Aaron Rank rankam

## foo.R
library(tidyverse)
train_raw <- read_csv("/Users/aaron/Downloads/train 2.csv")

library(lubridate)

train_raw %>%
  mutate(
    age_upon_outcome = as.period(as.Date(datetime) - date_of_birth),
    age_upon_outcome = time_length(age_upon_outcome, unit = "weeks")
  ) %>%

## cohort.py
import pandas as pd
import datetime

def diff_month(d1, d2):
    return (d1.year - d2.year) * 12 + d1.month - d2.month

# add the location to your csv
data = pd.read_csv(csv_file)

# change values in by=[] to the 1) account_id/name, 2) open date 3) close date to whatever you've named the

## brice-sankey.py
# add code

## fasttext.txt
__label__sauce __label__cheese How much does potato starch affect a cheese sauce recipe?
__label__food-safety __label__acidity Dangerous pathogens capable of growing in acidic environments
__label__cast-iron __label__stove How do I cover up the white spots on my cast iron stove?
__label__restaurant Michelin Three Star Restaurant; but if the chef is not there
__label__knife-skills __label__dicing Without knife skills, how can I quickly and accurately dice vegetables?
__label__storage-method __label__equipment __label__bread What's the purpose of a bread box?
__label__baking __label__food-safety __label__substitutions __label__peanuts how to seperate peanut oil from roasted peanuts at home?
__label__chocolate American equivalent for British chocolate terms
__label__baking __label__oven __label__convection Fan bake vs bake
__label__sauce __label__storage-lifetime __label__acidity __label__mayonnaise Regulation and balancing of readymade packed mayonnaise and other sauces

## word_count.py
import pandas as pd

keywords = ['nhs', 'love']
words = ['i love football', 'nhs is awful', 'what is this', 'i love nhs because nhs is love', 'who are you']
df = pd.DataFrame(words, columns=['words'])
all_results = []
for words in df.words:
    tmp_results = []
    for keyword in keywords:
        keyword_count = 0

## helpers.date.js
import Ember from 'ember';

export function date(params) {
  return moment(date[0]).format('MMM Do');;
}

export default Ember.Helper.helper(date);

## sunburst
<?xml version="1.0" encoding="utf-16"?>
<VisualDefinition xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <Html>&lt;link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap.min.css"&gt;
&lt;div&gt;
&lt;a id="left-menu" data-open="true"  class="btn btn-default mobileMenu"&gt;
&lt;i class="glyphicon glyphicon-list"&gt;&lt;/i&gt;&lt;/a&gt;
&lt;/div&gt;

&lt;div id='chart'&gt;

## Lingvist Challenge (Updated)
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {

## Lingvist Challenge
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {

## cities.GeoJson

      
        
          
            
              
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                rankam
                / cities.GeoJson
            
            
              Last active
              August 29, 2015 14:15
            
          
        
      
        
  
    
    

          
    
      
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	library(tidyverse)
	train_raw <- read_csv("/Users/aaron/Downloads/train 2.csv")

	library(lubridate)

	train_raw %>%
	mutate(
	age_upon_outcome = as.period(as.Date(datetime) - date_of_birth),
	age_upon_outcome = time_length(age_upon_outcome, unit = "weeks")
	) %>%
	import pandas as pd
	import datetime

	def diff_month(d1, d2):
	return (d1.year - d2.year) * 12 + d1.month - d2.month

	# add the location to your csv
	data = pd.read_csv(csv_file)

	# change values in by=[] to the 1) account_id/name, 2) open date 3) close date to whatever you've named the
	__label__sauce __label__cheese How much does potato starch affect a cheese sauce recipe?
	__label__food-safety __label__acidity Dangerous pathogens capable of growing in acidic environments
	__label__cast-iron __label__stove How do I cover up the white spots on my cast iron stove?
	__label__restaurant Michelin Three Star Restaurant; but if the chef is not there
	__label__knife-skills __label__dicing Without knife skills, how can I quickly and accurately dice vegetables?
	__label__storage-method __label__equipment __label__bread What's the purpose of a bread box?
	__label__baking __label__food-safety __label__substitutions __label__peanuts how to seperate peanut oil from roasted peanuts at home?
	__label__chocolate American equivalent for British chocolate terms
	__label__baking __label__oven __label__convection Fan bake vs bake
	__label__sauce __label__storage-lifetime __label__acidity __label__mayonnaise Regulation and balancing of readymade packed mayonnaise and other sauces
	import pandas as pd

	keywords = ['nhs', 'love']
	words = ['i love football', 'nhs is awful', 'what is this', 'i love nhs because nhs is love', 'who are you']
	df = pd.DataFrame(words, columns=['words'])
	all_results = []
	for words in df.words:
	tmp_results = []
	for keyword in keywords:
	keyword_count = 0
	import Ember from 'ember';

	export function date(params) {
	return moment(date[0]).format('MMM Do');;
	}

	export default Ember.Helper.helper(date);
	<?xml version="1.0" encoding="utf-16"?>
	<VisualDefinition xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
	<Html><link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap.min.css">
	<div>
	<a id="left-menu" data-open="true" class="btn btn-default mobileMenu">
	<i class="glyphicon glyphicon-list"></i></a>
	</div>

	<div id='chart'>
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 13,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{