README.Rmd

---
output: github_document
editor_options: 
  chunk_output_type: console
bibliography: references.bib
---

<!-- 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
)

library(tidyverse)
library(epoxy)
library(gt)
```

# fsmglobal

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This data was first published as part of a journal article by [@greene2021] and contained in the supplemental material as a table in a DOCX file. The following summary table was produced from the data and the code is shown further below.

```{r, echo=FALSE}
library(fsmglobal)
library(gt)
library(dplyr)
```

```{r, echo=FALSE}
tbl_emptying_demand_global <- fsmglobal |> 
  group_by(emptying_method) |> 
  summarise(
    sum_population_emptying_method = sum(population_emptying_method)
  ) |> 
  mutate(
    percent = sum_population_emptying_method / 
      sum(sum_population_emptying_method) * 100
  )
```


```{r, echo=FALSE}
tbl_emptying_demand_global |> 
  janitor::adorn_totals() |>
  
  # use gt R package to prepare a table
  gt(rowname_col = "emptying_method") |>
  tab_header(title = md("**Demand for faecal sludge emptying services**"), 
             subtitle = "summarised for 175 countries") |> 
  fmt_number(columns = "sum_population_emptying_method", decimals = 0) |> 
  fmt_percent(columns = "percent", decimals = 0, scale_values = FALSE) |> 
  cols_label(sum_population_emptying_method = "population") 
```

## Installation

You can install fsmglobal from [GitHub](https://github.com/) with:

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

Alternatively, you can download the individual datasets as a CSV or XLSX file from the table below.

```{r, echo=FALSE}

extdata_path <- "https://github.com/openwashdata/fsmglobal/raw/main/inst/extdata/"

read_csv("data-raw/dictionary.csv") |> 
  distinct(file_name) |> 
  mutate(file_name = str_remove(file_name, ".rda")) |> 
  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.

```{r}
library(fsmglobal)
```

```{epoxy}
The `fsmglobal` data set has {ncol(fsmglobal)} variables and {nrow(fsmglobal)} observations. For an overview of the variable names, see the following table.  
```

```{r, eval=FALSE}
fsmglobal
```

```{r, echo=FALSE}
readr::read_csv("data-raw/dictionary.csv") |> 
  dplyr::filter(file_name == "fsmglobal.rda") |> 
  dplyr::select(variable_name:description) |> 
  knitr::kable()
```

## Examples

The code and table below show a summary for the demand for faecal sludge emptying services. Find this and more examples in `vignette("examples")`.

```{r,eval=FALSE}
library(fsmglobal)
library(gt)
library(dplyr)
```

```{r, eval=FALSE}
tbl_emptying_demand_global <- fsmglobal |> 
  group_by(emptying_method) |> 
  summarise(
    sum_population_emptying_method = sum(population_emptying_method)
  ) |> 
  mutate(
    percent = sum_population_emptying_method / 
      sum(sum_population_emptying_method) * 100
  )
```

```{r}
tbl_emptying_demand_global |> 
  janitor::adorn_totals() |>
  
  # use gt R package to prepare a table
  gt(rowname_col = "emptying_method") |>
  tab_header(title = md("**Demand for faecal sludge emptying services**"), 
             subtitle = "summarised for 175 countries") |> 
  fmt_number(columns = "sum_population_emptying_method", decimals = 0) |> 
  fmt_percent(columns = "percent", decimals = 0, scale_values = FALSE) |> 
  cols_label(sum_population_emptying_method = "population") 
```

## License

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

## Citation

Please cite using:

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

## References