diverging_lollipop.R

cities <- structure(
  list(
    name = c(
      "Mesa",
      "Phoenix",
      "Tucson",
      "Fresno",
      "Los Angeles",
      "Sacramento",
      "San Diego",
      "San Francisco",
      "San Jose",
      "Denver",
      "Washington",
      "Jacksonville",
      "Chicago",
      "Indianapolis",
      "Louisville",
      "Baltimore",
      "Boston",
      "Detroit",
      "Kansas City",
      "Las Vegas",
      "Albuquerque",
      "New York",
      "Charlotte",
      "Columbus",
      "Oklahoma City",
      "Portland",
      "Philadelphia",
      "Memphis",
      "Nashville",
      "Austin",
      "Dallas",
      "El Paso",
      "Fort Worth",
      "Houston",
      "San Antonio",
      "Seattle",
      "Milwaukee"
    ),
    pctchange = c(
      15.15,
      17.89,
      7.07,
      14.09,
      3.16,
      17.91,
      14.34,
      13.37,
      10.84,
      30.5,
      30.08,
      24.21,
      -0.2,
      15.29,
      14.74,
      -5.26,
      25.65,
      -24.34,
      15.32,
      20.07,
      15.25,
      6.43,
      46.23,
      30.74,
      33.34,
      25.04,
      12.08,
      -2.2,
      29.88,
      42.09,
      12.53,
      16.28,
      55.56,
      17.8,
      20.76,
      36.68,
      2.22
    )
  ),
  row.names = c(NA,-37L),
  class = c("tbl_df",
            "tbl", "data.frame")
)

# Not great; we can make this better!
ggplot(cities, aes(x = pctchange, y = name)) + 
  geom_col()

cities$type <- ifelse(cities$pctchange > 0, 
                      "growth", "decline")

gg <- ggplot(cities, 
             aes(x = pctchange, 
                 y = reorder(name,
                             pctchange), 
                 color = type,
                 fill = type)) + 
  geom_col(width = 0.01)

gg <- gg + geom_point(size = 3) + 
  theme_minimal()

gg <- gg + theme(panel.grid.major.x = element_blank(),
                 panel.grid.minor.x = element_blank(),
                 legend.position = "none")

gg <- gg + scale_x_continuous(labels = scales::label_percent(scale = 1)) + 
  scale_color_manual(values = c("red", "#90b4d2"))

gg <- gg + labs(x = "Percent change between 2005 and 2021", 
                y = "", 
                title = "Municipal population change, 2005-2021", 
                subtitle = "Cities with populations above 500,000", 
                caption = "Data acquired with the R tidycensus package. Chart by @kyle_e_walker.")

gg