visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Analysis of Recent Research Trends\n",
    "\n",
    "DSEP Mapping Research Team, UC Berkeley\n",
    "\n",
    "Berkeley Institute for Data Science\n",
    "\n",
    "Zhongling Jiang, Vinitra Swamy\n",
    "\n",
    "The research_grant_history dataset contains historical grants information of all Berkeley research taken place from 1987 to 2016. The information includes activity type, sponsor class, fund amount, department, project information, PI, etc. Our goal is to visualize the trend of recent ten years' research. The component that we look at includes:\n",
    "\n",
    "- which department gets most funding? what projects do they conduct?\n",
    "- what are the largest funding source of these research? \n",
    "- which type of research gets most funding?\n",
    "- who are supervising funds (popular PIs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "import pandas as pd\n",
    "\n",
    "from datascience import *\n",
    "import numpy as np\n",
    "\n",
    "import locale\n",
    "import re\n",
    "import csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By department data\n",
    "\n",
    "Based on previous work on data cleaning and exploration, we create two datasets: by_dept_funding.csv and by_dept_activity.csv. Both datasets group data by department and respectively show funding info. and reseach type info. for each department. They are sorted by grant amount."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dept/Division</th>\n",
       "      <th>Grant Amount</th>\n",
       "      <th>Federal</th>\n",
       "      <th>State of California</th>\n",
       "      <th>Non Profit</th>\n",
       "      <th>University of California</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ERSO Engineering Research Support Organization</td>\n",
       "      <td>$1,024,039,099</td>\n",
       "      <td>3808</td>\n",
       "      <td>192</td>\n",
       "      <td>614</td>\n",
       "      <td>344</td>\n",
       "      <td>1127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SSL Space Sciences Lab</td>\n",
       "      <td>$551,464,465</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MCB Molecular &amp; Cell Biology</td>\n",
       "      <td>$438,743,186</td>\n",
       "      <td>1404</td>\n",
       "      <td>14</td>\n",
       "      <td>1262</td>\n",
       "      <td>44</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Dept/Division    Grant Amount  Federal  \\\n",
       "0  ERSO Engineering Research Support Organization  $1,024,039,099     3808   \n",
       "1                          SSL Space Sciences Lab    $551,464,465     1969   \n",
       "2                    MCB Molecular & Cell Biology    $438,743,186     1404   \n",
       "\n",
       "   State of California  Non Profit  University of California  Other  \n",
       "0                  192         614                       344   1127  \n",
       "1                    0         105                         2     35  \n",
       "2                   14        1262                        44    155  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_dept_funding = pd.read_csv('by_dept_funding.csv')\n",
    "by_dept_activity = pd.read_csv('by_dept_activity.csv')\n",
    "by_dept_funding.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dept/Division</th>\n",
       "      <th>Grant Amount</th>\n",
       "      <th>Applied Research</th>\n",
       "      <th>Basic Research</th>\n",
       "      <th>Training</th>\n",
       "      <th>Instruction</th>\n",
       "      <th>Services</th>\n",
       "      <th>Other</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ERSO Engineering Research Support Organization</td>\n",
       "      <td>$1,024,039,099</td>\n",
       "      <td>282</td>\n",
       "      <td>5242</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>521</td>\n",
       "      <td>6085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SSL Space Sciences Lab</td>\n",
       "      <td>$551,464,465</td>\n",
       "      <td>108</td>\n",
       "      <td>1769</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>122</td>\n",
       "      <td>2111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MCB Molecular &amp; Cell Biology</td>\n",
       "      <td>$438,743,186</td>\n",
       "      <td>24</td>\n",
       "      <td>2748</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>2879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Dept/Division    Grant Amount  \\\n",
       "0  ERSO Engineering Research Support Organization  $1,024,039,099   \n",
       "1                          SSL Space Sciences Lab    $551,464,465   \n",
       "2                    MCB Molecular & Cell Biology    $438,743,186   \n",
       "\n",
       "   Applied Research  Basic Research  Training  Instruction  Services  Other  \\\n",
       "0               282            5242        26            8         6    521   \n",
       "1               108            1769         2            0       110    122   \n",
       "2                24            2748        62            0         1     44   \n",
       "\n",
       "   Total  \n",
       "0   6085  \n",
       "1   2111  \n",
       "2   2879  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_dept_activity.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dept/Division</th>\n",
       "      <th>Grant Amount</th>\n",
       "      <th>Federal</th>\n",
       "      <th>State of California</th>\n",
       "      <th>Non Profit</th>\n",
       "      <th>University of California</th>\n",
       "      <th>Other</th>\n",
       "      <th>num_of_sponsors</th>\n",
       "      <th>num_of_research</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ERSO Engineering Research Support Organization</td>\n",
       "      <td>$1,024,039,099</td>\n",
       "      <td>3808</td>\n",
       "      <td>192</td>\n",
       "      <td>614</td>\n",
       "      <td>344</td>\n",
       "      <td>1127</td>\n",
       "      <td>6085</td>\n",
       "      <td>6085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SSL Space Sciences Lab</td>\n",
       "      <td>$551,464,465</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>2111</td>\n",
       "      <td>2111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MCB Molecular &amp; Cell Biology</td>\n",
       "      <td>$438,743,186</td>\n",
       "      <td>1404</td>\n",
       "      <td>14</td>\n",
       "      <td>1262</td>\n",
       "      <td>44</td>\n",
       "      <td>155</td>\n",
       "      <td>2879</td>\n",
       "      <td>2879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The California Institute for Quantitative Bios...</td>\n",
       "      <td>$371,026,612</td>\n",
       "      <td>1388</td>\n",
       "      <td>25</td>\n",
       "      <td>476</td>\n",
       "      <td>41</td>\n",
       "      <td>263</td>\n",
       "      <td>2193</td>\n",
       "      <td>2193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Dept/Division    Grant Amount  Federal  \\\n",
       "0     ERSO Engineering Research Support Organization  $1,024,039,099     3808   \n",
       "1                             SSL Space Sciences Lab    $551,464,465     1969   \n",
       "2                       MCB Molecular & Cell Biology    $438,743,186     1404   \n",
       "3  The California Institute for Quantitative Bios...    $371,026,612     1388   \n",
       "\n",
       "   State of California  Non Profit  University of California  Other  \\\n",
       "0                  192         614                       344   1127   \n",
       "1                    0         105                         2     35   \n",
       "2                   14        1262                        44    155   \n",
       "3                   25         476                        41    263   \n",
       "\n",
       "   num_of_sponsors  num_of_research  \n",
       "0             6085             6085  \n",
       "1             2111             2111  \n",
       "2             2879             2879  \n",
       "3             2193             2193  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Add total number of sponsors and research\n",
    "by_dept_funding['num_of_sponsors'] = by_dept_funding['Federal'] + by_dept_funding['State of California'] + by_dept_funding['Non Profit'] + by_dept_funding['University of California'] + by_dept_funding['Other']\n",
    "by_dept_funding['num_of_research'] = by_dept_activity['Total']\n",
    "by_dept_funding.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bubble chart\n",
    "\n",
    "We create the bubble chart using plot.ly. Each bubble represents a department, the size represents the number of research from that department, and the distance from origin represents the amount of fundings.\n",
    "\n",
    "The top four organizations that recieve most fundings: ERSO Engineering, SSL Space Lab, MCB Molecular & Cell, and The California Institute for Qualitative Biosciences.\n",
    "\n",
    "The top four organizations that produces most research: ERSO Engineering, MCB Molecular & Cell, Graduate division Dean, and The California Institute for Qualitative Biosciences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named 'plotly'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m\u001b[0m",
      "\u001b[1;31mImportError\u001b[0mTraceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-b67fae576f64>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplotly\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpy\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcufflinks\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcf\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mcf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_config_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moffline\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mworld_readable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtheme\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'pearl'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: No module named 'plotly'"
     ]
    }
   ],
   "source": [
    "import plotly.plotly as py\n",
    "import cufflinks as cf\n",
    "import pandas as pd\n",
    "\n",
    "cf.set_config_file(offline=False, world_readable=True, theme='pearl')\n",
    "\n",
    "by_dept_funding.iplot(kind='bubble', x='Grant Amount', y='num_of_research', size = 'num_of_sponsors',text='Dept/Division',\n",
    "             xTitle='Funding Recieved', yTitle='Number of Research',\n",
    "             filename='simple-bubble-chart2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recent activity\n",
    "\n",
    "We are curious to know which departments are most active recently (in past 10 years) by producing high quantity of research. We pick top four from above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cleaned_research = pd.read_csv('cleaned_research_spo_data.csv')\n",
    "recent_data = cleaned_research[cleaned_research['Year'] > 2006] \n",
    "# Find number of research\n",
    "grouped = recent_data.groupby([recent_data['Department'], recent_data['Year']]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OEqRrtBxPK0KSL0PcWQ2DyceNESNGEBMTw6FDhww/PXv25IknnuDQoUMEBAS0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\nIgiCICwWClIEQRCExUJBiiAIgrBY/h8b8RzjNO075AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9202655710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(2,2,1)\n",
    "grouped['ERSO Engineering Research Support Organization'].plot.line()\n",
    "plt.subplot(2,2,2)\n",
    "grouped['SSL Space Sciences Lab'].plot.line()\n",
    "plt.subplot(2,2,3)\n",
    "grouped['MCB Molecular & Cell Biology'].plot.line()\n",
    "plt.subplot(2,2,4)\n",
    "grouped['The California Institute for Quantitative Biosciences (QB3)'].plot.line()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Type of research\n",
    "The number of different types of research over time. Basic research are most popular fund recievers in recent 10- 15 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wMJDp06cD5StpvPDCC/Tp0wd/f380TWP79u38+c9/BuDAgQNMnjyZkydPMnPm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\nI/kHH820T5sFungAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9203c0dc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cleaned_research = pd.read_csv('cleaned_research_spo_data.csv')\n",
    "a = cleaned_research['Grant Amount'].groupby([cleaned_research['Activity Type'], cleaned_research['Year']]).sum()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(a['Applied research'], label = 'Applied Research')\n",
    "plt.plot(a['Basic research'], label = 'Basic research')\n",
    "plt.plot(a['Services'], label = 'Services')\n",
    "plt.plot(a['Training'], label = 'Training')\n",
    "plt.plot(a['Other'], label = 'Other')\n",
    "plt.legend(loc=2,prop={'size':10})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization with Ipython widget \n",
    "### Funding source & reserach type analysis \n",
    "With the drop-down list, we can visualize the funding source by department. For example, federal funding accounts for the greatest percentage in funding ERSO Engineering. Meanwhile, onw subplot shows which type of research is more hearvily funded in department.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from ipywidgets import interact, interactive, fixed\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ei4cPH+b999+nS5cuvPPOOzg4OHD16lWNbbLbz8PDg8uXL2NnZ4eDg4PGR3a/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\neARBEARBqPb+P+W1o3Uln/T1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f92000cd160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ERSO Engineering Research Lab\n",
    "# funding source count\n",
    "# apply ipywidget\n",
    "grant_data = pd.read_csv('grant_data.csv')\n",
    "\n",
    "def plot_funding_and_research_type(dept):\n",
    "    x = by_dept_funding.loc[by_dept_funding['Dept/Division'] == dept,['Federal', 'State of California', 'Non Profit', 'University of California', 'Other']].values.flatten().tolist()\n",
    "    y = [1,2,3,4,5] \n",
    "\n",
    "    \n",
    "    z = cleaned_research.loc[cleaned_research['Department'] == dept, ['Activity Type','Grant Amount', 'Department','Year']]\n",
    "    a = z['Grant Amount'].groupby([z['Activity Type'], z['Year']]).sum()\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.subplot(2,1,1)\n",
    "    LABELS = ['Federal', 'State of California', 'Non Profit', 'University of California', 'Other'] \n",
    "    plt.barh(y, x, align = 'center')\n",
    "    plt.yticks(y, LABELS)\n",
    "\n",
    "    plt.subplot(2,1,2)\n",
    "    types_of_research = ['Applied research','Basic research', 'Services', 'Training', 'Other']\n",
    "    \n",
    "    for research_type in types_of_research:\n",
    "        plt.plot(a[research_type], label = research_type)\n",
    "    plt.legend(loc=2,prop={'size':10})\n",
    "    plt.show()\n",
    "\n",
    "plot_funding_and_research_type('ERSO Engineering Research Support Organization')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Onz9PXFwcw4cPf+b9eXJ07doVU1NT+vXrx8mTJ9m5cyfDhg2jU6dOz5R4PomTkxNqtZqI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\ntkYLdGFPoPFrgmJJc9rFGUVGzjiH9ep/sd06jKTzQvIKQ5LceyWyMF/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bt6pjySsamt8m\nOjrapfcsEAgEAoGraCdPnjytsgchuDcpKSmcP3+e06dPs379enr37o3RaMTDwwNPT0+CgoI4cOAA\nK1asoHXr1iQmJjJmzBg6duyoqv1GRESwfv16YmNjadmyJSdPnmTChAkMHjyYAQMGANC4cWOWLFnC\nsWPHaNKkCfHx8bz77ruMHTtWLYhu2LAh8+fP5/r164SHh7NhwwbmzZvHBx98QNOmTQEICQlh5syZ\nGAwGgoKC+Pbbb1m2bBn/7//9P7Vo2s/Pj5kzZ1K3bl28vLyYNWsWe/bs4bPPPsPfXyj+CgQCgaDs\nENvS3YSPPvqIjz/+uEAPns8//5whQ4YAkJaWxsSJE/nll18AGDBgALNmzXIIHhITExk3bhxxcXF4\nenryzDPPMGPGDPR6vWpz8uRJxo8fz8GDBwkICGDYsGFMmDDB4bm7d+/mrbfe4tSpUwQHBzNmzBiG\nDh3qYLNu3To++OADLly4QMOGDXnnnXd49NFHHWy+/vpr5s+fr4qGzpw5U6zwCAQCgaDMEQGPQCAQ\nCASCao+o4RFUGLt372bIkCG0aNGCwMBAli9f7vD69evXeeWVV2jevDn16tXj6aef5vz58w42Fy5c\n4O9//zuRkZGEh4czbNgwrl+/7mDjisSGO1BR/mrVqlWB3YEzZswo9/dXVsydO5c+ffoQHh5OZGQk\nzz77LCdPnixgN3PmTJo3b05ISAiPPfYYp06dcng9OzubCRMm0LhxY0JDQxkyZAhXr151sHH3uVWR\nvhLzyk5ZyQJVdSrSXyWdWyLgEVQYmZmZtGzZko8++ghvb+8Cr//1r3/lwoULLF++nLi4OMLCwnjy\nySfJysoC7PIaMTExAPz8889s2rQJs9lcYKfbvSQ23IWK8pckSUyePJkzZ85w+vRpEhISGD9+fPm/\nwTJi9+7dDB8+nM2bN7N+/Xp0Oh0DBw4kNTVVtfn0009ZuHAhs2fPZuvWrdSuXZuYmBgyMzNVm8mT\nJ7Nhwwa+/vprfvnlF9LT0xk8eDCKkrsI7u5zqyJ9JeaVnbKQBXIHKtJfJZ1bIqUlqBTCwsKYPXu2\nWn907tw5OnbsyK5du2jRogUAiqLQtGlT/vnPf/Lcc88RGxvL008/zR9//KHWJd25c4eIiAhWr15N\nz549SUhIIDo6ukiJDXekvPwF0Lp1a15++WVee+21ynlzZUxmZibh4eEsW7aMhx9+GICoqChGjBjB\nmDFjADCZTDRp0oT333+foUOHcufOHSIjI1m4cCGDBg0C7PVurVq1YuXKlfTu3btazq3y8hWIeZW/\nprE0skDuSHn5C0o+t8QKj6BKYDabkSQJDw8P9VzO8Z49ewD7Mnp+Gw8PDzQajWqzf//+e0psVAfK\nyl85fPbZZzRq1Iju3bvzySefYLFYKuaNlAPp6enIskxAQABgT+slJyc7SJ14enry0EMPqXPi0KFD\nWK1WB5vQ0FCaNWvmIM1S3eZWefkqh/t9XrmCK7JA7kh5+SuHkswtEfAIqgRNmzYlNDSUGTNmkJqa\nSnZ2Np9++imJiYkkJycD0KlTJ3x8fJg6dSpGo5HMzEymTp2KLMuqjSsSG9WBsvIXwMiRI1m0aBE/\n//wzL7/8Ml988YVbpR7yM3nyZNq0aUPnzp0B+5yQJKmArEpeGZPr16+j1WqpWbNmoTbVcW6Vl69A\nzCtXqY7zCsrPX1DyuSU6LQuqBDqdjqVLlzJ69GgaNmyITqejV69e9OvXT60LyOnnM3bsWBYvXoxW\nq2XQoEG0bt0ajeb+it3L0l+jRo1Sf27RogX+/v688MILTJ8+Xf3rzF1466232LdvHxs3biw0/y+w\nU96+EvPq/qWqzq3767eEoErTpk0bduzYwaVLl0hISGDFihXcvHmTBg0aqDa9evXi4MGDnDt3jnPn\nzvGvf/2LpKQk1cYViY3qQln4yxnt27dHUZQCO76qOlOmTGH16tWsX79e7QoO9jmhKEqB3Wl5ZUzq\n1KmDzWbj1q1bRdpUl7lV3r5yxv04r1yhOs0rKH9/OcPVuSUCHkGVw8/Pj5o1a3Lu3DkOHTrEY489\nVsAmMDAQf39/tm/fzo0bN9RO0a5IbFQ3SuMvZxw9ehRJklR9NXdg0qRJ6ods/iLPiIgI6taty9at\nW9VzJpOJ+Ph4tcll27Zt0el0DjaJiYlqoTJUn7lVEb5yxv04r1yhuswrqBh/OcPVuSWkJQQVRmZm\nJgkJCSQnJ/Pdd9/RsmVL/P39sVgs+Pv7s3btWq5fv46iKOzatYvhw4fTq1cv3nzzTfUe//nPfzCb\nzVgsFjZt2sTo0aN56aWX1N0irkhsuAsV4a/9+/ezdu1aPD09MZlMbN26lcmTJ9OjRw+ef/75Snrn\nxWP8+PH88MMPLFmyhNDQUDIzM9VtrgaDAQCbzca8efOIjIzEZrPx9ttvk5KSwrx58zAYDHh4eHDt\n2jUWLVpEy5YtSUtLY+zYsQQEBDBt2jQkSaoWc6uifCXm1TzVpixkgdyBivJXaeaW2JYuqDB27tzJ\n448/XiCnO2TIED7//HO+/PJLFixYwPXr16lbty5DhgxhwoQJ6HS5pWbTp09n2bJlpKamqo30Xnnl\nFYf7uSKx4Q5UhL+OHDnC+PHjOXPmDNnZ2dSvX59Bgwbx+uuvuyxcW9kEBgY6rROYNGkSkyZNUo8/\n/vhjlixZQmpqKh06dGDOnDlERUWpr1ssFqZOncpPP/2EyWSiZ8+ezJkzh3r16qk27j63KspXYl7l\n+qqsZIGqOhXlr9LMLRHwCAQCgUAgqPaIGh6BQCAQCATVHhHwCAQCgUAgqPaIgEcgEAgEAkG1RwQ8\nAoFAIBAIqj0i4BEIBAKBQFDtEQGPQCAQCASCao8IeAQCgUAgEFR7RMAjEAgEAoGg2iMCHoFAIBAI\nBNWe/w/q3/VW7j0TAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f91fb3aaa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_funding_and_research_type>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_divisions = list(set(by_dept_funding['Dept/Division']))[1:]\n",
    "interact(plot_funding_and_research_type, dept=unique_divisions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dept/Division</th>\n",
       "      <th>Grant Amount</th>\n",
       "      <th>Federal</th>\n",
       "      <th>State of California</th>\n",
       "      <th>Non Profit</th>\n",
       "      <th>University of California</th>\n",
       "      <th>Other</th>\n",
       "      <th>num_of_sponsors</th>\n",
       "      <th>num_of_research</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ERSO Engineering Research Support Organization</td>\n",
       "      <td>$1,024,039,099</td>\n",
       "      <td>3808</td>\n",
       "      <td>192</td>\n",
       "      <td>614</td>\n",
       "      <td>344</td>\n",
       "      <td>1127</td>\n",
       "      <td>6085</td>\n",
       "      <td>6085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SSL Space Sciences Lab</td>\n",
       "      <td>$551,464,465</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>2111</td>\n",
       "      <td>2111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MCB Molecular &amp; Cell Biology</td>\n",
       "      <td>$438,743,186</td>\n",
       "      <td>1404</td>\n",
       "      <td>14</td>\n",
       "      <td>1262</td>\n",
       "      <td>44</td>\n",
       "      <td>155</td>\n",
       "      <td>2879</td>\n",
       "      <td>2879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Dept/Division    Grant Amount  Federal  \\\n",
       "0  ERSO Engineering Research Support Organization  $1,024,039,099     3808   \n",
       "1                          SSL Space Sciences Lab    $551,464,465     1969   \n",
       "2                    MCB Molecular & Cell Biology    $438,743,186     1404   \n",
       "\n",
       "   State of California  Non Profit  University of California  Other  \\\n",
       "0                  192         614                       344   1127   \n",
       "1                    0         105                         2     35   \n",
       "2                   14        1262                        44    155   \n",
       "\n",
       "   num_of_sponsors  num_of_research  \n",
       "0             6085             6085  \n",
       "1             2111             2111  \n",
       "2             2879             2879  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_dept_funding.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top funding projects \n",
    "\n",
    "We could also see the most popular projects i.e, the ones that recieve most fundings within each department. Further text analysis could be conducted to investigate the trend in research topics. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Department</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Title</th>\n",
       "      <th>Activity Type</th>\n",
       "      <th>Project Begin Date</th>\n",
       "      <th>Project End Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Center for Educational Partnerships</td>\n",
       "      <td>$3,321,578</td>\n",
       "      <td>TRIO-Talent Search</td>\n",
       "      <td>Services</td>\n",
       "      <td>9/1/2011</td>\n",
       "      <td>8/31/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>Center for Educational Partnerships</td>\n",
       "      <td>$3,254,050</td>\n",
       "      <td>University of California, Berkeley Upward Boun...</td>\n",
       "      <td>Services</td>\n",
       "      <td>6/1/2013</td>\n",
       "      <td>5/31/2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Center for Educational Partnerships</td>\n",
       "      <td>$2,616,100</td>\n",
       "      <td>Upward Bound Program (84.047A) Regular Upward ...</td>\n",
       "      <td>Services</td>\n",
       "      <td>6/1/2009</td>\n",
       "      <td>5/31/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>Center for Educational Partnerships</td>\n",
       "      <td>$1,770,070</td>\n",
       "      <td>CEP - Upward Bound Math and Science 2012-17</td>\n",
       "      <td>Services</td>\n",
       "      <td>11/1/2012</td>\n",
       "      <td>10/31/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>Center for Educational Partnerships</td>\n",
       "      <td>$1,665,000</td>\n",
       "      <td>Fisher Counseling Initiative</td>\n",
       "      <td>Services</td>\n",
       "      <td>2/1/2009</td>\n",
       "      <td>7/31/2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>Center for Educational Partnerships</td>\n",
       "      <td>$1,416,164</td>\n",
       "      <td>Upward Bound Math and Science (UBMS) Program (...</td>\n",
       "      <td>Instruction</td>\n",
       "      <td>11/1/2007</td>\n",
       "      <td>10/31/2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>Center for Educational Partnerships</td>\n",
       "      <td>$1,259,564</td>\n",
       "      <td>Berkeley Bridges to the Baccalaureate</td>\n",
       "      <td>Services</td>\n",
       "      <td>4/1/2011</td>\n",
       "      <td>3/31/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1332</th>\n",
       "      <td>Center for Educational Partnerships</td>\n",
       "      <td>$1,043,316</td>\n",
       "      <td>National College Access Initative; Destination...</td>\n",
       "      <td>Services</td>\n",
       "      <td>5/1/2007</td>\n",
       "      <td>5/31/2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1417</th>\n",
       "      <td>Center for Educational Partnerships</td>\n",
       "      <td>$1,000,000</td>\n",
       "      <td>Community College Transfer Initiative</td>\n",
       "      <td>Services</td>\n",
       "      <td>5/1/2006</td>\n",
       "      <td>6/30/2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1604</th>\n",
       "      <td>Center for Educational Partnerships</td>\n",
       "      <td>$858,435</td>\n",
       "      <td>The Puente Project CA Community College Progra...</td>\n",
       "      <td>Services</td>\n",
       "      <td>7/1/2014</td>\n",
       "      <td>6/30/2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Department       Amount  \\\n",
       "245   Center for Educational Partnerships  $3,321,578    \n",
       "257   Center for Educational Partnerships  $3,254,050    \n",
       "340   Center for Educational Partnerships  $2,616,100    \n",
       "646   Center for Educational Partnerships  $1,770,070    \n",
       "705   Center for Educational Partnerships  $1,665,000    \n",
       "940   Center for Educational Partnerships  $1,416,164    \n",
       "1077  Center for Educational Partnerships  $1,259,564    \n",
       "1332  Center for Educational Partnerships  $1,043,316    \n",
       "1417  Center for Educational Partnerships  $1,000,000    \n",
       "1604  Center for Educational Partnerships    $858,435    \n",
       "\n",
       "                                                  Title Activity Type  \\\n",
       "245                                  TRIO-Talent Search      Services   \n",
       "257   University of California, Berkeley Upward Boun...      Services   \n",
       "340   Upward Bound Program (84.047A) Regular Upward ...      Services   \n",
       "646         CEP - Upward Bound Math and Science 2012-17      Services   \n",
       "705                        Fisher Counseling Initiative      Services   \n",
       "940   Upward Bound Math and Science (UBMS) Program (...   Instruction   \n",
       "1077              Berkeley Bridges to the Baccalaureate      Services   \n",
       "1332  National College Access Initative; Destination...      Services   \n",
       "1417              Community College Transfer Initiative      Services   \n",
       "1604  The Puente Project CA Community College Progra...      Services   \n",
       "\n",
       "     Project Begin Date Project End Date  \n",
       "245            9/1/2011        8/31/2016  \n",
       "257            6/1/2013        5/31/2018  \n",
       "340            6/1/2009        5/31/2013  \n",
       "646           11/1/2012       10/31/2017  \n",
       "705            2/1/2009        7/31/2011  \n",
       "940           11/1/2007       10/31/2011  \n",
       "1077           4/1/2011        3/31/2016  \n",
       "1332           5/1/2007        5/31/2011  \n",
       "1417           5/1/2006        6/30/2010  \n",
       "1604           7/1/2014        6/30/2015  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.top_project>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what ERSO project recieves top funding (or in each department)\n",
    "\n",
    "clean_grant = pd.read_csv('cleaned_research_spo_data.csv') # the dataset has been sorted by grant amount\n",
    "def top_project(data, dept, n):\n",
    "    by_dept = data.loc[data['Dept/Division'] == dept, ] \n",
    "    return by_dept[['Department', 'Amount', 'Title', 'Activity Type','Project Begin Date', 'Project End Date']].head(n)\n",
    "\n",
    "# top_project(clean_grant, 'ERSO Engineering Research Support Organization', 10)\n",
    "\n",
    "interact(top_project, data=fixed(clean_grant), dept=unique_divisions, n=widgets.IntSlider(min=0, max=50, step=5, value=10))"
   ]
  }
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