haterzmapper

An R package for mapping diversity (ethnic, racial, linguistic or otherwise).

haterzmapper extends some of the functionality of the sf and tidycensus packages making it simple to subset simple feature data structures and gather census related data pertaining to specific geographies. It also provides some handy diversity functions (ex. shannon index).

Why is it called "haterzmapper"

This package is a product of my research, which involves understanding the geography of diversity and peoples' perceptions of other populations. This project is called haterz and, since this is a primarily geographic extension of that project, haterzmapper seemed most appropriate.

Example

library(sf)
library(ggplot2)
library(haterzmapper)
library(tidycensus)


# gather census tracts of California from tidycensus
ca <- tidycensus::get_acs(geography = "tract", variables = "B00001_001",
                          state = "CA", geometry = TRUE)

# lat and lon of San Fran center
sflon <- topcities[topcities$full_name == "San Francisco, CA",]$lon 
sflat <- topcities[topcities$full_name == "San Francisco, CA",]$lat

# subset San Fran by 80 km radius
sf <- subset_map(ca, long = sflon, lat = sflat, dist = 80000)

# plot both layers
ggplot() +
  geom_sf(data = ca, colour = '#828282', fill = 'white', size = 0.05, alpha = 0) +
  geom_sf(data = sf, colour = '#828282', fill = '#00bf98', size = 0.001) +
  map_theme_stark()

library(haterzmapper)
library(ggplot2)
library(sf)
library(tidycensus)

colors <- c('#CF3A24', '#8F1D21', '#f08f90', '#FCC9B9', '#763568', '#A87CA0', '#48929B', '#264348', '#7A942E', '#87D37C',
            '#D9B611', '#F5D76E', '#6C7A89')

# collect all census tracts from tidycensus
geographies <- reduce(
  map(us, function(x) {
    get_acs(geography = "tract", variables = 'B00001_001',
            state = x, geometry = TRUE, year = 2015)
  }),
  rbind
)


# water shapefile and subset it 
water <- st_read("~/Desktop/seattle_water.shp")
water <- subset_map(df = water, long = (topcities %>% filter(city == "Seattle"))$lon, lat = (topcities %>% filter(city == "Seattle"))$lat, 
                    dist = 50000)

# collect tracts from a 10km perimeter around the 40km radius
outer <- subset_map(df = geographies, long = (topcities %>% filter(city == "Seattle"))$lon, 
                    lat = (topcities %>% filter(city == "Seattle"))$lat, 
                    dist = 50000) 

# city data sample
city <- subset_map(df = geographies, long = (topcities %>% filter(city == "Seattle"))$lon, 
                   lat = (topcities %>% filter(city == "Seattle"))$lat)

# randomly create lab column
city$lab <- sample(1:13, nrow(city), replace = T)

ggplot() +
  geom_sf(data = outer, color = '#e2e2de', fill = '#e2e2de', alpha = 0) + 
  geom_sf(data = city, aes(fill = factor(lab)) ,colour = '#ffffff', size = 0.01) +
  geom_sf(data = water, color = "#4f5a6b", fill = "#d2ddef", size = 0.15) +
  geom_sf(data = water, color = "#d2ddef", fill = "#d2ddef", size = 0.001) +
  map_theme_stark() +
  scale_color_manual(values = colors) +
  scale_fill_manual(values = colors) +
  ggtitle(label = "Seattle", subtitle = "Demographic Clusters")