# bangladesh: introduction

The bangladesh package provides ready-to-use shapefiles for different administrative regions of Bangladesh (e.g., Division, District, Upazila, and Union). Usually, it is difficult to plot choropleth maps for Bangladesh in R. This package will help users to draw thematic maps of administrative regions of Bangladesh easily as it comes with the sf objects for the boundaries and regions’ names in English. It also provides functions allowing users to efficiently get specific area maps and center coordinates for regions. Users can also search for a specific area and calculate the centroids of those areas.

## Getting Started

This packages comes with sf objects for administrative levels 0-4 (Country, Division, District, Upazila, Union). The easiest way to get the shapefile for a level is to is to use `get_map()` function.

    # remotes::install_github("ovirahman/bangladesh")
    library(bangladesh)

    country <- get_map("country")
    division <- get_map("division")
    district <- get_map("district")
    upazila <- get_map("upazila")
    union <- get_map("union")

## Plotting Map

To start with we can check the sample function `bd_plot()` to draw the map of different administrative levels of Bangladesh, which uses `tmap`, a very flexible and cool package to visualize thematic maps.

    bd_plot("country")

    bd_plot("division")

    bd_plot("district")

We can also plot beautiful interactive maps with this.

## Choropleth map with data

Using the `tmap` package (my favorite for creating thematic maps), we can make cool choropleths, both static and interactive. When plotting mode is chosen as static (plot) it returns a `ggplot` object, when interactive (view) it returns a `leaflet` object.

    library(tmap)
    population <- bangladesh::pop_district_2011[, c("district", "population")]
    district <- get_map("district")

    map_data <- dplyr::left_join(district, population, by = c("District" = "district"))

    map <- tm_shape(map_data) +
      tm_polygons("population",id = "District",palette = "Reds", title = "Population") +
      tm_style("cobalt")+
      tm_layout(
        "Bangladesh District Wise Population Map\nSource: BBS",
        title.position = c("left", "bottom"),
        legend.position = c("right", "top")
        )

    tmap::tmap_mode("plot")
    map

To make it interactive we can do the following:

## Using ggplot2 and leaflet

We can also use `ggplot2` and `leaflet` to draw customized choropleths with the sf objects provided in `bangladesh` package.

    library(ggplot2)
    ggplot(data = map_data) +
      geom_sf(aes(fill = population))+
      theme_void()+
      viridis::scale_fill_viridis(trans = "log", name="Population", labels = scales::unit_format(unit = "M", scale = 1e-6)) +
      labs(
        title = "Bangladesh Population Map",
        subtitle = "Population & Housing Census 2011",
        caption = "Data Source: BBS"
      )

## Other useful functions

It is also possible to get the approximate center points (centroids) of administrative regions easily

by using `get_coordinates()` function in `bangladesh` package.

    division_map <- get_map("division")
    division_centroids <- bangladesh::get_coordinates(level = "division")
    knitr::kable(division_centroids, format = "html")

Division | lat | lon
--- | --- | ---
Barisal | 22.41889 | 90.34684
Chittagong | 22.70692 | 91.73546
Dhaka | 23.83870 | 90.24064
Khulna | 22.91367 | 89.29437
Mymensingh | 24.84675 | 90.38088
Rajshahi | 24.58846 | 89.04540
Rangpur | 25.77920 | 89.05685
Sylhet | 24.71515 | 91.66400

    ggplot(data = division_map) +
      geom_sf() +
      geom_sf_label(aes(label = Division)) +
      geom_point(data = division_centroids, x = division_centroids$lon, y = division_centroids$lat, col = "red", size = 3) +
      xlab("")+ ylab("")+
      theme_minimal()

Suppose someone needs to plot partially a single or selected number of divisions instead of whole country map, in that case the function `get_divisions()` might be beneficial.

    sylhet <- get_divisions(divisions = "Sylhet",level =  "upazila")
    # single division
    ggplot(data = sylhet) +
      geom_sf() +
      xlab("")+ ylab("")+
      theme_minimal()

    #multiple division
    sylhet_chittagong_dhaka <- get_divisions(divisions = c("Sylhet", "Chittagong", "Dhaka"),level =  "upazila")
    ggplot(data = sylhet_chittagong_dhaka) +
      geom_sf() +
      xlab("")+ ylab("")+
      theme_minimal()

To search for an area within the provided names for administrative regions we can apply the `bd_search()` function. The result can also include centroids for those areas.

    amtali <- bd_search("amtali", level = "union", as.is = TRUE, coordinates = TRUE)
    knitr::kable(amtali, format = "html")

Division | District | Upazila | Union | lat | lon
--- | --- | --- | --- | --- | ---
Barisal | Barguna | Amtali | Amtali | 22.07556 | 90.24699
Chittagong | Rangamati | Baghai Chhari | Amtali | 23.10559 | 92.18832
Dhaka | Gopalganj | Kotali Para | Amtali | 22.98264 | 90.03070
Chittagong | Khagrachhari | Matiranga | Amtali | 23.11701 | 91.88545

    ggplot(bangladesh::map_union) +
      geom_sf() +
      geom_point(data = amtali, x = amtali$lon, y = amtali$lat, col = "red", size = 3)