kjgarza

exports.main = function() {
    require("widget").Widget({
        id: "widgetID1",
        label: "My Mozilla Widget",
        contentURL: "http://www.mozilla.org/favicon.ico",
        onClick: function(event) {

            var XMLHttpRequest =  require("xhr");
            var request = new XMLHttpRequest();
            request.open("GET", "http://www.myexperiment.org/search.xml?query=goble", false);

kjgarza

var publ = document.querySelectorAll(".gs_a");
var dl =[];
for(i=0; i<publ.length; i++) {
   var n  = (publ[i].innerHTML).split("-");
   dl[i] = n[1];
} 
    
var result = {}, max = 0, res;
for(var v in dl) {
  result[dl[v]] = (result[dl[v]] || 0) + 1;

kjgarza

var publ = document.querySelectorAll(".gs_rt");
var dl =[];
for(i=0; i<publ.length; i++) {
   var n  = publ[i].textContent;
   dl[i] = n;
} 
    

console.log(dl);

kjgarza

y1 <- read.csv(file="/Users/kristian/Documents/statementforR.csv",sep=",",head=TRUE)

#y2 <- y1[y1$item == 'LIDL UK MANCHESTER',]


y3 <- y1[grep("CASH", y1$item), ]


plot(as.Date(y1$date, format='%d/%m/%Y'),y1$debit,type="l", col="blue", xlab="Time", ylab="Debit", ylim=range(c(y1$debit,y2$debit)), xlim=range(c(as.Date(y1$date, format='%d/%m/%Y'),as.Date(y2$date, format='%d/%m/%Y'))))

kjgarza

var json =
{
    "max":196419,
    "buckets":
    [
        {
            "d":1346835600,
            "i":
            [
                [

kjgarza

var auth = "Floridi"
var authors = document.querySelectorAll(".gs_a");
var c = 0;

for(i=0; i<authors.length; i++) {
   var name = authors[i].innerHTML;
    if (name.indexOf(auth) != -1){
        c++;
    }
}    

kjgarza

    (function() {
      var el=document.createElement('div'),
          b=document.getElementsByTagName('body')[0],
          otherlib=false,
          msg='';
      el.style.position='fixed';
      el.style.height='32px';
      el.style.width='220px';

kjgarza

df <- data.frame(
                 coun)

profession.code <- c(`0` = 0, "1"="mexico", Accountants=2, Veterinarians=3, 
                     `Financial analysts`=4,  Nurses=5)

df$profession.str <- names(profession.code)[match(df$coun, profession.code)]

kjgarza

wordSearch<- "problem"
i<-1

files<-RQDAQuery("select file from source where status=1")

file<-files$file[i][1]

lines<- unlist(strsplit(file, "\n[ \t\n]*\n*"))

r<-grep(wordSearch, lines)

kjgarza

# load required libraries
require(maps)
require(RColorBrewer)

# Create a dataframe with the reported observations
# get data from https://www.cia.gov/library/publications/the-world-factbook/rankorder/rawdata_2228.txt
# transform it to csv
r <- read.csv(file=paste0("/Dropbox/Data/rawdata_2228.csv"),sep=",",head=FALSE)
loc<-r$V2
cases<-r$V3