ggmapcn is a ggplot2 extension package for visualizing China’s map with customizable projections and styling. This package includes province-level map data and supports adding mainland borders, coastlines, and buffer areas, making it easy to create geographic visualizations of China.

Installation

Install the development version of ggmapcn from GitHub with:

# install.packages("devtools")
devtools::install_github("Rimagination/ggmapcn", force = TRUE)

Usage

Plotting a Map of China

To plot a map of China with province boundaries, use geom_mapcn():

library(ggplot2)
library(ggmapcn)

ggplot() +
  geom_mapcn() +
  theme_minimal()

Province Map

Custom Projection and Styling

If you want to try the Albers projection, you can customize it.

ggplot() +
  geom_mapcn(crs = "+proj=aea +lat_1=25 +lat_2=47 +lat_0=0 +lon_0=105 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs", color = "black", fill = "white", size = 0.7) +
  theme_minimal()

Basic Map

Adding Mainland Borders and Coastlines

Use geom_boundary_cn() to add mainland borders and coastlines to the map. You can set colors and line widths for both the mainland and coastline boundaries:

ggplot() +
  geom_mapcn(fill = NA) +
  geom_boundary_cn(
    mainland_color = "black",
    mainland_size = 0.5,
    coastline_color = "skyblue",
    coastline_size = 0.5
  ) +
  theme_minimal()

Map with Boundary

Adding Buffer Zones

The geom_buffer_cn() function adds buffer zones around China’s borders. You can specify buffer distances, colors, and projections. The example below shows buffer zones with varying distances:

ggplot() +
  geom_buffer_cn(mainland_dist = 40000) +
  geom_buffer_cn(mainland_dist = 20000, fill = "#BBB3D8") +
  geom_mapcn(fill = "white") +
  geom_boundary_cn() +
  theme_minimal()

Map of China

Data Source

The data used in this package is sourced from Tianditu (https://cloudcenter.tianditu.gov.cn/administrativeDivision/), a reliable provider of province-, city-, and county-level boundary information in China. This administrative division data has been processed into GeoJSON format for seamless integration into the package, enabling easy access and visualization.