Google Developers Live BigQuery IRT Demo

gistfile1.txt

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Item Analysis With Google BigQuery and pandas"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### What is our goal?\n",
      "  * Perform a simple confirmatory analysis (using a specific method to test a specific question)\n",
      "  * Show how BigQuery fits into our workflow\n",
      "  * Demonstrate using pandas to filter and cleanup data from result sets\n",
      "  * Demonstrate using R through `rpy2` package\n",
      "\n",
      "#### What is the question we are asking?\n",
      "  * How difficult are the assessments in one of Pearson's online learning platforms?\n",
      "  * We will look specifically at a an assessment from a junior high reading course\n",
      "  * It is important to bear in mind these \"assessments\" are for student practice, not grading\n",
      "  * We have access to a fairly large dataset of anonymous student response date in BigQuery\n",
      "  * We know which questions belong to each particular assessment\n",
      "\n",
      "#### What method will we use to answer this?\n",
      "  * Rasch Item Response Model\n",
      "  * Establishes question difficulty within an assessment\n",
      "  * Takes into account students' varying abilities\n",
      "  * Implemented in `ltm` package for GNU R\n",
      "\n",
      "#### What is pandas?\n",
      "  * Data analytics library for Python\n",
      "  * Key datastructure is DataFrame, analogous to that in R\n",
      "    * Slicing, sorting, indexing, etc.\n",
      "  * Lots of IO modules - CSV, STATA, HDF5, MySQL, Google Analytics\n",
      "    * We helped add a BigQuery connector as well\n",
      "  * See http://pandas.pydata.org , or the book *Python for Data Analysis* by Wes McKinney"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Let's start with some imports, including the new experimental pandas submodule to connect to Google BigQuery"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For more details on the *EXPERIMENTAL* BigQuery submodule, see (http://pandas.pydata.org/pandas-docs/stable/io.html#google-bigquery-experimental)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as PD\n",
      "from pandas.io import gbq"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Prepare our Query."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### What are data requirements?\n",
      "\n",
      "* Must use data about *first* time a student has seen the data\n",
      "    * Students may have seen data in a different assessment\n",
      "    * Students may have reattempted the entire assessment\n",
      "    * Students may have reattemtped the question\n",
      "    * Automated test suites may have submissions that occur within same second\n",
      "* Use `order_answered` field to filter for reattempts within a quiz\n",
      "* Make sure elapsed time and question times are valid (to eliminate automated submissions)\n",
      "* Order the results chronologically for further filtering in pandas\n",
      "* Use conditional to get boolean out of strings"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "query = \"\"\"SELECT id,\n",
      "                  item_title as item_id,\n",
      "                  if(status == \"CORRECT\", 1,0) as correct,\n",
      "                  date_created as created,\n",
      "                  last_updated as updated\n",
      "           FROM [analysis.table]\n",
      "           WHERE item_title in (\"001303_MC10\",\n",
      "                                \"001321_MC09\",\n",
      "                                \"001303_MC09\",\n",
      "                                \"001321_MC07\",\n",
      "                                \"001322_MC06\",\n",
      "                                \"001304_MC10\",\n",
      "                                \"001322_MC09\",\n",
      "                                \"001321_MC08\",\n",
      "                                \"001303_MC07\",\n",
      "                                \"001322_MC07\",\n",
      "                                \"001304_MC09\",\n",
      "                                \"001304_MC06\",\n",
      "                                \"001322_MC10\",\n",
      "                                \"001304_MC08\")\n",
      "           AND data_order_answered = 1\n",
      "           AND data_time_in_seconds is not NULL\n",
      "           AND status in (\"CORRECT\", \"INCORRECT\")\n",
      "           AND elapsed_time_in_millis > 0\n",
      "           ORDER by id, item_id, created, updated\"\"\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Now, let's ask Google BigQuery for the data using the new pandas \"read_gbq\" method"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### GBQ Tricks\n",
      "\n",
      "* There are a handful of additional parameters the method can take\n",
      "  * Column reindexing if desired\n",
      "  * Some of `bq` flags (not thoroughly tested)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%time df = gbq.read_gbq(query, project_id)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Waiting on bqjob_r3090d7319be9d738_00000143da0b7483_1 ... (0s) Current status: DONE   \n",
        "CPU times: user 1.12 s, sys: 193 ms, total: 1.31 s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Wall time: 32.1 s\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Now we filter any subsequent attempts, and lose the unecessary columns\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* At this point data still contains repeat attempts across assessments\n",
      "  * Since the rows are already ordered chronologically keeping only the first occurence of each will work\n",
      "  * Pandas provides a particularly useful `drop_duplicates()` method that works well for this."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2 = df[['stud_id','item_id','correct']].drop_duplicates(['stud_id', 'item_id'])\n",
      "df2[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>stud_id</th>\n",
        "      <th>item_id</th>\n",
        "      <th>correct</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 2600</td>\n",
        "      <td> SMRE_ITR_001303_MC07</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 2600</td>\n",
        "      <td> SMRE_ITR_001303_MC09</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 2600</td>\n",
        "      <td> SMRE_ITR_001304_MC06</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 2600</td>\n",
        "      <td> SMRE_ITR_001304_MC08</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 2600</td>\n",
        "      <td> SMRE_ITR_001304_MC09</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 2600</td>\n",
        "      <td> SMRE_ITR_001321_MC07</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 2600</td>\n",
        "      <td> SMRE_ITR_001321_MC08</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 2600</td>\n",
        "      <td> SMRE_ITR_001322_MC06</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 2600</td>\n",
        "      <td> SMRE_ITR_001322_MC09</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 2600</td>\n",
        "      <td> SMRE_ITR_001322_MC10</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "    stud_id               item_id  correct\n",
        "0      2600  SMRE_ITR_001303_MC07        1\n",
        "1      2600  SMRE_ITR_001303_MC09        1\n",
        "2      2600  SMRE_ITR_001304_MC06        1\n",
        "4      2600  SMRE_ITR_001304_MC08        1\n",
        "5      2600  SMRE_ITR_001304_MC09        1\n",
        "7      2600  SMRE_ITR_001321_MC07        1\n",
        "9      2600  SMRE_ITR_001321_MC08        1\n",
        "10     2600  SMRE_ITR_001322_MC06        1\n",
        "11     2600  SMRE_ITR_001322_MC09        1\n",
        "13     2600  SMRE_ITR_001322_MC10        1"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Now to pivot the data."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Data should be in a matrix for analysis\n",
      "  * Each student is a row\n",
      "  * Each item_id is a column\n",
      "  * Cells are 1 or 0 if student got the question correct."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2_pivot = df2.pivot(index='stud_id', columns='item_id', values='correct')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Let's take a look at what we have so far."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* \"randomly\" audit rows 20 through 30.\n",
      "* In reality should be more thorough"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2_pivot[20:30]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>item_id</th>\n",
        "      <th>SMRE_ITR_001303_MC07</th>\n",
        "      <th>SMRE_ITR_001303_MC09</th>\n",
        "      <th>SMRE_ITR_001303_MC10</th>\n",
        "      <th>SMRE_ITR_001304_MC06</th>\n",
        "      <th>SMRE_ITR_001304_MC08</th>\n",
        "      <th>SMRE_ITR_001304_MC09</th>\n",
        "      <th>SMRE_ITR_001304_MC10</th>\n",
        "      <th>SMRE_ITR_001321_MC07</th>\n",
        "      <th>SMRE_ITR_001321_MC08</th>\n",
        "      <th>SMRE_ITR_001321_MC09</th>\n",
        "      <th>SMRE_ITR_001322_MC06</th>\n",
        "      <th>SMRE_ITR_001322_MC07</th>\n",
        "      <th>SMRE_ITR_001322_MC09</th>\n",
        "      <th>SMRE_ITR_001322_MC10</th>\n",
        "    </tr>\n",
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       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "item_id  SMRE_ITR_001303_MC07  SMRE_ITR_001303_MC09  SMRE_ITR_001303_MC10  \\\n",
        "stud_id                                                                     \n",
        "2692                        1                     1                     1   \n",
        "2699                        1                     1                   NaN   \n",
        "2718                        1                     1                     1   \n",
        "2720                      NaN                   NaN                     0   \n",
        "2725                        1                     1                     1   \n",
        "2732                        1                   NaN                   NaN   \n",
        "2737                      NaN                     1                     1   \n",
        "2747                      NaN                   NaN                     1   \n",
        "2751                      NaN                   NaN                     1   \n",
        "2759                      NaN                   NaN                   NaN   \n",
        "\n",
        "item_id  SMRE_ITR_001304_MC06  SMRE_ITR_001304_MC08  SMRE_ITR_001304_MC09  \\\n",
        "stud_id                                                                     \n",
        "2692                        1                     1                     1   \n",
        "2699                        1                     1                     1   \n",
        "2718                        1                     1                     0   \n",
        "2720                      NaN                     1                     1   \n",
        "2725                        1                     1                     1   \n",
        "2732                        1                   NaN                   NaN   \n",
        "2737                      NaN                   NaN                   NaN   \n",
        "2747                        1                     1                     1   \n",
        "2751                      NaN                   NaN                   NaN   \n",
        "2759                        1                     1                     1   \n",
        "\n",
        "item_id  SMRE_ITR_001304_MC10  SMRE_ITR_001321_MC07  SMRE_ITR_001321_MC08  \\\n",
        "stud_id                                                                     \n",
        "2692                        1                     1                     1   \n",
        "2699                      NaN                   NaN                     1   \n",
        "2718                        1                     1                     1   \n",
        "2720                      NaN                   NaN                     1   \n",
        "2725                        0                     1                     0   \n",
        "2732                      NaN                     1                   NaN   \n",
        "2737                      NaN                   NaN                     1   \n",
        "2747                        1                   NaN                     0   \n",
        "2751                      NaN                     1                     1   \n",
        "2759                        1                     1                     1   \n",
        "\n",
        "item_id  SMRE_ITR_001321_MC09  SMRE_ITR_001322_MC06  SMRE_ITR_001322_MC07  \\\n",
        "stud_id                                                                     \n",
        "2692                        1                     1                     1   \n",
        "2699                        0                     1                     0   \n",
        "2718                        1                     1                     1   \n",
        "2720                        1                     1                   NaN   \n",
        "2725                        1                     1                     1   \n",
        "2732                      NaN                   NaN                   NaN   \n",
        "2737                      NaN                     1                     1   \n",
        "2747                        1                     1                     1   \n",
        "2751                      NaN                     1                   NaN   \n",
        "2759                        1                   NaN                   NaN   \n",
        "\n",
        "item_id  SMRE_ITR_001322_MC09  SMRE_ITR_001322_MC10  \n",
        "stud_id                                              \n",
        "2692                        1                     1  \n",
        "2699                        1                     1  \n",
        "2718                        1                     1  \n",
        "2720                      NaN                     1  \n",
        "2725                        1                     1  \n",
        "2732                      NaN                   NaN  \n",
        "2737                      NaN                     1  \n",
        "2747                      NaN                     1  \n",
        "2751                      NaN                     1  \n",
        "2759                      NaN                   NaN  "
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* We have some empty data that will complicate analysis especially for a demo\n",
      "  * No gaurantee the students will finish an assessment (voluntary participation)\n",
      "* Use pandas to drop rows containing NaN values"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Let's drop these pesky duplicates"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2_pivot = df2_pivot.dropna(how=\"any\")\n",
      "df2_pivot[20:30]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>item_id</th>\n",
        "      <th>SMRE_ITR_001303_MC07</th>\n",
        "      <th>SMRE_ITR_001303_MC09</th>\n",
        "      <th>SMRE_ITR_001303_MC10</th>\n",
        "      <th>SMRE_ITR_001304_MC06</th>\n",
        "      <th>SMRE_ITR_001304_MC08</th>\n",
        "      <th>SMRE_ITR_001304_MC09</th>\n",
        "      <th>SMRE_ITR_001304_MC10</th>\n",
        "      <th>SMRE_ITR_001321_MC07</th>\n",
        "      <th>SMRE_ITR_001321_MC08</th>\n",
        "      <th>SMRE_ITR_001321_MC09</th>\n",
        "      <th>SMRE_ITR_001322_MC06</th>\n",
        "      <th>SMRE_ITR_001322_MC07</th>\n",
        "      <th>SMRE_ITR_001322_MC09</th>\n",
        "      <th>SMRE_ITR_001322_MC10</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>stud_id</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>3061</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3067</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3135</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3138</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3158</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3175</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6287</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6376</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6410</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6413</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "item_id  SMRE_ITR_001303_MC07  SMRE_ITR_001303_MC09  SMRE_ITR_001303_MC10  \\\n",
        "stud_id                                                                     \n",
        "3061                        1                     1                     1   \n",
        "3067                        0                     1                     0   \n",
        "3135                        1                     1                     1   \n",
        "3138                        1                     1                     1   \n",
        "3158                        1                     1                     1   \n",
        "3175                        1                     1                     1   \n",
        "6287                        1                     1                     1   \n",
        "6376                        1                     1                     1   \n",
        "6410                        1                     1                     1   \n",
        "6413                        1                     1                     1   \n",
        "\n",
        "item_id  SMRE_ITR_001304_MC06  SMRE_ITR_001304_MC08  SMRE_ITR_001304_MC09  \\\n",
        "stud_id                                                                     \n",
        "3061                        1                     1                     1   \n",
        "3067                        1                     1                     0   \n",
        "3135                        1                     1                     1   \n",
        "3138                        1                     0                     1   \n",
        "3158                        1                     1                     1   \n",
        "3175                        1                     1                     1   \n",
        "6287                        1                     1                     1   \n",
        "6376                        1                     1                     1   \n",
        "6410                        1                     1                     1   \n",
        "6413                        1                     1                     1   \n",
        "\n",
        "item_id  SMRE_ITR_001304_MC10  SMRE_ITR_001321_MC07  SMRE_ITR_001321_MC08  \\\n",
        "stud_id                                                                     \n",
        "3061                        1                     1                     1   \n",
        "3067                        0                     0                     1   \n",
        "3135                        0                     1                     1   \n",
        "3138                        0                     1                     1   \n",
        "3158                        1                     1                     1   \n",
        "3175                        1                     1                     1   \n",
        "6287                        1                     1                     1   \n",
        "6376                        1                     1                     1   \n",
        "6410                        1                     1                     1   \n",
        "6413                        1                     1                     1   \n",
        "\n",
        "item_id  SMRE_ITR_001321_MC09  SMRE_ITR_001322_MC06  SMRE_ITR_001322_MC07  \\\n",
        "stud_id                                                                     \n",
        "3061                        1                     1                     1   \n",
        "3067                        1                     1                     1   \n",
        "3135                        1                     1                     0   \n",
        "3138                        1                     1                     1   \n",
        "3158                        1                     1                     1   \n",
        "3175                        1                     1                     1   \n",
        "6287                        1                     1                     1   \n",
        "6376                        1                     1                     1   \n",
        "6410                        1                     1                     1   \n",
        "6413                        1                     1                     1   \n",
        "\n",
        "item_id  SMRE_ITR_001322_MC09  SMRE_ITR_001322_MC10  \n",
        "stud_id                                              \n",
        "3061                        1                     1  \n",
        "3067                        0                     1  \n",
        "3135                        1                     1  \n",
        "3138                        1                     1  \n",
        "3158                        1                     1  \n",
        "3175                        1                     1  \n",
        "6287                        1                     1  \n",
        "6376                        1                     1  \n",
        "6410                        1                     0  \n",
        "6413                        1                     1  "
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Analysis time!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Could possibly be done with pandas\n",
      "* GNU R already has `ltm` package which covers Rasch Item Response Theory\n",
      "* Use `rpy2` to convert pandas dataframes to R dataframes\n",
      "* Use `rpy2` to bring R functionality into Python\n",
      "* Can parse resultant R objects using delegators\n",
      "  * http://rpy.sourceforge.net/rpy2/doc-2.2/html/vector.html#extracting-r-style"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from rpy2.robjects import *\n",
      "import pandas.rpy.common as com\n",
      "r(\"library('ltm')\")\n",
      "r_dataframe = com.convert_to_r_dataframe(df2_pivot)\n",
      "r_rasch = r['rasch']\n",
      "r_descript = r['descript']\n",
      "\n",
      "rasch_dataframe = r_rasch(r_dataframe)\n",
      "descript_dataframe = r_descript(r_dataframe)\n",
      "print 'Frequencies for Total Scores'\n",
      "print descript_dataframe.rx('items')\n",
      "print 'Alpha Values: '\n",
      "print descript_dataframe.rx('alpha')\n",
      "print 'Biserial List Inclusive'\n",
      "print descript_dataframe.rx('bisCorr')\n",
      "print 'Biserial List Exclusive'\n",
      "print descript_dataframe.rx('ExBisCorr')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Loading required package: MASS\n",
        "Loading required package: msm\n",
        "Loading required package: mvtnorm\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Loading required package: polycor\n",
        "Loading required package: sfsmisc\n",
        "Frequencies for Total Scores"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "$items\n",
        "     0 1 2 3 4 5 6 7 8 9 10 11 12 13 14\n",
        "Freq 0 0 0 0 1 0 1 1 6 5  6  9 33 40 52\n",
        "\n",
        "\n",
        "Alpha Values: \n",
        "$alpha\n",
        "                                   value\n",
        "All Items                      0.6549320\n",
        "Excluding SMRE_ITR_001303_MC07 0.6225958\n",
        "Excluding SMRE_ITR_001303_MC09 0.6349281\n",
        "Excluding SMRE_ITR_001303_MC10 0.6314788\n",
        "Excluding SMRE_ITR_001304_MC06 0.6451940\n",
        "Excluding SMRE_ITR_001304_MC08 0.6359128\n",
        "Excluding SMRE_ITR_001304_MC09 0.6381103\n",
        "Excluding SMRE_ITR_001304_MC10 0.6123571\n",
        "Excluding SMRE_ITR_001321_MC07 0.6219429\n",
        "Excluding SMRE_ITR_001321_MC08 0.6545952\n",
        "Excluding SMRE_ITR_001321_MC09 0.6341581\n",
        "Excluding SMRE_ITR_001322_MC06 0.6520212\n",
        "Excluding SMRE_ITR_001322_MC07 0.6439346\n",
        "Excluding SMRE_ITR_001322_MC09 0.6327125\n",
        "Excluding SMRE_ITR_001322_MC10 0.6665947\n",
        "\n",
        "\n",
        "Biserial List Inclusive\n",
        "$bisCorr\n",
        "SMRE_ITR_001303_MC07 SMRE_ITR_001303_MC09 SMRE_ITR_001303_MC10 \n",
        "           0.5373696            0.4395862            0.4716578 \n",
        "SMRE_ITR_001304_MC06 SMRE_ITR_001304_MC08 SMRE_ITR_001304_MC09 \n",
        "           0.3424764            0.4762909            0.4198854 \n",
        "SMRE_ITR_001304_MC10 SMRE_ITR_001321_MC07 SMRE_ITR_001321_MC08 \n",
        "           0.5891776            0.5405685            0.3355866 \n",
        "SMRE_ITR_001321_MC09 SMRE_ITR_001322_MC06 SMRE_ITR_001322_MC07 \n",
        "           0.4321656            0.2362544            0.4222147 \n",
        "SMRE_ITR_001322_MC09 SMRE_ITR_001322_MC10 \n",
        "           0.4804807            0.3930858 \n",
        "\n",
        "\n",
        "Biserial List Exclusive\n",
        "$ExBisCorr\n",
        " [1] 0.3628059 0.3572117 0.3156812 0.2233073 0.2936628 0.2745146 0.4111982\n",
        " [8] 0.4532215 0.1687659 0.3143018 0.1637309 0.2448341 0.4100541 0.1644669\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Picture Time!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Raw data is useful, but doesn't tell story as well\n",
      "* We make use of R's very nice drawing capabilities"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "r_plot_rasch = r['plot.rasch']\n",
      "r_png_device = r['png']\n",
      "r_png_device_off = r['dev.off']\n",
      "r_c = r['c']\n",
      "\n",
      "r_png_device(filename=\"rasch_graph.png\", width = 800, height = 800,\n",
      "             units = \"px\", pointsize = 12, bg = \"white\", res = \"NA\",\n",
      "             type = r_c(\"cairo\", \"cairo-png\", \"Xlib\", \"quartz\"))\n",
      "\n",
      "r_plot_rasch(rasch_dataframe, legend=True)\n",
      "r_png_device_off()\n",
      "\n",
      "display(Image(filename='rasch_graph.png'))\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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oPonXJEEX7BYxI4VLTUFKClJTO2qbnPxZze32uL4rrLMHI/u0GWjovEpmcAIp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h4eGpqakhWP0shg6lYzC/Aw8dOnTFFVdoD9+9m5MnA4SvTp4M6eXBBcrhq4KCgtWrV3/k0zIw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\njXQFPProoyVKlLjqi5UqVdLJLREREbnB5WbAurYyZcq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       "text": [
        "<IPython.core.display.Image at 0x10ce6cb10>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* The \"Ability\" can be thought of as relative intelligence as measured by this test\n",
      "  * 0 being the average student.\n",
      "  * Above average students have positive scores\n",
      "* Average student will get each question right with a probability in excess of 80%\n",
      "* Graph shows questions tend to be fairly easy\n",
      "  * This is good for students, though perhaps not good for teachers\n",
      "  * ... or, perhaps students were all very smart\n",
      " * We provide this feedback to the curriculum teams who adjust the difficulty of items to have a better distribution of difficulty levels"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}