R Statistics and Web Scraping with R

R Statistics and Web Scraping with R

Introduction:

In this brief introduction to web scraping, we will explore the application of the “rvest” package in a real-world scenario. Our goal is to scrape data from Formula 1 Wikipedia’s page and create a CSV file containing various statistics for every pilot, such as their name, nationality, and number of podiums.

To achieve this, we will use HTTP GET requests to retrieve the HTML content of the webpage. Then, we will parse the HTML code and extract the desired attributes using the “rvest” package. Once we have the data in a tibble format, we can perform further analysis and visualization using the “tidyverse” ecosystem.

By following this tutorial, you will not only learn the basics of web scraping in R, but you will also gain valuable insights into the world of Formula 1 racing. So let’s dive in and start scraping!

Full Article: R Statistics and Web Scraping with R

Scraping Formula 1 Wikipedia to Create a CSV File: A Real-World Application

Web scraping has become increasingly popular in recent years, and the rvest package provides a convenient way to scrape data from websites. In this article, we will demonstrate a real-world application of web scraping by using the rvest package to scrape data from Formula 1 Wikipedia’s page. Our goal is to create a CSV file containing information such as the name, nationality, number of podiums, and other statistics for every Formula 1 pilot.

HTTP GET Request

One of the easiest parts of scraping is performing a GET request. All we need to do is execute the following line of code:

link <- "

Parsing HTML Content and Getting Attributes

Just like in our previous example with the New York Times, we need to parse the HTML content and extract the desired attributes. By examining the HTML code, we find that the table we want to scrape has the table element with the sortable attribute. Therefore, we can use the following code to extract the table from the HTML code:

page <- read_html(link)
drivers_F1 <- html_element(page, "table.sortable") %>%
  html_table()

To inspect the data, we display the first and last observations, as well as the structure of the dataset:

head(drivers_F1) # first 6 rows
tail(drivers_F1) # last 6 rows
str(drivers_F1) # structure of the dataset

Cleaning the Data

Now that we have the data in a tibble format (similar to a dataframe in the tidyverse universe), we can select the variables of interest and remove the last row that contains the name of the variables:

drivers_F1 <- drivers_F1[c(1:4, 7:9)] # select variables
drivers_F1 <- drivers_F1[-nrow(drivers_F1), ] # remove last row

We may also want to clean the data. For example, the variable “Drivers’ Championships” contains not only the number of championships won but also the years of the victories. To extract only the number of victories without the years, we can use the substr() function:

drivers_F1$`Drivers' Championships` <- substr(drivers_F1$`Drivers' Championships`,
  start = 1, stop = 1
)

Saving the Dataset

If you want to save the dataset, you can do so using the write.csv() function:

write.csv(drivers_F1, "F1_drivers.csv", row.names = FALSE)

Analysis on the Database

To showcase the usefulness of this scraped database, we will answer a few simple questions.

1. Which country has the largest number of wins?

drivers_F1 %>
  group_by(Nationality) %>
  summarise(championship_country = sum(as.double(`Drivers' Championships`))) %>
  arrange(desc(championship_country))

2. Who has the most Championships?

drivers_F1 %>
  group_by(`Driver name`) %>
  summarise(championship_pilot = sum(as.double(`Drivers' Championships`))) %>
  arrange(desc(championship_pilot))

3. Is there a relationship between the number of Championships won and the number of race pole positions?

drivers_F1 %>
  filter(`Pole positions` > 1) %>
  ggplot(aes(x = as.double(`Pole positions`), y = as.double(`Drivers' Championships`))) +
  geom_point(position = "jitter") +
  labs(y = "Championships won", x = "Pole positions") +
  theme_minimal()

Conclusion

With the rvest package, we can easily scrape data from websites and perform various analyses. In this real-world application, we scraped data from Formula 1 Wikipedia’s page and created a CSV file containing valuable information about Formula 1 pilots. Furthermore, we analyzed the data to answer questions about the countries with the most wins and the pilots with the most Championships. The scraped data opens up countless possibilities for further analysis and insights into the world of Formula 1.

Summary: R Statistics and Web Scraping with R

In this introduction to web scraping, we have used the rvest package in a real-world application to scrape data from the Formula 1 Wikipedia page. The goal was to create a CSV file containing the name, nationality, number of podiums, and other statistics for each pilot. We demonstrated how to make an HTTP GET request, parse HTML content, and extract attributes. We also showed how to clean and analyze the data. This process allows us to gather valuable insights, such as identifying the country with the most wins and the pilot with the most Championships. The analysis also suggests a positive relationship between the number of pole positions and the number of Championships won.

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