You can easily increase the business value of your existing data by gathering and joining publicly available information from the web. Let’s walk through two web scraping examples through the lens of a Disney World competitor compiling their own competitive dataset.
Note: with all web scraping, make sure you are mindful of the website’s Terms of Service/Use. Even in light of recent news, it’s important to understand the boundaries websites are putting in place. Additionally, if you are making many automated requests, you can implement sleep functions to slow down your process and avoid overloading a site. This post is for educational purposes only.
Data from Wikipedia Tables
Wikipedia has a lot of rich information from contributors. Many tables can be extracted with a few lines of code. We’re using R to scrape this Disney World event timeline table; the rvest package for web scraping/HTML parsing and dplyr package for data cleaning. This post assumes you have basic HTML structure knowledge, but it’s important to take a look at the DOM and understand how you’re going to access the data you want (in this case, we’re accessing the first <table> element with class wikitable).
#call the rvest and tidyverse libraries library(rvest) library(tidyverse) #read in html from page wdw_wiki_html <- read_html("https://en.wikipedia.org/wiki/Walt_Disney_World") #select and convert html table to data frame wdw_event_timeline <- wdw_wiki_html %>% #select first table with class "wikitable" html_node("table.wikitable") %>% #convert html node to character string as.character() %>% #replace all break tags with new lines for formatting str_replace_all("<br>", "\n") %>% #read back into html read_html() %>% #parse html table into data frame html_table() %>% #get first result in list .[] #convert column names to lowercase names(wdw_event_timeline) <- names(wdw_event_timeline) %>% tolower()
If all goes well, you’ll end up with a neat data frame! Here’s a preview:
year 1 1965 2 1966 3 1967 4 1971 5 1972 event 1 Walt Disney announces Florida Project 2 Walt Disney dies of lung cancer at age 65 3 Construction of Walt Disney World Resort begins 4 Magic Kingdom opens\nPalm and Magnolia Golf Courses opens\nDisney's Contemporary Resort opens\nDisney's Polynesian Village Resort opens\nDisney's Fort Wilderness Resort & Campground opens\nRoy O. Disney dies at age 78 5 Disney's Village Resort opens
Data from Article Text
In many cases the data you’re interested in collecting won’t be in a nice table format. You’ll have to parse HTML more creatively to wrangle the data. In this example, we’ll be extracting historical Disney World ticket pricing from an article that provides data as text in different HTML elements. Again using rvest and dplyr, we’ll create a data frame of year, historical price, and adjusted price, ready to be joined to the Disney World event timeline. The year and historical prices (bolded text below) are all <h2> elements on the page and the adjusted price can be found in <li> elements. These will be explicitly extracted through an XPath expression.
#read in html from page wdw_prices_article_html <- read_html("https://www.gobankingrates.com/saving-money/entertainment/how-much-disney-world-cost-year-born/") #format historical prices wdw_prices_historical <- wdw_prices_article_html %>% #get all h2 elements html_nodes("h2") %>% #convert to list of character values (ie: "1983: $17") html_text() %>% #convert to data frame data.frame(year = .) %>% #separate the year and price into two columns based on the colon and space separate(year,c("year","historical_price"),sep=": ") %>% #remove dollar sign mutate(historical_price = str_replace(historical_price,"\\$", "")) %>% #convert both columns to numeric mutate_all(as.numeric) %>% #filter NA rows filter(!is.na(year)) #format adjusted prices wdw_prices_adjusted <- wdw_prices_article_html %>% #extract all li elements containing the relevant text html_nodes(xpath = "//li[strong[contains(text(),'Cost adjusted for 2019 inflation:')]]") %>% #convert to list of character values html_text() %>% #convert to data frame data.frame(adjusted_price = .) %>% #remove preceding text mutate(adjusted_price = str_replace(adjusted_price,"Cost adjusted for 2019 inflation\\:", "")) %>% #remove whitespace mutate(adjusted_price = str_replace(adjusted_price,"\u00A0", "")) %>% #remove dollar sign mutate(adjusted_price = str_replace(adjusted_price,"\\$", "")) %>% #convert column to numeric mutate(adjusted_price = as.numeric(adjusted_price)) #combine two pricing data frames wdw_prices_combined <- wdw_prices_historical %>% #add wdw_prices_adjusted as a column cbind(wdw_prices_adjusted) %>% #if 2019, use "historical" price as adjusted mutate(adjusted_price = ifelse(year == 2019,historical_price,adjusted_price))
If all goes well, you’ll end up with another neat data frame ready to be joined to the event timeline data frame. Here’s a preview:
year historical_price adjusted_price 1971 3.50 21.92 1972 3.75 23.26 1973 4.50 26.44 1974 5.25 27.44 1975 6.00 27.99
Combining the Data
Bringing these two datasets together could look something like the below visualization (code can be found here). Think about the output you have in mind before starting to gather data. Are you creating another table in your internal database? Are you collecting this information for reporting/sharing? What does the deliverable look like?
Other Data Sources To Consider
Depending on the domain, chances are high that there is an existing dataset out there that will meet your needs (or better yet, an API for the data you need). Here are a few examples of R packages that might prove useful in a similar project.
Google Trends: gtrendsR
Search volume trend for “Walt Disney World Resort” topic on Google Trends
#call the gtrendsR package library(gtrendsR) #download trend data for the “Walt Disney World Resort” topic (%2Fm%2F09b1k) in the United States, for all time (back to 2004), and only download interest trend data wdw_gtrends <- gtrends("%2Fm%2F09b1k","US",time = "all",onlyInterest = T) wdw_gtrends <- wdw_gtrends$interest_over_time #preview data wdw_gtrends %>% select(date,hits) %>% head(5) date hits 2004-01-01 67 2004-02-01 61 2004-03-01 60 2004-04-01 52 2004-05-01 51
#call the quantmod package library(quantmod) #define stock symbol (DIS) and data source (Yahoo Finance) getSymbols("DIS",src = "yahoo") #extract data to data frame and convert row names (date) to column DIS <- as.data.frame(DIS) %>% rownames_to_column("date") #preview DIS %>% select(date,DIS.Close) %>% head(5) date DIS.Close 2007-01-03 33.73830 2007-01-04 34.00465 2007-01-05 33.72844 2007-01-08 34.03425 2007-01-09 33.98492
#call the rtweet package library(rtweet) #define API token credentials (see this guide on Twitter API authentication) create_token( app = "app", consumer_key = "consumer_key", consumer_secret = "consumer_secret", access_token = "access_token", access_secret = "access_secret") #define handle and get max tweets allowed in call wdw_twitter_timeline <- get_timeline('WaltDisneyWorld',n = 3200) #preview wdw_twitter_timeline %>% select(created_at,text) %>% head(5) created_at text 2019-09-13 13:52:38 Check out the latest color … 2019-09-09 17:10:40 VIDEO: Six-year-old Jermain… 2019-09-06 15:21:18 Ready to live your own Star… 2019-09-03 20:13:52 Based on the most recent we… 2019-09-02 21:18:32 Based on the most recent fo…
Thinking Outside the Box
Web scraping and gathering data from around the web can be taken much further to enhance the value of your business. Any attribute of or interaction with a web page can be transformed into a dataset; image gathering for image recognition algorithm creation, competitive SEO (search engine optimization) research, text scraping/mining for sentiment analysis, the possibilities are endless. What other applications do you think this kind of information gathering has? Leave a comment below!