notebooks/notebook-5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xian/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import glob\n",
    "%matplotlib inline\n",
    "\n",
    "#import warnings\n",
    "#warnings.filterwarnings('ignore')\n",
    "#warnings.filterwarnings(action='once')\n",
    "#warnings.filterwarnings('default')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>age</th>\n",
       "      <th>stimulus</th>\n",
       "      <th>trial</th>\n",
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       "      <td>black</td>\n",
       "      <td>nogun</td>\n",
       "      <td>639</td>\n",
       "      <td>correct</td>\n",
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       "      <td>nogun</td>\n",
       "      <td>494</td>\n",
       "      <td>correct</td>\n",
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       "      <td>black</td>\n",
       "      <td>nogun</td>\n",
       "      <td>562</td>\n",
       "      <td>correct</td>\n",
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      ],
      "text/plain": [
       "   subject  age stimulus  trial target object   rt response\n",
       "0        1   35      w05     19  white  nogun  658  correct\n",
       "1        1   35      w15     20  white  nogun  622  correct\n",
       "2        1   35      b92     21  black  nogun  639  correct\n",
       "3        1   35      w11     22  white  nogun  494  correct\n",
       "4        1   35      b93     23  black  nogun  562  correct"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./shooter-data/shooter-s1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gather the names of all the data files\n",
    "files = glob.glob('./shooter-data/shooter-s*.csv')\n",
    "\n",
    "# place to keep the subjects' data frames\n",
    "data_frames = []\n",
    "\n",
    "# loop over files\n",
    "for file in files:\n",
    "    # read in the data and add it to the list\n",
    "    data_frames.append(pd.read_csv(file, sep=',', index_col=False))\n",
    "\n",
    "# merge all the data into a single dataframe\n",
    "df = pd.concat(data_frames, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <td>black</td>\n",
       "      <td>nogun</td>\n",
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       "      <td>white</td>\n",
       "      <td>nogun</td>\n",
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       "      <td>incorrect</td>\n",
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       "      <th>4</th>\n",
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       "      <td>27</td>\n",
       "      <td>b17</td>\n",
       "      <td>23</td>\n",
       "      <td>black</td>\n",
       "      <td>nogun</td>\n",
       "      <td>719</td>\n",
       "      <td>correct</td>\n",
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      "text/plain": [
       "   subject  age stimulus  trial target object   rt   response\n",
       "0       18   27      b95     19  black    gun    0  incorrect\n",
       "1       18   27      b05     20  black    gun  615    correct\n",
       "2       18   27      b04     21  black  nogun  559    correct\n",
       "3       18   27      w04     22  white  nogun  488  incorrect\n",
       "4       18   27      b17     23  black  nogun  719    correct"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject      int64\n",
       "age          int64\n",
       "stimulus    object\n",
       "trial        int64\n",
       "target      object\n",
       "object      object\n",
       "rt           int64\n",
       "response    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rename columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>black</td>\n",
       "      <td>nogun</td>\n",
       "      <td>719</td>\n",
       "      <td>correct</td>\n",
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      "text/plain": [
       "   subject  age stimulus  trial   race    gun   rt    correct\n",
       "0       18   27      b95     19  black    gun    0  incorrect\n",
       "1       18   27      b05     20  black    gun  615    correct\n",
       "2       18   27      b04     21  black  nogun  559    correct\n",
       "3       18   27      w04     22  white  nogun  488  incorrect\n",
       "4       18   27      b17     23  black  nogun  719    correct"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename({'target' : 'race', 'object' : 'gun', 'response' : 'correct'}, axis='columns', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Replace the string codings for correct, race, and gun with integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "   subject  age stimulus  trial  race  gun   rt  correct\n",
       "0       18   27      b95     19     1    1    0        0\n",
       "1       18   27      b05     20     1    1  615        1\n",
       "2       18   27      b04     21     1    0  559        1\n",
       "3       18   27      w04     22     0    0  488        0\n",
       "4       18   27      b17     23     1    0  719        1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace({'race': {'white' : 0, 'black' : 1}}, inplace=True)\n",
    "df.replace({'correct': {'incorrect' : 0, 'correct' : 1}}, inplace=True)\n",
    "df.replace({'gun': {'nogun' : 0, 'gun' : 1}}, inplace=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <td>588.70</td>\n",
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       "      <td>68.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>578.52</td>\n",
       "      <td>0.97</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>575.10</td>\n",
       "      <td>1.00</td>\n",
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       "      <th>5</th>\n",
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       "      <td>68.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>579.43</td>\n",
       "      <td>0.93</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33.0</td>\n",
       "      <td>68.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>577.18</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>32.0</td>\n",
       "      <td>68.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>587.96</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>32.0</td>\n",
       "      <td>68.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>622.74</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31.0</td>\n",
       "      <td>68.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>559.64</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age  trial  race  gun      rt  correct\n",
       "subject                                         \n",
       "1        35.0   68.5   0.5  0.5  588.70     0.97\n",
       "2        35.0   68.5   0.5  0.5  609.34     0.97\n",
       "3        34.0   68.5   0.5  0.5  578.52     0.97\n",
       "4        34.0   68.5   0.5  0.5  575.10     1.00\n",
       "5        33.0   68.5   0.5  0.5  579.43     0.93\n",
       "6        33.0   68.5   0.5  0.5  577.18     0.97\n",
       "7        32.0   68.5   0.5  0.5  587.96     0.95\n",
       "8        32.0   68.5   0.5  0.5  622.74     0.98\n",
       "9        31.0   68.5   0.5  0.5  559.64     0.85"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('subject').mean()[0:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt</th>\n",
       "      <th>correct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>559.64</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>597.07</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>591.60</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>616.38</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>561.75</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>621.93</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>591.41</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>579.43</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>603.17</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             rt  correct\n",
       "subject                 \n",
       "9        559.64     0.85\n",
       "30       597.07     0.87\n",
       "18       591.60     0.88\n",
       "12       616.38     0.90\n",
       "27       561.75     0.92\n",
       "20       621.93     0.92\n",
       "10       591.41     0.92\n",
       "5        579.43     0.93\n",
       "24       603.17     0.93"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# least accurate subjects\n",
    "df[['subject', 'rt', 'correct']].groupby('subject').mean().sort_values(by='correct', ascending=True)[:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt</th>\n",
       "      <th>correct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>536.44</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>545.42</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>553.33</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>557.81</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>557.98</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>559.64</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>560.95</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>561.55</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>561.75</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             rt  correct\n",
       "subject                 \n",
       "33       536.44     0.94\n",
       "16       545.42     0.93\n",
       "31       553.33     0.95\n",
       "25       557.81     0.95\n",
       "32       557.98     0.97\n",
       "9        559.64     0.85\n",
       "28       560.95     0.99\n",
       "26       561.55     0.95\n",
       "27       561.75     0.92"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fastest subjects\n",
    "df[['subject', 'rt', 'correct']].groupby('subject').mean().sort_values(by='rt', ascending=True)[:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# note that this is the distribution of all subjects'/all trials' RTs\n",
    "#plt.hist(df['rt']);\n",
    "df['rt'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rt'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      98\n",
       "551    25\n",
       "591    24\n",
       "592    22\n",
       "583    21\n",
       "593    21\n",
       "573    20\n",
       "569    20\n",
       "602    20\n",
       "Name: rt, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rt'].value_counts()[:9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So it looks like we have some RTs of zero.  We ask our student/collaborator and are told these are 'non-reponses' (too slow)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's eliminate trials where rt == 0\n",
    "df.drop(df[df.rt == 0].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.hist(df['rt']);\n",
    "df['rt'].hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if we wanted to drop fast and slow RTs, we could do something like this\n",
    "df = df.drop(df[ (df.rt == 0) | (df.rt > 1500) ].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/EfD5HpbTSGY+nZkPleWfAPuA1bOGbQRuzY5dwIqIOBt4F3BPZj5f5s89wCXDUPOwzemG+3k+A5nTJ1HzUMzpbqoK9xO0mc5R2jEJ3B0Re8pHIwyTt0XEtyPiroh4c2lbDfxwxpiDNP8l6ouIeBWdEPzSjOaB7+eIGAN+G9g9q2u+fTrwfb1AzTMN1ZzuUvNQzulu+3lY5/RcevKRv8MuIjbQ+UX4nRnNF2XmoYh4HXBPRHy3HKEO2kN0Pj9iOiIuA/4VWEfDj3kYsHcD/56ZM4/yB7qfI2KEzi/mhzLzhdndc9wlF2jviy41HxszVHO6S81DOaeb7GeGcE7P55fuyD0ifgu4CdiYmf9zrD0zD5WfzwJfofNfxIHLzBcyc7os3wmcGhGrWBof83A1s/77Osj9HBGn0vnl/VxmfnmOIfPt04Ht6wY1D92c7lbzMM7pJvu5GKo5vaBBn/Rv+waMMf+Lk78G7AfePqv9DODVM5YfoPPJlsNQ86/yizebXQA8SecIZxmdF/bO4RcvPr15GGou/cvpnJc/Yxj2c9lntwKfXGDM5Rz/gup/lPYzgR/QeTF1ZVk+c0hqHqo53bDmoZrTTWou44ZqTne7VXVaJiI+T+eV+FURcRD4GHAqQGb+I/BR4CzgHyIC4Gh2PuFtFPhKaVsG/HNmfm1Iar4S+JOIOAq8BFydnZk0sI95aFAzwB8Cd2fmizPuOrD9DFwEvAfYGxEPl7aP0AnHY3XfSeeKmf3AT4H3lb7nI+Jv6HxuEsBf5/H/LR9kzcM2p5vUPGxzuknNMHxzekF+/IAkVeiX7py7JP0yMNwlqUKGuyRVyHCXpAoZ7pJUIcNdkipkuEtShf4fe2CplT7J1/wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transform RT and place in a new column\n",
    "df['rate'] = 1000.0/df['rt']\n",
    "#df['rate'] = np.log(df['rt'])\n",
    "#plt.hist(df['rate']);\n",
    "df['rate'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_means = df[['subject','correct', 'rt']].groupby('subject').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAANkUlEQVR4nO3df4ykd13A8fentz24dvoLe9noFbkagVi52LQr8iOU3SLxbJFGaUJNS3pVsyEIFnOYnFFDQkI02iOpwT/cLNjwQ0goDTGeSCuyaTSl6rWFa3tKf1Btr/UqItWtTdqLH/+Y52A55rpzM/PMfHbn/UomN7vPMzPfz87c++ae2dmNzESSVNdpk16AJOnFGWpJKs5QS1JxhlqSijPUklTcTBtXev755+f27ds588wz27j6DeHZZ5+d2vmneXZw/mmef5jZDx48+K3M3N5rWyuh3rlzJzfddBPz8/NtXP2GsLKyMrXzT/Ps4PzTPP8ws0fEv55sm4c+JKk4Qy1JxRlqSSrOUEtScYZakooz1JJUnKGWpOIMtSQVZ6glqbhW3pkoqY6d+w6M9fb27jrGnn0HeOwPrhzr7W5mPqOWpOIMtSQVZ6glqThDLUnFGWpJKs5QS1JxhlqSijPUklScoZak4gy1JBVnqCWpOEMtScUZakkqzlBLUnGGWpKKM9SSVJyhlqTiDLUkFWeoJam4vkIdEb8ZEQ9ExP0R8ZmIeGnbC5Mkda0b6ojYAfwGMJeZrwG2ANe0vTBJUle/hz5mgG0RMQOcATzZ3pIkSWtFZq6/U8SNwIeB54DbM/PaHvssAosAs7Ozly4vL9PpdEa83I1jdXV1auef5tmh3vyHjjwz1tub3QZHnxvrTf6AXTvOmcjtDnPfLywsHMzMuV7bZta7cEScB1wFXAh8B/hcRFyXmZ9au19mLgFLAHNzc9npdJifnx9owZvBysrK1M4/zbNDvfn37Dsw1tvbu+sY+w+tm5ZWPXbt/ERut637vp9DHz8LfDMz/yMzXwBuA94w8pVIknrqJ9T/BrwuIs6IiADeAhxud1mSpOPWDXVm3g3cCtwDHGous9TyuiRJjb4OJGXmB4EPtrwWSVIPvjNRkooz1JJUnKGWpOIMtSQVZ6glqThDLUnFGWpJKs5QS1JxhlqSijPUklScoZak4gy1JBVnqCWpOEMtScUZakkqzlBLUnGGWpKKM9SSVJyhlqTiDLUkFWeoJak4Qy1JxRlqSSrOUEtScYZakooz1JJUnKGWpOIMtSQVZ6glqThDLUnFGWpJKs5QS1JxhlqSijPUklScoZak4gy1JBVnqCWpuL5CHRHnRsStEfHPEXE4Il7f9sIkSV0zfe53M/DXmXl1RGwFzmhxTZKkNdYNdUScDVwG7AHIzOeB59tdliTpuMjMF98h4mJgCXgQ+CngIHBjZj57wn6LwCLA7OzspcvLy3Q6nVYWvRGsrq5O7fzTPDv0nv/QkWcmtJrxm90GR5+b7Bp27ThnIrc7zGN/YWHhYGbO9drWz6GPGeAS4H2ZeXdE3AzsA35v7U6ZuUQ36MzNzWWn02F+fn6gBW8GKysrUzv/NM8Oveffs+/AZBYzAXt3HWP/oX6PqrbjsWvnJ3K7bT32+3kx8Qngicy8u/n4VrrhliSNwbqhzsx/Bx6PiFc3n3oL3cMgkqQx6Pf/J+8DPt18x8ejwA3tLUmStFZfoc7M+4CeB7klSe3ynYmSVJyhlqTiDLUkFWeoJak4Qy1JxRlqSSrOUEtScYZakooz1JJUnKGWpOIMtSQVZ6glqThDLUnFGWpJKs5QS1JxhlqSijPUklScoZak4gy1JBVnqCWpOEMtScUZakkqzlBLUnGGWpKKM9SSVJyhlqTiDLUkFWeoJak4Qy1JxRlqSSrOUEtScYZakooz1JJUnKGWpOIMtSQVZ6glqThDLUnF9R3qiNgSEfdGxF+2uSBJ0vc7lWfUNwKH21qIJKm3vkIdERcAVwLL7S5HknSiyMz1d4q4Ffh94CzgA5n5th77LAKLALOzs5cuLy/T6XRGvNyNY3V1dWrnn+bZoff8h448M6HVjN/sNjj63KRXMRkXnrNl4Mf+wsLCwcyc67VtZr0LR8TbgKcz82BEzJ9sv8xcApYA5ubmstPpMD9/0t03vZWVlamdf5pnh97z79l3YDKLmYC9u46x/9C6admUbtl9ZiuP/X4OfbwReHtEPAZ8Frg8Ij418pVIknpaN9SZ+duZeUFm7gSuAf42M69rfWWSJMDvo5ak8k7pQFJmrgArraxEktSTz6glqThDLUnFGWpJKs5QS1JxhlqSijPUklScoZak4gy1JBVnqCWpOEMtScUZakkqzlBLUnGGWpKKM9SSVJyhlqTiDLUkFWeoJak4Qy1JxRlqSSrOUEtScYZakooz1JJUnKGWpOIMtSQVZ6glqThDLUnFGWpJKs5QS1JxhlqSijPUklScoZak4gy1JBVnqCWpOEMtScUZakkqzlBLUnGGWpKKWzfUEfHyiPhKRByOiAci4sZxLEyS1DXTxz7HgL2ZeU9EnAUcjIg7MvPBltcmSaKPZ9SZ+VRm3tOc/x/gMLCj7YVJkroiM/vfOWIncCfwmsz87xO2LQKLALOzs5cuLy/T6XROeUGHjjxzypcZhV07zhnp9a2urvY9/2aZ+bh+Zp/UzOMwuw2OPjfpVUzONM9/4TlbBuoewMLCwsHMnOu1rZ9DHwBERAf4PPD+EyMNkJlLwBLA3Nxcdjod5ufnT3mxe/YdOOXLjMJj186P9PpWVlb6nn+zzHxcP7NPauZx2LvrGPsP9f1Xa9OZ5vlv2X3mQN1bT1/f9RERp9ON9Kcz87aRr0KSdFL9fNdHAB8DDmfmR9pfkiRprX6eUb8ReBdweUTc15yuaHldkqTGugeSMvPvgBjDWiRJPfjOREkqzlBLUnGGWpKKM9SSVJyhlqTiDLUkFWeoJak4Qy1JxRlqSSrOUEtScYZakooz1JJUnKGWpOIMtSQVZ6glqThDLUnFGWpJKm46f1WwANjZ0m8C37vr2Kb+LePSuPmMWpKKM9SSVJyhlqTiDLUkFWeoJak4Qy1JxRlqSSrOUEtScYZakooz1JJUnKGWpOIMtSQVZ6glqThDLUnFGWpJKs5QS1JxhlqSijPUklScoZak4voKdUTsjoh/iYiHI2Jf24uSJH3PuqGOiC3AnwA/D1wE/HJEXNT2wiRJXf08o34t8HBmPpqZzwOfBa5qd1mSpOMiM198h4irgd2Z+WvNx+8CfiYz33vCfovAYvPhq4H/BL418hVvHOczvfNP8+zg/NM8/zCzvyIzt/faMNPHhaPH536g7pm5BCx990IR/5SZc30vcZOZ5vmneXZw/mmev63Z+zn08QTw8jUfXwA8OeqFSJJ66yfU/wi8MiIujIitwDXAX7S7LEnScese+sjMYxHxXuBLwBbg45n5QB/XvbT+LpvaNM8/zbOD80/z/K3Mvu6LiZKkyfKdiZJUnKGWpOIGCvV6bymPiFdExJcj4usRsRIRF6zZdn1EPNScrh9m8ZMw6OwRcXFE3BURDzTb3jn+1Q9vmPu+2X52RByJiI+Ob9WjM+Rj/0cj4vaIOBwRD0bEznGufVhDzv6HzWP/cET8cUT0+rbfsiLi4xHxdETcf5Lt0cz1cDP/JWu2Dd+8zDylE90XFB8BfgzYCnwNuOiEfT4HXN+cvxz4ZHP+ZcCjzZ/nNefPO9U1TOo05OyvAl7ZnP8R4Cng3EnPNK7512y/Gfhz4KOTnmfc8wMrwFub8x3gjEnPNI7ZgTcAf99cxxbgLmB+0jOd4vyXAZcA959k+xXAF+m+7+R1wN3N50fSvEGeUffzlvKLgC8357+yZvvPAXdk5rcz87+AO4DdA6xhUgaePTO/kZkPNeefBJ4Ger4LqbBh7nsi4lJgFrh9DGttw8DzNz8fZyYz7wDIzNXM/N/xLHskhrnvE3gp3cC/BDgdONr6ikcoM+8Evv0iu1wFfCK7vgqcGxE/zIiaN0iodwCPr/n4ieZza30NeEdz/heBsyLih/q8bGXDzP5dEfFaug/aR1paZ1sGnj8iTgP2A7/V+irbM8z9/yrgOxFxW0TcGxF/1PzAs41i4Nkz8y664X6qOX0pMw+3vN5xO9nXZyTNGyTU/byl/APAmyPiXuDNwBHgWJ+XrWyY2btX0P1X9pPADZn5f20ttCXDzP8e4K8y83E2rmHmnwHe1Gz/abqHEPa0ttLRG3j2iPhx4Cfovqt5B3B5RFzW5mIn4GRfn5E0r5+f9XGidd9S3vzX/pcAIqIDvCMzn4mIJ4D5Ey67MsAaJmXg2ZuPzwYOAL/b/Pdooxnmvn898KaIeA/d47NbI2I1MzfSzzcf9rF/b2Y+2mz7At1jmR8bx8JHYJjZF4GvZuZqs+2LdGe/cxwLH5OTfX1G07wBDqrP0D0gfiHfe1HhJ0/Y53zgtOb8h4EPrTmw/k26B9XPa86/bNIvFIxp9q10j9+9f9JzTGL+E/bZw8Z8MXGY+39Ls//25uM/A3590jONafZ3An/TXMfpzd+DX5j0TAN8DXZy8hcTr+T7X0z8h+bzI2neoAu+AvgG3WOsv9N87kPA25vzVwMPNfssAy9Zc9lfAR5uTjdM+os/rtmB64AXgPvWnC6e9DzjvO/XXMeGDPWw8wNvBb4OHAJuAbZOep5xzE73H6k/BQ4DDwIfmfQsA8z+GbrH11+g+yz5V4F3A+9utgfdX7DySHP/zq257NDN8y3kklSc70yUpOIMtSQVZ6glqThDLUnFGWpJKs5QS1JxhlqSivt/bSJhWT90x3wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.hist(sub_means['correct']);\n",
    "sub_means['correct'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.hist(sub_means['rt']);\n",
    "sub_means['rt'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.plot(sub_means['rt'], sub_means['correct'], 'bo')\n",
    "#plt.xlabel('rt')\n",
    "#plt.ylabel('p(correct)');\n",
    "sub_means.plot.scatter(x='rt', y='correct');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save wrangled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('./shooter-data/shooter-group.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='race', y='rt', hue='gun', data=df)\n",
    "plt.ylim(530, 660);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.94, 1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='race', y='correct', hue='gun', data=df)\n",
    "plt.ylim(.94, 1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}