README.Rmd

---
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'
)
```

# fungalgrowth

<!-- 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/)

<!-- badges: end -->

Data from a laboratory assay investigating the growth of fungal species on 
human faeces and their effect on faecal physicochemical properties and 
bacterial indicator concentrations over 14 days post inoculation. Five fungal 
species were tested alongside untreated controls: Pleurotus ostreatus (MG1015, 
MG1010), Ganoderma lucidum, Stropharia fimicola (F35), and Trichoderma 
harzianum (T22).

## Installation

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

``` r
# install.packages("devtools")
devtools::install_github("global-health-engineering/fungalgrowth")
```

```{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/global-health-engineering/fungalgrowth/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 datasets capture a 14-day laboratory assay designed to study how fungal 
mycelium grows on human faeces and how that growth alters the substrate's 
physicochemical and microbial properties. Five datasets describe the assay end 
to end: the experimental setup, the characterization of the inputs (faeces 
samples and fungal inocula), the daily time series of mycelium growth, and the 
endpoint measurements recorded 14 days post inoculation. Each dataset is 
lazy-loaded and ready to use as soon as the package is attached.

```{r}
library(fungalgrowth)
```

### experiment_setup

The dataset `experiment_setup` contains data about the experimental setup recorded at 0 days post inoculation (0 dpi), linking each treatment unit to its faeces sample and fungal inoculum along with the initial faeces wet weight on the plate. It has
`r nrow(experiment_setup)` observations and `r ncol(experiment_setup)`
variables

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

### experiment_endpoint

The dataset `experiment_endpoint` contains data about the experimental measurements taken at 14 days post inoculation (14 dpi), including faeces wet and dry weights, pH, and bacterial indicator concentrations (E. coli, Enterococcus, total plate count) for each treatment unit. It has
`r nrow(experiment_endpoint)` observations and `r ncol(experiment_endpoint)`
variables

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

### faecal_measurements

The dataset `faecal_measurements` contains data about the characterization of each faeces sample at 0 days post inoculation (0 dpi), including total faeces weight, additive composition, pH, water content, and bacterial indicator concentrations (E. coli, Enterococcus, total plate count) averaged across three replicates. It has
`r nrow(faecal_measurements)` observations and `r ncol(faecal_measurements)`
variables

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

### inoculum_species

The dataset `inoculum_species` contains data about the fungal inocula used in the assay, providing the mapping from each inoculum identifier to the fungal species. It has
`r nrow(inoculum_species)` observations and `r ncol(inoculum_species)`
variables

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

### fungal_growth

The dataset `fungal_growth` contains time-series observations of fungal mycelium growth on each treatment unit, with one row per treatment-observation date. Variables include mycelium area, contamination metrics, and growth descriptors recorded at each timepoint. It has
`r nrow(fungal_growth)` observations and `r ncol(fungal_growth)`
variables

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

## Example

```{r, message=FALSE, warning=FALSE}
library(fungalgrowth)
library(ggplot2)

ggplot(fungal_growth, aes(x = dpi, y = area_size, group = id_treatment)) +
  geom_line(alpha = 0.3) +
  labs(
    title = "Fungal mycelium growth over time",
    x = "Days post inoculation",
    y = "Area size (cm²)"
  ) +
  theme_minimal()
```

## License

Data are available as
[CC-BY](https://github.com/Global-Health-Engineering/fungalgrowth/blob/main/LICENSE.md).

## Citation

Please cite this package using:

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