Spatial Data Visualization in R: NYC Subway Art Map

Combining geocoding, spatial cropping, smart labeling, and base maps with tidygeocoder, sf, ggrepel, and ggmap

#TidyTuesday
{sf}
Maps
Geocoding
{tidygeocoder}
Author

Aditya Dahiya

Published

July 24, 2025

About the Data

This dataset comes from the New York Metropolitan Transportation Authority (MTA) Permanent Art Catalog, which documents public artworks commissioned through the MTA’s Permanent Art Program. Administered by MTA Arts & Design (formerly Arts for Transit), this program commissions public art installations that are viewed by millions of daily commuters and visitors across the MTA’s subway and rail systems. The program works collaboratively with architects and engineers from MTA NYC Transit, Long Island Rail Road, and Metro-North Railroad to integrate artwork into station renovations using materials native to the transit system including mosaic, ceramic, tile, bronze, steel, and glass. Artists are selected through a competitive process involving panels of visual arts professionals and community representatives. The dataset provides comprehensive information about each artwork including the station location, transit lines served, artist details, installation dates, materials used, and descriptive information, offering a unique window into one of the world’s largest public art collections integrated into urban transit infrastructure.

Figure 1: A map of public artworks displayed across Manhattan’s subway stations, showing the Metropolitan Transportation Authority’s permanent art collection. Each label identifies the artwork title, artist, and station location along the transit network.

How the Graphic Was Created

This visualization was built using R’s powerful ecosystem of spatial and visualization packages. The workflow began by geocoding subway station names using tidygeocoder to obtain precise coordinates, then converting the data to spatial format with sf. The base map was created using ggmap with Stadia Maps terrain tiles, processed as raster data through terra. NYC subway line geometries were loaded from a shapefile, spatially cropped to the Manhattan area, and overlaid on the map. The challenging task of labeling multiple artworks without overlap was solved using ggrepel, which intelligently positions labels on opposite sides of the map based on longitude coordinates. Custom nudging variables and curved connection segments ensure clean, readable labels that don’t obscure the underlying geography.

Loading required libraries

Code 
pacman::p_load(
  tidyverse,            # All things tidy
  
  scales,               # Nice Scales for ggplot2
  fontawesome,          # Icons display in ggplot2
  ggtext,               # Markdown text support for ggplot2
  showtext,             # Display fonts in ggplot2
  colorspace,           # Lighten and Darken colours

  patchwork             # Composing Plots
)

mta_art <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-07-22/mta_art.csv')
station_lines <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-07-22/station_lines.csv')

Visualization Parameters

Code 
# Font for titles
font_add_google("Saira",
  family = "title_font"
) 

# Font for the caption
font_add_google("Saira Condensed",
  family = "body_font"
) 

# Font for plot text
font_add_google("Saira Extra Condensed",
  family = "caption_font"
) 

showtext_auto()

# A base Colour
bg_col <- "white"
seecolor::print_color(bg_col)

# Colour for highlighted text
text_hil <- "grey20"
seecolor::print_color(text_hil)

# Colour for the text
text_col <- "grey20"
seecolor::print_color(text_col)

line_col <- "grey30"

# Define Base Text Size
bts <- 120

mypal <- paletteer::paletteer_d("fishualize::Balistapus_undulatus")
mypal <- c("#DD75D3", "#E16305", "#F2CB05", "#719F4F", "#7E8CFF")

# Caption stuff for the plot
sysfonts::font_add(
  family = "Font Awesome 6 Brands",
  regular = here::here("docs", "Font Awesome 6 Brands-Regular-400.otf")
)
github <- "&#xf09b"
github_username <- "aditya-dahiya"
xtwitter <- "&#xe61b"
xtwitter_username <- "@adityadahiyaias"
social_caption_1 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{github};</span> <span style='color: {text_hil}'>{github_username}  </span>")
social_caption_2 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{xtwitter};</span> <span style='color: {text_hil}'>{xtwitter_username}</span>")
plot_caption <- paste0(
  "**Data:** Metropolitan Transportation Authority (New York City)", 
  " |  **Code:** ", 
  social_caption_1, 
  " |  **Graphics:** ", 
  social_caption_2
  )
rm(github, github_username, xtwitter, 
   xtwitter_username, social_caption_1, 
   social_caption_2)

# Add text to plot-------------------------------------------------
plot_title <- "Art Underground: NYC Subway Gallery"

Exploratory Data Analysis and Wrangling

Code 
pacman::p_load(summarytools)

mta_art |> dfSummary() |> view()

station_lines |> dfSummary() |> view()

pacman::p_load(tidygeocoder)


# Try to get geographical locations of the maps
df1 <- station_lines |> 
  distinct(station_name) |> 
  mutate(station_name = paste0(station_name, " Station, New York City")) |> 
  geocode(
    station_name, method = 'osm', 
    lat = latitude , long = longitude
  )

df2 <- df1 |> 
  drop_na() |> 
  mutate(
    full_station_name = station_name,
    station_name = str_remove(station_name, " Station, New York City")
  ) |> 
  st_as_sf(
    coords = c("longitude", "latitude"),
    crs = "EPSG:4326"
  )

df3 <- df2 |> 
  bind_cols(
    st_coordinates(df2) |> janitor::clean_names()
  )


plotdf <- mta_art |> 
  left_join(df3) |> 
  filter(!is.na(x) & !is.na(y)) |> 
  
  # Remove values outside my map's bounding box
  filter(x < -73.83 & x > -74.1) |> 
  filter(y < 40.88 & y > 40.6) |> 
  st_as_sf() |> 
  
  # Add a minor jitter in the positions to prevent overlaps in multiple
  # artworks within the same subway station
  st_jitter(0.001)

mid_lon <- mean(plotdf$x, na.rm = T)

mid_lat <- mean(plotdf$y, na.rm = T)

# Create new directional variables using tidyverse
plotdf2 <- plotdf |> 
  select(-agency, -art_image_link, -art_description, art_date, -line) |> 
  
  # Create the directional variables
  mutate(
    # lon_var: east if longitude > mid_lon, west otherwise
    lon_var = case_when(
      x > mid_lon ~ "east",
      x <= mid_lon ~ "west"
    ),
    # lat_var: north if latitude > mid_lat, south otherwise
    lat_var = case_when(
      y > mid_lat ~ "north",
      y <= mid_lat ~ "south"
    ),
    
    # nudge_x_var to create a variable for nudge x with ggrepel
    nudge_x_var = if_else(
      lon_var == "east", 
      -73.82 - x,
      -74.05 - x
    ),
    segment_curv_var = if_else(
      lat_var == "north",
      -0.5,
      0.5
    )
  )

Getting New York City Base Map

Code 
pacman::p_load(ggmap, tidyterra)

# Define your bounding box
my_bbox <- c(
  bottom = 40.6, 
  top = 40.88,
  left = -74.08, 
  right = -73.78
)

base_map_rast <- get_stadiamap(
  bbox = my_bbox,
  zoom = 11,
  maptype = "stamen_terrain_background"
) |> 
  terra::rast()

ggplot() +
  geom_spatraster_rgb(
    data = base_map_rast
  )

Get New York City Subway Lines

Code 
# Source: https://geo.nyu.edu/catalog/nyu-2451-34758



subway_lines <- read_sf("C:/Users/dradi/Downloads/nyu_2451_34758/routes_nyc_subway_may2016.shp") |> 
  st_transform("EPSG:4326") |> 
  # Fix invalid geometries
  mutate(geometry = st_make_valid(geometry))

st_crs(subway_lines)

st_crop(subway_lines, 
                               c(xmin = my_bbox["left"], 
                                 ymin = my_bbox["bottom"],
                                 xmax = my_bbox["right"], 
                                 ymax = my_bbox["top"]))

subway_lines |> 
  ggplot() +
  geom_sf()

The Plot

Code 
# A jitter position for removing overlapping points in 
# some central Subway Stations in New York City

g <- ggplot() +
  
  geom_spatraster_rgb(
    data = base_map_rast,
    alpha = 0.7
  ) +
  geom_sf(
    data = subway_lines,
    colour = "darkred",
    linewidth = 0.5
  ) +
  
  geom_sf(
    data = plotdf2,
    alpha = 0.6,
    size = 3,
    pch = 19
  ) +
  
  # West side stations' labels
  ggrepel::geom_label_repel(
    data = plotdf2 |> filter(lon_var == "west"),
    mapping = aes(
      geometry = geometry,
      label = paste0(
        str_sub(art_title, 1, 20), 
        "  (", 
        str_sub(artist, 1, 15), 
        ")  ", 
        str_sub(station_name, 1, 10),
        " Stn. "
      ),
      segment.curvature = segment_curv_var
    ),
    
    nudge_x = plotdf2 |> filter(lon_var == "west") |> pull(nudge_x_var),
    direction = "y",
    
    stat = "sf_coordinates",
    seed = 42,
    hjust = 1,
    arrow = arrow(
      ends = "first",
      length = unit(0.4, "mm")
    ),

    min.segment.length = unit(0.05, "mm"),
    segment.size = 0.15,
    segment.color = alpha("black", 0.4),
    lineheight = 0.25,
    size = 6,
    family = "caption_font",
    
    fill = alpha("white", 0.6),
    label.padding = 0.2,
    label.r = 0.1,
    label.size = NA
  ) +
  
  
  # East side stations' labels
  ggrepel::geom_label_repel(
    data = plotdf2 |> filter(lon_var == "east"),
    mapping = aes(
      geometry = geometry,
      label = paste0(
        str_sub(art_title, 1, 20), 
        "  (", 
        str_sub(artist, 1, 15), 
        ")  ", 
        str_sub(station_name, 1, 10),
        " Stn. "
      ),
      segment.curvature = -segment_curv_var
    ),
    
    nudge_x = plotdf2 |> filter(lon_var == "east") |> pull(nudge_x_var),
    direction = "y",
    
    stat = "sf_coordinates",
    seed = 42,
    hjust = 0,
    arrow = arrow(
      ends = "first",
      length = unit(0.4, "mm")
    ),

    min.segment.length = unit(0.05, "mm"),
    segment.size = 0.15,
    segment.color = alpha("black", 0.4),
    lineheight = 0.25,
    size = 6,
    family = "caption_font",
    
    fill = alpha("white", 0.6),
    label.padding = 0.2,
    label.r = 0.1,
    label.size = NA
  ) +
  
  coord_sf(
    xlim = c(-74.08, -73.78),
    ylim = c(40.60,  40.88),
    default_crs = "EPSG:4326",
    clip = "on",
    expand = FALSE
  ) +
  labs(
    title = plot_title,
    caption = plot_caption
  ) +
  ggthemes::theme_map(
    base_family = "body_font",
    base_size = bts
  ) +
  theme(
    
    # Overall
    text = element_text(
      margin = margin(0,0,0,0, "mm"),
      colour = text_col,
      lineheight = 0.3
    ),
    
    # Labels and Strip Text
    plot.title = element_text(
      margin = margin(5,0,5,0, "mm"),
      hjust = 0.5,
      vjust = 0.5,
      colour = text_hil,
      size = 2 * bts,
      family = "body_font",
      face = "bold"
      ),
    plot.caption = element_textbox(
      margin = margin(-5,0,5,0, "mm"),
      hjust = 0.5,
      halign = 0.5,
      colour = text_hil,
      size = 0.5 * bts,
      family = "caption_font",
      fill = alpha("white", 0.6),
      box.color = NA,
      padding = unit(0.3, "lines"),
      r = unit(5, "mm")
    ),
    plot.caption.position = "plot",
    plot.title.position = "plot",
    plot.margin = margin(0,0,0,0, "mm")
  )

ggsave(
  filename = here::here(
    "data_vizs",
    "tidy_mta_art_catalog.png"
  ),
  plot = g,
  width = 400,
  height = 500,
  units = "mm",
  bg = bg_col
)

Savings the thumbnail for the webpage

Code 
# Saving a thumbnail

library(magick)

# Saving a thumbnail for the webpage
image_read(here::here("data_vizs", 
                      "tidy_mta_art_catalog.png")) |> 
  image_resize(geometry = "x400") |> 
  image_write(
    here::here(
      "data_vizs", 
      "thumbnails", 
      "tidy_mta_art_catalog.png"
    )
  )

Session Info

Code 
pacman::p_load(
  tidyverse,            # All things tidy
  
  scales,               # Nice Scales for ggplot2
  fontawesome,          # Icons display in ggplot2
  ggtext,               # Markdown text support for ggplot2
  showtext,             # Display fonts in ggplot2
  colorspace,           # Lighten and Darken colours

  patchwork             # Composing Plots
)

sessioninfo::session_info()$packages |> 
  as_tibble() |> 
  dplyr::select(package, 
         version = loadedversion, 
         date, source) |> 
  dplyr::arrange(package) |> 
  janitor::clean_names(
    case = "title"
  ) |> 
  gt::gt() |> 
  gt::opt_interactive(
    use_search = TRUE
  ) |> 
  gtExtras::gt_theme_espn()
Table 1: R Packages and their versions used in the creation of this page and graphics

Links