Visualizing Hurricane History: Landfalls, Wind Speed & Scale

This graphic maps all U.S. hurricane landfalls from 1850 to 2024, showing location and intensity at landfall. Inset charts reveal seasonal patterns, decadal trends in storm strength, and rising maximum wind speeds over time.

Geocomputation

{ggmap}

Maps

Webscraping

{rvest}

{sf}

Data Is Plural

{ggrepel}

Author

Aditya Dahiya

Published

April 11, 2025

About the Data

The data on U.S. hurricane landfalls is sourced from NOAA’s Hurricane Research Division, which maintains two key tables. The first table provides an overview of hurricanes since the 1850s, including year, month, name, affected states, Saffir-Simpson category, central pressure, and wind speed. A second, more detailed table adds landfall dates, coordinates, and other metrics but has gaps in the late 1970s–early 1980s. These datasets are valuable for climatological research, though users should note inconsistencies in record-keeping over time. For methodology, see NOAA’s Saffir-Simpson scale.

Figure 1: This visualization maps every recorded hurricane landfall along the U.S. coast from 1850 to 2024, with dot colors representing storm intensity on the Saffir-Simpson scale. The inset charts provide additional context: hurricanes are most frequent in September, higher-intensity storms have become more common in recent decades, and maximum wind speeds show a gradual upward trend over time. The graphic draws on NOAA’s detailed hurricane records and offers a long-term view of how these powerful storms have impacted the U.S. coastline over nearly two centuries.

How I made this graphic?

Loading required libraries, data import

Code

# Plot touch-up tools
library(scales)               # Nice Scales for ggplot2
library(fontawesome)          # Icons display in ggplot2
library(ggtext)               # Markdown text support for ggplot2
library(showtext)             # Display fonts in ggplot2
library(colorspace)           # Lighten and Darken colours


# Getting geographic data 
library(sf)                   # Simple Features in R
library(ggmap)                # Getting raster maps
library(terra)                # Cropping / Masking rasters
library(tidyterra)            # Rasters with ggplot2
library(osmdata)              # Open Street Maps data
library(ggspatial)            # Scales and Arrows on maps

# Data Wrangling & ggplot2
library(tidyverse)            # All things tidy
library(patchwork)            # Composing plots

library(janitor)  # For clean column name handling

library(rvest)                # Web Scraping

# URL of the webpage
url <- "https://www.aoml.noaa.gov/hrd/hurdat/UShurrs_detailed.html"

# Read the HTML content of the page
page <- read_html(url)

# Extract the first table from the page and convert it to a tibble
raw_data <- page |> 
  html_element("table")  |>    # Target the table element
  html_table()  |>             # Convert to data frame
  as_tibble()                  # Convert to tibble

Date/Time: Date and time when the circulation center crosses the U.S. coastline (including barrier islands). Time is estimated to the nearest hour.
Max Winds: Estimated maximum sustained (1 min) surface (10 m) winds to occur along the U. S. coast.
SSHWS: The estimated Saffir-Simpson Hurricane Scale at landfall based upon maximum 1-min surface winds.
Cent Press: The central pressure of the hurricane at landfall. Central pressure values in parentheses indicate that the value is a simple estimation (based upon a wind-pressure relationship), not directly measured or calculated.

Visualization Parameters

Code

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

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

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

showtext_auto()
mypal <- c("#009392FF", "#9CCB86FF", "#EEB479FF", "#E88471FF", "#CF597EFF")

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

# Colour for highlighted text
text_hil <- "grey30"
seecolor::print_color(text_hil)

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

# Define Base Text Size
bts <- 90 

# 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:** Hurricane Research Division (NOAA)", 
  " |  **Code:** ", 
  social_caption_1, 
  " |  **Graphics:** ", 
  social_caption_2
  )
rm(github, github_username, xtwitter, 
   xtwitter_username, social_caption_1, 
   social_caption_2)

# Alpha Values for dots and colours
alpha_value = 0.6

# Add text to plot-------------------------------------------------
plot_title <- "U.S. Hurricane Landfalls (1850–2024): Trends"

plot_subtitle <- "All recorded U.S. hurricane landfalls from 1850 to 2024, with colored dots indicating the location and intensity (Saffir-Simpson Hurricane Scale) at landfall. Charts above highlight seasonal patterns, changes in hurricane strength by decade, and trends in maximum wind speed over time."

Data Wrangling

Code

drop_rows <- paste0(185:202, "0s")

base_map <- usmapdata::us_map()

df1 <- raw_data |> 
  # Remove first 3 rows
  slice(-1:-3) |> 
  # Select first 10 columns
  select(1:13) |>  
  # Use row 4 (now first row after slicing) as column names
  janitor::row_to_names(1) |>  
  # Convert all columns to appropriate types
  readr::type_convert() |> 
  # Clean the names of the columns
  janitor::clean_names() |> 
  # Drop the rows that are row-headers (i.e. 1850s, 1860s, ...)
  filter(!(number %in% drop_rows)) |> 
  mutate(
    # Extract numeric value and direction from latitude
    lat = as.numeric(str_extract(latitude, "\\d+\\.?\\d*")),
    lat_dir = str_extract(latitude, "[NS]"),
    # Extract numeric value and direction from longitude
    lon = as.numeric(str_extract(longitude, "\\d+\\.?\\d*")),
    lon_dir = str_extract(longitude, "[WE]"),
    # Convert to signed decimal degrees
    lat = if_else(lat_dir == "S", -lat, lat),
    lon = if_else(lon_dir == "W", -lon, lon)
  ) |> 
  filter(!is.na(lat) & !is.na(lon)) |> 
  # Convert to sf object (WGS84 coordinate system)
  st_as_sf(coords = c("lon", "lat"), crs = 4326) |> 
  # Remove intermediate columns
  select(-ends_with("_dir"), -latitude, -longitude) |> 
  mutate(
    # Convert date column into proper dates
    date = date |> 
      str_replace("\\D+$", "") |> # Remove non-digits at end
      mdy(),  # Convert to date
    
    # Extract maximum wind speed, and Saffir-Simpson Hurricane Scale
    max_winds_kt = parse_number(max_winds_kt),
    ss_hws = parse_number(ss_hws),
    pressure = parse_number(central_pressure_mb),
    
    # Parse time column (handles UTC/Zulu times)
    time = parse_time(time, format = "%H%MZ"),
    
    # Remove all quotation marks
    storm_names = str_remove_all(storm_names, pattern = '\"') |> 
                    str_to_title(),
    storm_names = if_else(
                    str_detect(storm_names, "---"),
                    NA,
                    storm_names
                  ),
    decade = paste0(floor(year(date) / 10) * 10, "s")
  ) |> 
  select(-rm_wnm, -oci_mb, -central_pressure_mb) |> 
  relocate(geometry, .after = everything()) |> 
  filter(!is.na(date) & !is.na(ss_hws)) |> 
  
  # Convert to CRS of US Map
  st_transform(crs = st_crs(base_map))

# Set the values for controlling the extent of the map
st_bbox(df1)
xlim_values <- st_bbox(df1)[c(1, 3)]
ylim_values <- st_bbox(df1)[c(2, 4)]

bbox_raster <- st_bbox(df1 |> st_transform("EPSG:4326"))
names(bbox_raster) <- c("left", "bottom", "right", "top")

Composing Final Plot

Code

# Base Plot ------------------------------------------------------------
g_base <- ggplot() +
  geom_sf(
    data = base_map,
    colour = text_col,
    alpha = 0.6
  ) +
  
  geom_sf_text(
    data = base_map,
    mapping = aes(label = abbr),
    size = bts / 3,
    colour = alpha(text_col, 0.5),
    check_overlap = T
  ) +
  geom_sf(
    data = df1 |> st_jitter(factor = 0.02),
    mapping = aes(
      size = ss_hws,
      colour = as.character(ss_hws)
    ),
    alpha = alpha_value
  ) +
  annotate(
    geom = "label",
    x = 537727.1,
    y = -303554.2,
    label = str_wrap(plot_subtitle, 25),
    size = bts / 2.5,
    lineheight = 0.3,
    hjust = 0.5,
    vjust = 0.7,
    family = "body_font",
    fill = alpha(bg_col, 0.7),
    label.size = NA,
    label.padding = unit(0., "lines"),
    colour = text_hil
  ) +
  coord_sf(
    xlim = xlim_values,
    ylim = ylim_values
  ) +
  labs(
    colour = "Saffir-Simpson Hurricane Scale",
    size = "Saffir-Simpson Hurricane Scale",
    x = NULL, y = NULL
  ) +
  scale_colour_manual(
    values = mypal
  ) +
  scale_size(
    range = c(1, 15),
    transform = "exp"
  ) +

  labs(
    title = plot_title,
    # subtitle = str_wrap(plot_subtitle, 95),
    caption = plot_caption
  ) +
  
  # Theme and beautification of plot
  theme_minimal(
    base_family = "body_font",
    base_size = bts
  ) +
  theme(
    # Overall
    plot.margin = margin(5,5,5,5, "mm"),
    plot.title.position = "plot",
    plot.caption.position = "plot",
    text = element_text(
        colour = text_hil
    ), 
    
    # Grid and Axes
    panel.grid = element_line(
      linewidth = 0.2,
      colour = text_hil,
      linetype = 3
    ),
    axis.text.x = element_text(margin = margin(0,0,0,0, "mm")),
    axis.text.y = element_text(margin = margin(0,0,0,0, "mm")),
    axis.ticks = element_blank(),
    axis.ticks.length = unit(0, "mm"),
    
    # Legend
    legend.position = "inside",
    legend.position.inside = c(0, 1),
    legend.justification = c(0, 1),
    legend.title.position = "top",
    legend.direction = "horizontal",
    legend.box = "horizontal",
    legend.title = element_text(
      margin = margin(0,0,2,0, "mm"),
      hjust = 0.5
    ),
    legend.text = element_text(
      margin = margin(2,0,0,0, "mm"),
      hjust = 0.5,
      face = "bold"
    ),
    legend.text.position = "left",
    legend.background = element_rect(
      fill = alpha("white", 0.6), colour = NA
    ),
    legend.margin = margin(5,5,5,5, "mm"),
    legend.box.margin = margin(5,5,5,5, "mm"),
    legend.spacing.y = unit(1, "cm"),
    legend.spacing.x = unit(3, "cm"),
    
    # Labels
    plot.title = element_text(
      hjust = 0.5,
      size = bts * 2.2,
      margin = margin(5,0,100,0, "mm"),
      family = "body_font",
      lineheight = 0.3,
      face = "bold"
    ),
    plot.subtitle = element_text(
      hjust = 0.5, 
      family = "body_font",
      lineheight = 0.3,
      margin = margin(0,0,0,0, "mm")
    ),
    plot.caption = element_textbox(
      hjust = 0.5,
      halign = 0.5, 
      margin = margin(10,0,0,0, "mm"),
      family = "caption_font",
      size = 0.7 * bts
    )
  )

# Inset : Bar Chart of number of Hurricanes by SS_HWS each decade
inset_barchart <- df1 |> 
  st_drop_geometry() |> 
  filter(decade != "1970s") |> 
  count(decade, ss_hws) |> 
  ggplot(
    aes(
      x = decade, 
      y = n,
      fill = as.character(ss_hws)
    )
  ) +
  ggchicklet::geom_chicklet(
    alpha = alpha_value,
    colour = bg_col,
    position = position_stack(reverse = T)
  ) +
  geom_text(
    aes(label = n),
    family = "body_font",
    position = position_stack(reverse = T, vjust = 0.5),
    colour = alpha(text_col, 0.5),
    size = bts / 10
  ) +
  scale_fill_manual(values = mypal) +
  labs(
    x = NULL,
    y = "Number of hurricanes (in each decade)"
  ) +
  theme_minimal(
    base_size = bts / 2,
    base_family = "body_font"
  ) +
  theme(
    text = element_text(colour = text_hil),
    legend.position = "none",
    axis.text.x = element_text(
      margin = margin(0.5,0,0,0, "mm"),
      angle = 90,
      hjust = 0.5, vjust = 0.5
    ),
    panel.grid = element_blank(),
    axis.ticks.x = element_blank(),
    axis.ticks.length.x = unit(0, "mm"),
    axis.ticks.y = element_blank(),
    axis.ticks.length.y = unit(0, "mm"),
    axis.text.y = element_text(margin = margin(0,0,0,0, "mm")),
    axis.title.y = element_text(margin = margin(0,0,0,0, "mm"))
  )

# Inset bar chart on number of hurricanes per month (historically)  
inset_barchart2 <- df1 |> 
  st_drop_geometry() |> 
  filter(decade != "1970s") |> 
  mutate(month_var = month(date, label = TRUE)) |> 
  count(month_var, ss_hws) |> 
  ggplot(
    aes(
      x = month_var, 
      y = n,
      fill = as.character(ss_hws)
    )
  ) +
  ggchicklet::geom_chicklet(
    alpha = alpha_value,
    colour = bg_col,
    position = position_stack(reverse = T)
  ) +
  geom_text(
    mapping = aes(label = n, size = n),
    family = "body_font",
    position = position_stack(reverse = T, vjust = 0.5),
    colour = alpha(text_col, 0.5)
  ) +
  scale_fill_manual(values = mypal) +
  labs(
    x = NULL,
    y = "Number of hurricanes (in each month)"
  ) +
  theme_minimal(
    base_size = bts/2,
    base_family = "body_font"
  ) +
  theme(
    text = element_text(colour = text_hil),
    legend.position = "none",
    axis.text.x = element_text(
      margin = margin(1,0,0,0, "mm")
    ),
    panel.grid = element_blank(),
    axis.ticks.x = element_blank(),
    axis.ticks.length.x = unit(0, "mm"),
    axis.ticks.y = element_blank(),
    axis.ticks.length.y = unit(0, "mm"),
    axis.text.y = element_text(margin = margin(0,0,0,0, "mm")),
    axis.title.y = element_text(margin = margin(0,0,0,0, "mm"))
  )
  
inset_scatterplot <- df1 |> 
  st_drop_geometry() |> 
  ggplot(aes(x = date, y = max_winds_kt)) +
  geom_jitter(
    aes(colour = as.character(ss_hws)),
    alpha = alpha_value
  ) +
  geom_smooth(
    colour = alpha("black", 0.7)
  ) +
  scale_y_continuous(
    limits = c(60, 135),
    breaks = seq(70, 130, 10),
    expand = expansion(0)
  ) +
  scale_x_date(
    expand = expansion(0),
    breaks = seq(as.Date("1840-01-01"), as.Date("2030-01-01"), by = "20 years"),
    date_labels = "%Y"
  ) +
  scale_colour_manual(values = mypal) +
  coord_cartesian(
    clip = "off"
  ) +
  labs(
    x = NULL,
    y = "Maximum Wind Speed"
  ) +
  theme_classic(
    base_size = bts/2,
    base_family = "body_font"
  ) +
  theme(
    text = element_text(colour = text_hil),
    legend.position = "none",
    axis.text.x = element_text(
      margin = margin(2,0,0,0, "mm")
    ),
    panel.grid.major.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.line = element_line(
      linewidth = 0.2,
      colour = text_hil,
      arrow = arrow(length = unit(5, "mm"))
    ),
    panel.grid = element_blank(),
    axis.ticks.length.x = unit(0, "mm"),
    axis.ticks.y = element_blank(),
    axis.ticks.length.y = unit(0, "mm"),
    axis.text.y = element_text(margin = margin(0,1,0,0, "mm")),
    axis.title.y = element_text(margin = margin(0,0,0,0, "mm")),
    panel.background = element_rect(fill = NA, colour = NA),
    plot.background = element_rect(fill = NA, colour = NA)
  ) 

library(patchwork)

g <- g_base +
  inset_element(
    p = inset_barchart,
    align_to = "full",
    left = 0, right = 0.5,
    bottom = 0.78, top = 0.96,
    clip = FALSE
  ) +
  inset_element(
    p = inset_barchart2,
    align_to = "full",
    left = 0.48, right = 0.75,
    bottom = 0.78, top = 0.96,
    clip = FALSE
  ) +
  inset_element(
    p = inset_scatterplot,
    align_to = "full",
    left = 0.72, right = 1,
    bottom = 0.78, top = 0.96,
    clip = FALSE
  )


ggsave(
  plot = g,
  filename = here::here(
    "data_vizs", "viz_usa_hurricanes.png"
  ),
  height = 60,
  width = 40,
  units = "cm",
  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", 
                      "viz_usa_hurricanes.png")) |> 
  image_resize(geometry = "x400") |> 
  image_write(
    here::here(
      "data_vizs", 
      "thumbnails", 
      "viz_usa_hurricanes.png"
    )
  )

Combining {ggrepel} with {sf}

The following code generates a map showcasing named hurricanes that made landfall in the USA between 1850 and 2024. It utilizes a suite of packages to handle data processing, spatial operations, and plotting. The script begins by filtering the dataset df1 using filter() from the {dplyr} package to exclude entries with missing storm names, and selects the first occurrence for each using slice(), ensuring that only the earliest landfall per storm is visualized.

The visualization is built using {ggplot2}. The map is constructed by adding the hurricane landfall points with geom_sf(), colored and sized based on the Saffir-Simpson scale. The {ggblend} package’s blend() function is employed to apply a “darken” blending mode, enhancing contrast between layers. Labels for hurricane names and years are added with geom_text_repel() from {ggrepel}, which ensures non-overlapping text annotations. State abbreviations are added using geom_sf_text(), and a subtitle is included using annotate().

Code

# Get data only on the named Hurricanes
df1 |> 
  st_drop_geometry() |> 
  filter(!is.na(storm_names)) |> 
  count(storm_names, sort = T)

df2 <- df1 |> 
  filter(!is.na(storm_names)) |> 
  group_by(storm_names) |> 
  slice(1) |> 
  ungroup()

# BBOX for names hurricanes landfall points
xlim_values <- st_bbox(df2)[c(1, 3)]
ylim_values <- st_bbox(df2)[c(2, 4)]

library(ggrepel)
library(ggmap)
base_raster_raw <- get_stadiamap(
  bbox = bbox_raster,
  zoom = 7,
  maptype = "stamen_terrain_background"
)

base_raster <- rast(base_raster_raw) |>
  terra::crop(base_map |> st_transform("EPSG:4326"))
  terra::mask(base_map |> st_transform("EPSG:4326"))

ggplot() +
  geom_spatraster_rgb(
    data = base_raster,
    alpha = alpha_value
  )

base_map <- geodata::gadm(
  country = "USA",
  level = 1,
)

plot_subtitle <- "Names of the hurricanes that made landfalls in USA from 1850 to 2024, with colored dots indicating the location and intensity (Saffir-Simpson Hurricane Scale) at landfall. Labels indicate the given name, and the year of landfall in parenthesis."

sshws_annotation <- "The **Saffir-Simpson Hurricane Wind Scale** is a 1 to 5 rating system that categorizes hurricanes based on their sustained wind speeds. Developed in the 1970s, it estimates potential damage, with **Category 1** indicating minimal damage (winds of 74–95 mph) and **Category 5** representing catastrophic destruction (winds exceeding 157 mph). The scale helps communicate storm intensity and prepare communities for potential impacts." |> 
  str_wrap(90) |> 
  str_replace_all("\\n", "<br>")

g <- ggplot() +
  # geom_spatraster_rgb(
  #   data = base_raster,
  #   alpha = alpha_value
  # ) +
  geom_sf(
    data = base_map,
    colour = text_col,
    alpha = alpha_value
  ) +
  geom_sf(
    data = df2,
    mapping = aes(
      colour = as.character(ss_hws),
      size = ss_hws
    ),
    alpha = alpha_value
  ) |> ggblend::blend("darken") +
  scale_size(
    range = c(4, 24),
    transform = "exp"
  ) +
  
  ggrepel::geom_text_repel(
    data = df2,
    mapping = aes(
      geometry = geometry,
      label = paste0(storm_names, " (", year(date), ")"),
      colour = as.character(ss_hws)
    ),
    family = "caption_font",
    segment.size = 0.2,
    min.segment.length = unit(0.1, "pt"),
    force = 10,
    force_pull = 0.1,
    stat = "sf_coordinates",
    position = ggrepel::position_nudge_repel(
      x = 1, y = -2
      ),
    lineheight = 0.25,
    size = bts / 5
  ) +
  geom_sf_text(
    data = base_map,
    mapping = aes(label = abbr),
    size = bts / 3,
    colour = alpha(text_col, 0.3),
    check_overlap = T,
    family = "title_font",
    fontface = "bold"
  ) +
  
  # Add subtitle
  annotate(
    geom = "label",
    x = -100.5,
    y = 46,
    label = str_wrap(plot_subtitle, 25),
    size = bts / 2.5,
    lineheight = 0.3,
    hjust = 0,
    vjust = 1,
    family = "body_font",
    fill = alpha(bg_col, 0.6),
    label.size = NA,
    label.padding = unit(0.1, "lines"),
    colour = text_hil,
    fontface = "bold"
  ) +
  
  # Add Text Annotation on SS HWS Scale
  annotate(
    geom = "richtext",
    x = -87.5,
    y = 19.5,
    label = sshws_annotation,
    size = bts / 4.5,
    lineheight = 0.33,
    hjust = 0,
    vjust = 1,
    family = "caption_font",
    fill = alpha(bg_col, 0.6),
    label.size = NA,
    label.padding = unit(0.1, "lines"),
    colour = text_hil
  ) +
  
  coord_sf(
    crs = st_crs(df2),
    default_crs = "EPSG:4326",
    xlim = c(-100, -70),
    ylim = c(17, 45)
  ) +
  labs(
    colour = "Saffir-Simpson Hurricane Scale",
    size = "Saffir-Simpson Hurricane Scale",
    x = NULL, y = NULL
  ) +
  scale_colour_manual(
    values = mypal
  ) +
  labs(
    title = "U.S. Hurricane Landfalls: 1850-2024\nA History in Names and Winds",
    # subtitle = str_wrap(plot_subtitle, 90),
    caption = plot_caption
  ) +
  
  # Theme and beautification of plot
  theme_minimal(
    base_family = "body_font",
    base_size = bts
  ) +
  theme(
    # Overall
    plot.margin = margin(5,5,5,5, "mm"),
    plot.title.position = "plot",
    plot.caption.position = "plot",
    text = element_text(
        colour = text_hil
    ), 
    
    # Grid and Axes
    panel.grid = element_line(
      linewidth = 0.2,
      colour = text_hil,
      linetype = 3
    ),
    axis.text.x = element_text(margin = margin(0,0,0,0, "mm")),
    axis.text.y = element_text(margin = margin(0,0,0,0, "mm")),
    axis.ticks = element_blank(),
    axis.ticks.length = unit(0, "mm"),
    
    # Legend
    legend.position = "inside",
    legend.position.inside = c(0.05, 0.02),
    legend.justification = c(0, 0),
    legend.title.position = "top",
    legend.direction = "horizontal",
    legend.box = "horizontal",
    legend.title = element_text(
      margin = margin(0,0,2,0, "mm"),
      hjust = 0.5
    ),
    legend.text = element_text(
      margin = margin(2,0,0,0, "mm"),
      hjust = 0.5,
      face = "bold"
    ),
    legend.text.position = "left",
    legend.background = element_rect(
      fill = alpha("white", 0.7), colour = NA
    ),
    legend.margin = margin(5,5,5,5, "mm"),
    legend.box.margin = margin(5,5,5,5, "mm"),
    legend.spacing.y = unit(1, "cm"),
    legend.spacing.x = unit(3, "cm"),
    
    # Labels
    plot.title = element_text(
      hjust = 0.5,
      size = bts * 2.7,
      margin = margin(5,0,5,0, "mm"),
      family = "body_font",
      lineheight = 0.3,
      face = "bold"
    ),
    plot.subtitle = element_text(
      hjust = 0.5, 
      family = "body_font",
      lineheight = 0.3,
      margin = margin(0,0,0,0, "mm")
    ),
    plot.caption = element_textbox(
      hjust = 0.5,
      halign = 0.5, 
      margin = margin(10,0,0,0, "mm"),
      family = "caption_font",
      size = 0.7 * bts
    )
  )

ggsave(
  plot = g,
  filename = here::here(
    "data_vizs", "viz_usa_hurricanes_2.png"
  ),
  height = 50,
  width = 40,
  units = "cm",
  bg = bg_col
)

This map visualizes all named hurricanes that made landfall in the USA between 1850 and 2024. Each colored dot marks a landfall location, with size and color representing the storm’s intensity based on the Saffir-Simpson Hurricane Wind Scale. Labels display the hurricane’s name and year of landfall. The background terrain map and state boundaries provide geographical context, offering a clear view of historical hurricane patterns across the U.S. coastline.

Saving a thumbnail

Code

# Saving a thumbnail

library(magick)
# Saving a thumbnail for the webpage
image_read(here::here("data_vizs", 
                      "viz_usa_hurricanes_2.png")) |> 
  image_resize(geometry = "x400") |> 
  image_write(
    here::here(
      "data_vizs", 
      "thumbnails", 
      "viz_usa_hurricanes.png"
    )
  )

Session Info

Code

# Plot touch-up tools
library(scales)               # Nice Scales for ggplot2
library(fontawesome)          # Icons display in ggplot2
library(ggtext)               # Markdown text support for ggplot2
library(showtext)             # Display fonts in ggplot2
library(colorspace)           # Lighten and Darken colours


# Getting geographic data 
library(sf)                   # Simple Features in R
library(ggmap)                # Getting raster maps
library(terra)                # Cropping / Masking rasters
library(tidyterra)            # Rasters with ggplot2
library(osmdata)              # Open Street Maps data
library(ggspatial)            # Scales and Arrows on maps

# Data Wrangling & ggplot2
library(tidyverse)            # All things tidy
library(patchwork)            # Composing plots

library(janitor)  # For clean column name handling

library(rvest)                # Web Scraping

sessioninfo::session_info()$packages |> 
  as_tibble() |> 
  select(package, 
         version = loadedversion, 
         date, source) |> 
  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