Yankim

library(shiny); library(maps); library(mapproj); library(dplyr); library(plotly); library(googleVis); library(car)
data("county.fips")
#Join data by county indentification numbe (fips) to the county data in the maps package
flipjoin = function(x) {
    y = read.csv(x)
    left_join(county.fips, y, by = c("fips" = "FIPS"))
}

#Data wrangling for map plot
health2 = flipjoin("data/health.csv")

Yankim

#Loading packages
library(dplyr); library(ggplot2); library(RColorBrewer); library(rworldmap)

#Loading datasets
indicators = read.csv("subbeddata.csv", header = TRUE, stringsAsFactors = FALSE)
allindicators = read.csv("/Users/YannickMac/Dropbox/Applications/Data science/NYCDSA/Data_visualization_project/world-development-indicators/Indicators.csv")

counts <- indicators %>%
    group_by(IndicatorCode, IndicatorName) %>%
    summarise(NumCountries = n_distinct(CountryName),

Yankim

import numpy as np
import pymongo
import pandas as pd
# Connection to Mongo DB and import recipe and ingredients collections as Pandas
try:
    conn=pymongo.MongoClient()
    print "Connected successfully!!!"
except pymongo.errors.ConnectionFailure, e:
    print "Could not connect to MongoDB: %s" % e 
conn

Yankim

def scrape_recipe(br, year, idnumber):
	#This is called when user wants to scrape for specific recipe site
	#Try functions were used to prevent any one element from stopping the operation
	
	#recipe title
	try:
		rtitle = br.find_element_by_tag_name('h1').text
	except:
		rtitle = 'NA'

Yankim

br = webdriver.Firefox() #open firefox
br.get('https://www.allrecipes.com/recipes/'+str(yearurls[i])) 
###ID number for year, example 1997 has ID of 14486

html_list = br.find_element_by_id("grid")
urls = html_list.find_elements(By.CLASS_NAME, "favorite") 
#All top 20 recipes have hearts associated with them. Inside 
#the heart contains the unique ID number for the given recipe

for i, e in enumerate(urls):