Isabella Velásquez
Isabella Velásquez
Jul 27, 2021 3 min read

Politely Scraping Wikipedia Tables

Walkthrough on “politely” scraping Wikipedia tables and transforming them into analysis-ready datasets.

Wikipedia is such an amazing website, and also a fantastic source of data for analysis projects. I often find myself scraping Wikipedia’s tables for information and then cleaning and using the data for whatever I am working on.

However, scraping a website can create issues if not done properly. Though not required, I like to use the {polite} package to introduce myself to the website and ask for permission to scrape.

Recently, I submitted the Wikipedia table on independence days around the world to TidyTuesday - resulting in many beautiful and creative visualizations from the R Community ! This post walks through how to “politely” scrape the table and pull it into a flat data frame so that it’s ready for use.

Load libraries

There are several libraries needed for this walkthrough:

# To clean data

# To scrape data

Scrape table

First, we save the web page with the table that we would like as url:

url <- ""

Next, we use polite::bow() to introduce ourselves to the host, Wikipedia. This reads the rules from robots.txt and makes sure we follow them. The object (url_bow in this case) is saved as an object of class polite.

url_bow <- polite::bow(url)
## <polite session>
##     User-agent: polite R package -
##     robots.txt: 456 rules are defined for 33 bots
##    Crawl delay: 5 sec
##   The path is scrapable for this user-agent

Next, we actually ‘scrape’ (pull the content of) the web page using polite::scrape(). This needs an object of class polite (created with bow() from before).

Since we politely scraped the entire web page, we want to use {rvest} to specify what exact content we’d like to pull out. We can do this using html_nodes().

How do we know which node we want? There are probably other ways of doing this. As a Firefox and Mac user, I click Cmd + Shift + C which opens up the Developer Tools so that I can select a specific element from the web page. I hover over the table to determine what the HTML is called, in this case, table.wikitable.

This object is saved as an HTML table which is great, but a data frame would be preferable for analysis. So the final step is to use rvest::html_table() to read this table as something with which we can use tidyverse tools. The parameter fill = TRUE allows you to fill empty rows with NA.

ind_html <-
  polite::scrape(url_bow) %>%  # scrape web page
  rvest::html_nodes("table.wikitable") %>% # pull out specific table
  rvest::html_table(fill = TRUE) 

Flatten table

You will notice that ind_html is saved as a single object (a list) in which each element is a data frame. If we want to convert it to a flat data frame, we can specify that we want the content from only the first element [[1]]. We can then use janitor::clean_names() for nice, standardized column names.

ind_tab <- 
  ind_html[[1]] %>% 

That’s it! Now we’ve “politely” scraped the Wikipedia table into an analysis-ready data frame.


Additional steps to clean the file can be found in my GitHub repo. After doing so, I submitted to #TidyTuesday by submitting an issue on their page. Then they approved and shared the dataset!

This was a very quick walkthrough. I recommend Ryo Nakagawara’s blog post on politely scraping websites, especially if you would like (1) more in-depth explanation of what {polite} does and (2) more complex scraping examples.

Which Wikipedia table will you analyze next?