README.Rmd

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# choleramalawi

<!-- badges: start -->

[![License: CC BY
4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.13920530.svg)](https://zenodo.org/doi/10.5281/zenodo.13920530)

<!-- badges: end -->

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")
```