lexical_decision_task_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's preprocess the data first!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns \n",
    "import plotnine as gg \n",
    "from plotnine import ggplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yr/vf28v1mn2y1ck9bp7ljl0p8h0000gn/T/ipykernel_69139/71405584.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  trials = trials.append(participant_data)\n",
      "/var/folders/yr/vf28v1mn2y1ck9bp7ljl0p8h0000gn/T/ipykernel_69139/71405584.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  trials = trials.append(participant_data)\n",
      "/var/folders/yr/vf28v1mn2y1ck9bp7ljl0p8h0000gn/T/ipykernel_69139/71405584.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  trials = trials.append(participant_data)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>trial</th>\n",
       "      <th>cond</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>key</th>\n",
       "      <th>id</th>\n",
       "      <th>reaction_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>recht</td>\n",
       "      <td>HF</td>\n",
       "      <td>2.718182</td>\n",
       "      <td>3.838300</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>1.120117</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>gals</td>\n",
       "      <td>none</td>\n",
       "      <td>5.352657</td>\n",
       "      <td>6.295809</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.943152</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>slont</td>\n",
       "      <td>none</td>\n",
       "      <td>7.810418</td>\n",
       "      <td>8.713127</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.902709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>baby</td>\n",
       "      <td>HF</td>\n",
       "      <td>10.226432</td>\n",
       "      <td>10.973984</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.747552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>boek</td>\n",
       "      <td>HF</td>\n",
       "      <td>12.486627</td>\n",
       "      <td>13.213963</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.727336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>imams</td>\n",
       "      <td>LF</td>\n",
       "      <td>14.726606</td>\n",
       "      <td>15.430361</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.703755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>goont</td>\n",
       "      <td>none</td>\n",
       "      <td>16.943319</td>\n",
       "      <td>17.694142</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.750824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>plan</td>\n",
       "      <td>HF</td>\n",
       "      <td>19.207660</td>\n",
       "      <td>19.846547</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.638888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>women</td>\n",
       "      <td>none</td>\n",
       "      <td>21.359490</td>\n",
       "      <td>22.022061</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.662571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>buidels</td>\n",
       "      <td>LF</td>\n",
       "      <td>23.540104</td>\n",
       "      <td>24.230132</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.690029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>hield</td>\n",
       "      <td>HF</td>\n",
       "      <td>25.742965</td>\n",
       "      <td>26.511447</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.768482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>polders</td>\n",
       "      <td>LF</td>\n",
       "      <td>28.025344</td>\n",
       "      <td>28.688923</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.663579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>ponten</td>\n",
       "      <td>LF</td>\n",
       "      <td>30.202228</td>\n",
       "      <td>31.081533</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.879305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>geslonden</td>\n",
       "      <td>none</td>\n",
       "      <td>32.595859</td>\n",
       "      <td>33.344787</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.748927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>giep</td>\n",
       "      <td>none</td>\n",
       "      <td>34.859374</td>\n",
       "      <td>35.646302</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.786928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>labber</td>\n",
       "      <td>none</td>\n",
       "      <td>37.160373</td>\n",
       "      <td>37.926389</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.766016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>jugen</td>\n",
       "      <td>none</td>\n",
       "      <td>39.440160</td>\n",
       "      <td>40.254621</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.814461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>hese</td>\n",
       "      <td>LF</td>\n",
       "      <td>41.768017</td>\n",
       "      <td>42.638338</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.870322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>brum</td>\n",
       "      <td>none</td>\n",
       "      <td>44.151668</td>\n",
       "      <td>44.798299</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.646631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>millag</td>\n",
       "      <td>none</td>\n",
       "      <td>46.311550</td>\n",
       "      <td>46.925954</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165435</td>\n",
       "      <td>0.614404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>plan</td>\n",
       "      <td>HF</td>\n",
       "      <td>2.732804</td>\n",
       "      <td>3.668818</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.936014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>imams</td>\n",
       "      <td>LF</td>\n",
       "      <td>5.182351</td>\n",
       "      <td>5.936878</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.754527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>hese</td>\n",
       "      <td>LF</td>\n",
       "      <td>7.450601</td>\n",
       "      <td>8.209015</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.758414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>polders</td>\n",
       "      <td>LF</td>\n",
       "      <td>9.722794</td>\n",
       "      <td>10.497127</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.774332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>brum</td>\n",
       "      <td>none</td>\n",
       "      <td>12.010234</td>\n",
       "      <td>12.747876</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.737642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>ponten</td>\n",
       "      <td>LF</td>\n",
       "      <td>14.264084</td>\n",
       "      <td>15.056405</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.792321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>geslonden</td>\n",
       "      <td>none</td>\n",
       "      <td>16.569832</td>\n",
       "      <td>17.352689</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.782857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>gals</td>\n",
       "      <td>none</td>\n",
       "      <td>18.865736</td>\n",
       "      <td>19.553098</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.687362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>recht</td>\n",
       "      <td>HF</td>\n",
       "      <td>21.066896</td>\n",
       "      <td>21.785232</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.718336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>buidels</td>\n",
       "      <td>LF</td>\n",
       "      <td>23.299407</td>\n",
       "      <td>24.008707</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.709300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>hield</td>\n",
       "      <td>HF</td>\n",
       "      <td>25.522279</td>\n",
       "      <td>26.368387</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.846108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>baby</td>\n",
       "      <td>HF</td>\n",
       "      <td>27.882381</td>\n",
       "      <td>28.616453</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.734072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>slont</td>\n",
       "      <td>none</td>\n",
       "      <td>30.130719</td>\n",
       "      <td>30.952576</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.821857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>giep</td>\n",
       "      <td>none</td>\n",
       "      <td>32.465605</td>\n",
       "      <td>33.192580</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.726975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>jugen</td>\n",
       "      <td>none</td>\n",
       "      <td>34.705926</td>\n",
       "      <td>35.560509</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.854583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>women</td>\n",
       "      <td>none</td>\n",
       "      <td>37.074502</td>\n",
       "      <td>37.872475</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.797973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>labber</td>\n",
       "      <td>none</td>\n",
       "      <td>39.386173</td>\n",
       "      <td>40.209063</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.822890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>millag</td>\n",
       "      <td>none</td>\n",
       "      <td>41.721994</td>\n",
       "      <td>42.624504</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.902511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>boek</td>\n",
       "      <td>HF</td>\n",
       "      <td>44.137905</td>\n",
       "      <td>45.048897</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.910992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>goont</td>\n",
       "      <td>none</td>\n",
       "      <td>46.561676</td>\n",
       "      <td>47.441955</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165618</td>\n",
       "      <td>0.880279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>women</td>\n",
       "      <td>none</td>\n",
       "      <td>2.715994</td>\n",
       "      <td>3.768022</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>1.052028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>goont</td>\n",
       "      <td>none</td>\n",
       "      <td>5.281677</td>\n",
       "      <td>6.272063</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.990385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>recht</td>\n",
       "      <td>HF</td>\n",
       "      <td>7.786454</td>\n",
       "      <td>8.504235</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.717781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>slont</td>\n",
       "      <td>none</td>\n",
       "      <td>10.018325</td>\n",
       "      <td>10.767570</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.749245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>giep</td>\n",
       "      <td>none</td>\n",
       "      <td>12.280973</td>\n",
       "      <td>12.919527</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.638554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>brum</td>\n",
       "      <td>none</td>\n",
       "      <td>14.431786</td>\n",
       "      <td>15.159372</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.727586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>labber</td>\n",
       "      <td>none</td>\n",
       "      <td>16.672149</td>\n",
       "      <td>17.371160</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.699010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>plan</td>\n",
       "      <td>HF</td>\n",
       "      <td>18.885155</td>\n",
       "      <td>19.503246</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.618091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>ponten</td>\n",
       "      <td>LF</td>\n",
       "      <td>21.016644</td>\n",
       "      <td>21.704053</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.687409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>imams</td>\n",
       "      <td>LF</td>\n",
       "      <td>23.218531</td>\n",
       "      <td>23.855498</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.636967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>jugen</td>\n",
       "      <td>none</td>\n",
       "      <td>25.369400</td>\n",
       "      <td>26.015500</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.646100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>boek</td>\n",
       "      <td>HF</td>\n",
       "      <td>27.527800</td>\n",
       "      <td>28.095664</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.567864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>buidels</td>\n",
       "      <td>LF</td>\n",
       "      <td>29.608214</td>\n",
       "      <td>30.247373</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.639159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>baby</td>\n",
       "      <td>HF</td>\n",
       "      <td>31.760937</td>\n",
       "      <td>32.335657</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.574720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>geslonden</td>\n",
       "      <td>none</td>\n",
       "      <td>33.847859</td>\n",
       "      <td>34.431657</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.583798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>hese</td>\n",
       "      <td>LF</td>\n",
       "      <td>35.945448</td>\n",
       "      <td>36.544224</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.598776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>millag</td>\n",
       "      <td>none</td>\n",
       "      <td>38.061550</td>\n",
       "      <td>38.552478</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.490928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>hield</td>\n",
       "      <td>HF</td>\n",
       "      <td>40.066236</td>\n",
       "      <td>40.999554</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.933318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>gals</td>\n",
       "      <td>none</td>\n",
       "      <td>42.513991</td>\n",
       "      <td>43.071706</td>\n",
       "      <td>m</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.557715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>polders</td>\n",
       "      <td>LF</td>\n",
       "      <td>44.585127</td>\n",
       "      <td>45.071525</td>\n",
       "      <td>z</td>\n",
       "      <td>P_165752</td>\n",
       "      <td>0.486398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0      trial  cond  start_time   end_time key        id  \\\n",
       "0            0      recht    HF    2.718182   3.838300   z  P_165435   \n",
       "1            1       gals  none    5.352657   6.295809   m  P_165435   \n",
       "2            2      slont  none    7.810418   8.713127   m  P_165435   \n",
       "3            3       baby    HF   10.226432  10.973984   z  P_165435   \n",
       "4            4       boek    HF   12.486627  13.213963   z  P_165435   \n",
       "5            5      imams    LF   14.726606  15.430361   m  P_165435   \n",
       "6            6      goont  none   16.943319  17.694142   m  P_165435   \n",
       "7            7       plan    HF   19.207660  19.846547   z  P_165435   \n",
       "8            8      women  none   21.359490  22.022061   z  P_165435   \n",
       "9            9    buidels    LF   23.540104  24.230132   m  P_165435   \n",
       "10          10      hield    HF   25.742965  26.511447   m  P_165435   \n",
       "11          11    polders    LF   28.025344  28.688923   z  P_165435   \n",
       "12          12     ponten    LF   30.202228  31.081533   z  P_165435   \n",
       "13          13  geslonden  none   32.595859  33.344787   m  P_165435   \n",
       "14          14       giep  none   34.859374  35.646302   m  P_165435   \n",
       "15          15     labber  none   37.160373  37.926389   z  P_165435   \n",
       "16          16      jugen  none   39.440160  40.254621   z  P_165435   \n",
       "17          17       hese    LF   41.768017  42.638338   z  P_165435   \n",
       "18          18       brum  none   44.151668  44.798299   m  P_165435   \n",
       "19          19     millag  none   46.311550  46.925954   m  P_165435   \n",
       "0            0       plan    HF    2.732804   3.668818   m  P_165618   \n",
       "1            1      imams    LF    5.182351   5.936878   z  P_165618   \n",
       "2            2       hese    LF    7.450601   8.209015   m  P_165618   \n",
       "3            3    polders    LF    9.722794  10.497127   z  P_165618   \n",
       "4            4       brum  none   12.010234  12.747876   z  P_165618   \n",
       "5            5     ponten    LF   14.264084  15.056405   m  P_165618   \n",
       "6            6  geslonden  none   16.569832  17.352689   m  P_165618   \n",
       "7            7       gals  none   18.865736  19.553098   m  P_165618   \n",
       "8            8      recht    HF   21.066896  21.785232   m  P_165618   \n",
       "9            9    buidels    LF   23.299407  24.008707   z  P_165618   \n",
       "10          10      hield    HF   25.522279  26.368387   z  P_165618   \n",
       "11          11       baby    HF   27.882381  28.616453   m  P_165618   \n",
       "12          12      slont  none   30.130719  30.952576   z  P_165618   \n",
       "13          13       giep  none   32.465605  33.192580   m  P_165618   \n",
       "14          14      jugen  none   34.705926  35.560509   z  P_165618   \n",
       "15          15      women  none   37.074502  37.872475   z  P_165618   \n",
       "16          16     labber  none   39.386173  40.209063   m  P_165618   \n",
       "17          17     millag  none   41.721994  42.624504   m  P_165618   \n",
       "18          18       boek    HF   44.137905  45.048897   z  P_165618   \n",
       "19          19      goont  none   46.561676  47.441955   z  P_165618   \n",
       "0            0      women  none    2.715994   3.768022   m  P_165752   \n",
       "1            1      goont  none    5.281677   6.272063   z  P_165752   \n",
       "2            2      recht    HF    7.786454   8.504235   m  P_165752   \n",
       "3            3      slont  none   10.018325  10.767570   z  P_165752   \n",
       "4            4       giep  none   12.280973  12.919527   m  P_165752   \n",
       "5            5       brum  none   14.431786  15.159372   z  P_165752   \n",
       "6            6     labber  none   16.672149  17.371160   z  P_165752   \n",
       "7            7       plan    HF   18.885155  19.503246   m  P_165752   \n",
       "8            8     ponten    LF   21.016644  21.704053   z  P_165752   \n",
       "9            9      imams    LF   23.218531  23.855498   m  P_165752   \n",
       "10          10      jugen  none   25.369400  26.015500   z  P_165752   \n",
       "11          11       boek    HF   27.527800  28.095664   m  P_165752   \n",
       "12          12    buidels    LF   29.608214  30.247373   z  P_165752   \n",
       "13          13       baby    HF   31.760937  32.335657   m  P_165752   \n",
       "14          14  geslonden  none   33.847859  34.431657   z  P_165752   \n",
       "15          15       hese    LF   35.945448  36.544224   z  P_165752   \n",
       "16          16     millag  none   38.061550  38.552478   m  P_165752   \n",
       "17          17      hield    HF   40.066236  40.999554   m  P_165752   \n",
       "18          18       gals  none   42.513991  43.071706   m  P_165752   \n",
       "19          19    polders    LF   44.585127  45.071525   z  P_165752   \n",
       "\n",
       "    reaction_time  \n",
       "0        1.120117  \n",
       "1        0.943152  \n",
       "2        0.902709  \n",
       "3        0.747552  \n",
       "4        0.727336  \n",
       "5        0.703755  \n",
       "6        0.750824  \n",
       "7        0.638888  \n",
       "8        0.662571  \n",
       "9        0.690029  \n",
       "10       0.768482  \n",
       "11       0.663579  \n",
       "12       0.879305  \n",
       "13       0.748927  \n",
       "14       0.786928  \n",
       "15       0.766016  \n",
       "16       0.814461  \n",
       "17       0.870322  \n",
       "18       0.646631  \n",
       "19       0.614404  \n",
       "0        0.936014  \n",
       "1        0.754527  \n",
       "2        0.758414  \n",
       "3        0.774332  \n",
       "4        0.737642  \n",
       "5        0.792321  \n",
       "6        0.782857  \n",
       "7        0.687362  \n",
       "8        0.718336  \n",
       "9        0.709300  \n",
       "10       0.846108  \n",
       "11       0.734072  \n",
       "12       0.821857  \n",
       "13       0.726975  \n",
       "14       0.854583  \n",
       "15       0.797973  \n",
       "16       0.822890  \n",
       "17       0.902511  \n",
       "18       0.910992  \n",
       "19       0.880279  \n",
       "0        1.052028  \n",
       "1        0.990385  \n",
       "2        0.717781  \n",
       "3        0.749245  \n",
       "4        0.638554  \n",
       "5        0.727586  \n",
       "6        0.699010  \n",
       "7        0.618091  \n",
       "8        0.687409  \n",
       "9        0.636967  \n",
       "10       0.646100  \n",
       "11       0.567864  \n",
       "12       0.639159  \n",
       "13       0.574720  \n",
       "14       0.583798  \n",
       "15       0.598776  \n",
       "16       0.490928  \n",
       "17       0.933318  \n",
       "18       0.557715  \n",
       "19       0.486398  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "participants = pd.read_csv('participants.csv')\n",
    "\n",
    "trials = pd.DataFrame()\n",
    "for participant_id in participants['id']:\n",
    "    participant_data = pd.read_csv(f'results/{participant_id}.csv')\n",
    "    trials = trials.append(participant_data)\n",
    "\n",
    "trials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "trials.rename(columns={'Unnamed: 0': 'trial_order'}, inplace=True)\n",
    "trials.to_csv('trials.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocessing done!\n",
    "\n",
    "It's time for summarizing and visualizing the data!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>cond</th>\n",
       "      <th>mean_RT</th>\n",
       "      <th>std_RT</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HF</td>\n",
       "      <td>0.770645</td>\n",
       "      <td>0.153705</td>\n",
       "      <td>25.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LF</td>\n",
       "      <td>0.709639</td>\n",
       "      <td>0.101922</td>\n",
       "      <td>25.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>none</td>\n",
       "      <td>0.759563</td>\n",
       "      <td>0.128258</td>\n",
       "      <td>25.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cond   mean_RT    std_RT        age\n",
       "0    HF  0.770645  0.153705  25.666667\n",
       "1    LF  0.709639  0.101922  25.666667\n",
       "2  none  0.759563  0.128258  25.666667"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "participants = pd.read_csv('participants.csv')\n",
    "trials = pd.read_csv('trials.csv')\n",
    "\n",
    "merged = trials.merge(participants, on='id')\n",
    "\n",
    "# trials.groupby(by='id').describe()\n",
    "summary = merged.groupby(by='cond').aggregate(\n",
    "    mean_RT=pd.NamedAgg('reaction_time', np.mean),\n",
    "    std_RT=pd.NamedAgg('reaction_time', np.std),\n",
    "    age=pd.NamedAgg('age', np.mean)\n",
    ")\n",
    "\n",
    "summary.reset_index(inplace=True)\n",
    "\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = (\n",
    "    ggplot(gg.aes(x='cond', y='reaction_time', fill='cond'), data=trials) +\n",
    "    gg.geom_point(alpha=0.5)\n",
    ")\n",
    "plot.draw()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = (\n",
    "    ggplot(gg.aes(x = 'reaction_time', fill = 'cond'), data=trials) +\n",
    "    gg.geom_density(alpha = 0.5)\n",
    ")\n",
    "plot.draw()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = (\n",
    "    ggplot(gg.aes(x = 'cond', y = 'reaction_time', fill = 'cond'), data=trials) +\n",
    "    gg.geom_boxplot()\n",
    ")\n",
    "plot.draw()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
  },
  "kernelspec": {
   "display_name": "Python 3.9.12 64-bit",
   "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": "3.9.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}