nrathnam

#Extract the Cost, Facility, Faculty, Programme, Student and Recruiter data from The Economist 
# Ranking, Application and Accreditation details are not required for our analysis

root_web = 'http://www.economist.com/'
university_school_details = []
for  j in range(100):
    for k in range(9):
        if (k != 0 and k != 2 and k != 8):
            school_full_url = root_web + univ_school_url[j] + '?tab=' + str(k)
            response = opener.open(school_full_url)

nrathnam

#Python code for scraping Economist website and fetching Business school ranking data 
#using Beautiful Soup
import urllib2
import re, requests
from bs4 import BeautifulSoup
opener = urllib2.build_opener()
opener.addheaders = [('User-agent', 'Chrome/52.0')]
university = []
#Extract the first level of information about the university B'schools, ranks and their locations
for i in range(10):

nrathnam

  ### Code for Insight: Responses tab
  
  choiceResponseInsight <- reactive({
    capitalize(input$responseInsightRB)
  })
  output$txtResponseInsight <- renderText({
    if  (choiceResponseInsight() == "% Disputed Responses")   {  "Percentage of Disputed Responses"    }
    if  (choiceResponseInsight() == "% Delayed Responses")   { "Percentage of Delayed Responses"  }
    if  (choiceResponseInsight() == "% Cases closed with Monetary Relief")   { "Percentage of Cases closed with Monetary Relief"  }
    print (choiceResponseInsight())

nrathnam

### Code for Insight: Map tab
  # Displaying data of interest
  choiceResponseMap <- reactive({
    capitalize(input$responseMapRB)
  })
  output$txtMap <- renderText({
    if (choiceResponseMap() == "Volume of Complaints")    {  "Volume of Complaints" }
    if  (choiceResponseMap() == "% Disputed Responses")   {  "Percentage of Disputed Responses"    }
    if  (choiceResponseMap() == "% Delayed Responses")   { "Percentage of Delayed Responses"  }
    print (choiceResponseMap())