This is a Graphviz diagram that shows major US cities (with population greater than 300,000 people). Uses chromatography.js to provide some color by US state, and, the DiagrammeR R package (https://github.com/rich-iannone/DiagrammeR) to render the graph.

us_cities_graph.R

# install.packages("downloader")
# devtools::install_github("rich-iannone/DiagrammeR")
# devtools::install_github("jeroenooms/V8")

library("downloader")
library("DiagrammeR")
library("V8")

# Create a function that uses the 'chromatography' JS library to generate a random
# set of colors from the CIE Lab color space 
hex_palette <- function(no_colors,
                        hue_range = NULL,
                        chroma_range = NULL,
                        lightness_range = NULL,
                        display_colors = TRUE){
  
  # Construct the JS call
  js_call <- paste0("var colors = createPalette.generate(",
                    no_colors, ", function(color){ var hcl = color.hcl(); return hcl[0]>=",
                    hue_range[1], " && hcl[0]<=",
                    hue_range[2], "&& hcl[1]>=",
                    chroma_range[1], "&& hcl[1]<=",
                    chroma_range[2], "&& hcl[2]>=",
                    lightness_range[1], "&& hcl[2]<=",
                    lightness_range[2], "; }, true, 50);",
                    "var colors = createPalette.diffSort(colors); colors;")
  
  # Add function to display colors in the RStudio Viewer
  display_in_viewer <- function(col, border = "light gray", ...){
    n <- length(col)
    plot(0, 0, type = "n", xlim = c(0, 1), ylim = c(0, 1),
         axes = FALSE, xlab = "", ylab = "", ...)
    rect(0:(n-1)/n, 0, 1:n/n, 1, col = col, border = border)
  }
  
  ct <- new_context("window")
  invisible(ct$source(system.file("htmlwidgets/lib/chromatography/chromatography.js",
                                  package = "DiagrammeR")))
  
  hex_colors <- unlist(strsplit(ct$eval(js_call), ","))
  
  if (display_colors == TRUE){
    display_in_viewer(hex_colors)
  }
  
  return(hex_colors) 
}

# Download a CSV file containing information on US cities (e.g., names, states, population,
# lonlat coordinates); uses the 'download' function from the 'downloader' package
tmpfile <- tempfile()

download("https://raw.githubusercontent.com/rich-iannone/DiagrammeR/master/inst/examples/us_cities.csv",
         tmpfile, mode = "wb")

# Create the 'us_cities' data frame object using the CSV file
us_cities <- read.csv(tmpfile, header = TRUE, stringsAsFactors = FALSE)

# Subset the data frame so that only cities with populations greater than
# 300,000 people remain
population_us_cities <- us_cities[us_cities[,5] > 300000,]

# Subset the data frame by a latitude and longitude bounding box so that only the
# lower 48 states are included (column 6 is latitude, 7 is longitude)
population_us_cities_lower48 <-
  population_us_cities[population_us_cities[,6] < 49.384358,]

population_us_cities_lower48 <-
  population_us_cities_lower48[population_us_cities_lower48[,6] > 24.396308,]

population_us_cities_lower48 <-
  population_us_cities[population_us_cities[,7] < -66.885444,]

population_us_cities_lower48 <-
  population_us_cities_lower48[population_us_cities_lower48[,7] > -124.848974,]

# Get a vector of the states available in the dataframe
us_states <-
  unique(population_us_cities_lower48[,4])

# Define a number of distinct, yet light, colors for coloring nodes for each state
us_state_colors <- hex_palette(no_colors = length(us_states),
                               hue_range = c(0, 360),
                               chroma_range = c(0, 3),
                               lightness_range = c(0.75, 1.5))

# Create a block of node statements for subsitution into the Graphviz DOT statement
for (i in 1:length(us_states)){
  if (i == 1) us_cities_nodes <- vector(mode = "character", length = 0)
  
  # Create the node block element for each statement (includes an informative tooltip)
  us_state_cities_nodes <-
    paste("'",
          population_us_cities[population_us_cities[,4] == us_states[i], 3],
          "' [fillcolor = '",
          us_state_colors[i],
          "', tooltip = '",
          population_us_cities[population_us_cities[,4] == us_states[i], 3], "\n",
          "pop: ", prettyNum(population_us_cities[population_us_cities[,4] == us_states[i], 5],
                             big.mark = ",", scientific = FALSE),
          "' pos = '",
          population_us_cities[population_us_cities[,4] == us_states[i], 7], ",",
          population_us_cities[population_us_cities[,4] == us_states[i], 6], "!']",
          sep = "", collapse = "\n")
  
  us_cities_nodes <- c(us_cities_nodes, us_state_cities_nodes)
  
  if (i == length(us_states)){
    us_cities_nodes <- paste(us_cities_nodes, collapse = "\n")
  }
}

# Create a block of edge statements for subsitution into the Graphviz DOT statement
for (i in 1:length(us_states)){
  if (i == 1) us_cities_edges <- vector(mode = "character", length = 0)
  
  # Create the edge block element for each statement
  us_state_cities_edges <-
    paste("'",
          population_us_cities[i,3], "'--'", population_us_cities[-i, 3],
          "'",
          sep = "", collapse = "\n")
  
  us_cities_edges <- c(us_cities_edges, us_state_cities_edges)
  
  if (i == length(us_states)){
    us_cities_edges <- paste(us_cities_edges, collapse = "\n")
  }
}

# Create the graph diagram, showing major US cities with populations greater than
# 300,000 people. Colored by state, using coordinate information to position nodes on
# the graph canvas
grViz("
graph USA {

graph [layout = neato,
       overlap = true,
       outputorder = edgesfirst]

# US cities
node [shape = circle, style = filled, height = 1, label = '']
@@1

# Connections between cities
edge [color = '#5B8FA550']
@@2

}
[1]: us_cities_nodes
[2]: us_cities_edges
")

# After creating the graph in the RStudio Viewer, hover over city
# nodes to get city names and population data