README.Rmd

---
output: github_document
editor_options: 
  chunk_output_type: console
  markdown: 
    wrap: 72
execute:
  echo: false
---

<!-- 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'
)

library(sf)
library(tidyverse)
library(tmap)
library(wasteskipsblantyre)
```

# wasteskipsblantyre

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The goal of wasteskipsblantyre is to provide a dataset for
research and planning of solid waste management in Blantyre, Malawi. The
dataset includes locations of the publicly accessible waste skips in in
the city. The data was collected in 2021 and has not been updated since.

```{r, echo=FALSE, message=FALSE, warning=FALSE, dpi=300}
census_boundaries <- 
  st_read(here::here("vignettes", "articles",
                     "traditional-authorities", "Malawi_TA_2018.shp"), 
          quiet = TRUE) |> 
  st_as_sf()

census_data <- read_csv(here::here("vignettes", "articles", 
                                   "traditional-authorities", 
                                   "census_data.csv"))


sf_wsb <- st_as_sf(wasteskipsblantyre, coords = c('long', 'lat')) |>
  st_set_crs(value = 4326) |> 
  st_transform(st_crs(census_boundaries))

joined_dataset <- st_join(census_boundaries, sf_wsb) |> 
  mutate(name = ifelse((DIST_NAME == "Blantyre City")*is.na(name),
                       yes = TA_NAME, no = name)) |> 
  mutate(capacity_l = ifelse((DIST_NAME == "Blantyre City")*(is.na(capacity_l)), 
                             yes = 0, no =capacity_l)) |> 
  drop_na(capacity_l)

pop_ta <- joined_dataset |> 
  group_by(TA_NAME) |> 
  summarise(n = sum(capacity_l)/7000) |> 
  left_join(census_data) |> 
  summarise(ta_pop = sum(TOTAL_POP), .by = TA_NAME)

count_ta <- joined_dataset |> 
  group_by(TA_NAME) |> 
  summarise(n = sum(capacity_l)/7000) |> 
  mutate(mean_number = mean(n))

#st_drop_geometry(count_ta) |> 
#  left_join(st_drop_geometry(pop_ta)) |> 
#  ggplot(aes(x = ta_pop, y = forcats::fct_rev(TA_NAME))) +
#  geom_col(aes(fill = factor(n))) +
#  scale_fill_brewer(palette = "RdPu") +
#  labs(title = "Ward population and number of waste skips",
#       subtitle = "Greater population does not indicate greater number of #skips",
#       x = "Ward Population",
#       y = "Ward in Blantyre",
#       fill = "Number of publicly\naccessible waste skips") +
#  theme_bw()
```

```{r, echo=FALSE, message=FALSE, warning=FALSE}
st_drop_geometry(count_ta) |> 
  left_join(st_drop_geometry(pop_ta)) |> 
  mutate(n = factor(n)) |> 
  ggplot(aes(x = n, y = ta_pop, color = n)) +
  geom_boxplot(outlier.shape = NA, lwd = 0.8) +
  geom_jitter(width = 0.15, size = 4, alpha = 0.4, color = "black") +
  labs(title = "Number of waste skips and population",
       subtitle = "Greater population does not indicate greater number of skips",
       y = "Population",
       x = "Waste skips (n)",
       color = "Number of publicly\naccessible waste skips") +
  scale_color_brewer(palette = "RdPu") +
  scale_y_continuous(breaks = seq(0, 100000, 10000), 
                     limits = c(0, 100000),
                     expand = c(0, 0, 0.05, 0),
                     labels = scales::label_number(
                       scale_cut = scales::cut_short_scale()
                       )) +
  theme_gray() +
  theme(panel.grid.minor = element_blank(),
        panel.grid.major.x = element_blank(),
        axis.line = element_line(),
        panel.background = element_rect(fill = "white"),
        panel.grid.major = element_line("grey95", linewidth = 0.25),
        legend.key  = element_rect(fill = NA))


```

## Installation

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

```{r eval=FALSE, echo=TRUE}
# install.packages("devtools")
devtools::install_github("openwashdata/wasteskipsblantyre")
```

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/wasteskipsblantyre/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()

```

## Project goal

Data on the location of public waste skips in Blanytre was not
available. Without such data, it becomes difficult to develop a solid
waste management plan and logistics. The goal of this project was to
identify the locations of public waste skips in Blantyre, Malawi.

## Data

The data set includes the locations of the publicly accessible waste
skips in Blantyre, Malawi. The data was collected in 2021. 

```{r, echo = TRUE}
library(wasteskipsblantyre)
```

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

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

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

```{r, echo=FALSE}
#| label: fig-waste-skips-simple
#| fig-cap: Locations of waste skips in this dataset

sf_wsb <- st_as_sf(wasteskipsblantyre, coords = c('long', 'lat'), crs = 4326)

tmap_mode("plot")

sf_wsb |>
  tm_shape() +
  tm_dots() +
  tm_graticules()
```

## Example

The code below is an example which shows how you could use the data to
prepare a map in R. Find this and more examples in the [prepared
examples
article](https://openwashdata.github.io/wasteskipsblantyre/articles/examples.html)
(`vignette("examples")`).

```{r example, eval=FALSE, echo=TRUE}
library(wasteskipsblantyre)
library(sf)
library(tmap)

# read data set into a simple feature (spatial vector data)
sf_wsb <- st_as_sf(wasteskipsblantyre, coords = c('long', 'lat'), crs = 4326)
# set mapping mode to interactive ("view")
tmap_mode("view")
# create an interactive map 
qtm(sf_wsb)
```

```{r, echo=FALSE, fig.cap="Screenshot of the an interactive map with OpenStreetMap layer."}
knitr::include_graphics("man/figures/screenshot-map-waste-skips-blantyre.png")
```

## License

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

## Citation

Please cite using:

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