd-001.github.io/project-ljc3084/) completed by Joseph Lwere for the ["data science for openwashdata" course](https://ds4owd-001.github.io/website/) offered by [openwashdata.org](https://openwashdata.org/).
## Installation
You can install the development version of ugabore from
[GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("openwashdata/ugabore")
```
Alternatively, you can download the individual datasets as a CSV or XLSX
file from the table below.
```{r, echo=FALSE, message=FALSE, warning=FALSE}
library(dplyr)
library(knitr)
library(readr)
library(stringr)
library(gt)
library(kableExtra)
library(ggtext)
extdata_path <- "https://github.com/openwashdata/ugabore/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()
```
## Project goal
Boreholes are the main technology used to access groundwater in Uganda, according to [@owor2022permeability], and they are also a source of drinking water for households in rural communities in Africa, including Uganda [@lapworth2020drinking]. Therefore, it is crucial to have good quality data to inform decision-making and planning. This project examines data collected from two districts in central Uganda where a borehole operation and maintenance program is run. As professional operation and maintenance is considered the future for borehole functionality in Uganda [@smith2023does], this project report offers more insights into research on this topic.
## Data
The dataset includes information about borehole repair records used by the borehole operation and maintenance company operating in central Uganda. The package provides access to one dataset.
```{r}
library(ugabore)
```
The `ugabore` data set has `r ncol(ugabore)` variables and `r nrow(ugabore)` observations. 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 == "ugabore.rda") |>
dplyr::select(variable_name:description) |>
knitr::kable() |>
kableExtra::kable_styling("striped")
```
## Example: Water production capacity versus number of people collecting
Here is an example illustrating the relationship between the number of people collecting water from boreholes in a sub-county and the water production capacity of the corresponding boreholes. From the plot, we see that the sub-county of Kalagala has the highest water production capacity by far. However, it is not the sub-county that serves the most people, suggesting it may be the richest one. On another note, the sub-counties Bombo Town Council and Luwero Town Council have the most people collecting water from their boreholes but do not have high water production capacity, indicating a need for improvement.
```{r, warning=FALSE}
library(ugabore)
library(ggplot2)
library(dplyr)
# Define custom colors for plotting
custom_colors <- c(
"Bamunanika" = "dodgerblue2", "Bombo Tc" = "#E31A1C",
"Busukuma" = "green4",
"Butuntumula" = "#6A3D9A",
"Gombe" = "#FF7F00",
"Kajjansi Town Council" = "black", "Kakiri" = "gold1",
"Kakiri Town Council" = "skyblue2", "Kalagala" = "#FB9A99",
"Kamira" = "palegreen2",
"Kasangati Town Council" = "#CAB2D6",
"Kasangombe" = "#FDBF6F",
"Katikamu" = "gray70", "Kikyusa" = "khaki2",
"Kira" = "maroon", "Luwero" = "orchid1", "Luwero Tc" = "deeppink1",
"Makulubita" = "blue1", "Namayumba Town Council" = "steelblue4",
"Wobulenzi Tc" = "darkturquoise", "Zirobwe" = "green1",
"NA" = "yellow4")
# Summarize data by sub_county
summary_data <- ugabore |>
group_by(sub_county) |>
summarise(mean_well_yield = mean(well_yield, na.rm = TRUE),
mean_population_served = mean(population_served, na.rm = TRUE))
# Plot summarized data
ggplot(summary_data, aes(x = mean_well_yield, y = mean_population_served,
color = sub_county)) +
geom_point(size = 3, alpha = 0.7) +
labs(title = "Water collection versus production by sub-county",
x = "\naverage water production capacity in m3/h",
y = "average number of people\n",
color = "sub-county") +
scale_color_manual(values = custom_colors) +
theme_minimal()
```
## License
Data are available as
[CC-BY](https://github.com/openwashdata/ugabore/blob/main/LICENSE.md).
## Citation
Please cite this package using:
```{r}
citation("ugabore")
```