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 1,500\r\n </span>
## [2] <span>Paid INR 300\r\n </span>
## [3] <span>Paid INR 500\r\n </span>
## [4] <span>Paid INR 500\r\n </span>
## [5] <span>Paid INR 1,500\r\n </span>
## [6] <span>Paid INR 4,000\r\n </span>
## [7] <span>Paid INR 530\r\n </span>
## [8] <span>Paid INR 150\r\n </span>
## [9] <span>Paid INR 100\r\n </span>
## [10] <span>Paid INR 1,000\r\n </span>html_text(amounts)## [1] "Paid INR 1,500\r\n "
## [2] "Paid INR 300\r\n "
## [3] "Paid INR 500\r\n "
## [4] "Paid INR 500\r\n "
## [5] "Paid INR 1,500\r\n "
## [6] "Paid INR 4,000\r\n "
## [7] "Paid INR 530\r\n "
## [8] "Paid INR 150\r\n "
## [9] "Paid INR 100\r\n "
## [10] "Paid INR 1,000\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" "300" "500" "500" "1500" "4000" "530" "150" "100" "1000"(amounts <- as.numeric(amounts)) # convert to numeric## [1] 1500 300 500 500 1500 4000 530 150 100 1000Let’s do another one: transactions during which the bribe ocurred
transaction <- html_nodes(bribes, ".transaction a")
(transaction <- html_text(transaction))## [1] "Family Certificate" "Police Verification for Passport"
## [3] "Traffic Violations" "Permission to sell Wood"
## [5] "Police Verification for Passport" "Building Completion Certificate"
## [7] "Registration of Mortgage Deed" "Medical Certificate"
## [9] "Speed Post" "False Allegations"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] "Health and Family Welfare" "Police"
## [3] "Transport" "Forest"
## [5] "Police" "Municipal Services"
## [7] "Stamps and Registration" "Health and Family Welfare"
## [9] "Post Office" "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] 1500 300 500 500 1500 4000 530 150 100 1000
## ..$ transaction: chr [1:10] "Family Certificate" "Police Verification for Passport" "Traffic Violations" "Permission to sell Wood" ...
## ..$ dept : chr [1:10] "Health and Family Welfare" "Police" "Transport" "Forest" ...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 Family Certificate Health and Family Welfare
## 2 300 Police Verification for Passport Police
## 3 500 Traffic Violations Transport
## 4 500 Permission to sell Wood Forest
## 5 1500 Police Verification for Passport Police
## 6 4000 Building Completion Certificate Municipal Servicesstr(bribes)## 'data.frame': 50 obs. of 3 variables:
## $ amounts : num 1500 300 500 500 1500 4000 530 150 100 1000 ...
## $ transaction: chr "Family Certificate" "Police Verification for Passport" "Traffic Violations" "Permission to sell Wood" ...
## $ dept : chr "Health and Family Welfare" "Police" "Transport" "Forest" ...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$transaction) # frequency table
tab <- sort(tab, decreasing=TRUE) # sorting the table from most to least common
head(tab)##
## Police Verification for Passport Traffic Violations
## 6 4
## Activities on Beat Customs Check and Clearance
## 3 3
## False Allegations Filing FIR
## 2 2What was the average bribe payment?
summary(bribes$amount)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 50 500 1500 28758 9000 901000And 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
## 1 Customs, Excise and Service Tax 307000.000
## 7 Others 100000.000
## 2 Education 43500.000
## 15 Transport 25320.000
## 14 Stamps and Registration 19004.286
## 12 Railways 5350.000
## 6 Municipal Services 4000.000
## 8 Passport 3066.667
## 10 Post Office 2550.000
## 13 Revenue 2500.000
## 9 Police 1991.667
## 5 Health and Family Welfare 825.000
## 4 Forest 500.000
## 11 Public Services 500.000
## 3 Electricity and Power Supply 250.000