FrontEndVisualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import plotly\n",
    "plotly.tools.set_credentials_file(username = 'jjuncho', api_key = 'bfdyUJIiTO2T4BMakGCz')\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from pymongo import MongoClient\n",
    "client = MongoClient()\n",
    "repo = client.repo\n",
    "final_data  = repo[\"delphi.finalData\"]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "act_type = ['Email', 'Call', 'Letter']\n",
    "male_data = final_data.find({'sex':1})\n",
    "female_data = final_data.find({'sex':0})\n",
    "male_values = {\n",
    "    \"Email\": 0,\n",
    "    \"Call\": 0,\n",
    "    \"Letter\": 0\n",
    "}\n",
    "female_values = {\n",
    "    \"Email\": 0,\n",
    "    \"Call\": 0,\n",
    "    \"Letter\": 0\n",
    "}\n",
    "        \n",
    "for person in male_data:\n",
    "    male_values[person['activity_type']] += 1\n",
    "    \n",
    "for person in female_data:\n",
    "    female_values[person['activity_type']] += 1\n",
    "\n",
    "for key in male_values:\n",
    "    male_values[key] = male_values[key]/male_data.count()\n",
    "    \n",
    "for key in female_values:\n",
    "    female_values[key] = female_values[key]/female_data.count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~jjuncho/2.embed\" height=\"525px\" width=\"100%\"></iframe>"
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      "text/plain": [
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace1 = go.Bar(\n",
    "    x=\"Email\",\n",
    "    y=male_values['Email'],\n",
    "    name='Male',\n",
    "    marker=dict(color=['rgb(0,0,200)'])\n",
    ")\n",
    "trace2 = go.Bar(\n",
    "    x=\"Email\",\n",
    "    y=female_values['Email'],\n",
    "    name='Female',\n",
    "    marker=dict(color=['rgb(200,0,0)'])\n",
    ")\n",
    "\n",
    "trace3 = go.Bar(\n",
    "    x=\"Call\",\n",
    "    y=male_values['Call'],\n",
    "    name='Male',\n",
    "    marker=dict(color=['rgb(0,0,200)']),\n",
    "    showlegend=False\n",
    ")\n",
    "trace4 = go.Bar(\n",
    "    x=\"Call\",\n",
    "    y=female_values['Call'],\n",
    "    name='Female',\n",
    "    marker=dict(color=['rgb(200,0,0)']),\n",
    "    showlegend=False\n",
    ")\n",
    "\n",
    "trace5 = go.Bar(\n",
    "    x=\"Letter\",\n",
    "    y=male_values['Letter'],\n",
    "    name='Male',\n",
    "    marker=dict(color=['rgb(0,0,200)']),\n",
    "    showlegend=False\n",
    ")\n",
    "trace6 = go.Bar(\n",
    "    x=\"Letter\",\n",
    "    y=female_values['Letter'],\n",
    "    name='Female',\n",
    "    marker=dict(color=['rgb(200,0,0)']),\n",
    "    showlegend=False\n",
    ")\n",
    "\n",
    "data = [trace1, trace2, trace3, trace4, trace5, trace6]\n",
    "layout = go.Layout(\n",
    "    barmode='group',\n",
    "    title = \"% of Male & Females vs. Effective Means of Advertising\",\n",
    "    xaxis=dict(\n",
    "        title='Types of Advertising' \n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title='% of Males & Females'\n",
    "    )\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='grouped-bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "act_type = ['Email', 'Call', 'Letter']\n",
    "single_data = final_data.find({'married':0})\n",
    "married_data = final_data.find({'married':1})\n",
    "single_values = {\n",
    "    \"Email\": 0,\n",
    "    \"Call\": 0,\n",
    "    \"Letter\": 0\n",
    "}\n",
    "married_values = {\n",
    "    \"Email\": 0,\n",
    "    \"Call\": 0,\n",
    "    \"Letter\": 0\n",
    "}\n",
    "        \n",
    "for person in married_data:\n",
    "    married_values[person['activity_type']] += 1\n",
    "    \n",
    "for person in single_data:\n",
    "    single_values[person['activity_type']] += 1\n",
    "\n",
    "for key in married_values:\n",
    "    married_values[key] = married_values[key]/married_data.count()\n",
    "    \n",
    "for key in single_values:\n",
    "    single_values[key] = single_values[key]/single_data.count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~jjuncho/2.embed\" height=\"525px\" width=\"100%\"></iframe>"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "trace1 = go.Bar(\n",
    "    x=\"Email\",\n",
    "    y=married_values['Email'],\n",
    "    name='Married',\n",
    "    marker=dict(color=['rgb(0,0,200)'])\n",
    ")\n",
    "trace2 = go.Bar(\n",
    "    x=\"Email\",\n",
    "    y=single_values['Email'],\n",
    "    name='Single',\n",
    "    marker=dict(color=['rgb(200,0,0)'])\n",
    ")\n",
    "\n",
    "trace3 = go.Bar(\n",
    "    x=\"Call\",\n",
    "    y=married_values['Call'],\n",
    "    name='Married',\n",
    "    marker=dict(color=['rgb(0,0,200)']),\n",
    "    showlegend=False\n",
    ")\n",
    "trace4 = go.Bar(\n",
    "    x=\"Call\",\n",
    "    y=single_values['Call'],\n",
    "    name='Single',\n",
    "    marker=dict(color=['rgb(200,0,0)']),\n",
    "    showlegend=False\n",
    ")\n",
    "\n",
    "trace5 = go.Bar(\n",
    "    x=\"Letter\",\n",
    "    y=married_values['Letter'],\n",
    "    name='Married',\n",
    "    marker=dict(color=['rgb(0,0,200)']),\n",
    "    showlegend=False\n",
    ")\n",
    "trace6 = go.Bar(\n",
    "    x=\"Letter\",\n",
    "    y=single_values['Letter'],\n",
    "    name='Single',\n",
    "    marker=dict(color=['rgb(200,0,0)']),\n",
    "    showlegend=False\n",
    ")\n",
    "\n",
    "data = [trace1, trace2,trace3, trace4, trace5, trace6]\n",
    "layout = go.Layout(\n",
    "    barmode='group',\n",
    "    title = \"% of Married & Single Individuals vs. Effective Means of Advertising\",\n",
    "    xaxis=dict(\n",
    "        title='Types of Advertising' \n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title='% of Married & Single Individuals'\n",
    "    )\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='grouped-bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "act_type = ['Email', 'Call', 'Letter']\n",
    "et_data = final_data.find({'age': {\"$lt\": 20}, 'age': {\"$gt\": 0}})\n",
    "tf_data = final_data.find({'age': {\"$lt\": 40}, 'age': {\"$gt\": 20}})\n",
    "fs_data = final_data.find({'age': {\"$lt\": 60}, 'age': {\"$gt\": 40}})\n",
    "sp_data = final_data.find({'age': {\"$lt\": 200}, 'age': {\"$gt\": 60}})\n",
    "et_values = {\n",
    "    \"Email\": 0,\n",
    "    \"Call\": 0,\n",
    "    \"Letter\": 0\n",
    "}\n",
    "tf_values = {\n",
    "    \"Email\": 0,\n",
    "    \"Call\": 0,\n",
    "    \"Letter\": 0\n",
    "}\n",
    "fs_values = {\n",
    "    \"Email\": 0,\n",
    "    \"Call\": 0,\n",
    "    \"Letter\": 0\n",
    "}\n",
    "sp_values = {\n",
    "    \"Email\": 0,\n",
    "    \"Call\": 0,\n",
    "    \"Letter\": 0\n",
    "}\n",
    "        \n",
    "for person in et_data:\n",
    "    et_values[person['activity_type']] += 1\n",
    "    \n",
    "for person in tf_data:\n",
    "    tf_values[person['activity_type']] += 1\n",
    "    \n",
    "for person in fs_data:\n",
    "    fs_values[person['activity_type']] += 1\n",
    "    \n",
    "for person in sp_data:\n",
    "    sp_values[person['activity_type']] += 1\n",
    "\n",
    "for key in et_values:\n",
    "    et_values[key] = et_values[key]/et_data.count()\n",
    "    \n",
    "for key in tf_values:\n",
    "    tf_values[key] = tf_values[key]/tf_data.count()\n",
    "\n",
    "for key in fs_values:\n",
    "    fs_values[key] = fs_values[key]/fs_data.count()\n",
    "    \n",
    "for key in sp_values:\n",
    "    sp_values[key] = sp_values[key]/sp_data.count()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 8,
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   ],
   "source": [
    "trace1 = go.Bar(\n",
    "    x=\"Email\",\n",
    "    y=et_values['Email'],\n",
    "    name='0-20',\n",
    "    marker=dict(color=['rgb(0,0,200)'])\n",
    ")\n",
    "trace2 = go.Bar(\n",
    "    x=\"Email\",\n",
    "    y=tf_values['Email'],\n",
    "    name='20-40',\n",
    "    marker=dict(color=['rgb(0,200,0)'])\n",
    ")\n",
    "trace3 = go.Bar(\n",
    "    x=\"Email\",\n",
    "    y=fs_values['Email'],\n",
    "    name='40-60',\n",
    "    marker=dict(color=['rgb(150,50,200)'])\n",
    ")\n",
    "trace4 = go.Bar(\n",
    "    x=\"Email\",\n",
    "    y=sp_values['Email'],\n",
    "    name='60+',\n",
    "    marker=dict(color=['rgb(200,0,0)'])\n",
    ")\n",
    "\n",
    "trace5 = go.Bar(\n",
    "    x=\"Call\",\n",
    "    y=et_values['Call'],\n",
    "    name='0-20',\n",
    "    showlegend=False,\n",
    "    marker=dict(color=['rgb(0,0,200)'])\n",
    ")\n",
    "trace6 = go.Bar(\n",
    "    x=\"Call\",\n",
    "    y=tf_values['Call'],\n",
    "    name='20-40',\n",
    "    showlegend=False,\n",
    "    marker=dict(color=['rgb(0,200,0)'])\n",
    ")\n",
    "trace7 = go.Bar(\n",
    "    x=\"Call\",\n",
    "    y=fs_values['Call'],\n",
    "    name='40-60',\n",
    "    showlegend=False,\n",
    "    marker=dict(color=['rgb(150,50,200)'])\n",
    ")\n",
    "trace8 = go.Bar(\n",
    "    x=\"Call\",\n",
    "    y=sp_values['Call'],\n",
    "    name='60+',\n",
    "    showlegend=False,\n",
    "    marker=dict(color=['rgb(200,0,0)'])\n",
    ")\n",
    "\n",
    "trace9 = go.Bar(\n",
    "    x=\"Letter\",\n",
    "    y=et_values['Letter'],\n",
    "    name='0-20',\n",
    "    showlegend=False,\n",
    "    marker=dict(color=['rgb(0,0,200)'])\n",
    ")\n",
    "trace10 = go.Bar(\n",
    "    x=\"Letter\",\n",
    "    y=tf_values['Letter'],\n",
    "    name='20-40',\n",
    "    showlegend=False,\n",
    "    marker=dict(color=['rgb(0,200,0)'])\n",
    ")\n",
    "trace11 = go.Bar(\n",
    "    x=\"Letter\",\n",
    "    y=fs_values['Letter'],\n",
    "    name='40-60',\n",
    "    showlegend=False,\n",
    "    marker=dict(color=['rgb(150,50,200)'])\n",
    ")\n",
    "trace12 = go.Bar(\n",
    "    x=\"Letter\",\n",
    "    y=sp_values['Letter'],\n",
    "    name='60+',\n",
    "    showlegend=False,\n",
    "    marker=dict(color=['rgb(200,0,0)'])\n",
    ")\n",
    "\n",
    "\n",
    "data = [trace1, trace2, trace3, trace4, trace5, trace6, trace7, trace8, trace9, trace10, trace11, trace12]\n",
    "layout = go.Layout(\n",
    "    barmode='group',\n",
    "    title = '% of Age Groups vs. Effective Means of Advertising',\n",
    "    xaxis=dict(\n",
    "        title='Types of Advertising' \n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title='% of Age Groups'\n",
    "    )\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='grouped-bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~jjuncho/11.embed\" height=\"525px\" width=\"100%\"></iframe>"
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      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e_data_lat = [t[\"lat\"] for t in final_data.find({'activity_type':'Email'}).limit(1000)]\n",
    "c_data_lat = [t[\"lat\"] for t in final_data.find({'activity_type': 'Call'}).limit(1000)]\n",
    "l_data_lat = [t[\"lat\"] for t in final_data.find({'activity_type': 'Letter'}).limit(1000)]\n",
    "\n",
    "e_data_lng = [t[\"lng\"] for t in final_data.find({'activity_type':'Email'}).limit(1000)]\n",
    "c_data_lng = [t[\"lng\"] for t in final_data.find({'activity_type': 'Call'}).limit(1000)]\n",
    "l_data_lng = [t[\"lng\"] for t in final_data.find({'activity_type': 'Letter'}).limit(1000)]\n",
    "\n",
    "data = [ dict(\n",
    "        type = 'scattergeo',\n",
    "        lon = e_data_lng,\n",
    "        lat = e_data_lat,\n",
    "        name = 'Emails',\n",
    "        mode = 'markers',\n",
    "        marker = dict(\n",
    "            size = 8,\n",
    "            opacity = 0.8,\n",
    "            autocolorscale = False,\n",
    "            symbol = 'circle',\n",
    "            line = dict(\n",
    "                width=1,\n",
    "                color='rgba(0,0,200)'\n",
    "            ),\n",
    "            color = 'rgb[0,0,200]',\n",
    "            \n",
    "            )),\n",
    "        dict(\n",
    "        type = 'scattergeo',\n",
    "        lon = c_data_lng,\n",
    "        lat = c_data_lat,\n",
    "        name = 'Calls',\n",
    "        mode = 'markers',\n",
    "        marker = dict(\n",
    "            size = 8,\n",
    "            opacity = 0.8,\n",
    "            autocolorscale = False,\n",
    "            symbol = 'square',\n",
    "            line = dict(\n",
    "                width=1,\n",
    "                color='rgba(0,200,0)'\n",
    "            ),\n",
    "            color = 'rgba(0,200,0)',\n",
    "            \n",
    "            )),\n",
    "        dict(\n",
    "        type = 'scattergeo',\n",
    "        lon = l_data_lng,\n",
    "        lat = l_data_lat,\n",
    "        name = 'Letters',\n",
    "        mode = 'markers',\n",
    "        marker = dict(\n",
    "            size = 8,\n",
    "            opacity = 0.8,\n",
    "            autocolorscale = False,\n",
    "            symbol = 'square',\n",
    "            line = dict(\n",
    "                width=1,\n",
    "                color='rgba(200,0,0)'\n",
    "            ),\n",
    "            color = 'rgba(200,0,0)',\n",
    "            \n",
    "            \n",
    "        ))]\n",
    "\n",
    "layout = dict(\n",
    "        title = 'Effectiveness of Campaigns by Location',\n",
    "        colorbar = True,\n",
    "        geo = dict(\n",
    "            scope='north america',\n",
    "            resolution=50,\n",
    "            lonaxis=dict(\n",
    "                range=[-130, -55]\n",
    "            ),\n",
    "            lataxis=dict(\n",
    "                range=[40, 70]\n",
    "            ),\n",
    "            showrivers=True,\n",
    "            rivercolor='#fff',\n",
    "            showlakes=True,\n",
    "            lakecolor='#fff',\n",
    "            showland=True,\n",
    "            landcolor='#EAEAAE',\n",
    "            countrycolor='#d3d3d3',\n",
    "            countrywidth=1.5,\n",
    "            subunitcolor='#d3d3d3'\n",
    "        ),\n",
    "    )\n",
    "\n",
    "fig = dict( data=data, layout=layout )\n",
    "py.iplot( fig, validate=False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~jjuncho/31.embed\" height=\"525px\" width=\"100%\"></iframe>"
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       "<plotly.tools.PlotlyDisplay object>"
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     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalCapitals  = repo[\"delphi.finalCapitals\"]\n",
    "lats = [t[\"lat\"] for t in finalCapitals.find()]\n",
    "lngs = [t[\"lng\"] for t in finalCapitals.find()]\n",
    "texts = [t[\"city\"] + \"<br>\" + repr(t[\"activity_type\"]) for t in finalCapitals.find()]\n",
    "\n",
    "data = [ dict(\n",
    "        type = 'scattergeo',\n",
    "        lon = lngs,\n",
    "        lat = lats,\n",
    "        name=\"Cities\",\n",
    "        text = texts,\n",
    "        mode = 'markers',\n",
    "        marker = dict(\n",
    "            size = 8,\n",
    "            opacity = 0.8,\n",
    "            autocolorscale = False,\n",
    "            symbol = 'circle',\n",
    "            line = dict(\n",
    "                width=1,\n",
    "                color='rgba(0,0,200)'\n",
    "            ),\n",
    "            color = 'rgb[0,0,200]',\n",
    "            \n",
    "            ))]\n",
    "\n",
    "layout = dict(\n",
    "        title = 'Effectiveness of Campaigns by City',\n",
    "        colorbar = True,\n",
    "        geo = dict(\n",
    "            scope='north america',\n",
    "            resolution=50,\n",
    "            lonaxis=dict(\n",
    "                range=[-130, -55]\n",
    "            ),\n",
    "            lataxis=dict(\n",
    "                range=[40, 70]\n",
    "            ),\n",
    "            showrivers=True,\n",
    "            rivercolor='#fff',\n",
    "            showlakes=True,\n",
    "            lakecolor='#fff',\n",
    "            showland=True,\n",
    "            landcolor='#EAEAAE',\n",
    "            countrycolor='#d3d3d3',\n",
    "            countrywidth=1.5,\n",
    "            subunitcolor='#d3d3d3'\n",
    "        ),\n",
    "    )\n",
    "\n",
    "fig = dict( data=data, layout=layout )\n",
    "py.iplot( fig, validate=False )"
   ]
  }
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