Michelin 3 Star Restaurants

Author

Ken

Cuisine of Michelin 3-Star Restaurants

Given my love for food, I wanted to explore a topic related to fine dining. Michelin stars often come up in conversation about restaurant quality and culinary trends, so I decided to explore what cuisine appears most often among U.S. restaurants that have earned three Michelin stars.

By scraping and analyzing the data-set, my goal is to determine most popular cuisine types at the highest tier of recognition. Hopefully, this provides some insight on what styles of cooking are most valued or recognized in the U.S. fine-dining scene.

library(tidyverse)  # The tidyverse collection of packages
library(httr)       # Useful for web authentication
library(rvest)      # Useful tools for working with HTML and XML
library(lubridate)  # Working with dates
library(magrittr)   # Piping output easily with loops

Process

To explore this question, data was scraped from Wikipedia page List of Michelin 3- star restaurants in the United States. This page contains a strcutured table listing all U.S. restaurants that have held three stars, along with details like chef name, cuisine types, and locations.

Retrieving Data

#Define user agent 
set_config(user_agent("<whitek25@xavier.edu>; +https://www.xavier.edu/business-analytics-program"))

#define HTML 
michelin <- 
  read_html("https://en.wikipedia.org/wiki/List_of_Michelin_3-star_restaurants_in_the_United_States")


#retreive tables from page 
michelin_tables <- 
   michelin %>% 
  html_table()


michelin_df <- 
  michelin_tables %>% 
  extract2(2) %>% 
  data.frame()

Cleaning

After pulling data into R, columns like cuisine type, chef, restaurant name and location were selected for analysis. One challenge faced was that both cuisine and location included very specific and overly detailed labels (I .e Cuisine values like Japanese sushi” or “Nouveau American.”)

To make analysis clearer, I grouped varying cuisine and location names into borader categories to accurately account for total amounts of each cuisine type and location. These categories were then mutated by a grouping function to assess their frequency.

michelinstar <- 
  michelin_df %>% 
  select(Restaurant,Chef.s.,Cuisine,Location)


 michelinstar <- 
  michelinstar %>% 
  mutate(cuisine_grouped = ifelse(
    str_detect(Cuisine, "Japanese"), "Japanese",
    ifelse(str_detect(Cuisine, "French"), "French",
    ifelse(str_detect(Cuisine, "Seafood"), "Seafood", 
    ifelse(str_detect(Cuisine, "California"), "California",
    ifelse(str_detect(Cuisine, "Contemporary|New American|Nouveau"), 
        "Contemporary/New American",
        Cuisine 
    ))))))

 
  
 michelinstar <- 
   michelinstar %>% 
   mutate(location_grouped = ifelse(
     str_detect(Location, "California|San Francisco|San Diego"), "California",
     Location ))

Analysis

#Explore restaurant count by cuisine 
michelin_count <- 
  michelinstar %>%  
group_by(cuisine_grouped) %>%  
  summarize(n_restaurants = n()) %>% 
  arrange(desc(n_restaurants))


#Explore restaurant count by location
location_count <- 
  michelinstar %>%  
  group_by(location_grouped) %>%  
  summarize(n_location = n()) %>% 
  arrange(desc(n_location))

#| echo: true
#| message: false 
#Visualizatons 

michelin_count  %>%  
  ggplot(aes(x = reorder(cuisine_grouped, n_restaurants), y = n_restaurants )) +
  geom_col() + 
  labs(title = "Number of U.S. 3-star Michelin Restaurants by Cuisine",
       x = "Cuisine",
       y = "Number of Restaurants") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

#| echo: false
#| message: false 

location_count  %>%  
  ggplot(aes(x = reorder(location_grouped, n_location), y = n_location )) +
  geom_col() + 
  labs(title = "Number of U.S. 3-star Michelin Restaurants by Location",
       x = "Location",
       y = "Number of Restaurants") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

From the visualization, French and Contemporary/New American cuisines appear the most among three-star restaurants. This makes sence given their influence:

French cuisine has historically shaped the fine dining world, and Michelin guide itself originated in France. Its strong presence in the U.S. suggsts that classical French technique still plays a large role in how restaurants earn top recognition.

Contemporary/New American cuisine reflects a more modern and innovative approach to fine dining. Its popularity indicates that Michelin also recognizes and appreciates creativity and reinterpretation of tadtional American cuisine. It’s popularity is also suggests U.S. fine finding has evolved into its own identity rather that leaning solely on European standards.

Seafood and California form a middle tear. Several high-end West Coast restraunts emphasize fresh, coastal, or produce-driven cooking, which aligns with the popularity of West-coast region and shows its influence on food culture.

--- title: "Michelin 3 Star Restaurants" author: "Ken" editor: visual toc: true # Generates an automatic table of contents. format: # Options related to formatting. html: # Options related to HTML output. code-tools: TRUE # Allow the code tools option showing in the output. embed-resources: TRUE # Embeds all components into a single HTML file. execute: # Options related to the execution of code chunks. warning: FALSE # FALSE: Code chunk sarnings are hidden by default. message: FALSE # FALSE: Code chunk messages are hidden by default. echo: TRUE # TRUE: Show all code in the output. --- ## Cuisine of Michelin 3-Star Restaurants Given my love for food, I wanted to explore a topic related to fine dining. Michelin stars often come up in conversation about restaurant quality and culinary trends, so I decided to explore what cuisine appears most often among U.S. restaurants that have earned three Michelin stars. By scraping and analyzing the data-set, my goal is to determine most popular cuisine types at the highest tier of recognition. Hopefully, this provides some insight on what styles of cooking are most valued or recognized in the U.S. fine-dining scene. ```{r} #| echo: TRUE #| message: false library(tidyverse) # The tidyverse collection of packages library(httr) # Useful for web authentication library(rvest) # Useful tools for working with HTML and XML library(lubridate) # Working with dates library(magrittr) # Piping output easily with loops ``` ## Process To explore this question, data was scraped from Wikipedia page [List of Michelin 3- star restaurants in the United States.](https://en.wikipedia.org/wiki/List_of_Michelin_3-star_restaurants_in_the_United_States) This page contains a strcutured table listing all U.S. restaurants that have held three stars, along with details like chef name, cuisine types, and locations. ## Retrieving Data ```{r} #| echo: true #| message: false #Define user agent set_config(user_agent("<whitek25@xavier.edu>; +https://www.xavier.edu/business-analytics-program")) #define HTML michelin <- read_html("https://en.wikipedia.org/wiki/List_of_Michelin_3-star_restaurants_in_the_United_States") #retreive tables from page michelin_tables <- michelin %>% html_table() michelin_df <- michelin_tables %>% extract2(2) %>% data.frame() ``` ## Cleaning After pulling data into R, columns like cuisine type, chef, restaurant name and location were selected for analysis. One challenge faced was that both cuisine and location included very specific and overly detailed labels (I .e Cuisine values like Japanese sushi" or "Nouveau American.") To make analysis clearer, I grouped varying cuisine and location names into borader categories to accurately account for total amounts of each cuisine type and location. These categories were then mutated by a grouping function to assess their frequency. ```{r} #| echo: true #| message: false michelinstar <- michelin_df %>% select(Restaurant,Chef.s.,Cuisine,Location) michelinstar <- michelinstar %>% mutate(cuisine_grouped = ifelse( str_detect(Cuisine, "Japanese"), "Japanese", ifelse(str_detect(Cuisine, "French"), "French", ifelse(str_detect(Cuisine, "Seafood"), "Seafood", ifelse(str_detect(Cuisine, "California"), "California", ifelse(str_detect(Cuisine, "Contemporary|New American|Nouveau"), "Contemporary/New American", Cuisine )))))) michelinstar <- michelinstar %>% mutate(location_grouped = ifelse( str_detect(Location, "California|San Francisco|San Diego"), "California", Location )) ``` ## Analysis ```{r} #| echo: true #| message: false #Explore restaurant count by cuisine michelin_count <- michelinstar %>% group_by(cuisine_grouped) %>% summarize(n_restaurants = n()) %>% arrange(desc(n_restaurants)) #Explore restaurant count by location location_count <- michelinstar %>% group_by(location_grouped) %>% summarize(n_location = n()) %>% arrange(desc(n_location)) ``` ```{r} #| echo: true #| message: false #Visualizatons michelin_count %>% ggplot(aes(x = reorder(cuisine_grouped, n_restaurants), y = n_restaurants )) + geom_col() + labs(title = "Number of U.S. 3-star Michelin Restaurants by Cuisine", x = "Cuisine", y = "Number of Restaurants") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) ``` ```{r} #| echo: false #| message: false location_count %>% ggplot(aes(x = reorder(location_grouped, n_location), y = n_location )) + geom_col() + labs(title = "Number of U.S. 3-star Michelin Restaurants by Location", x = "Location", y = "Number of Restaurants") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) ``` From the visualization, French and Contemporary/New American cuisines appear the most among three-star restaurants. This makes sence given their influence: French cuisine has historically shaped the fine dining world, and Michelin guide itself originated in France. Its strong presence in the U.S. suggsts that classical French technique still plays a large role in how restaurants earn top recognition. Contemporary/New American cuisine reflects a more modern and innovative approach to fine dining. Its popularity indicates that Michelin also recognizes and appreciates creativity and reinterpretation of tadtional American cuisine. It's popularity is also suggests U.S. fine finding has evolved into its own identity rather that leaning solely on European standards. Seafood and California form a middle tear. Several high-end West Coast restraunts emphasize fresh, coastal, or produce-driven cooking, which aligns with the popularity of West-coast region and shows its influence on food culture.