Dimitris Lamprinos pkakelas

## index.r
library("arules")


# Exercise 1

# Import csv and create a unique set of all the grocery names
groceries <- read.csv("./GroceriesInitial.csv", header=TRUE, sep=",")
product_names = unique(unlist(groceries[,4,35]))

# Transform groceries to a binary correlation values

## covid.js
const rp = require('request-promise')
const baseUrl = 'https://api.covid19api.com/'
const {getCases, getCountryNames} = require('./request')

//request.js
const doMakeRequest = async (endpoint) => {
    const res = await rp(`${baseUrl}${endpoint}`)
    return JSON.parse(res)
}

## index.py
#Kailyn Richardson
#12/4/19
#Total Sales for the Week (avg, highest, and total calculated)

weekdays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'] # weekdays

def salesEntry(): # this function will return saleslist for whole week
    salesList = [] # empty saleslist

    print("Enter sales details for each day")

## index.php
<!DOCTYPE html>
<?php
  $host    = "localhost";
  $user    = "root";
  $pass    = "root";
  $db_name = "DAVAPS";

  //create connection
  $connection = mysqli_connect($host, $user, $pass, $db_name);

## battleship.c
#include <stdio.h>
#include <stdlib.h>
#include <stdbool.h>
#include <time.h>


#define SIZE 6
// Do you understand this code? This is how proper validation is done

//Board print

## index.py
def prime_eratosthenes(n):
    numbers = list(range(2, n + 1))
    primes = []

    divisor = -1
    while len(numbers) > 0:
        divisor = numbers[0]
        primes.append(divisor)
        numbers = filterNumber(numbers, divisor)


## solutions.sql
-- a) the most populated city in each country.
SELECT CountryCode, MAX(Population) FROM city GROUP BY CountryCode;

-- b) the second most populated city in each country.
SELECT
    city.name, MAX(city.population)
FROM city
LEFT OUTER JOIN (
    SELECT *, MAX(population)
        FROM city group by CountryCode

## Phrase.css
body {
 font-family: Arial, Helvetica, sans-serif;
}

#content {
 width: 450px;
 margin: 0 auto;
 padding: 0px 20px 20px;
 background: white;
 border: 2px solid navy;

## style.css
.page-header {
    margin: 2%;
    padding-bottom: 20px;
    border-bottom-width:1px;
    border-bottom-color: grey;
    border-bottom-style: dotted;
}

.page-header h1 {
    font-weight: bold;

## index.py
from twython import Twython
import pandas as pd

TTL_TWEETS = 10000

consumer_key="4Y0swuxyRoDNZNWjboh5FI1OP"
consumer_secret="WKbjpokMp9ds3Yg2GwDo69AKeX704ywm2BOLVcR8ieGOri9fWG"
access_token_key="1186727191888957441-piNS1GzpeDw5Mmbjc8w0HzWiSJr2sX"
access_token_secret="LgXyWMbtti2FNnNa1sZrPTIcfPb4sj1azs9vUbJXJQipr"
	library("arules")


	# Exercise 1

	# Import csv and create a unique set of all the grocery names
	groceries <- read.csv("./GroceriesInitial.csv", header=TRUE, sep=",")
	product_names = unique(unlist(groceries[,4,35]))

	# Transform groceries to a binary correlation values
	const rp = require('request-promise')
	const baseUrl = 'https://api.covid19api.com/'
	const {getCases, getCountryNames} = require('./request')

	//request.js
	const doMakeRequest = async (endpoint) => {
	const res = await rp(`${baseUrl}${endpoint}`)
	return JSON.parse(res)
	}
	#Kailyn Richardson
	#12/4/19
	#Total Sales for the Week (avg, highest, and total calculated)

	weekdays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'] # weekdays

	def salesEntry(): # this function will return saleslist for whole week
	salesList = [] # empty saleslist

	print("Enter sales details for each day")
	<!DOCTYPE html>
	<?php
	$host = "localhost";
	$user = "root";
	$pass = "root";
	$db_name = "DAVAPS";

	//create connection
	$connection = mysqli_connect($host, $user, $pass, $db_name);
	#include <stdio.h>
	#include <stdlib.h>
	#include <stdbool.h>
	#include <time.h>


	#define SIZE 6
	// Do you understand this code? This is how proper validation is done

	//Board print
	def prime_eratosthenes(n):
	numbers = list(range(2, n + 1))
	primes = []

	divisor = -1
	while len(numbers) > 0:
	divisor = numbers[0]
	primes.append(divisor)
	numbers = filterNumber(numbers, divisor)
	-- a) the most populated city in each country.
	SELECT CountryCode, MAX(Population) FROM city GROUP BY CountryCode;

	-- b) the second most populated city in each country.
	SELECT
	city.name, MAX(city.population)
	FROM city
	LEFT OUTER JOIN (
	SELECT *, MAX(population)
	FROM city group by CountryCode
	body {
	font-family: Arial, Helvetica, sans-serif;
	}

	#content {
	width: 450px;
	margin: 0 auto;
	padding: 0px 20px 20px;
	background: white;
	border: 2px solid navy;
	.page-header {
	margin: 2%;
	padding-bottom: 20px;
	border-bottom-width:1px;
	border-bottom-color: grey;
	border-bottom-style: dotted;
	}

	.page-header h1 {
	font-weight: bold;
	from twython import Twython
	import pandas as pd

	TTL_TWEETS = 10000

	consumer_key="4Y0swuxyRoDNZNWjboh5FI1OP"
	consumer_secret="WKbjpokMp9ds3Yg2GwDo69AKeX704ywm2BOLVcR8ieGOri9fWG"
	access_token_key="1186727191888957441-piNS1GzpeDw5Mmbjc8w0HzWiSJr2sX"
	access_token_secret="LgXyWMbtti2FNnNa1sZrPTIcfPb4sj1azs9vUbJXJQipr"