Steps

• Loading required packages
• Getting web page content
• Extract relevant attributes / data from the content
• Building the final dataframe (to be written as csv) or for further analysis

Note: This post also assumes you’re familiar with Browser Devtools and CSS Selectors

Packages

We’ll use the R-packages rvest for web scraping and tidyverse for Data Analysis and Visualization

Loading the libraries

library(rvest)
library(tidyverse)

zomato web scraping

Getting Web Content from Zomato

zom <- read_html("https://www.zomato.com/bangalore/restaurants?buffet=1")

Extracting relevant attributes

Considering, It’s Restaurant listing - the columns that we can try to build are - Name of the Restaurant, Place / City where it’s, Average Price (or as Zomato says, Price for two)

Name of the Restaurant

This is how the html code for the name is placed:

<a class="result-title hover_feedback zred bold ln24   fontsize0 " href="https://www.zomato.com/bangalore/barbeque-nation-indiranagar" title="barbeque nation Restaurant, Indiranagar" data-result-type="ResCard_Name">Barbeque Nation</a>

So, what we need is for a tag with class value result-title, the value of attribute title.

zom %>% html_nodes("a.result-title") %>% 
  html_attr("title") %>% 
  stringr::str_split(pattern = ',') -> listing

As a good thing for us, Zomato’s website is designed in such a way that the name and place of the Restaurant are within the same css selector a.result-title - so it’s one scraping. And it’s separated by a , so we can use str_split() to split and the final output is now saved into listing which is a list.

Converting List to Dataframe

zom_df <- do.call(rbind.data.frame, listing)
names(zom_df) <- c("Name","Place")

In the above two lines, we’re trying to convert the listing list to a dataframe zom_df and then rename the columns into Name and Place

Extracting Price and Adding a New Price Column

zom_df$Price <- zom %>% html_nodes("div.res-cost > span.pl0") %>% 
  html_text() %>% 
  parse_number()

Since the Price field is actually a combination of Indian Currency and Comma-separated Number (which is ultimately a character), we’ll use parse_number() function remove the Indian currency unicode from the text and extract only the price value number.

Dataset

head(zom_df)
##                                Name             Place Price
## 1 abs absolute barbecues Restaurant      Marathahalli  1600
## 2            big pitcher Restaurant  Old Airport Road  1800
## 3                 pallet Restaurant        Whitefield  1600
## 4        barbeque nation Restaurant       Indiranagar  1600
## 5            black pearl Restaurant      Marathahalli  1500
## 6      empire restaurant Restaurant       Indiranagar   500

Price Graph

zom_df %>% 
  ggplot() + geom_line(aes(Name,Price,group = 1)) +
  theme_minimal() +
  coord_flip() +
  labs(title = "Top Zomato Buffet Restaurants",
       caption = "Data: Zomato.com")

Summary

Thus, We’ve learnt how to build a new dataset by scraping web content and in this case, from Zomato to build a Price Graph.

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