README.Rmd

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# fslogisticskampala

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[![License: CC BY
4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
[![R-CMD-check](https://github.com/openwashdata/fslogisticskampala/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/openwashdata/fslogisticskampala/actions/workflows/R-CMD-check.yaml)
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The goal of fslogisticskampala is to provide data sources on faecal sludge transporting logistics in Kampala, Uganda collected from 30th March 2015 until 25th June 2015. 

## Installation

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

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

```{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.

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

extdata_path <- "https://github.com/openwashdata/fslogisticskampala/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 two datasets `trips` and `trucks`. 

```{r}
library(fslogisticskampala)
```

### trips

The dataset `trips` contains data about the GPS locations of faecal sludge trucks collecting sludge from pit latrines and septic tanks in Kampala, Uganda. Each trip is recorded with a unique identifier, the numberplate of the truck, the date and time of the record. Data was collected from 30th March 2015 until 25th June 2015.
It has `r nrow(trips)` observations and `r ncol(trips)` variables

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

### trucks

The dataset `trucks` contains data about additional information on the volume of each truck used in the dataset `trips`.
It has `r nrow(trucks)` observations and `r ncol(trucks)` variables

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

## Example

```{r}
library(fslogisticskampala)
library(ggplot2)
library(lubridate)

aus <- trips |>
  dplyr::filter(numberplate == "AUS 119X") |>
  dplyr::filter(date < ymd("2015-04-06"))

ggplot(aus, aes(x = lon, y = lat, color = date)) +
  geom_point() +
  labs(title = "GPS Locations of Faecal Sludge Trucks",
       x = "Longitude",
       y = "Latitude",
       color = "Treatment Plant") +
  theme_minimal()
# Provide some example code here
```


## License

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

## Citation

Please cite this package using:

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