ggplot US state and China province heatmap

R examples for drawing US state and China province heatmaps with ggplot2, usmap, shapefiles, labels, scale bars, and leaflet-based alternatives.

• 1. US Map by state
  • Method 1. Use usmap
  • Method 2. Use map_data and build from shape files
• 2. China map by province
  • Method 1. China map by province using downloaded shapefiles
  • Method 2. Using geojsonMap (leaflet)

Updated on 2020-06-12; first posted on 2019-04-17.

This post collects a few R approaches for drawing state- and province-level thematic maps. The goal is to keep the examples practical: labels, scale bars, color fills, and enough flexibility to adjust the final plot.

I will show:

1. 50-state (including Alaska and Hawaii) United States thematic map, with map scale and with state abbreviations

2. China thematic map, with map scale and province names in either English or Chinese

1. US Map by state

The dt1 below can be used by both methods. With usmap, only two variables are needed: state names and the value to plot.

suppressPackageStartupMessages({ library(ggplot2) library(maps) library(usmap) library(rgeos) # for rgeos::gCentroid library(data.table) library(ggsn) # for adding map scale bar ggsn::scalebar library(shadowtext) # for adding label library(ggrepel) # if need to repel labels }) # A sample dataset of 77 states: dt1 <- as.data.table(copy(state.x77)) dt1$state <- tolower(rownames(state.x77)) dt1 <- dt1[,.(state, Population)] setkey(dt1, state)

states <- setDT(ggplot2::map_data("state")) setkey(states, region) # join data to map: left join states to dt1 dt1 <- dt1[states] # rmarkdown::paged_table(dt1[1:50,])

Method 1. Use usmap

The easiest way is to use the usmap package. State abbreviations can be added directly with usmap::plot_usmap. This is easy to start with, though harder to fine-tune in detail.

dt1_simple <- unique(dt1[,.(state, Population)])
usmap::plot_usmap(data = dt1_simple, values = "Population", labels = TRUE) +
  labs(fill = 'State Population (1975)') + 
  scale_fill_gradientn(colours = RColorBrewer::brewer.pal(9, "GnBu"),
                       na.value = "grey90",
                       guide = guide_colourbar(barwidth = 25, 
                                               barheight = 0.4,
                                               #put legend title on top of legend 
                                               title.position = "top")) +
  # put legend at the bottom, adjust legend title and text font sizes
  theme(legend.position = "bottom",
        legend.title=element_text(size=12), 
        legend.text=element_text(size=10))

The plot_usmap returns a ggplot object so it is possible to further revise by just adding more layers.

Method 2. Use map_data and build from shape files

This approach makes it possible to fine-tune label locations, as in the China map later. Here the label is placed near the center of each state: mean(lon), min(lat).

# create labels
# incl `Population` (the value to plot) in the label dataset, if want to fill with color. 
#' get centroids from a shp file dataset
get.centroids <- function(
  data = dt1, 
  long = "long", lat = "lat", 
  by_var = "state",  # the grouping variable, e.g. state: get centroid by state
  fill_var = NULL # the variable to plot 
  ){
  data <- data[!is.na(data[[by_var]]),]
  data[[by_var]] <- as.character(data[[by_var]]) # sometimes there is empty factor level
  dt1_df <- sp::SpatialPointsDataFrame(coords = data[, c(long, lat), with = FALSE], data = data)
  dt1_geo <- by(dt1_df, dt1_df[[by_var]], function(x) {sp::Polygon(x[c(long, lat)])@labpt})
  centroids <- stats::setNames(do.call("rbind.data.frame", dt1_geo), c(long, lat))
  centroids$name <- names(dt1_geo) 
  if(!is.null(fill_var)){ # if need to join fill value 
    setkeyv(setDT(centroids), "name")
    dt_var <- unique(data[,c(by_var, fill_var), with = FALSE])
    setkeyv(dt_var, by_var)
    centroids <- dt_var[centroids]
  }
  return(centroids)
}
centroids <- get.centroids(data = dt1, long = "long", lat = "lat", by_var = "state", fill_var = "Population")
# join short state names (e.g. VA, SC)
abb_names <- data.table(state = tolower(state.name), abb = state.abb) # these are R dataset
setkey(setDT(centroids), state)
setkey(abb_names, state)
centroids <- abb_names[centroids]
head(centroids, 3)

##       state abb Population       long      lat
## 1:  alabama  AL       3615  -86.83042 32.80316
## 2:  arizona  AZ       2212 -111.66786 34.30060
## 3: arkansas  AR       2110  -92.44013 34.90418

x_boundary = -77
ggplot(data = dt1, aes(x = long, y = lat, group = group))+
  geom_polygon(aes(fill = Population))+
  geom_path()+ 
  scale_fill_gradientn(colours = RColorBrewer::brewer.pal(9, "GnBu"), 
                       na.value = "grey90",
                       guide = guide_colourbar(barwidth = 25, barheight = 0.4,
                                               #put legend title on top of legend
                                               title.position = "top")) +
  # shadowtext::geom_shadowtext(data = centroids,
  #                             aes(x = long, y = lat, label = abb),
  #                             size = 3,
  #                             inherit.aes = FALSE) + 
  # if need to repel labels... could further fine-tune
  ggrepel::geom_label_repel(data = centroids[long >= x_boundary,],
                   aes(x = long, y = lat, label = abb, fill = Population),
    force = 1, size = 3,
    inherit.aes = F
  ) +
  # the normal labels:
  geom_text(data=centroids[long < x_boundary,],
            aes(x = long, y = lat, label = abb),
            size = 3, inherit.aes=F) +
  labs(fill = "Population", x = "Longitude", y = "Latitude") + 
  coord_map() + 
  # map scale
  ggsn::scalebar(data = dt1, dist = 500, dist_unit = "km",
           border.size = 0.4, st.size = 4,
           box.fill = c('black','white'),
           transform = TRUE, model = "WGS84") + 
  theme_void() + 
  # put legend at the bottom, adjust legend title and text font sizes
  theme(legend.position = "bottom",
        legend.title=element_text(size=12),  # font size of the legend 
        legend.text=element_text(size=10))

# 
# ggsave("us_example.png", width = 10, height = 6)