Juanan Pereira juananpe

## Lag.java
package hasiera;

import java.awt.Image;
import java.net.URL;
import java.util.Vector;

import javax.swing.ImageIcon;

public class Lag {
	String izena;

## intro.R
?cars
str(cars)
summary(cars)

gg <- ggplot(cars, aes(x=speed, y=dist)) +
      geom_point()

plot(gg)

gg <- ggplot(cars, aes(x=speed, y=dist)) +

## linearReg.R
library(ggplot2)

# Price, Age, KM(kilometers driven), Fuel Type,
# HP(horsepower), Automatic or Manual, Number of Doors,
# and Weight in pounds are the data collected in this file for
# Toyota Corollas.

toyota <- read.csv("https://raw.githubusercontent.com/datailluminations/PredictingToyotaPricesBlog/master/ToyotaCorolla.csv")

g <- ggplot(toyota, aes(Price))

## films.R
library('rvest')
url <- 'http://www.imdb.com/search/title?count=100&release_date=2018,2016&title_type=feature'

webpage <- read_html(url)
#Using CSS selectors to scrap the rankings section
rank_data_html <- html_nodes(webpage,'.text-primary')

#Converting the ranking data to text
rank_data <- html_text(rank_data_html)
#Data-Preprocessing: Converting rankings to numerical

## ejercicio.html
<!doctype html>
<html>
<head>
<link rel="stylesheet" href="estiloa.css">
</head>
<body>
<div id="menu">
<ul>
    <li><a href="index.html"> ERREZETAK</a></li>
    <li><a href="formularioa.html">BIDALI ZURE ERREZETA </a></li>

## debugger.html
<!doctype html>
<html>
<head>

<script src="debugger.js"></script>

</head>
<body>

</body>

## debugger.js
class Ejercicio {


    constructor(){
        this.w = this.width = 194, this.height= 122;
        this.g = Array(this.width*(this.height+2)).fill(0);

        for(let i=0; i< this.width; i++){
           for(let j=0; j< this.height; j++){
               if (j <= 2 || j >= this.height - 2 )

## qunit-assert-canvas.js
QUnit.extend(QUnit.assert, {
  pixelEqual: function(canvas, x, y, r, g, b, a, message) {
    var actual = Array.prototype.slice.apply(canvas.getContext("2d").getImageData(x, y, 1, 1).data),
        expected = [r, g, b, a];
    QUnit.push(QUnit.equiv(actual, expected), actual, expected, message);
  },
  pixelNotEqual: function(canvas, x, y, r, g, b, a, message) {
    var actual = Array.prototype.slice.apply(canvas.getContext("2d").getImageData(x, y, 1, 1).data),
        expected = [r, g, b, a];
    message = message || "should not be " + expected;

## fifa.R
rm(list=ls())

library(readr)      # importation
library(RTextTools) # classification de textes
library(textreuse)  # plagiat
library(tm)         # text mining
library(ggplot2)    # visualisations
library(lsa)        # latent semantic analysis
library(stringr)    # manipulation de chaines de caractères

## fifa.csv
"journal";"note";"auteur";"url";"annee";"texte"
"gamekult";"70";"puyo";"http://www.gamekult.com/jeux/test-fifa-17-SU3050706473t.html#ps4";"2017";"Si vous avez un tant soit peu suivi l'actualité de FIFA 17 depuis son annonce, vous savez déjà qu'il ne s'agit pas d'un FIFA +1 comme il en paraît chaque année. Pour ce millésime 2016, la simu d'EA Sports fait peau neuve en se dotant d'un nouveau moteur, le Frostbite cher à Battlefield, histoire de se mettre à niveau tout en bénéficiant des améliorations régulières apportées par les ingénieurs maison. Forcément, qui dit nouveau moteur sous-entend une liste considérable de changements et de petits ajustements, pour ne pas dire de concessions ; Konami avait d'ailleurs essuyé les plâtres il y a quelques années en osant le pari du Fox Engine, qui a longtemps tâtonné avant de parvenir à un compromis satisfaisant en 2016. FIFA 17 saura-t-il trouver la bonne formule dès le coup d'envoi ? \n Si l'on se place du point de vue dév, FIFA 17 tient du petit exploit. Même si le pr
	package hasiera;

	import java.awt.Image;
	import java.net.URL;
	import java.util.Vector;

	import javax.swing.ImageIcon;

	public class Lag {
	String izena;
	?cars
	str(cars)
	summary(cars)

	gg <- ggplot(cars, aes(x=speed, y=dist)) +
	geom_point()

	plot(gg)

	gg <- ggplot(cars, aes(x=speed, y=dist)) +
	library(ggplot2)

	# Price, Age, KM(kilometers driven), Fuel Type,
	# HP(horsepower), Automatic or Manual, Number of Doors,
	# and Weight in pounds are the data collected in this file for
	# Toyota Corollas.

	toyota <- read.csv("https://raw.githubusercontent.com/datailluminations/PredictingToyotaPricesBlog/master/ToyotaCorolla.csv")

	g <- ggplot(toyota, aes(Price))
	library('rvest')
	url <- 'http://www.imdb.com/search/title?count=100&release_date=2018,2016&title_type=feature'

	webpage <- read_html(url)
	#Using CSS selectors to scrap the rankings section
	rank_data_html <- html_nodes(webpage,'.text-primary')

	#Converting the ranking data to text
	rank_data <- html_text(rank_data_html)
	#Data-Preprocessing: Converting rankings to numerical
	<!doctype html>
	<html>
	<head>
	<link rel="stylesheet" href="estiloa.css">
	</head>
	<body>
	<div id="menu">
	<ul>
	<li><a href="index.html"> ERREZETAK</a></li>
	<li><a href="formularioa.html">BIDALI ZURE ERREZETA </a></li>
	class Ejercicio {


	constructor(){
	this.w = this.width = 194, this.height= 122;
	this.g = Array(this.width*(this.height+2)).fill(0);

	for(let i=0; i< this.width; i++){
	for(let j=0; j< this.height; j++){
	if (j <= 2 \|\| j >= this.height - 2 )
	QUnit.extend(QUnit.assert, {
	pixelEqual: function(canvas, x, y, r, g, b, a, message) {
	var actual = Array.prototype.slice.apply(canvas.getContext("2d").getImageData(x, y, 1, 1).data),
	expected = [r, g, b, a];
	QUnit.push(QUnit.equiv(actual, expected), actual, expected, message);
	},
	pixelNotEqual: function(canvas, x, y, r, g, b, a, message) {
	var actual = Array.prototype.slice.apply(canvas.getContext("2d").getImageData(x, y, 1, 1).data),
	expected = [r, g, b, a];
	message = message \|\| "should not be " + expected;
	rm(list=ls())

	library(readr) # importation
	library(RTextTools) # classification de textes
	library(textreuse) # plagiat
	library(tm) # text mining
	library(ggplot2) # visualisations
	library(lsa) # latent semantic analysis
	library(stringr) # manipulation de chaines de caractères