Mapping India’s Cuisines with R: {sf}, {ggpattern}, {magick} and more

Diverse culinary heritage of India with a vibrant map created in R, blending spatial data from {sf}, creative patterns from {ggpattern}, and precise labeling from {ggrepel}. Images of iconic state cuisines were curated using {httr} and {magick}, seamlessly integrated into the visual using the power of {ggplot2}.

Discover the rich culinary diversity of India with this map, showcasing each state and union territory filled with its most iconic cuisine, accompanied by state names for easy reference. A harmonious blend of geography and gastronomy, crafted with R.

Step 1: Get India’s Map Data, and make a base tibble

# Data Import and Wrangling Tools
library(tidyverse)            # All things tidy
library(sf)                   # Handling simple features in R
library(here)                 # Folder management

# Final plot tools
library(scales)               # Nice Scales for ggplot2
library(fontawesome)          # Icons display in ggplot2
library(ggtext)               # Markdown text in ggplot2
library(showtext)             # Display fonts in ggplot2
library(colorspace)           # Lighten and Darken colours
library(ggpattern)            # Patterns / Images in geoms
library(magick)               # Handling images
library(httr)                 # Downloading images from Google


india_map <- read_sf(
  here::here(
    "data", "india_map", "India_State_Boundary.shp"
  )
) |> 
  janitor::clean_names() |> 
  st_simplify(dTolerance = 1000) |> 
  st_transform("EPSG:4326") |> 
  mutate(
    id = row_number(),
    area = st_area(geometry)
  ) |> 
  relocate(id)

india_map
india_map$state_name

Step 2: Get images of states’ cuisines from Google’s Programme Search Engine through an API

# Get a custom google search engine and API key
# Tutorial: https://developers.google.com/custom-search/v1/overview
# Tutorial 2: https://programmablesearchengine.google.com/

# From:https://developers.google.com/custom-search/v1/overview
# google_api_key <- "LOAD YOUR GOOGLE API KEY HERE"

# From: https://programmablesearchengine.google.com/controlpanel/all
# my_cx <- "GET YOUR CUSTOM SEARCH ENGINE ID HERE"

# Define function to download and save movie poster
download_food_images <- function(i) {
  
  api_key <- google_api_key
  cx <- my_cx
  
  # Build the API request URL
  url <- paste0(
    "https://www.googleapis.com/customsearch/v1?q=", 
    URLencode(
      paste0(
        india_map$state_name[i], 
        " state cuisine photo HD"
      )), 
      "&cx=", cx, "&searchType=image&key=", api_key,
      "&imgSize=large",       # Prioritize larger images
      "&imgType=photo"
    )
  
  # Make the request
  response <- GET(url)
  result <- content(response, "parsed")
  
  # Get the URL of the first image result
  image_url <- result$items[[1]]$link
  
  im <- magick::image_read(image_url) |> 
    image_resize("x1000")
  
  # set background as white
  image_write(
    image = im,
    path = here::here("geocomputation", "images",
                      paste0("temp_food_india_", i,".png")),
    format = "png"
    )
}

for (i in 18:nrow(india_map)) {
  download_food_images(i)
}



# Custom run the function for id 1, 2, 10, 20
custom_ids <- c(1, 2, 10, 20)

Step 2.1: The same Code improved with ChatGPT

# Add a new column to store food names in the tibble
india_map <- india_map |> mutate(food_name = NA_character_)

# Function to extract food item name from the search query result
extract_food_name <- function(items) {
  if (length(items) > 0) {
    # Attempt to extract meaningful food names from the title or snippet
    food_name <- items[[1]]$title
    return(food_name)
  } else {
    return(NA) # Return NA if no items are found
  }
}

# Improved function to download and save food images
download_food_images <- function(i) {
  
  api_key <- google_api_key
  cx <- my_cx
  
  # Build the API request URL with additional filters
  url <- paste0(
    "https://www.googleapis.com/customsearch/v1?q=",
    URLencode(paste0(india_map$state_name[i], 
                     " traditional cuisine food photo")),
    "&cx=", cx,
    "&searchType=image",
    "&key=", api_key,
    "&imgSize=large",       # Restrict to medium-sized images
    "&imgType=photo",
    "&num=1"                 # Fetch only one result
  )
  
  # Make the request
  response <- GET(url)
  if (response$status_code != 200) {
    warning("Failed to fetch data for state: ", india_map$state_name[i])
    return(NULL)
  }
  
  # Parse the response
  result <- content(response, "parsed")
  
  # Extract the image URL
  if (!is.null(result$items)) {
    image_url <- result$items[[1]]$link
    food_name <- extract_food_name(result$items)
  } else {
    warning("No results found for state: ", india_map$state_name[i])
    return(NULL)
  }
  
  # Validate and process the image
  tryCatch({
    im <- magick::image_read(image_url) |> 
      image_resize("x1000") # Resize image
    # Save the image
    image_write(
      image = im,
      path = here("geocomputation", "images", 
                  paste0("temp_food_india_", i, ".png")),
      format = "png"
    )
    
    # Add the food name to the tibble
    india_map$food_name[i] <- food_name
    
  }, error = function(e) {
    warning("Failed to process image for state: ", india_map$state_name[i])
  })
}

# Iterate through each state and download images
for (i in 1:nrow(india_map)) {
  download_food_images(i)
}

Step 3: Set up Visualization Parameters

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

# Font for the caption
font_add_google("Voltaire",
  family = "caption_font"
) 

font_add_google("Saira Extra Condensed",
  family = "caption_font2"
) 
showtext_auto()

# A base Colour
bg_col <- "grey10"
seecolor::print_color(bg_col)

# Colour for highlighted text
text_hil <- "grey90"
seecolor::print_color(text_hil)

# Colour for the text
text_col <- "white"
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:** Google & Census of India", 
  " |  **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 <- "Flavors of India:\nA Culinary Map"

Step 4: Use ggpattern to make final map Customizing options here.

g <- ggplot(data = india_map) +
  ggpattern::geom_sf_pattern(
    mapping = aes(
      pattern_filename = I(
        paste0(
          "geocomputation/images/temp_food_india_",
          id, ".png"
          )
        )
    ),
    pattern = "image",
    pattern_type = "expand",
    linewidth = 1.2,
    colour = bg_col
  ) +
  # A place filler code to try iterations while arriving
  # at the final plot
  # geom_sf(
  #   colour = bg_col,
  #   linewidth = 0.5,
  #   fill = "grey60"
  # ) +
  ggrepel::geom_label_repel(
    mapping = aes(
      label = state_name,
      geometry = geometry
    ),
    stat = "sf_coordinates",
    family = "caption_font",
    colour = text_col,
    fill = alpha(bg_col, 0.5),
    size = bts / 5,
    label.size = unit(0, "mm")
  ) +
  coord_sf(clip = "off") +
  labs(
    title = plot_title,
    caption = plot_caption
  ) +
  ggthemes::theme_map(
    base_size = bts,
    base_family = "title_font"
  ) +
  theme(
    # Overall plot
    plot.margin = margin(0,-15,0,-15, "mm"),
    
    # Labels
    plot.title = element_text(
      colour = text_hil,
      margin = margin(0,25,-50,0, "mm"),
      size = bts * 2.5,
      lineheight = 0.3,
      hjust = 1
    ),
    plot.caption = element_textbox(
      family = "caption_font2",
      margin = margin(-40,0,0,20, "mm"),
      hjust = 0,
      size = 0.8 * bts,
      colour = text_hil
    ),
    plot.background = element_rect(
      fill = "transparent",
      colour = "transparent"
    )
    
  )

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

Step 5: Save thumbnail and clean up temporary files

# Saving a thumbnail

library(magick)
# Reducing Image Size - its 15 Mb plus
image_read(
  here::here(
    "geocomputation", "images",
    "ggpattern_with_sf_india_cuisines.png"
    )
  ) |> 
  image_resize(geometry = "x400")

# Clean Up: Do no harm and leave the world an untouched place!
# Remove temporary image files
unlink(paste0("geocomputation/images/temp_food_india_", 1:nrow(india_map), ".png"))