README.Rmd

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# fecalcanuga

<!-- 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.20795917.svg)](https://zenodo.org/doi/10.5281/zenodo.20795917)
<!-- badges: end -->

The goal of `fecalcanuga` is to provide data representing 5 months of field work from April - June 2023 in Southern Coastal BC (Canada) and in Kampala (Uganda) from January - February 2024 where household and commercial non-sewered containment data was collected, characterizing demographic, technical, environmental, physical, chemical and biological data for 22 non-sewered sanitation sites in Southern Coastal British Columbia, Canada and 19 sites in Kampala, Uganda.

## Installation

You can install the development version of fecalcanuga from
[GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("openwashdata/fecalcanuga")
```

```{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 file from the table below.

```{r, echo=FALSE, message=FALSE, warning=FALSE}

extdata_path <- "https://github.com/openwashdata/fecalcanuga/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)"),
      ) |> 
  knitr::kable()

```

## Data

The package provides access to 3 datasets: `containment`, `household_survey`, and `phys_chem_parameter`.

```{r}
library(fecalcanuga)
```

### containment

The dataset `containment` contains data about the technical data (containment size, volume, fecal sludge depth, etc.) for each non-sewered sanitation system containment that was sampled as a part of this field work. It has `r nrow(containment)` observations and `r ncol(containment)` variables

```{r}
containment |> 
  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 == "containment.rda") |>
  dplyr::select(variable_name:description) |> 
  knitr::kable() |> 
  kableExtra::kable_styling("striped") |> 
  kableExtra::scroll_box(height = "200px")
```

### household_survey

The dataset `household_survey` contains data about the collected household / institutional survey data for each location where a non-sewered sanitation containment was sampled. This includes demographic infromation, operational and maintenance information and some technical and environmental parameters.
It has `r nrow(household_survey)` observations and `r ncol(household_survey)` variables

```{r}
household_survey |> 
  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 == "household_survey.rda") |>
  dplyr::select(variable_name:description) |> 
  knitr::kable() |> 
  kableExtra::kable_styling("striped") |> 
  kableExtra::scroll_box(height = "200px")
```

### phys_chem_parameter

The dataset `phys_chem_parameter` contains data about the measured in situ and analyzed in laboratory physical, chemical and biological parameters pertaining to each containment sampled as well as at different veritcal locations in each containment (i.e., Top of containment, middle and bottom).
It has `r nrow(phys_chem_parameter)` observations and `r ncol(phys_chem_parameter)` variables

```{r}
phys_chem_parameter |> 
  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 == "phys_chem_parameter.rda") |>
  dplyr::select(variable_name:description) |> 
  knitr::kable() |> 
  kableExtra::kable_styling("striped") |> 
  kableExtra::scroll_box(height = "200px")
```


## Example

```{r}
# Plot of Volatile Suspended Solids (VSS) versus Chemical Oxygen Demand (COD) for all samples. This plot demonstrates how changes in organic loads (COD) can influence biomass (VSS)
library(fecalcanuga)
library(ggplot2)
# Filter out rows with NA values in COD or vss
filtered_data <- phys_chem_parameter %>%
  filter(!is.na(COD) & !is.na(vss))

# Plot VSS vs COD
ggplot(filtered_data, aes(x = vss, y = COD)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "blue") +
  theme_minimal() +
  labs(title = "VSS vs COD",
       x = "VSS (mg/L)",
       y = "COD (mg/L)")

```


## License

Data are available as
[CC-BY](https://github.com/openwashdata/fecalcanuga/blob/main/LICENSE.md).

## Citation

Please cite this package using:

```{r}
citation("fecalcanuga")
```