A common scenario for web scraping is when the data we want is available in plain html, but in different parts of the web, and not in a table format. In this scenario, we will need to find a way to extract each element, and then put it together into a data frame manually.
The motivating example here will be the website ipaidabribe.com, which contains a database of self-reports of bribes in India. We want to learn how much people were asked to pay for different services, and by which departments.
url <- 'http://ipaidabribe.com/reports/paid'We will also be using rvest, but in a slightly different way: prior to scraping, we need to identify the CSS selector of each element we want to extract.
A very useful tool for this purpose is selectorGadget, an extension to the Google Chrome browser. Go to the following website to install it: http://selectorgadget.com/. Now, go back to the ipaidabribe website and open the extension. Then, click on the element you want to extract, and then on the rest of highlighted elements that you do not want to extract. After only the elements you’re interested in are highlighted, copy and paste the CSS selector into R.
Now we’re ready to scrape the website:
library(rvest, warn.conflicts=FALSE)## Loading required package: xml2bribes <- read_html(url) # reading the HTML code
amounts <- html_nodes(bribes, ".paid-amount span") # identify the CSS selector
amounts # content of CSS selector## {xml_nodeset (10)}
## [1] <span>Paid INR 200\r\n </span>
## [2] <span>Paid INR 54,000\r\n </span>
## [3] <span>Paid INR 1,000\r\n </span>
## [4] <span>Paid INR 5,000\r\n </span>
## [5] <span>Paid INR 3,000\r\n </span>
## [6] <span>Paid INR 500\r\n </span>
## [7] <span>Paid INR 1,00,000\r\n </span>
## [8] <span>Paid INR 2,000\r\n </span>
## [9] <span>Paid INR 1,200\r\n </span>
## [10] <span>Paid INR 500\r\n </span>html_text(amounts)## [1] "Paid INR 200\r\n "
## [2] "Paid INR 54,000\r\n "
## [3] "Paid INR 1,000\r\n "
## [4] "Paid INR 5,000\r\n "
## [5] "Paid INR 3,000\r\n "
## [6] "Paid INR 500\r\n "
## [7] "Paid INR 1,00,000\r\n "
## [8] "Paid INR 2,000\r\n "
## [9] "Paid INR 1,200\r\n "
## [10] "Paid INR 500\r\n "We still need to do some cleaning before the data is usable:
amounts <- html_text(amounts)
(amounts <- gsub("Paid INR | |\r|\n|,", "", amounts)) # remove text, white space, and commas## [1] "200" "54000" "1000" "5000" "3000" "500" "100000"
## [8] "2000" "1200" "500"(amounts <- as.numeric(amounts)) # convert to numeric## [1] 200 54000 1000 5000 3000 500 100000 2000 1200 500Let’s do another one: transactions during which the bribe ocurred
transaction <- html_nodes(bribes, ".transaction a")
(transaction <- html_text(transaction))## [1] "Customs Check and Clearance" "Temporary Connection"
## [3] "Bribing of Government Officers" "VAT Registration"
## [5] "Duplicate Driving License" "Police Verification for Passport"
## [7] "Meter Installation" "Tax Clearance Certificate"
## [9] "Berth Allocation" "Police Verification for Passport"And one more: the department that is responsible for these transactions
# and one more
dept <- html_nodes(bribes, ".name a")
(dept <- html_text(dept))## [1] "Customs, Excise and Service Tax" "Electricity and Power Supply"
## [3] "Public Services" "Commercial Tax, Sales Tax, VAT"
## [5] "Transport" "Police"
## [7] "Electricity and Power Supply" "Commercial Tax, Sales Tax, VAT"
## [9] "Railways" "Police"This was just for one page, but note that there are many pages. How do we scrape the rest? First, following the best practices on coding, we will write a function that takes the URL of each page, scrapes it, and returns the information we want.
scrape_bribe <- function(url){
bribes <- read_html(url)
# variables that we're interested in
amounts <- html_text(html_nodes(bribes, ".paid-amount span"))
amounts <- as.numeric(gsub("Paid INR | |\r|\n|,", "", amounts))
transaction <- html_text(html_nodes(bribes, ".transaction a"))
dept <- html_text(html_nodes(bribes, ".name a"))
# putting together into a data frame
df <- data.frame(
amounts = amounts,
transaction = transaction,
dept = dept,
stringsAsFactors=F)
return(df)
}And we will start a list of data frames, and put the data frame for the initial page in the first position of that list.
bribes <- list()
bribes[[1]] <- scrape_bribe(url)
str(bribes)## List of 1
## $ :'data.frame': 10 obs. of 3 variables:
## ..$ amounts : num [1:10] 200 54000 1000 5000 3000 500 100000 2000 1200 500
## ..$ transaction: chr [1:10] "Customs Check and Clearance" "Temporary Connection" "Bribing of Government Officers" "VAT Registration" ...
## ..$ dept : chr [1:10] "Customs, Excise and Service Tax" "Electricity and Power Supply" "Public Services" "Commercial Tax, Sales Tax, VAT" ...How should we go about the following pages? Note that the following urls had page=XX, where XX is 10, 20, 30… So we will create a base url and then add these additional numbers. (Note that for this exercise we will only scrape the first 5 pages.)
base_url <- "http://ipaidabribe.com/reports/paid?page="
pages <- seq(0, 40, by=10)And now we just need to loop over pages, and use the function we created earlier to scrape the information, and add it to the list. Note that we’re adding a couple of seconds between HTTP requests to avoid overloading the page, as well as a message that will informs us of the progress of the loop.
for (i in 2:length(pages)){
# informative message about progress of loop
message(i, '/', length(pages))
# prepare URL
url <- paste(base_url, pages[i], sep="")
# scrape website
bribes[[i]] <- scrape_bribe(url)
# wait a couple of seconds between URL calls
Sys.sleep(2)
}## 2/5## 3/5## 4/5## 5/5The final step is to convert the list of data frames into a single data frame that we can work with, using the function do.call(rbind, LIST) (where LIST is a list of data frames).
bribes <- do.call(rbind, bribes)
head(bribes)## amounts transaction dept
## 1 200 Customs Check and Clearance Customs, Excise and Service Tax
## 2 54000 Temporary Connection Electricity and Power Supply
## 3 1000 Bribing of Government Officers Public Services
## 4 5000 VAT Registration Commercial Tax, Sales Tax, VAT
## 5 3000 Duplicate Driving License Transport
## 6 500 Police Verification for Passport Policestr(bribes)## 'data.frame': 50 obs. of 3 variables:
## $ amounts : num 200 54000 1000 5000 3000 500 100000 2000 1200 500 ...
## $ transaction: chr "Customs Check and Clearance" "Temporary Connection" "Bribing of Government Officers" "VAT Registration" ...
## $ dept : chr "Customs, Excise and Service Tax" "Electricity and Power Supply" "Public Services" "Commercial Tax, Sales Tax, VAT" ...Let’s get some quick descriptive statistics to check everything worked. First, what is the most common transaction during which a bribe was paid?
tab <- table(bribes$transaction) # frequency table
tab <- sort(tab, decreasing=TRUE) # sorting the table from most to least common
head(tab)##
## Police Verification for Passport Traffic Violations
## 7 4
## Background Verification Check Post
## 2 2
## Contract Work Garbage Collection
## 2 2What was the average bribe payment?
summary(bribes$amount)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 100 500 2000 32000 3500 1000000And what was the average payment for each department?
agg <- aggregate(bribes$amount, by=list(dept=bribes$dept), FUN=mean)
agg[order(agg$x, decreasing = TRUE),] # ordering from highest to lowest## dept x
## 7 Health and Family Welfare 501750.000
## 16 Revenue 123000.000
## 5 Electricity and Power Supply 77000.000
## 12 Police 22070.000
## 8 Income Tax 10000.000
## 9 Municipal Services 6000.000
## 4 Education 5000.000
## 19 Urban Development Authorities 5000.000
## 17 Stamps and Registration 4166.667
## 2 Commercial Tax, Sales Tax, VAT 3500.000
## 6 Forest 3000.000
## 3 Customs, Excise and Service Tax 1600.000
## 18 Transport 1485.714
## 1 Airports 1000.000
## 11 Passport 1000.000
## 14 Public Services 1000.000
## 13 Public Sector Undertakings 800.000
## 15 Railways 800.000
## 10 Others 500.000