https://github.com/openwashdata/choleramalawi
Data package tracking the course of the cholera epidemic in Malawi (2023-24)
https://github.com/openwashdata/choleramalawi
Last synced: 4 months ago
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Data package tracking the course of the cholera epidemic in Malawi (2023-24)
- Host: GitHub
- URL: https://github.com/openwashdata/choleramalawi
- Owner: openwashdata
- License: cc-by-4.0
- Created: 2024-10-04T13:46:47.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-16T06:58:09.000Z (over 1 year ago)
- Last Synced: 2025-09-04T22:46:18.426Z (9 months ago)
- Language: R
- Homepage: https://openwashdata.github.io/choleramalawi/
- Size: 1.31 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE.md
- 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
---
```{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'
)
```
# choleramalawi
[](https://creativecommons.org/licenses/by/4.0/)
[](https://zenodo.org/doi/10.5281/zenodo.13920530)
The goal of choleramalawi is to analyse the progress of the Cholera epidemic in Malawi (2023-24)
## Installation
You can install the development version of choleramalawi from
[GitHub](https://github.com/) with:
``` {r eval=FALSE}
# install.packages("devtools")
devtools::install_github("openwashdata/choleramalawi")
```
```{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)
library(rnaturalearth)
library(rnaturalearthdata)
library(sf)
library(RColorBrewer)
#devtools::load_all()
```
Alternatively, you can download the individual datasets as a CSV or XLSX
file from the table below.
```{r, echo=FALSE, message=FALSE, warning=FALSE}
extdata_path <- "https://github.com/openwashdata/choleramalawi/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 one dataset `choleramalawi`. It contains data on the progress of the cholera epidemic in each district of Malawi during 2022-23. The data focuses on cases and deaths for every week as well as cumulative cases and deaths.
```{r}
library(choleramalawi)
```
### choleramalawi
The dataset `choleramalawi` contains data about the progress of the cholera epidemic in Malawi (2022-23).
It has `r nrow(choleramalawi)` observations and `r ncol(choleramalawi)` variables
```{r}
choleramalawi |>
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 == "choleramalawi.rda") |>
dplyr::select(variable_name:description) |>
knitr::kable() |>
kableExtra::kable_styling("striped") |>
kableExtra::scroll_box(height = "200px")
```
## Example
```{r}
library(choleramalawi)
# Top districts by total cases
choleramalawi |>
group_by(district) |>
summarise(total_cases = sum(cases, na.rm = TRUE)) |>
arrange(desc(total_cases)) |>
head(10) |>
gt() |>
as_raw_html()
```
```{r}
# Plotting the number of cases over time in Lilongwe district
library(ggplot2)
choleramalawi |>
filter(district == "Lilongwe") |>
ggplot(aes(x = week, y = cases)) +
geom_line() +
labs(title = "Number of cases over time in Lilongwe district",
x = "Week",
y = "Cases")
```
```{r}
# Plot a map of districts of Malawi colored by the number of cases
total_cases_district <- choleramalawi |>
group_by(district) |>
summarise(total_cases = sum(cases, na.rm = TRUE)) |>
mutate(total_cases = ifelse(is.na(total_cases), 0, total_cases))
malawi_map <- ne_states(country = "Malawi", returnclass = "sf")
malawi_map <- malawi_map %>%
left_join(total_cases_district, by = c("name" = "district"))
ggplot(malawi_map) +
geom_sf(aes(fill = total_cases)) +
scale_fill_viridis_c(guide="none") +
theme_void() +
labs(title = "Cholera Cases by District in Malawi")
```
## License
Data are available as
[CC-BY](https://github.com/openwashdata/choleramalawi/blob/main/LICENSE.md).
## Citation
Please cite this package using:
```{r}
citation("choleramalawi")
```