Restaurant Rating

Note: This is a presentation of the project expressing on how consumers evaluate each unique restaurant

1.Loading data

Prepare and setup data for working

library(tidyverse) 

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library(dplyr)
library(ggplot2)
setwd("C:/Users/admin/OneDrive/Documents/Code/R/Data frames")
restaurant <- read.csv("restaurants.csv")
consumer <- read.csv("consumers.csv")
rating <- read.csv("ratings.csv")

2. Dataset walkthrough

The data consists of 3 main parts

• Restaurants

• Consumers

• Ratings Below is the overview of the datasets:

head(restaurant)

head(consumer)

head(rating)

3. Illustrating the graphs

 ggplot(restaurant, aes(x = "", y = Price, fill = Price)) + geom_col() + coord_polar(theta = "y") +scale_fill_manual(values = c("steelblue", "grey", "aquamarine")) + labs(title = "Restaurant by Price" + geom_text(aes(label = paste0(round((count_price/sum(price))*100), "%")), position = position_stack(vjust = 2)))  

The chart below shows the Distribution of Alcohol Services on each Price

ggplot(data = restaurant,aes(x = Price, fill = Alcohol_Service)) + geom_bar( stat = "count", position = "dodge") + labs(title = "Alcohol by Price")

And another chart showing the Distribution of Smoking Service on each Price

ggplot(data = restaurant, aes(x= Price, fill = Smoking_Allowed))+geom_bar(stat= "count", position = "dodge") + labs(title = "Smoking by Price")

• From this chart we can see that the Medium price takes the largest proportion while the others “Low” and “High” price roughly shares the same slice width