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.
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")
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.
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
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"
)
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 = T, coordinates = T)
knitr::kable(amtali, format = "html")
Division | District | Upazila | Union | lat | lon |
---|---|---|---|---|---|
Barisal | Barguna | Amtali | Amtali | 22.07564 | 90.24704 |
Chittagong | Rangamati | Baghai Chhari | Amtali | 23.10561 | 92.18835 |
Dhaka | Gopalganj | Kotali Para | Amtali | 22.98264 | 90.03085 |
Chittagong | Khagrachhari | Matiranga | Amtali | 23.11700 | 91.88539 |
ggplot(bangladesh::map_union) +
geom_sf() +
geom_point(data = amtali, x = amtali$lon, y = amtali$lat, col = "red", size = 3)