A common scenario for web scraping is when the data we want is available in plain html, but in different parts of the site, 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 1,500\r\n </span>
## [2] <span>Paid INR 10,000\r\n </span>
## [3] <span>Paid INR 50\r\n </span>
## [4] <span>Paid INR 20,000\r\n </span>
## [5] <span>Paid INR 20,000\r\n </span>
## [6] <span>Paid INR 10,000\r\n </span>
## [7] <span>Paid INR 800\r\n </span>
## [8] <span>Paid INR 1,000\r\n </span>
## [9] <span>Paid INR 2,500\r\n </span>
## [10] <span>Paid INR 650\r\n </span>html_text(amounts)## [1] "Paid INR 1,500\r\n "
## [2] "Paid INR 10,000\r\n "
## [3] "Paid INR 50\r\n "
## [4] "Paid INR 20,000\r\n "
## [5] "Paid INR 20,000\r\n "
## [6] "Paid INR 10,000\r\n "
## [7] "Paid INR 800\r\n "
## [8] "Paid INR 1,000\r\n "
## [9] "Paid INR 2,500\r\n "
## [10] "Paid INR 650\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] "1500" "10000" "50" "20000" "20000" "10000" "800" "1000" "2500"
## [10] "650"(amounts <- as.numeric(amounts)) # convert to numeric## [1] 1500 10000 50 20000 20000 10000 800 1000 2500 650Let’s do another one: transactions during which the bribe ocurred
transaction <- html_nodes(bribes, ".transaction a")
(transaction <- html_text(transaction))## [1] "Certificates" "Caste Certificate"
## [3] "Encumbrance Certificate" "Property Registration"
## [5] "Property Registration" "Building and Housing"
## [7] "Traffic Fines, Penalties, Rules" "Traffic Fines, Penalties, Rules"
## [9] "Customs Check and Clearance" "Traffic Fines, Penalties, Rules"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] "" "Revenue"
## [3] "Stamps and Registration" ""
## [5] "" ""
## [7] "" ""
## [9] "Customs, Excise and Service Tax" ""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] 1500 10000 50 20000 20000 10000 800 1000 2500 650
## ..$ transaction: chr [1:10] "Certificates" "Caste Certificate" "Encumbrance Certificate" "Property Registration" ...
## ..$ dept : chr [1:10] "" "Revenue" "Stamps and Registration" "" ...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 1500 Certificates
## 2 10000 Caste Certificate Revenue
## 3 50 Encumbrance Certificate Stamps and Registration
## 4 20000 Property Registration
## 5 20000 Property Registration
## 6 10000 Building and Housingstr(bribes)## 'data.frame': 50 obs. of 3 variables:
## $ amounts : num 1500 10000 50 20000 20000 10000 800 1000 2500 650 ...
## $ transaction: chr "Certificates" "Caste Certificate" "Encumbrance Certificate" "Property Registration" ...
## $ dept : chr "" "Revenue" "Stamps and Registration" "" ...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$transaction) # frequency table
tab <- sort(tab, decreasing=TRUE) # sorting the table from most to least common
head(tab)##
## Traffic Fines, Penalties, Rules Property Registration
## 9 6
## Certificates Autorickshaws
## 5 4
## Others Building and Housing
## 4 2What was the average bribe payment?
summary(bribes$amount)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 50 500 2500 7452 10000 100000And what was the average payment for each department?
agg <- aggregate(amounts~dept, data=bribes, FUN=mean)
agg[order(agg$amount, decreasing = TRUE),] # ordering from highest to lowest## dept amounts
## 4 Others 32500.000
## 6 Revenue 10000.000
## 3 Municipal Services 8250.000
## 1 5910.286
## 8 Transport 2603.000
## 2 Customs, Excise and Service Tax 2500.000
## 5 Police 400.000
## 7 Stamps and Registration 50.000