Chris Essig csessig86

Tabletop.js is a fantastic, open-source JavaScript library that lets developers easily integrate data from Google Spreadsheets into their online projects. I've used it, even contributed a minor feature, and love it for prototyping. Non-programmers love being able to update a project via Google Spreadsheets' hyper-intuitive interface.

That said, I'm extraordinarily wary of using Tabletop in production. Instead, at the Wall Street Journal, we use a bit of middleware to "prune" our Google Spreadsheets-based data and then cache it on our own servers. A few brief reasons:

Short-Term Reliability. With Tabletop, your project depends on Google not to rate-limit access to your spreadsheet. Google rate-limits access to their Spreadsheet API, though the thresholds aren't clear. If you're building an app you care about, you don't want to be i

Locate the section for your github remote in the .git/config file. It looks like this:

[remote "origin"]
	fetch = +refs/heads/*:refs/remotes/origin/*
	url = [email protected]:joyent/node.git

Now add the line fetch = +refs/pull/*/head:refs/remotes/origin/pr/* to this section. Obviously, change the github url to match your project's URL. It ends up looking like this:

I'm having trouble understanding the benefit of require.js. Can you help me out? I imagine other developers have a similar interest.

From Require.js - Why AMD:

The AMD format comes from wanting a module format that was better than today's "write a bunch of script tags with implicit dependencies that you have to manually order"

I don't quite understand why this methodology is so bad. The difficult part is that you have to manually order dependencies. But the benefit is that you don't have an additional layer of abstraction.

	#!/usr/bin/env python
	import csv, json
	from collections import defaultdict
	r = csv.reader(open("data.csv"))
	headers = r.next()
	countries = headers[1:]
	out_rows = []
	for row in r:
	area = row[0]
	out_row = [area]

	# We will be using the Python library Beautiful Soup
	# To scrape the information
	import urllib2
	from bs4 import BeautifulSoup
	import re

	# Note: This arrest log is available at:
	# http://chrisessig.com/arrestlog.PDF
	# It was taken from the Waterloo Police Department's website:
	# http://www.waterloopolice.com/images/arrestlog.PDF

	var geojson = L.geoJson(data, {

	// style for all vector layers (color, opacity, etc.) (optional)
	getStyle: function (feature) {
	return feature.properties && feature.properties.style;
	},

	// function for creating layers for GeoJSON point features (optional)
	pointToLayer: function (feature, latlng) {
	return L.marker(latlng, {

	#!/bin/sh

	###
	# SOME COMMANDS WILL NOT WORK ON macOS (Sierra or newer)
	# For Sierra or newer, see https://github.com/mathiasbynens/dotfiles/blob/master/.macos
	###

	# Alot of these configs have been taken from the various places
	# on the web, most from here
	# https://github.com/mathiasbynens/dotfiles/blob/5b3c8418ed42d93af2e647dc9d122f25cc034871/.osx

	<!DOCTYPE html>
	<html>
	<head>
	<meta http-equiv="Content-Type"
	content="text/html; charset=utf-8" />

	<script src="jquery-1.7.2.min.js" ></script>
	<script src="handlebars-1.0.0.beta.6.js" ></script>
	<script src="underscore-min.js" ></script>
	<script src="backbone-min.js" ></script>

	import pytz
	import datetime
	import time
	import urllib2
	import json
	import os
	import elementtree.ElementTree as ET

	# e.g. http://scores.nbcsports.msnbc.com/ticker/data/gamesMSNBC.js.asp?jsonp=true&sport=MLB&period=20120929
	url = 'http://scores.nbcsports.msnbc.com/ticker/data/gamesMSNBC.js.asp?jsonp=true&sport=%s&period=%d'

	import re
	import json

	ws_re = re.compile("\s+")
	line_num_re = re.compile("\s\d+\s{2,}", re.M)

	# first, pdftotext -layout <pdf> <text>
	with open("12-307_jnt1.txt", "r") as f:
	data = f.read()