tools/7_test_the_hypotheses.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considers each research questions and hypotheses studied in [1] and generates the results in [1]\n",
    "\n",
    "The research questions and hypotheses are listed as follows:\n",
    "\n",
    "* RQ1 Lexical alignment: How do the interlocutors use expressions related to the task? Is this associated with task success?\n",
    "    * H1.1: Task-specific referents become routine early for more successful teams.\n",
    "    * H1.2: Hesitation phenomena are more likely to occur in the vicinity of priming and establishment of task-specific referents for more successful teams.\n",
    "\n",
    "* RQ2 Behavioural alignment: How do the interlocutors follow up these expressions with actions? Is this associated with task success?\n",
    "    * H2.1: Instructions are more likely to be followed by a corresponding action early in the dialogue for more successful teams.\n",
    "    * H2.2: When instructions are followed by a corresponding or a different action, the action is more likely to be in the vicinity of information management phenomena for more successful teams.\n",
    "\n",
    "\n",
    "[1] U. Norman\\*, T. Dinkar\\*, B. Bruno, and C. Clavel, \"Studying Alignment in a Collaborative Learning Activity via Automatic Methods: The Link Between What We Say and Do,\" Dialogue & Discourse, 13(2), 1–48. https://doi.org/10.5210/dad.2022.201"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import pickle\n",
    "\n",
    "import pathlib as pl\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# for statistical tests\n",
    "# - to compute Spearman's Rank Correlation Coefficient\n",
    "# - to perform Mann-Whitney U test\n",
    "# - to perform Kruskal-Wallis H test\n",
    "from scipy.stats import spearmanr, mannwhitneyu, kruskal\n",
    "\n",
    "# to estimate effect size by Cliff's Delta (also others)\n",
    "from effsize.effsize import two_group_difference\n",
    "\n",
    "# for visualisation\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter, AutoMinorLocator\n",
    "import seaborn as sns\n",
    "\n",
    "# data reading utilities\n",
    "from read_utils import read_tables\n",
    "\n",
    "# matching utilities\n",
    "from match_utils import Instruction, \\\n",
    "    Do, Match, Mismatch, Nonmatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set figure style.\n",
    "sns.set_theme(style='whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define paths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inputs.\n",
    "processed_data_dir = pl.Path('../processed_data')\n",
    "log_features_file = processed_data_dir.joinpath(\n",
    "    'log_features/justhink19_log_features_task_level.csv')\n",
    "\n",
    "learning_file = processed_data_dir.joinpath(\n",
    "    'learning_features/justhink19_learning_features.csv')\n",
    "\n",
    "annot_corpus_dir = processed_data_dir.joinpath('annotated_corpus')\n",
    "annot_corpus_pickle_file = annot_corpus_dir.joinpath(\n",
    "    'justhing19_annotated_corpus.pickle')\n",
    "\n",
    "routines_dir = processed_data_dir.joinpath('routines')\n",
    "tokens_dir = processed_data_dir.joinpath('tokens')\n",
    "\n",
    "# Also log features at attempt and turn levels, for descriptive statistics.\n",
    "attempt_features_file = processed_data_dir.joinpath(\n",
    "    'log_features/justhink19_log_features_attempt_level.csv')\n",
    "turn_features_file = processed_data_dir.joinpath(\n",
    "    'log_features/justhink19_log_features_turn_level.csv')\n",
    "\n",
    "# Outputs.\n",
    "plots_dir = pl.Path('../figures')\n",
    "\n",
    "# Create output directories if they do not exist.\n",
    "for folder in [plots_dir]:\n",
    "    if not folder.exists():\n",
    "        folder.mkdir(parents=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": []
   },
   "source": [
    "## Load data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read learning data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_df = pd.read_csv(learning_file, index_col='team_no')\n",
    "\n",
    "# learning_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read task features data (at the team level)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "task_df = pd.read_csv(log_features_file, index_col='team_no')\n",
    "\n",
    "# task_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read task features data (at the attempt and turn level).\n",
    "These are only used to compute descriptive statistics like duration of a turn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "attempt_df = pd.read_csv(attempt_features_file, index_col='team_no')\n",
    "turn_df = pd.read_csv(turn_features_file, index_col='team_no')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read annotated corpus (of dialogue and actions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with annot_corpus_pickle_file.open('rb') as handle:\n",
    "    corpus_dfs = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read routine tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading routine files from ../processed_data/routines.\n",
      "routine 10 files found.\n",
      "File justhink19_routines_07 belongs to team  7\n",
      "File justhink19_routines_08 belongs to team  8\n",
      "File justhink19_routines_09 belongs to team  9\n",
      "File justhink19_routines_10 belongs to team 10\n",
      "File justhink19_routines_11 belongs to team 11\n",
      "File justhink19_routines_17 belongs to team 17\n",
      "File justhink19_routines_18 belongs to team 18\n",
      "File justhink19_routines_20 belongs to team 20\n",
      "File justhink19_routines_28 belongs to team 28\n",
      "File justhink19_routines_47 belongs to team 47\n"
     ]
    }
   ],
   "source": [
    "routine_dfs = read_tables(routines_dir, form='routine')\n",
    "\n",
    "# routines_dfs[7].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading transcript files from ../processed_data/tokens.\n",
      "transcript 10 files found.\n",
      "File justhink19_tokens_07 belongs to team  7\n",
      "File justhink19_tokens_08 belongs to team  8\n",
      "File justhink19_tokens_09 belongs to team  9\n",
      "File justhink19_tokens_10 belongs to team 10\n",
      "File justhink19_tokens_11 belongs to team 11\n",
      "File justhink19_tokens_17 belongs to team 17\n",
      "File justhink19_tokens_18 belongs to team 18\n",
      "File justhink19_tokens_20 belongs to team 20\n",
      "File justhink19_tokens_28 belongs to team 28\n",
      "File justhink19_tokens_47 belongs to team 47\n",
      "Transcript of  7 has 3287 utterances\n",
      "Transcript of  8 has 3464 utterances\n",
      "Transcript of  9 has 4536 utterances\n",
      "Transcript of 10 has 2149 utterances\n",
      "Transcript of 11 has 3092 utterances\n",
      "Transcript of 17 has 1762 utterances\n",
      "Transcript of 18 has 1446 utterances\n",
      "Transcript of 20 has 2533 utterances\n",
      "Transcript of 28 has 1816 utterances\n",
      "Transcript of 47 has 2331 utterances\n"
     ]
    }
   ],
   "source": [
    "tokens_dfs = read_tables(tokens_dir, form='transcript')\n",
    "\n",
    "# tokens_dfs[7].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construct task success tables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare a learning outcomes table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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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>Team No</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>20</th>\n",
       "      <th>28</th>\n",
       "      <th>47</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>learn (%)</th>\n",
       "      <td>14.2857</td>\n",
       "      <td>-8.75</td>\n",
       "      <td>22.5</td>\n",
       "      <td>36.6667</td>\n",
       "      <td>36.6667</td>\n",
       "      <td>25</td>\n",
       "      <td>-56.25</td>\n",
       "      <td>-52.0833</td>\n",
       "      <td>0</td>\n",
       "      <td>-12.6984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pretests</th>\n",
       "      <td>70, 30</td>\n",
       "      <td>50, 80</td>\n",
       "      <td>50, 60</td>\n",
       "      <td>40, 50</td>\n",
       "      <td>70, 50</td>\n",
       "      <td>40, 40</td>\n",
       "      <td>40, 80</td>\n",
       "      <td>80, 30</td>\n",
       "      <td>100, 60</td>\n",
       "      <td>90, 70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>posttests</th>\n",
       "      <td>70, 50</td>\n",
       "      <td>60, 50</td>\n",
       "      <td>60, 70</td>\n",
       "      <td>60, 70</td>\n",
       "      <td>80, 70</td>\n",
       "      <td>50, 60</td>\n",
       "      <td>10, 50</td>\n",
       "      <td>50, 10</td>\n",
       "      <td>100, 60</td>\n",
       "      <td>80, 60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Team No         7        8        9        10       11       17       18  \\\n",
       "learn (%)  14.2857    -8.75     22.5  36.6667  36.6667       25   -56.25   \n",
       "pretests    70, 30   50, 80   50, 60   40, 50   70, 50   40, 40   40, 80   \n",
       "posttests   70, 50   60, 50   60, 70   60, 70   80, 70   50, 60   10, 50   \n",
       "\n",
       "Team No         20       28       47  \n",
       "learn (%) -52.0833        0 -12.6984  \n",
       "pretests    80, 30  100, 60   90, 70  \n",
       "posttests   50, 10  100, 60   80, 60  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = learning_df[['RLG', 'A_pretest', 'A_posttest', 'B_pretest', 'B_posttest']].copy()\n",
    "df = df[df.index.isin(corpus_dfs.keys())]\n",
    "df.insert(0, 'learn (%)', 100*df['RLG'])\n",
    "df = df.drop(['RLG'], axis=1)\n",
    "df.index.name = 'Team No'\n",
    "for prefix in ['pretest', 'posttest']:\n",
    "    df['{}s'.format(prefix)] = [\n",
    "        '{:3.0f},{:3.0f}'.format(\n",
    "            100*df.loc[t]['A_{}'.format(prefix)],\n",
    "            100*df.loc[t]['B_{}'.format(prefix)])\n",
    "        for t in df.index]\n",
    "df = df[['learn (%)', 'pretests', 'posttests']]\n",
    "learn_df = df.T\n",
    "learn_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare a task performance table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([7, 8, 9, 10, 11, 17, 18, 20, 28, 47])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus_dfs.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>team_no</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>20</th>\n",
       "      <th>28</th>\n",
       "      <th>47</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>error (%)</th>\n",
       "      <td>13.636364</td>\n",
       "      <td>13.636364</td>\n",
       "      <td>4.545455</td>\n",
       "      <td>13.636364</td>\n",
       "      <td>4.545455</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.181818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.636364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "team_no           7          8         9          10        11   17  \\\n",
       "error (%)  13.636364  13.636364  4.545455  13.636364  4.545455  0.0   \n",
       "\n",
       "team_no           18   20   28         47  \n",
       "error (%)  18.181818  0.0  0.0  13.636364  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = task_df[['min_error']]\n",
    "df = df[df.index.isin(corpus_dfs.keys())]\n",
    "df['error (%)'] = 100 * df['min_error']\n",
    "df = df.drop(['min_error'], axis=1)\n",
    "perf_df = df.T\n",
    "perf_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare sorting orders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(        learn (%) pretests posttests\n",
       " Team No                             \n",
       " 10        36.6667   40, 50    60, 70\n",
       " 11        36.6667   70, 50    80, 70\n",
       " 17             25   40, 40    50, 60\n",
       " 9            22.5   50, 60    60, 70\n",
       " 7         14.2857   70, 30    70, 50\n",
       " 28              0  100, 60   100, 60\n",
       " 8           -8.75   50, 80    60, 50\n",
       " 47       -12.6984   90, 70    80, 60\n",
       " 20       -52.0833   80, 30    50, 10\n",
       " 18         -56.25   40, 80    10, 50,\n",
       " ['10', '11', '17', '9', '7', '28', '8', '47', '20', '18'],\n",
       " ['28'])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = learn_df.T.sort_values('learn (%)', ascending=False)\n",
    "learn_order = [str(t) for t in df.index]\n",
    "# learn_seps = ['28', '8']\n",
    "# learn_seps = ['7', '28']\n",
    "learn_seps = ['28']\n",
    "df, learn_order, learn_seps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(         error (%)\n",
       " team_no           \n",
       " 17        0.000000\n",
       " 20        0.000000\n",
       " 28        0.000000\n",
       " 9         4.545455\n",
       " 11        4.545455\n",
       " 7        13.636364\n",
       " 8        13.636364\n",
       " 10       13.636364\n",
       " 47       13.636364\n",
       " 18       18.181818,\n",
       " ['17', '20', '28', '9', '11', '7', '8', '10', '47', '18'],\n",
       " ['28'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = perf_df.T.sort_values(['error (%)'])\n",
    "perf_order = [str(t) for t in df.index]\n",
    "perf_seps = ['28']\n",
    "df, perf_order, perf_seps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['28', '17', '20', '11', '9', '47', '10', '8', '7', '18']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [28, 20, 17, 11, 9, 47, 10, 8, 7, 18]\n",
    "# Order first by performance and then by duration (for the ties).\n",
    "l = [28, 17, 20, 11, 9, 47, 10, 8, 7, 18]\n",
    "l = [str(t) for t in l]\n",
    "perf_order = l\n",
    "perf_seps = ['20']\n",
    "perf_order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Override utterance numbers in the corpus for relinking with the routines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for team_no, df in corpus_dfs.items():\n",
    "    \n",
    "    df['utterance_no'] = -1\n",
    "    \n",
    "    cond = df.subject.isin(['A', 'B']) & df.verb.isin(['says'])\n",
    "    df.loc[cond, 'utterance_no'] = range(len(df[cond]))\n",
    "    \n",
    "    corpus_dfs[team_no] = df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do additional annotations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Annotate the corpora with start and end times scaled to unit interval. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 7: 26.4 mins\n",
      " 8: 26.2 mins\n",
      " 9: 36.0 mins\n",
      "10: 23.1 mins\n",
      "11: 26.4 mins\n",
      "17: 17.9 mins\n",
      "18: 30.6 mins\n",
      "20: 19.3 mins\n",
      "28: 11.2 mins\n",
      "47: 19.6 mins\n"
     ]
    }
   ],
   "source": [
    "for team_no, df in corpus_dfs.items():\n",
    "    duration = df.iloc[-1]['end']\n",
    "    print('{:2d}: {:.1f} mins'.format(team_no, duration/60))\n",
    "    df['scaled_start'] = df['start'] / duration\n",
    "    df['scaled_end'] = df['end'] / duration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Annotate the corpora with utterance positions scaled to unit interval. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for team_no, df in corpus_dfs.items():\n",
    "    # Utterances are zero-based numbered (first utterance has number 0).\n",
    "    n_utterances = max(df['utterance_no']) + 1\n",
    "\n",
    "    df['scaled_utterance_no'] = [(no + 1) / n_utterances if no != -1 else -1\n",
    "                                 for no in df['utterance_no']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Annotate the corpora with error values upon submission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cost(x):\n",
    "    '''the sum of the edge costs of the submitted solution'''\n",
    "    submits = x[x['verb'].isin(['submits'])]\n",
    "    if len(submits) > 0:\n",
    "        return int(submits['object'].iloc[-1].split()[0].split('=')[-1])\n",
    "    else:\n",
    "        return np.nan\n",
    "    \n",
    "def error(x, opt_cost=22):\n",
    "    '''relative error, i.e. absolute error divided by the optimal cost'''\n",
    "    return 1.0 * (cost(x) - opt_cost) / opt_cost\n",
    "\n",
    "for team_no, df in corpus_dfs.items():\n",
    "    df.groupby('attempt_no')\n",
    "    df['cost'] = df.groupby(df.index).apply(cost)\n",
    "    df['error'] = df.groupby(df.index).apply(error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Annotate the routines with establishment and priming positions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 608\n",
      "8 624\n",
      "9 773\n",
      "10 431\n",
      "11 540\n",
      "17 297\n",
      "18 331\n",
      "20 494\n",
      "28 334\n",
      "47 355\n"
     ]
    }
   ],
   "source": [
    "def get_priming_utterance_no(df, num_utterances):\n",
    "    return [(row['utterances'][0]+1) / num_utterances\n",
    "            for i, row in df.iterrows()]\n",
    "\n",
    "\n",
    "# Annotate routine tables with establishment and priming position and times.\n",
    "# Normalise/scale as a fraction of the total number of utterances for comparison.\n",
    "for team_no, df in routine_dfs.items():\n",
    "    corpus_df = corpus_dfs[team_no]\n",
    "    df['team_no'] = team_no\n",
    "\n",
    "    # Utterances are zero-based numbered (first utterance has number 0).\n",
    "    n_utterances = max(corpus_df['utterance_no']) + 1\n",
    "    print(team_no, n_utterances)\n",
    "\n",
    "    # Retrieve the total duration.\n",
    "    duration = corpus_df.iloc[-1]['end']\n",
    "\n",
    "    # Annotate the establishment position (i.e. utterance no).\n",
    "    df['establish_no'] = df['Establishment turn']\n",
    "    # Annotate the establishment time (i.e. utterance end time).\n",
    "    df['establish_time'] = [corpus_df[corpus_df['utterance_no'] == i].iloc[0]['end']\n",
    "                            for i in df['establish_no']]\n",
    "    # Annotate establishment position and time scaled to unit interval.\n",
    "    df['scaled_establish_no'] = (df['establish_no'] + 1) / n_utterances\n",
    "    df['scaled_establish_time'] = df['establish_time'] / duration\n",
    "\n",
    "    # Annotate the priming position (i.e. utterance no).\n",
    "    df['priming_no'] = [ll[0] for ll in df['utterances']]\n",
    "    # Annotate the establishment time (i.e. utterance end time).\n",
    "    df['priming_time'] = [corpus_df[corpus_df['utterance_no'] == i].iloc[0]['end']\n",
    "                          for i in df['priming_no']]\n",
    "    # Annotate establishment position and time scaled to unit interval.\n",
    "    df['scaled_priming_no'] = (df['priming_no'] + 1) / n_utterances\n",
    "    df['scaled_priming_time'] = df['priming_time'] / duration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split matches, mismatches and non-matches into columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# flatten a bit\n",
    "for team_no, df in corpus_dfs.items():\n",
    "    df['n_instructions'] = df.instructions.apply(len)\n",
    "    matching = df.matching.apply(\n",
    "        lambda x: [a for a in x if isinstance(a, Do)])\n",
    "    df['do'] = matching.apply(lambda x: x[0] if len(x) > 0 else None)\n",
    "\n",
    "    matching = df.matching.apply(lambda x: [a for a in x if isinstance(a, Match)])\n",
    "    df['match'] = matching.apply(lambda x: x[0] if len(x) > 0 else None)\n",
    "    df['is_match'] =  df['match'].notna()\n",
    "\n",
    "    matching = df.matching.apply(lambda x: [a for a in x if isinstance(a, Mismatch)])\n",
    "    df['mismatch'] = matching.apply(lambda x: x[0] if len(x) > 0 else None)\n",
    "    df['is_mismatch'] =  df['mismatch'].notna()\n",
    "    \n",
    "    df['is_match_or_mismatch']  = df.is_match | df.is_mismatch\n",
    "    \n",
    "    matching = df.matching.apply(lambda x: [a for a in x if isinstance(a, Nonmatch)])\n",
    "    df['nonmatch'] = matching.apply(lambda x: x[0] if len(x) > 0 else None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# corpus_dfs[7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construct helper tables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge all of the corpus into one table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# corpus_dfs[7].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [corpus_dfs[k]  for k in sorted(corpus_dfs)]\n",
    "all_corpus_df = pd.concat(l, ignore_index=True)\n",
    "all_corpus_df['team_no'] = all_corpus_df['team_no'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge all of the routine tables into one table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [routine_dfs[k] for k in sorted(routine_dfs)]\n",
    "all_routines_df = pd.concat(l, ignore_index=True)\n",
    "all_routines_df['team_no'] = all_routines_df['team_no'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print number of utterances, tokens and duration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>num_tokens</th>\n",
       "      <th>num_utterances</th>\n",
       "      <th>duration (secs)</th>\n",
       "      <th>duration (mins)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team_no</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1816</td>\n",
       "      <td>334</td>\n",
       "      <td>670.087</td>\n",
       "      <td>11.168117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1762</td>\n",
       "      <td>297</td>\n",
       "      <td>1076.721</td>\n",
       "      <td>17.945350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2533</td>\n",
       "      <td>494</td>\n",
       "      <td>1156.613</td>\n",
       "      <td>19.276883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3092</td>\n",
       "      <td>540</td>\n",
       "      <td>1586.006</td>\n",
       "      <td>26.433433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4536</td>\n",
       "      <td>773</td>\n",
       "      <td>2160.630</td>\n",
       "      <td>36.010500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2331</td>\n",
       "      <td>355</td>\n",
       "      <td>1178.847</td>\n",
       "      <td>19.647450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2149</td>\n",
       "      <td>431</td>\n",
       "      <td>1384.955</td>\n",
       "      <td>23.082583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3464</td>\n",
       "      <td>624</td>\n",
       "      <td>1570.423</td>\n",
       "      <td>26.173717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3287</td>\n",
       "      <td>608</td>\n",
       "      <td>1581.443</td>\n",
       "      <td>26.357383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1446</td>\n",
       "      <td>331</td>\n",
       "      <td>1835.823</td>\n",
       "      <td>30.597050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_tokens  num_utterances  duration (secs)  duration (mins)\n",
       "team_no                                                              \n",
       "28             1816             334          670.087        11.168117\n",
       "17             1762             297         1076.721        17.945350\n",
       "20             2533             494         1156.613        19.276883\n",
       "11             3092             540         1586.006        26.433433\n",
       "9              4536             773         2160.630        36.010500\n",
       "47             2331             355         1178.847        19.647450\n",
       "10             2149             431         1384.955        23.082583\n",
       "8              3464             624         1570.423        26.173717\n",
       "7              3287             608         1581.443        26.357383\n",
       "18             1446             331         1835.823        30.597050"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {\n",
    "    'team_no': [],\n",
    "    'num_tokens': [],\n",
    "    'num_utterances': [],\n",
    "    'duration (secs)': [],\n",
    "}\n",
    "for team_no, tokens_df in tokens_dfs.items():\n",
    "    corpus_df = corpus_dfs[team_no]\n",
    "    d['team_no'].append(team_no)\n",
    "    d['num_tokens'].append(max(tokens_df['token_no'])+1)\n",
    "    d['num_utterances'].append(max(tokens_df['utterance_no'])+1)\n",
    "    d['duration (secs)'].append(corpus_df.iloc[-1]['end'])\n",
    "\n",
    "df = pd.DataFrame.from_dict(d, orient='index').T\n",
    "\n",
    "cols = ['team_no', 'num_tokens', 'num_utterances']\n",
    "for c in cols:\n",
    "    df[c] = df[c].astype(int)\n",
    "df['team_no'] = df['team_no'].astype(str)\n",
    "\n",
    "df.set_index('team_no', inplace=True)\n",
    "df = df.loc[perf_order]\n",
    "df['duration (mins)'] = df['duration (secs)'] / 60\n",
    "counts_df = df.copy()\n",
    "counts_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construct descriptive statistics table for the transcribed teams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>num_tokens</th>\n",
       "      <th>num_utterances</th>\n",
       "      <th>duration (secs)</th>\n",
       "      <th>duration (mins)</th>\n",
       "      <th>num_attempts</th>\n",
       "      <th>num_turns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2641.600000</td>\n",
       "      <td>478.700000</td>\n",
       "      <td>1420.154800</td>\n",
       "      <td>23.669247</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>50.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>949.864692</td>\n",
       "      <td>156.978449</td>\n",
       "      <td>422.513096</td>\n",
       "      <td>7.041885</td>\n",
       "      <td>4.742245</td>\n",
       "      <td>21.396781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1446.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>670.087000</td>\n",
       "      <td>11.168117</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4536.000000</td>\n",
       "      <td>773.000000</td>\n",
       "      <td>2160.630000</td>\n",
       "      <td>36.010500</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>98.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_tokens  num_utterances  duration (secs)  duration (mins)  \\\n",
       "mean  2641.600000      478.700000      1420.154800        23.669247   \n",
       "std    949.864692      156.978449       422.513096         7.041885   \n",
       "min   1446.000000      297.000000       670.087000        11.168117   \n",
       "max   4536.000000      773.000000      2160.630000        36.010500   \n",
       "\n",
       "      num_attempts  num_turns  \n",
       "mean      9.400000  50.400000  \n",
       "std       4.742245  21.396781  \n",
       "min       4.000000  28.000000  \n",
       "max      19.000000  98.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = counts_df.copy()\n",
    "df['num_attempts'] = [task_df.loc[int(t), 'num_attempt'] for t in df.index]\n",
    "df['num_turns'] = [task_df.loc[int(t), 'num_turn'] for t in df.index]\n",
    "\n",
    "l = ['mean', 'std', 'min', 'max']\n",
    "df = df.describe().T[l].T\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Attempts that are not submitted (via intervention -> error is None) are dropped.\n",
    "submitted_attempts = attempt_df.dropna().loc[counts_df.index.astype(int)]\n",
    "\n",
    "turns = turn_df.loc[counts_df.index.astype(int)]\n",
    "# Filter for only the turns with two edit actions.\n",
    "# Since a team can start a turn but not be able to finish it (via intervention).\n",
    "turns = turns[turns.num_edit == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['attempt_duration (secs)'] = submitted_attempts['duration'].agg(l)\n",
    "df['attempt_duration (min)'] = df['attempt_duration (secs)'] / 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_tokens = tokens_df.groupby('utterance_no').apply(len)\n",
    "df['num_tokens_per_utterance'] = num_tokens.agg(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['turn_duration (secs)'] = turns['duration'].agg(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>num_tokens</th>\n",
       "      <td>2641.600000</td>\n",
       "      <td>949.864692</td>\n",
       "      <td>1446.000000</td>\n",
       "      <td>4536.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_utterances</th>\n",
       "      <td>478.700000</td>\n",
       "      <td>156.978449</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>773.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration (secs)</th>\n",
       "      <td>1420.154800</td>\n",
       "      <td>422.513096</td>\n",
       "      <td>670.087000</td>\n",
       "      <td>2160.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration (mins)</th>\n",
       "      <td>23.669247</td>\n",
       "      <td>7.041885</td>\n",
       "      <td>11.168117</td>\n",
       "      <td>36.010500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_attempts</th>\n",
       "      <td>9.400000</td>\n",
       "      <td>4.742245</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_turns</th>\n",
       "      <td>50.400000</td>\n",
       "      <td>21.396781</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>98.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attempt_duration (secs)</th>\n",
       "      <td>153.411609</td>\n",
       "      <td>139.831531</td>\n",
       "      <td>40.820000</td>\n",
       "      <td>766.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attempt_duration (min)</th>\n",
       "      <td>2.556860</td>\n",
       "      <td>2.330526</td>\n",
       "      <td>0.680333</td>\n",
       "      <td>12.771833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_tokens_per_utterance</th>\n",
       "      <td>6.566197</td>\n",
       "      <td>5.352330</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>turn_duration (secs)</th>\n",
       "      <td>24.969746</td>\n",
       "      <td>30.327900</td>\n",
       "      <td>1.137000</td>\n",
       "      <td>240.198000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 mean         std          min          max\n",
       "num_tokens                2641.600000  949.864692  1446.000000  4536.000000\n",
       "num_utterances             478.700000  156.978449   297.000000   773.000000\n",
       "duration (secs)           1420.154800  422.513096   670.087000  2160.630000\n",
       "duration (mins)             23.669247    7.041885    11.168117    36.010500\n",
       "num_attempts                 9.400000    4.742245     4.000000    19.000000\n",
       "num_turns                   50.400000   21.396781    28.000000    98.000000\n",
       "attempt_duration (secs)    153.411609  139.831531    40.820000   766.310000\n",
       "attempt_duration (min)       2.556860    2.330526     0.680333    12.771833\n",
       "num_tokens_per_utterance     6.566197    5.352330     1.000000    32.000000\n",
       "turn_duration (secs)        24.969746   30.327900     1.137000   240.198000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mark the routines that are established in the common time interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.168116666666666"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_duration = counts_df['duration (secs)'].min()\n",
    "common_duration / 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_routines_df['in_common_time'] = all_routines_df.establish_time <= common_duration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mark the matchings that are in the common time interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_corpus_df['in_common_time'] = all_corpus_df.end <= common_duration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RQ1: How do the interlocutors use task specific referents? Does this link to task success?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## H1.1: Task specific referents become routine early for more successful teams."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute mean of medians and SD (and mean)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "combined mean = 60.99% , combined SD = 22.2%\n",
      "mean of the medians = 63.0%\n"
     ]
    }
   ],
   "source": [
    "def compute_combined_mean_median_sd(df, of, by='team_no'):\n",
    "    # Compute combined mean, median and SD.\n",
    "    A = [np.array(l) for l in (df.groupby(by)[of].apply(list))]\n",
    "\n",
    "    N = float(sum([i.size for i in A]))\n",
    "    mean_ = sum([i.sum() for i in A])/N\n",
    "    std_ = np.sqrt(sum([((i-mean_)**2).sum() for i in A])/N)\n",
    "    print('combined mean = {:.2%} , combined SD = {:.1%}'.format(mean_, std_))\n",
    "\n",
    "    medians = [np.median(i) for i in A]\n",
    "    mean_medians = np.mean(medians)\n",
    "    print('mean of the medians = {:.1%}'.format(mean_medians))\n",
    "\n",
    "\n",
    "of = 'scaled_establish_time'\n",
    "compute_combined_mean_median_sd(all_routines_df, of=of)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Spearman's correlation with task performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>in_common_median</th>\n",
       "      <th>in_common_Q1</th>\n",
       "      <th>in_common_Q3</th>\n",
       "      <th>in_common_IQR</th>\n",
       "      <th>median</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q3</th>\n",
       "      <th>IQR</th>\n",
       "      <th>scaled_median</th>\n",
       "      <th>scaled_Q1</th>\n",
       "      <th>scaled_Q3</th>\n",
       "      <th>scaled_IQR</th>\n",
       "      <th>error (%)</th>\n",
       "      <th>learn (%)</th>\n",
       "      <th>pretests</th>\n",
       "      <th>posttests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>560.0660</td>\n",
       "      <td>485.00500</td>\n",
       "      <td>560.06600</td>\n",
       "      <td>75.0610</td>\n",
       "      <td>782.8420</td>\n",
       "      <td>694.6340</td>\n",
       "      <td>877.55500</td>\n",
       "      <td>182.92100</td>\n",
       "      <td>0.727061</td>\n",
       "      <td>0.645138</td>\n",
       "      <td>0.815025</td>\n",
       "      <td>0.169887</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25</td>\n",
       "      <td>40, 40</td>\n",
       "      <td>50, 60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>559.0440</td>\n",
       "      <td>545.69300</td>\n",
       "      <td>628.13850</td>\n",
       "      <td>82.4455</td>\n",
       "      <td>662.4940</td>\n",
       "      <td>558.6325</td>\n",
       "      <td>800.08400</td>\n",
       "      <td>241.45150</td>\n",
       "      <td>0.572788</td>\n",
       "      <td>0.482990</td>\n",
       "      <td>0.691747</td>\n",
       "      <td>0.208757</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-52.0833</td>\n",
       "      <td>80, 30</td>\n",
       "      <td>50, 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>404.4535</td>\n",
       "      <td>288.41000</td>\n",
       "      <td>592.56200</td>\n",
       "      <td>304.1520</td>\n",
       "      <td>404.4535</td>\n",
       "      <td>288.4100</td>\n",
       "      <td>592.56200</td>\n",
       "      <td>304.15200</td>\n",
       "      <td>0.603584</td>\n",
       "      <td>0.430407</td>\n",
       "      <td>0.884306</td>\n",
       "      <td>0.453899</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>100, 60</td>\n",
       "      <td>100, 60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>442.3460</td>\n",
       "      <td>395.33100</td>\n",
       "      <td>493.30800</td>\n",
       "      <td>97.9770</td>\n",
       "      <td>1112.7810</td>\n",
       "      <td>578.6865</td>\n",
       "      <td>1449.82800</td>\n",
       "      <td>871.14150</td>\n",
       "      <td>0.515026</td>\n",
       "      <td>0.267832</td>\n",
       "      <td>0.671021</td>\n",
       "      <td>0.403189</td>\n",
       "      <td>4.545455</td>\n",
       "      <td>22.5</td>\n",
       "      <td>50, 60</td>\n",
       "      <td>60, 70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>119.3000</td>\n",
       "      <td>100.56500</td>\n",
       "      <td>508.10400</td>\n",
       "      <td>407.5390</td>\n",
       "      <td>899.3805</td>\n",
       "      <td>623.3015</td>\n",
       "      <td>1240.56375</td>\n",
       "      <td>617.26225</td>\n",
       "      <td>0.567073</td>\n",
       "      <td>0.393001</td>\n",
       "      <td>0.782194</td>\n",
       "      <td>0.389193</td>\n",
       "      <td>4.545455</td>\n",
       "      <td>36.6667</td>\n",
       "      <td>70, 50</td>\n",
       "      <td>80, 70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>645.6470</td>\n",
       "      <td>645.64700</td>\n",
       "      <td>645.64700</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1043.5060</td>\n",
       "      <td>869.4585</td>\n",
       "      <td>1322.89650</td>\n",
       "      <td>453.43800</td>\n",
       "      <td>0.659844</td>\n",
       "      <td>0.549788</td>\n",
       "      <td>0.836512</td>\n",
       "      <td>0.286724</td>\n",
       "      <td>13.636364</td>\n",
       "      <td>14.2857</td>\n",
       "      <td>70, 30</td>\n",
       "      <td>70, 50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>570.8535</td>\n",
       "      <td>431.58175</td>\n",
       "      <td>586.45475</td>\n",
       "      <td>154.8730</td>\n",
       "      <td>1022.9265</td>\n",
       "      <td>679.9960</td>\n",
       "      <td>1227.02775</td>\n",
       "      <td>547.03175</td>\n",
       "      <td>0.651370</td>\n",
       "      <td>0.433002</td>\n",
       "      <td>0.781336</td>\n",
       "      <td>0.348334</td>\n",
       "      <td>13.636364</td>\n",
       "      <td>-8.75</td>\n",
       "      <td>50, 80</td>\n",
       "      <td>60, 50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>574.9370</td>\n",
       "      <td>411.86150</td>\n",
       "      <td>578.53100</td>\n",
       "      <td>166.6695</td>\n",
       "      <td>902.1730</td>\n",
       "      <td>715.0690</td>\n",
       "      <td>1001.10200</td>\n",
       "      <td>286.03300</td>\n",
       "      <td>0.651410</td>\n",
       "      <td>0.516312</td>\n",
       "      <td>0.722841</td>\n",
       "      <td>0.206529</td>\n",
       "      <td>13.636364</td>\n",
       "      <td>36.6667</td>\n",
       "      <td>40, 50</td>\n",
       "      <td>60, 70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>501.6130</td>\n",
       "      <td>441.36300</td>\n",
       "      <td>575.87900</td>\n",
       "      <td>134.5160</td>\n",
       "      <td>782.7380</td>\n",
       "      <td>539.1305</td>\n",
       "      <td>989.79025</td>\n",
       "      <td>450.65975</td>\n",
       "      <td>0.663986</td>\n",
       "      <td>0.457337</td>\n",
       "      <td>0.839626</td>\n",
       "      <td>0.382289</td>\n",
       "      <td>13.636364</td>\n",
       "      <td>-12.6984</td>\n",
       "      <td>90, 70</td>\n",
       "      <td>80, 60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1265.6490</td>\n",
       "      <td>1007.0020</td>\n",
       "      <td>1550.69400</td>\n",
       "      <td>543.69200</td>\n",
       "      <td>0.689418</td>\n",
       "      <td>0.548529</td>\n",
       "      <td>0.844686</td>\n",
       "      <td>0.296157</td>\n",
       "      <td>18.181818</td>\n",
       "      <td>-56.25</td>\n",
       "      <td>40, 80</td>\n",
       "      <td>10, 50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    in_common_median  in_common_Q1  in_common_Q3  in_common_IQR     median  \\\n",
       "17          560.0660     485.00500     560.06600        75.0610   782.8420   \n",
       "20          559.0440     545.69300     628.13850        82.4455   662.4940   \n",
       "28          404.4535     288.41000     592.56200       304.1520   404.4535   \n",
       "9           442.3460     395.33100     493.30800        97.9770  1112.7810   \n",
       "11          119.3000     100.56500     508.10400       407.5390   899.3805   \n",
       "7           645.6470     645.64700     645.64700         0.0000  1043.5060   \n",
       "8           570.8535     431.58175     586.45475       154.8730  1022.9265   \n",
       "10          574.9370     411.86150     578.53100       166.6695   902.1730   \n",
       "47          501.6130     441.36300     575.87900       134.5160   782.7380   \n",
       "18               NaN           NaN           NaN            NaN  1265.6490   \n",
       "\n",
       "           Q1          Q3        IQR  scaled_median  scaled_Q1  scaled_Q3  \\\n",
       "17   694.6340   877.55500  182.92100       0.727061   0.645138   0.815025   \n",
       "20   558.6325   800.08400  241.45150       0.572788   0.482990   0.691747   \n",
       "28   288.4100   592.56200  304.15200       0.603584   0.430407   0.884306   \n",
       "9    578.6865  1449.82800  871.14150       0.515026   0.267832   0.671021   \n",
       "11   623.3015  1240.56375  617.26225       0.567073   0.393001   0.782194   \n",
       "7    869.4585  1322.89650  453.43800       0.659844   0.549788   0.836512   \n",
       "8    679.9960  1227.02775  547.03175       0.651370   0.433002   0.781336   \n",
       "10   715.0690  1001.10200  286.03300       0.651410   0.516312   0.722841   \n",
       "47   539.1305   989.79025  450.65975       0.663986   0.457337   0.839626   \n",
       "18  1007.0020  1550.69400  543.69200       0.689418   0.548529   0.844686   \n",
       "\n",
       "    scaled_IQR  error (%) learn (%) pretests posttests  \n",
       "17    0.169887   0.000000        25   40, 40    50, 60  \n",
       "20    0.208757   0.000000  -52.0833   80, 30    50, 10  \n",
       "28    0.453899   0.000000         0  100, 60   100, 60  \n",
       "9     0.403189   4.545455      22.5   50, 60    60, 70  \n",
       "11    0.389193   4.545455   36.6667   70, 50    80, 70  \n",
       "7     0.286724  13.636364   14.2857   70, 30    70, 50  \n",
       "8     0.348334  13.636364     -8.75   50, 80    60, 50  \n",
       "10    0.206529  13.636364   36.6667   40, 50    60, 70  \n",
       "47    0.382289  13.636364  -12.6984   90, 70    80, 60  \n",
       "18    0.296157  18.181818    -56.25   40, 80    10, 50  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = list()\n",
    "\n",
    "def compute_quartiles(grouped, prefix=''):\n",
    "    df = grouped.agg(\n",
    "        [np.median,\n",
    "         lambda x: np.percentile(x, q=25),\n",
    "         lambda x: np.percentile(x, q=75)])\n",
    "    df.columns = ['{}{}'.format(prefix, c) for c in ['median', 'Q1', 'Q3']]\n",
    "    df['{}IQR'.format(prefix)] = df['{}Q3'.format(prefix)] - df['{}Q1'.format(prefix)]\n",
    "    df.index = df.index.astype(int)\n",
    "    return df\n",
    "\n",
    "# In common time establishments.\n",
    "df = all_routines_df.copy()\n",
    "df = df[df.in_common_time]\n",
    "grouped = df.groupby('team_no')['establish_time']\n",
    "df = compute_quartiles(grouped, 'in_common_')\n",
    "l.append(df)\n",
    "\n",
    "# All establishments, scaled and absolute time.\n",
    "for prefix in ['', 'scaled_']:\n",
    "    grouped = all_routines_df.groupby(\n",
    "        'team_no')['{}establish_time'.format(prefix)]\n",
    "    df = compute_quartiles(grouped, prefix)\n",
    "    l.append(df)\n",
    "    \n",
    "result_df = pd.concat([*l, perf_df.T, learn_df.T], axis=1)\n",
    "result_df.sort_values('error (%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For median: 10 values\n",
      "SpearmanrResult(correlation=0.6928494815184638, pvalue=0.026342919713052852)\n",
      "For scaled_median: 10 values\n",
      "SpearmanrResult(correlation=0.3813850356982369, pvalue=0.27684626336915275)\n",
      "For in_common_median: 9 values\n",
      "SpearmanrResult(correlation=0.5167050676973538, pvalue=0.1543541306570348)\n"
     ]
    }
   ],
   "source": [
    "for prefix in ['', 'scaled_', 'in_common_']:\n",
    "    c = '{}median'.format(prefix)\n",
    "    print('For {}:'.format(c), end=' ')\n",
    "    df = result_df[[c, 'error (%)']].copy()\n",
    "    df.dropna(inplace=True)\n",
    "    print('{} values'.format(len(df)))\n",
    "    print(spearmanr(df[c], df['error (%)']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Kruskal-Wallis with learning outcomes.\n",
    "Note that although we have two groups, \n",
    "we can not use scipy's implementation of Mann-Whitney U test as it asks for $> 20$ samples.\n",
    "On the other hand, scipy's implementation of Kruskal-Wallis works with $\\geq 5$ samples.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, 9, 10, 11, 17] [8, 18, 20, 28, 47]\n"
     ]
    }
   ],
   "source": [
    "positive_teams = list(result_df[result_df['learn (%)'] > 0].index)\n",
    "nonpositive_teams = [t for t in result_df.index if t not in positive_teams]\n",
    "print(positive_teams, nonpositive_teams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For median: KruskalResult(statistic=0.8836363636363629, pvalue=0.34720763934942456)\n",
      "\n",
      "For scaled_median: KruskalResult(statistic=0.09818181818181415, pvalue=0.7540225300620795)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for prefix in ['', 'scaled_']:\n",
    "    c = '{}median'.format(prefix)\n",
    "    print('For {}:'.format(c), end=' ')\n",
    "\n",
    "    test = result_df.loc[positive_teams, c].astype(float).tolist()\n",
    "    control = result_df.loc[nonpositive_teams, c].astype(float).tolist()\n",
    "\n",
    "    # print(test, control)\n",
    "    print(kruskal(test, control))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot establishment time distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### in absolute time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../figures/h1_1_time_absolute_plot.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_establishment_absolute(df, x,\n",
    "                                order,\n",
    "                                seps=[],\n",
    "                                y='team_no',\n",
    "                                save=True,\n",
    "                                save_filename=None,\n",
    "                                xlabel=None,\n",
    "                                show_ylabel=True,\n",
    "                                show_yticks=True,\n",
    "                                show_perf_groups=True,\n",
    "                                fig=None, ax=None):\n",
    "    if fig is None:\n",
    "        fig, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "    my_pal = {'28': colors[0],\n",
    "              '17': colors[0],\n",
    "              '20': colors[0],\n",
    "              '11': colors[1],\n",
    "              '9': colors[1],\n",
    "              '47': colors[2],\n",
    "              '10': colors[2],\n",
    "              '8': colors[2],\n",
    "              '7': colors[2],\n",
    "              '18': colors[5]}\n",
    "    bp = sns.boxplot(x=x, y=y,\n",
    "                     order=order,\n",
    "                     palette=my_pal,\n",
    "                     boxprops=dict(alpha=.2),\n",
    "                     width=0.3,\n",
    "                     showfliers=True, \n",
    "                     fliersize=3,\n",
    "                     whis=0,\n",
    "                     data=df, ax=ax)\n",
    "\n",
    "    dff = df[df.in_common_time].copy()\n",
    "    bp = sns.boxplot(x=x, y=y,\n",
    "                     order=order,\n",
    "                     palette=my_pal,\n",
    "                     boxprops=dict(alpha=.6),\n",
    "#                      whis=0,\n",
    "                     data=dff, ax=ax)\n",
    "\n",
    "    # Set x-axis properties.\n",
    "    ax.set_xticks(np.arange(0, 2400+1, 300))\n",
    "    ax.set_xlim([0, 37*60])\n",
    "    ax.xaxis.set_major_formatter(\n",
    "        FuncFormatter(lambda x, _: '{:.0f}'.format(x/60)))\n",
    "    if xlabel is not None:\n",
    "        ax.set_xlabel(xlabel, fontsize=18)\n",
    "\n",
    "    ax.xaxis.set_minor_locator(AutoMinorLocator(5))\n",
    "    ax.xaxis.grid(True, alpha=0.2, which='minor')\n",
    "\n",
    "    # Set y-axis properties.\n",
    "    if show_ylabel:\n",
    "        ax.set_ylabel('team no', fontsize=18)\n",
    "    else:\n",
    "        ax.set_ylabel('')\n",
    "    if not show_yticks:\n",
    "        ax.set_yticks([], [])\n",
    "\n",
    "    legend = ax.get_legend()\n",
    "    if legend is not None:\n",
    "        legend.remove()\n",
    "\n",
    "    # Annotate by task performance.\n",
    "    if show_perf_groups:\n",
    "        l = [0] + seps + [1]\n",
    "        x = 1.02\n",
    "        offset = 0.02\n",
    "        labels = [('well-performing', 'g'),\n",
    "                  ('badly-performing', 'r')]\n",
    "        for i, (previous, current) in enumerate(zip(l, l[1:])):\n",
    "            if isinstance(previous, str):\n",
    "                previous = (order.index(previous) + 1) / len(order)\n",
    "            if isinstance(current, str):\n",
    "                current = (order.index(current) + 1) / len(order)\n",
    "            previous += offset\n",
    "            current -= offset\n",
    "            previous, current = 1 - previous, 1 - current\n",
    "            s, c = labels[i]\n",
    "            ax.annotate('',\n",
    "                        xy=(x, previous),\n",
    "                        xytext=(x, current),\n",
    "                        xycoords='axes fraction',\n",
    "                        arrowprops=dict(arrowstyle='-', linewidth=3, color=c))\n",
    "            t = ax.annotate(s,\n",
    "                            xy=(x+0.01, (previous+current)/2),\n",
    "                            fontsize=16,\n",
    "                            color=c,\n",
    "                            rotation=90,\n",
    "                            xycoords='axes fraction',\n",
    "                            ha='left', va='center')\n",
    "            \n",
    "            s = '(increasing error)'\n",
    "            x = 1.02\n",
    "            style = '<-,head_length=0.8,head_width=0.5'\n",
    "            props = dict(arrowstyle=style, linewidth=3, color='r')\n",
    "            ax.annotate('',\n",
    "                        xy=(x, 0.1),\n",
    "                        xytext=(x, 0.01),\n",
    "                        xycoords='axes fraction',\n",
    "                        arrowprops=props)\n",
    "            t = ax.annotate(s,\n",
    "                            xy=(x+0.01, 0.06),\n",
    "                            fontsize=12,\n",
    "                            rotation=90,\n",
    "                            xycoords='axes fraction',\n",
    "                            ha='left', va='bottom')\n",
    "\n",
    "    # Mark the common duration with a vertical line.\n",
    "    ax.axvline(common_duration, ls='--', color='green')\n",
    "\n",
    "    if fig is not None and save:\n",
    "        if save_filename is None:\n",
    "            save_filename = 'h1_1_{}_absolute_plot.png'.format(\n",
    "                xlabel.split()[-2])\n",
    "        export_file = plots_dir.joinpath(\n",
    "            pl.Path(save_filename).with_suffix('.png'))\n",
    "        # fig.savefig(export_file, bbox_inches='tight')\n",
    "        # print(export_file)\n",
    "\n",
    "        export_file = export_file.with_suffix('.pdf')\n",
    "        fig.savefig(export_file, bbox_inches='tight')\n",
    "        print(export_file)\n",
    "\n",
    "    return fig, ax\n",
    "\n",
    "\n",
    "order, seps = perf_order, perf_seps\n",
    "\n",
    "figsize = (12, 12)\n",
    "fig, ax = plt.subplots(figsize=figsize)\n",
    "\n",
    "# # Plot utterances by marking their end times.\n",
    "palette = sns.color_palette()\n",
    "colors = list(palette)\n",
    "cs = [colors[0], colors[3]]\n",
    "df = all_corpus_df.copy()\n",
    "# Filter for the utterances.\n",
    "df = df[df.utterance_no != -1]\n",
    "df = df[df.subject.isin(['A', 'B'])]\n",
    "sns.stripplot(x='end', y='team_no', hue='subject',\n",
    "              order=order, jitter=0,\n",
    "              data=df, ax=ax, palette=cs, size=3,\n",
    "              dodge=True, alpha=0.2)\n",
    "\n",
    "# Annotate the end of interaction times on the plot.\n",
    "for team_no, row in counts_df.iterrows():\n",
    "    if perf_df.T.loc[int(team_no), 'error (%)'] == 0:\n",
    "        c = 'g'\n",
    "    else:\n",
    "        c = 'r'\n",
    "    pos = row['duration (secs)']\n",
    "    y = order.index(str(team_no))\n",
    "    props = dict(arrowstyle='-', alpha=1.0, linewidth=4, color=c)\n",
    "    ax.annotate('',\n",
    "                xy=(pos, y+0.5),\n",
    "                xytext=(pos, y-0.5),\n",
    "                xycoords='data',\n",
    "                color='green', arrowprops=props)\n",
    "\n",
    "# Annotate the learning outcomes on the plot.\n",
    "text_pos, offset = 1, 0\n",
    "t = ax.annotate('learn',\n",
    "                xy=(text_pos, 1-0.01),\n",
    "                backgroundcolor='w',\n",
    "                fontsize=20,\n",
    "                xycoords='axes fraction',\n",
    "                ha='center', va='center')\n",
    "for team_no, row in learn_df.T.iterrows():\n",
    "    value = row['learn (%)'] \n",
    "    if value > 0:\n",
    "        s = '$+$'\n",
    "    else:\n",
    "        s = '$-$'\n",
    "    y = (order.index(str(team_no)) + .5) / len(order)\n",
    "    t = ax.annotate(s,\n",
    "                    xy=(text_pos, y+offset),\n",
    "                    backgroundcolor='w',\n",
    "                    fontsize=20,\n",
    "                    xycoords='axes fraction',\n",
    "                    ha='center', va='center')\n",
    "    \n",
    "# Annotate the submission costs on the plot.\n",
    "for team_no, _ in routine_dfs.items():\n",
    "    df = all_corpus_df.copy()\n",
    "    df = df[df.team_no == str(team_no)]\n",
    "    df = df[df.subject == 'T']\n",
    "    y = order.index(str(team_no))\n",
    "\n",
    "    for i, row in df.iterrows():\n",
    "        arrow_start, arrow_end = 0, 0\n",
    "        # text_pos = row['scaled_end']\n",
    "        text_pos = row['end']\n",
    "\n",
    "        val = row['error']\n",
    "        s = '{:2.0%}'.format(val).strip('%')\n",
    "        offset = 0\n",
    "        props = dict(arrowstyle='-', alpha=0.6, linewidth=2, color='b')\n",
    "        ax.annotate('',\n",
    "                    xy=(text_pos, y+0.3),\n",
    "                    xytext=(text_pos, y-0.3),\n",
    "                    xycoords='data',\n",
    "                    color='green', arrowprops=props)\n",
    "        t = ax.annotate(s,\n",
    "                        xy=(text_pos, y+offset),\n",
    "                        backgroundcolor='w',\n",
    "                        fontsize=10,\n",
    "                        xycoords='data',\n",
    "                        ha='center', va='center')\n",
    "        t.set_bbox(dict(fc='w', alpha=0.9, ec='none',\n",
    "                        boxstyle='square,pad=0.05'))\n",
    "\n",
    "\n",
    "# Plot the establishment box plots.\n",
    "x, xlabel = 'establish_time', 'establishment time (mins)'\n",
    "fig, ax = plot_establishment_absolute(\n",
    "    all_routines_df, x=x, xlabel=xlabel,\n",
    "    seps=seps, order=order,\n",
    "    fig=fig, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### in normalised time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../figures/h1_1_time_plot.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_establishment(df, x,\n",
    "                       order,\n",
    "                       seps=[],\n",
    "                       y='team_no',\n",
    "                       save=True,\n",
    "                       save_filename=None,\n",
    "                       xlabel=None,\n",
    "                       show_ylabel=True,\n",
    "                       show_yticks=True,\n",
    "                       show_perf_groups=True,\n",
    "                       fig=None, ax=None):\n",
    "    if fig is None:\n",
    "        fig, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "    my_pal = {'28': colors[0],\n",
    "              '17': colors[0],\n",
    "              '20': colors[0],\n",
    "              '11': colors[1],\n",
    "              '9': colors[1],\n",
    "              '47': colors[2],\n",
    "              '10': colors[2],\n",
    "              '8': colors[2],\n",
    "              '7': colors[2],\n",
    "              '18': colors[5]}\n",
    "    bp = sns.boxplot(x=x, y=y,\n",
    "                     order=order,\n",
    "                     palette=my_pal,\n",
    "                     boxprops=dict(alpha=.2),\n",
    "                     data=df, ax=ax)\n",
    "\n",
    "    # Set x-axis properties.\n",
    "    ax.set_xticks(np.arange(0, 1.001, 0.1))\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.xaxis.set_major_formatter(\n",
    "        FuncFormatter(lambda x, _: '{:.0%}'.format(x)))\n",
    "    if xlabel is not None:\n",
    "        ax.set_xlabel(xlabel, fontsize=18)\n",
    "\n",
    "    # Set y-axis properties.\n",
    "    if show_ylabel:\n",
    "        ax.set_ylabel('team no', fontsize=18)\n",
    "    else:\n",
    "        ax.set_ylabel('')\n",
    "    if not show_yticks:\n",
    "        ax.set_yticks([], [])\n",
    "\n",
    "    legend = ax.get_legend()\n",
    "    if legend is not None:\n",
    "        legend.remove()\n",
    "\n",
    "    # Annotate by task performance.\n",
    "    if show_perf_groups:\n",
    "        l = [0] + seps + [1]\n",
    "        x = 1.02\n",
    "        offset = 0.02\n",
    "        labels = [('well-performing', 'g'),\n",
    "                  ('badly-performing', 'r')]\n",
    "        for i, (previous, current) in enumerate(zip(l, l[1:])):\n",
    "            if isinstance(previous, str):\n",
    "                previous = (order.index(previous) + 1) / len(order)\n",
    "            if isinstance(current, str):\n",
    "                current = (order.index(current) + 1) / len(order)\n",
    "            previous += offset\n",
    "            current -= offset\n",
    "            previous, current = 1 - previous, 1 - current\n",
    "            s, c = labels[i]\n",
    "            ax.annotate('',\n",
    "                        xy=(x, previous),\n",
    "                        xytext=(x, current),\n",
    "                        xycoords='axes fraction',\n",
    "                        arrowprops=dict(arrowstyle='-', linewidth=3, color=c))\n",
    "            t = ax.annotate(s,\n",
    "                            xy=(x+0.01, (previous+current)/2),\n",
    "                            fontsize=16,\n",
    "                            color=c,\n",
    "                            rotation=90,\n",
    "                            xycoords='axes fraction',\n",
    "                            ha='left', va='center')\n",
    "\n",
    "        s = '(increasing error)'\n",
    "        x = 1.02\n",
    "        style = '<-,head_length=0.8,head_width=0.5'\n",
    "        props = dict(arrowstyle=style, linewidth=3, color='r')\n",
    "        ax.annotate('',\n",
    "                    xy=(x, 0.1),\n",
    "                    xytext=(x, 0.01),\n",
    "                    xycoords='axes fraction',\n",
    "                    arrowprops=props)\n",
    "        t = ax.annotate(s,\n",
    "                        xy=(x+0.01, 0.06),\n",
    "                        fontsize=12,\n",
    "                        rotation=90,\n",
    "                        xycoords='axes fraction',\n",
    "                        ha='left', va='bottom')\n",
    "\n",
    "    if fig is not None and save:\n",
    "        if save_filename is None:\n",
    "            save_filename = 'h1_1_{}_plot.png'.format(xlabel.split()[-2])\n",
    "        export_file = plots_dir.joinpath(\n",
    "            pl.Path(save_filename).with_suffix('.png'))\n",
    "        # fig.savefig(export_file, bbox_inches='tight')\n",
    "        # print(export_file)\n",
    "\n",
    "        export_file = export_file.with_suffix('.pdf')\n",
    "        fig.savefig(export_file, bbox_inches='tight')\n",
    "        print(export_file)\n",
    "\n",
    "    return fig, ax\n",
    "\n",
    "\n",
    "order, seps = perf_order, perf_seps\n",
    "\n",
    "figsize = (12, 12)\n",
    "fig, ax = plt.subplots(figsize=figsize)\n",
    "\n",
    "# Plot utterances by marking their end times.\n",
    "palette = sns.color_palette()\n",
    "colors = list(palette)\n",
    "cs = [colors[0], colors[3]]\n",
    "df = all_corpus_df.copy()\n",
    "# Filter for the utterances.\n",
    "df = df[df.utterance_no != -1]\n",
    "df = df[df.subject.isin(['A', 'B'])]\n",
    "sns.stripplot(x='scaled_end', y='team_no', hue='subject',\n",
    "              order=order, jitter=0,\n",
    "              data=df, ax=ax, palette=cs, size=3,\n",
    "              dodge=True, alpha=0.2)\n",
    "\n",
    "# Annotate the submission costs on the plot.\n",
    "for team_no, _ in routine_dfs.items():\n",
    "    df = all_corpus_df.copy()\n",
    "    df = df[df.team_no == str(team_no)]\n",
    "    df = df[df.subject == 'T']\n",
    "    y = order.index(str(team_no))\n",
    "\n",
    "    for i, row in df.iterrows():\n",
    "        arrow_start, arrow_end = 0, 0\n",
    "        text_pos = row['scaled_end']\n",
    "\n",
    "        val = row['error']\n",
    "        s = '{:2.0%}'.format(val).strip('%')\n",
    "        offset = 0\n",
    "        props = dict(arrowstyle='-', alpha=0.6, linewidth=2, color='b')\n",
    "        width = 0.6\n",
    "        ax.annotate('',\n",
    "                    xy=(text_pos, y+width/2),\n",
    "                    xytext=(text_pos, y-width/2),\n",
    "                    xycoords='data',\n",
    "                    color='green', arrowprops=props)\n",
    "        t = ax.annotate(s,\n",
    "                        xy=(text_pos, y+offset),\n",
    "                        backgroundcolor='w',\n",
    "                        fontsize=10,\n",
    "                        xycoords='data',\n",
    "                        ha='center', va='center')\n",
    "        t.set_bbox(dict(fc='w', alpha=0.9, ec='none',\n",
    "                        boxstyle='square,pad=0.05'))\n",
    "\n",
    "# Plot the establishment box plots.\n",
    "x, xlabel = 'scaled_establish_time', 'establishment time (%)'\n",
    "fig, ax = plot_establishment(\n",
    "    all_routines_df, x=x, xlabel=xlabel,\n",
    "    seps=seps, order=order,\n",
    "    fig=fig, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## H1.2: Hesitation phenomena are more likely to occur in the vicinity of priming and establishment of task-specific referents for more successful teams."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Report filler counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['um', 'uh', 'hmm', 'ahh', 'oops', 'eww']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fillers = ['um', 'uh', 'hmm', 'ahh', 'oops', 'eww']\n",
    "fillers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count the number of info markers i.e. oh's for each utterance in the corpus.\n",
    "for team_no, df in corpus_dfs.items():\n",
    "    df['n_info_markers'] = df.object.apply(\n",
    "        lambda x: len([t for t in x.split() if t == 'oh']))\n",
    "\n",
    "# Mark each token that is a filler in the tokens tables.\n",
    "for team_no, df in tokens_dfs.items():\n",
    "    df['is_filler'] = df.token.apply(lambda x: x.strip() in fillers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "um 236 23.6 13.51\n",
      "uh 173 17.3 9.69\n",
      "hmm 3 0.3 0.90\n",
      "ahh 4 0.4 0.66\n",
      "oops 4 0.4 0.66\n",
      "eww 1 0.1 0.30\n"
     ]
    }
   ],
   "source": [
    "for filler in fillers:\n",
    "    counts = list()\n",
    "    for team_no, df in tokens_dfs.items():\n",
    "        count = df.token.apply(lambda x: x.strip() == filler).sum()\n",
    "        counts.append(count)\n",
    "\n",
    "    print(filler, np.sum(counts), np.mean(counts), '{:.2f}'.format(np.std(counts)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform Mann-Whitney U tests and compute Cliff's Deltas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### for establishment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U is the Mann–Whitney U test statistic, where p \\< 0.05 indicates that the groups are statistically significantly different\n",
      "Cliff's delta \\> 0 indicates filler times (or equivalently utterance positions) larger than establishment times. \\< otherwise.\n",
      "filler positions vs.\\ establishment positions (sorted by increasing learning):\n",
      "For Team 28: $U =  828.5$, $p < .05$, $\\text{Cliff's delta} = -0.25$ \\\\\n",
      "For Team 17: $U =  431.0$, $p = .12$, $\\text{Cliff's delta} = -0.22$ \\\\\n",
      "For Team 20: $U = 1139.5$, $p < .05$, $\\text{Cliff's delta} = -0.24$ \\\\\n",
      "For Team 11: $U = 1624.0$, $p < .05$, $\\text{Cliff's delta} = 0.33$ \\\\\n",
      "For Team 09: $U = 6561.5$, $p < .05$, $\\text{Cliff's delta} = 0.24$ \\\\\n",
      "For Team 47: $U = 1627.0$, $p < .05$, $\\text{Cliff's delta} = -0.21$ \\\\\n",
      "For Team 10: $U =  660.0$, $p = .92$, $\\text{Cliff's delta} = 0.02$ \\\\\n",
      "For Team 08: $U =  461.0$, $p < .05$, $\\text{Cliff's delta} = -0.43$ \\\\\n",
      "For Team 07: $U =  259.0$, $p < .05$, $\\text{Cliff's delta} = -0.67$ \\\\\n",
      "For Team 18: $U = 1326.0$, $p = .34$, $\\text{Cliff's delta} = -0.11$ \\\\\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">N</th>\n",
       "      <th colspan=\"2\" halign=\"left\">median (%)</th>\n",
       "      <th colspan=\"4\" halign=\"left\">establishment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>filler</th>\n",
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       "      <th>filler</th>\n",
       "      <th>establishment</th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>d</th>\n",
       "      <th>d-full</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</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>28</th>\n",
       "      <td>38</td>\n",
       "      <td>58</td>\n",
       "      <td>11.6</td>\n",
       "      <td>15.2</td>\n",
       "      <td>828.5</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.248185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>27</td>\n",
       "      <td>41</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>431.0</td>\n",
       "      <td>$.12$</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.221319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>59</td>\n",
       "      <td>51</td>\n",
       "      <td>20.7</td>\n",
       "      <td>22.8</td>\n",
       "      <td>1139.5</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.242606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>35</td>\n",
       "      <td>70</td>\n",
       "      <td>34.5</td>\n",
       "      <td>24.4</td>\n",
       "      <td>1624.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.325714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>81</td>\n",
       "      <td>131</td>\n",
       "      <td>46.3</td>\n",
       "      <td>37.4</td>\n",
       "      <td>6561.5</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.236735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>56</td>\n",
       "      <td>74</td>\n",
       "      <td>19.5</td>\n",
       "      <td>21.9</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.214768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>20.1</td>\n",
       "      <td>20.4</td>\n",
       "      <td>660.0</td>\n",
       "      <td>$.92$</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.015385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>62</td>\n",
       "      <td>19.8</td>\n",
       "      <td>36.0</td>\n",
       "      <td>461.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.428040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>24.9</td>\n",
       "      <td>37.8</td>\n",
       "      <td>259.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.674827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>52</td>\n",
       "      <td>57</td>\n",
       "      <td>10.8</td>\n",
       "      <td>11.8</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>$.34$</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.105263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          N               median (%)               establishment           \\\n",
       "     filler establishment     filler establishment             U        p   \n",
       "Team                                                                        \n",
       "28       38            58       11.6          15.2         828.5  $< .05$   \n",
       "17       27            41       14.0          17.1         431.0    $.12$   \n",
       "20       59            51       20.7          22.8        1139.5  $< .05$   \n",
       "11       35            70       34.5          24.4        1624.0  $< .05$   \n",
       "9        81           131       46.3          37.4        6561.5  $< .05$   \n",
       "47       56            74       19.5          21.9        1627.0  $< .05$   \n",
       "10       20            65       20.1          20.4         660.0    $.92$   \n",
       "8        26            62       19.8          36.0         461.0  $< .05$   \n",
       "7        27            59       24.9          37.8         259.0  $< .05$   \n",
       "18       52            57       10.8          11.8        1326.0    $.34$   \n",
       "\n",
       "                       \n",
       "          d    d-full  \n",
       "Team                   \n",
       "28    -0.25 -0.248185  \n",
       "17    -0.22 -0.221319  \n",
       "20    -0.24 -0.242606  \n",
       "11     0.33  0.325714  \n",
       "9      0.24  0.236735  \n",
       "47    -0.21 -0.214768  \n",
       "10     0.02  0.015385  \n",
       "8     -0.43 -0.428040  \n",
       "7     -0.67 -0.674827  \n",
       "18    -0.11 -0.105263  "
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrllrllr}\n",
      "\\toprule\n",
      "{} & \\multicolumn{2}{l}{N} & \\multicolumn{2}{l}{median (%)} & \\multicolumn{4}{l}{establishment} \\\\\n",
      "{} & filler & establishment &     filler & establishment &             U &        p &      d &    d-full \\\\\n",
      "Team &        &               &            &               &               &          &        &           \\\\\n",
      "\\midrule\n",
      "28   &     38 &            58 &       11.6 &          15.2 &         828.5 &  $< .05$ &  -0.25 & -0.248185 \\\\\n",
      "17   &     27 &            41 &       14.0 &          17.1 &         431.0 &    $.12$ &  -0.22 & -0.221319 \\\\\n",
      "20   &     59 &            51 &       20.7 &          22.8 &        1139.5 &  $< .05$ &  -0.24 & -0.242606 \\\\\n",
      "11   &     35 &            70 &       34.5 &          24.4 &        1624.0 &  $< .05$ &   0.33 &  0.325714 \\\\\n",
      "9    &     81 &           131 &       46.3 &          37.4 &        6561.5 &  $< .05$ &   0.24 &  0.236735 \\\\\n",
      "47   &     56 &            74 &       19.5 &          21.9 &        1627.0 &  $< .05$ &  -0.21 & -0.214768 \\\\\n",
      "10   &     20 &            65 &       20.1 &          20.4 &         660.0 &    $.92$ &   0.02 &  0.015385 \\\\\n",
      "8    &     26 &            62 &       19.8 &          36.0 &         461.0 &  $< .05$ &  -0.43 & -0.428040 \\\\\n",
      "7    &     27 &            59 &       24.9 &          37.8 &         259.0 &  $< .05$ &  -0.67 & -0.674827 \\\\\n",
      "18   &     52 &            57 &       10.8 &          11.8 &        1326.0 &    $.34$ &  -0.11 & -0.105263 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def test_fillers_and_routines(filler_col, routine_col,\n",
    "                              n1='filler',\n",
    "                              n2='establishment',\n",
    "                              verbose=True):\n",
    "    n1_col = 'N_{}'.format(n1)\n",
    "    n2_col = 'N_{}'.format(n2)\n",
    "    m1_col = 'median (%)_{}'.format(n1)\n",
    "    m2_col = 'median (%)_{}'.format(n2)\n",
    "    u_col = '{}_U'.format(n2)\n",
    "    p_col = '{}_p'.format(n2)\n",
    "    d_col = '{}_d'.format(n2)\n",
    "    d_col_full = '{}_d-full'.format(n2)\n",
    "    data = {\n",
    "        'Team': [],\n",
    "        n1_col: [], n2_col: [],\n",
    "        m1_col: [], m2_col: [],\n",
    "        u_col: [],\n",
    "        p_col: [],\n",
    "        d_col: [], d_col_full: []\n",
    "    }\n",
    "\n",
    "    order = [int(t) for t in perf_order]\n",
    "\n",
    "    for team_no in order:\n",
    "        routine_df = routine_dfs[team_no]\n",
    "\n",
    "        # # filter for the fillers of this team.\n",
    "        df = tokens_dfs[team_no]\n",
    "        filler_df = df[df.is_filler]\n",
    "\n",
    "        x = filler_df[filler_col].values\n",
    "        y = routine_df[routine_col].values\n",
    "\n",
    "        u, pvalue = mannwhitneyu(x=x, y=y,\n",
    "                                 use_continuity=False,\n",
    "                                 alternative='two-sided')\n",
    "        thres = 0.05\n",
    "        if pvalue < thres:\n",
    "            ps = 'p < ' + '{:.2f}'.format(thres).lstrip('0')\n",
    "        else:\n",
    "            ps = 'p = ' + '{:.2f}'.format(pvalue).lstrip('0')\n",
    "\n",
    "        d = two_group_difference(control=y,\n",
    "                                 test=x,\n",
    "                                 effect_size='cliffs_delta')\n",
    "\n",
    "        if verbose:\n",
    "            print('For Team {:02d}: '.format(team_no), end='')\n",
    "            s = '$U = {:6.1f}$, ${}$'.format(u, ps)\n",
    "            s += r\", $\\text{Cliff's delta}\"\n",
    "            s += \" = {:.2f}$\".format(d).lstrip('0')\n",
    "            s += r' \\\\'\n",
    "            print(s)\n",
    "\n",
    "        data['Team'].append(team_no)\n",
    "        data[n1_col].append(len(x))\n",
    "        data[n2_col].append(len(y))\n",
    "        data[m1_col].append('{:.1f}'.format(np.median(x)/60))\n",
    "        data[m2_col].append('{:.1f}'.format(np.median(y)/60))\n",
    "        data[u_col].append(u)\n",
    "        data[p_col].append('< .05' if pvalue <\n",
    "                           0.05 else '{:.2f}'.format(pvalue).lstrip('0'))\n",
    "        data[d_col].append('{:.2f}'.format(d))\n",
    "        data[d_col_full].append(d)\n",
    "\n",
    "    data_df = pd.DataFrame.from_dict(data)\n",
    "    data_df.set_index('Team', inplace=True)\n",
    "\n",
    "    data_df[p_col] = [r'${}$'.format(p) for p in data_df[p_col]]\n",
    "\n",
    "    idx = data_df.columns.str.split('_', expand=True)\n",
    "    idx = list(idx)\n",
    "    for i, (level0, level1) in enumerate(idx):\n",
    "        if type(level1) is not str and math.isnan(level1):\n",
    "            idx[i] = (level0, '.')\n",
    "    idx = pd.MultiIndex.from_tuples(idx)\n",
    "    data_df.columns = idx\n",
    "\n",
    "    data_df = data_df.loc[order]\n",
    "    return data_df\n",
    "\n",
    "\n",
    "print(\"U is the Mann–Whitney U test statistic, where p \\< 0.05 indicates that the groups are statistically significantly different\")\n",
    "print(\"Cliff's delta \\> 0 indicates filler times (or equivalently utterance positions) larger than establishment times. \\< otherwise.\")\n",
    "print('filler positions vs.\\ establishment positions (sorted by increasing learning):')\n",
    "\n",
    "routine_col, filler_col = 'establish_token', 'token_no'\n",
    "establish_df = test_fillers_and_routines(\n",
    "    filler_col, routine_col, n2='establishment')\n",
    "\n",
    "display(establish_df)\n",
    "print(establish_df.to_latex(escape=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### for priming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Team 28: $U = 1600.5$, $p < .05$, $\\text{Cliff's delta} = 0.45$ \\\\\n",
      "For Team 17: $U =  823.0$, $p < .05$, $\\text{Cliff's delta} = 0.49$ \\\\\n",
      "For Team 20: $U = 1746.5$, $p = .15$, $\\text{Cliff's delta} = 0.16$ \\\\\n",
      "For Team 11: $U = 2229.0$, $p < .05$, $\\text{Cliff's delta} = 0.82$ \\\\\n",
      "For Team 09: $U = 8805.5$, $p < .05$, $\\text{Cliff's delta} = 0.66$ \\\\\n",
      "For Team 47: $U = 3189.0$, $p < .05$, $\\text{Cliff's delta} = 0.54$ \\\\\n",
      "For Team 10: $U =  982.0$, $p < .05$, $\\text{Cliff's delta} = 0.51$ \\\\\n",
      "For Team 08: $U =  883.0$, $p = .48$, $\\text{Cliff's delta} = 0.10$ \\\\\n",
      "For Team 07: $U =  562.0$, $p < .05$, $\\text{Cliff's delta} = -0.29$ \\\\\n",
      "For Team 18: $U = 2136.0$, $p < .05$, $\\text{Cliff's delta} = 0.44$ \\\\\n"
     ]
    },
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">N</th>\n",
       "      <th colspan=\"2\" halign=\"left\">median (%)</th>\n",
       "      <th colspan=\"4\" halign=\"left\">priming</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>filler</th>\n",
       "      <th>priming</th>\n",
       "      <th>filler</th>\n",
       "      <th>priming</th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>d</th>\n",
       "      <th>d-full</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</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>28</th>\n",
       "      <td>38</td>\n",
       "      <td>58</td>\n",
       "      <td>11.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1600.5</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.452359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>27</td>\n",
       "      <td>41</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>823.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.486902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>59</td>\n",
       "      <td>51</td>\n",
       "      <td>20.7</td>\n",
       "      <td>18.1</td>\n",
       "      <td>1746.5</td>\n",
       "      <td>$.15$</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.160851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>35</td>\n",
       "      <td>70</td>\n",
       "      <td>34.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2229.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.819592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>81</td>\n",
       "      <td>131</td>\n",
       "      <td>46.3</td>\n",
       "      <td>11.4</td>\n",
       "      <td>8805.5</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.659693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>56</td>\n",
       "      <td>74</td>\n",
       "      <td>19.5</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3189.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.539093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>20.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>982.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.510769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>62</td>\n",
       "      <td>19.8</td>\n",
       "      <td>15.7</td>\n",
       "      <td>883.0</td>\n",
       "      <td>$.48$</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.095533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>24.9</td>\n",
       "      <td>25.9</td>\n",
       "      <td>562.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.294413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>52</td>\n",
       "      <td>57</td>\n",
       "      <td>10.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.441296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          N         median (%)         priming                          \n",
       "     filler priming     filler priming       U        p      d    d-full\n",
       "Team                                                                    \n",
       "28       38      58       11.6     3.5  1600.5  $< .05$   0.45  0.452359\n",
       "17       27      41       14.0     6.2   823.0  $< .05$   0.49  0.486902\n",
       "20       59      51       20.7    18.1  1746.5    $.15$   0.16  0.160851\n",
       "11       35      70       34.5     3.5  2229.0  $< .05$   0.82  0.819592\n",
       "9        81     131       46.3    11.4  8805.5  $< .05$   0.66  0.659693\n",
       "47       56      74       19.5     6.7  3189.0  $< .05$   0.54  0.539093\n",
       "10       20      65       20.1     8.7   982.0  $< .05$   0.51  0.510769\n",
       "8        26      62       19.8    15.7   883.0    $.48$   0.10  0.095533\n",
       "7        27      59       24.9    25.9   562.0  $< .05$  -0.29 -0.294413\n",
       "18       52      57       10.8     4.3  2136.0  $< .05$   0.44  0.441296"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrllrllr}\n",
      "\\toprule\n",
      "{} & \\multicolumn{2}{l}{N} & \\multicolumn{2}{l}{median (%)} & \\multicolumn{4}{l}{priming} \\\\\n",
      "{} & filler & priming &     filler & priming &       U &        p &      d &    d-full \\\\\n",
      "Team &        &         &            &         &         &          &        &           \\\\\n",
      "\\midrule\n",
      "28   &     38 &      58 &       11.6 &     3.5 &  1600.5 &  $< .05$ &   0.45 &  0.452359 \\\\\n",
      "17   &     27 &      41 &       14.0 &     6.2 &   823.0 &  $< .05$ &   0.49 &  0.486902 \\\\\n",
      "20   &     59 &      51 &       20.7 &    18.1 &  1746.5 &    $.15$ &   0.16 &  0.160851 \\\\\n",
      "11   &     35 &      70 &       34.5 &     3.5 &  2229.0 &  $< .05$ &   0.82 &  0.819592 \\\\\n",
      "9    &     81 &     131 &       46.3 &    11.4 &  8805.5 &  $< .05$ &   0.66 &  0.659693 \\\\\n",
      "47   &     56 &      74 &       19.5 &     6.7 &  3189.0 &  $< .05$ &   0.54 &  0.539093 \\\\\n",
      "10   &     20 &      65 &       20.1 &     8.7 &   982.0 &  $< .05$ &   0.51 &  0.510769 \\\\\n",
      "8    &     26 &      62 &       19.8 &    15.7 &   883.0 &    $.48$ &   0.10 &  0.095533 \\\\\n",
      "7    &     27 &      59 &       24.9 &    25.9 &   562.0 &  $< .05$ &  -0.29 & -0.294413 \\\\\n",
      "18   &     52 &      57 &       10.8 &     4.3 &  2136.0 &  $< .05$ &   0.44 &  0.441296 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "routine_col, filler_col = 'priming_token', 'token_no'\n",
    "priming_df = test_fillers_and_routines(filler_col, routine_col, n2='priming')\n",
    "\n",
    "display(priming_df)\n",
    "print(priming_df.to_latex(escape=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### merge establishment and priming results into a single table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">N</th>\n",
       "      <th colspan=\"3\" halign=\"left\">median (%)</th>\n",
       "      <th colspan=\"3\" halign=\"left\">priming</th>\n",
       "      <th colspan=\"3\" halign=\"left\">establishment</th>\n",
       "      <th>priming</th>\n",
       "      <th>establishment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>filler</th>\n",
       "      <th>establishment</th>\n",
       "      <th>filler</th>\n",
       "      <th>priming</th>\n",
       "      <th>establishment</th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>d</th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>d</th>\n",
       "      <th>d-full</th>\n",
       "      <th>d-full</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</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>28</th>\n",
       "      <td>38</td>\n",
       "      <td>58</td>\n",
       "      <td>11.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>15.2</td>\n",
       "      <td>1600.5</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.45</td>\n",
       "      <td>828.5</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>0.452359</td>\n",
       "      <td>-0.248185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>27</td>\n",
       "      <td>41</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>17.1</td>\n",
       "      <td>823.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.49</td>\n",
       "      <td>431.0</td>\n",
       "      <td>$.12$</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>0.486902</td>\n",
       "      <td>-0.221319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>59</td>\n",
       "      <td>51</td>\n",
       "      <td>20.7</td>\n",
       "      <td>18.1</td>\n",
       "      <td>22.8</td>\n",
       "      <td>1746.5</td>\n",
       "      <td>$.15$</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1139.5</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.160851</td>\n",
       "      <td>-0.242606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>35</td>\n",
       "      <td>70</td>\n",
       "      <td>34.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>24.4</td>\n",
       "      <td>2229.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1624.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.819592</td>\n",
       "      <td>0.325714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>81</td>\n",
       "      <td>131</td>\n",
       "      <td>46.3</td>\n",
       "      <td>11.4</td>\n",
       "      <td>37.4</td>\n",
       "      <td>8805.5</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.66</td>\n",
       "      <td>6561.5</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.659693</td>\n",
       "      <td>0.236735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>56</td>\n",
       "      <td>74</td>\n",
       "      <td>19.5</td>\n",
       "      <td>6.7</td>\n",
       "      <td>21.9</td>\n",
       "      <td>3189.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.539093</td>\n",
       "      <td>-0.214768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>20.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>20.4</td>\n",
       "      <td>982.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.51</td>\n",
       "      <td>660.0</td>\n",
       "      <td>$.92$</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.510769</td>\n",
       "      <td>0.015385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>62</td>\n",
       "      <td>19.8</td>\n",
       "      <td>15.7</td>\n",
       "      <td>36.0</td>\n",
       "      <td>883.0</td>\n",
       "      <td>$.48$</td>\n",
       "      <td>0.10</td>\n",
       "      <td>461.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>0.095533</td>\n",
       "      <td>-0.428040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>24.9</td>\n",
       "      <td>25.9</td>\n",
       "      <td>37.8</td>\n",
       "      <td>562.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>259.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.294413</td>\n",
       "      <td>-0.674827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>52</td>\n",
       "      <td>57</td>\n",
       "      <td>10.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>11.8</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>$.34$</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.441296</td>\n",
       "      <td>-0.105263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          N               median (%)                       priming           \\\n",
       "     filler establishment     filler priming establishment       U        p   \n",
       "Team                                                                          \n",
       "28       38            58       11.6     3.5          15.2  1600.5  $< .05$   \n",
       "17       27            41       14.0     6.2          17.1   823.0  $< .05$   \n",
       "20       59            51       20.7    18.1          22.8  1746.5    $.15$   \n",
       "11       35            70       34.5     3.5          24.4  2229.0  $< .05$   \n",
       "9        81           131       46.3    11.4          37.4  8805.5  $< .05$   \n",
       "47       56            74       19.5     6.7          21.9  3189.0  $< .05$   \n",
       "10       20            65       20.1     8.7          20.4   982.0  $< .05$   \n",
       "8        26            62       19.8    15.7          36.0   883.0    $.48$   \n",
       "7        27            59       24.9    25.9          37.8   562.0  $< .05$   \n",
       "18       52            57       10.8     4.3          11.8  2136.0  $< .05$   \n",
       "\n",
       "            establishment                   priming establishment  \n",
       "          d             U        p      d    d-full        d-full  \n",
       "Team                                                               \n",
       "28     0.45         828.5  $< .05$  -0.25  0.452359     -0.248185  \n",
       "17     0.49         431.0    $.12$  -0.22  0.486902     -0.221319  \n",
       "20     0.16        1139.5  $< .05$  -0.24  0.160851     -0.242606  \n",
       "11     0.82        1624.0  $< .05$   0.33  0.819592      0.325714  \n",
       "9      0.66        6561.5  $< .05$   0.24  0.659693      0.236735  \n",
       "47     0.54        1627.0  $< .05$  -0.21  0.539093     -0.214768  \n",
       "10     0.51         660.0    $.92$   0.02  0.510769      0.015385  \n",
       "8      0.10         461.0  $< .05$  -0.43  0.095533     -0.428040  \n",
       "7     -0.29         259.0  $< .05$  -0.67 -0.294413     -0.674827  \n",
       "18     0.44        1326.0    $.34$  -0.11  0.441296     -0.105263  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrlllrllrllrr}\n",
      "\\toprule\n",
      "{} & \\multicolumn{2}{l}{N} & \\multicolumn{3}{l}{median (%)} & \\multicolumn{3}{l}{priming} & \\multicolumn{3}{l}{establishment} &   priming & establishment \\\\\n",
      "{} & filler & establishment &     filler & priming & establishment &       U &        p &      d &             U &        p &      d &    d-full &        d-full \\\\\n",
      "Team &        &               &            &         &               &         &          &        &               &          &        &           &               \\\\\n",
      "\\midrule\n",
      "28   &     38 &            58 &       11.6 &     3.5 &          15.2 &  1600.5 &  $< .05$ &   0.45 &         828.5 &  $< .05$ &  -0.25 &  0.452359 &     -0.248185 \\\\\n",
      "17   &     27 &            41 &       14.0 &     6.2 &          17.1 &   823.0 &  $< .05$ &   0.49 &         431.0 &    $.12$ &  -0.22 &  0.486902 &     -0.221319 \\\\\n",
      "20   &     59 &            51 &       20.7 &    18.1 &          22.8 &  1746.5 &    $.15$ &   0.16 &        1139.5 &  $< .05$ &  -0.24 &  0.160851 &     -0.242606 \\\\\n",
      "11   &     35 &            70 &       34.5 &     3.5 &          24.4 &  2229.0 &  $< .05$ &   0.82 &        1624.0 &  $< .05$ &   0.33 &  0.819592 &      0.325714 \\\\\n",
      "9    &     81 &           131 &       46.3 &    11.4 &          37.4 &  8805.5 &  $< .05$ &   0.66 &        6561.5 &  $< .05$ &   0.24 &  0.659693 &      0.236735 \\\\\n",
      "47   &     56 &            74 &       19.5 &     6.7 &          21.9 &  3189.0 &  $< .05$ &   0.54 &        1627.0 &  $< .05$ &  -0.21 &  0.539093 &     -0.214768 \\\\\n",
      "10   &     20 &            65 &       20.1 &     8.7 &          20.4 &   982.0 &  $< .05$ &   0.51 &         660.0 &    $.92$ &   0.02 &  0.510769 &      0.015385 \\\\\n",
      "8    &     26 &            62 &       19.8 &    15.7 &          36.0 &   883.0 &    $.48$ &   0.10 &         461.0 &  $< .05$ &  -0.43 &  0.095533 &     -0.428040 \\\\\n",
      "7    &     27 &            59 &       24.9 &    25.9 &          37.8 &   562.0 &  $< .05$ &  -0.29 &         259.0 &  $< .05$ &  -0.67 & -0.294413 &     -0.674827 \\\\\n",
      "18   &     52 &            57 &       10.8 &     4.3 &          11.8 &  2136.0 &  $< .05$ &   0.44 &        1326.0 &    $.34$ &  -0.11 &  0.441296 &     -0.105263 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "col_list = [('N', 'filler'),\n",
    "            ('N', 'establishment'),\n",
    "            ('median (%)', 'filler'),\n",
    "            ('median (%)', 'priming'),\n",
    "            ('median (%)', 'establishment'),\n",
    "            ('priming', 'U'),\n",
    "            ('priming', 'p'),\n",
    "            ('priming', 'd'),\n",
    "            ('establishment', 'U'),\n",
    "            ('establishment', 'p'),\n",
    "            ('establishment', 'd'),\n",
    "            # for tests\n",
    "            ('priming', 'd-full'),\n",
    "            ('establishment', 'd-full'),\n",
    "           ]\n",
    "           \n",
    "columns = pd.Index(col_list)\n",
    "\n",
    "df = pd.concat([establish_df, priming_df], axis=1)\n",
    "df = df.loc[:, columns]\n",
    "df = df.loc[:, ~df.columns.duplicated()]\n",
    "\n",
    "result_df = df\n",
    "display(result_df)\n",
    "print(result_df.to_latex(escape=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Spearman's correlation with task performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Team\n",
       "28     0.000000\n",
       "17     0.000000\n",
       "20     0.000000\n",
       "11     4.545455\n",
       "9      4.545455\n",
       "47    13.636364\n",
       "10    13.636364\n",
       "8     13.636364\n",
       "7     13.636364\n",
       "18    18.181818\n",
       "Name: error (%), dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df['error (%)'] = [perf_df.T.loc[int(i), 'error (%)']\n",
    "                          for i in result_df.index]\n",
    "\n",
    "result_df['error (%)'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For ('establishment', 'd-full'): 10\n",
      "SpearmanrResult(correlation=0.05720775535473554, pvalue=0.8752667870913545)\n",
      "For ('priming', 'd-full'): 10\n",
      "SpearmanrResult(correlation=-0.17797968332584388, pvalue=0.6227695474948176)\n"
     ]
    }
   ],
   "source": [
    "columns = [('establishment', 'd-full'), ('priming', 'd-full')]\n",
    "     \n",
    "for c in columns:\n",
    "    print('For {}:'.format(c), end=' ')\n",
    "    print(len(result_df))\n",
    "    print(spearmanr(result_df[c].tolist(), result_df['error (%)']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Kruskal-Wallis of Cliff's deltas for the learning outcomes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([7, 9, 10, 11, 17], [8, 18, 20, 28, 47])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_teams, nonpositive_teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For ('establishment', 'd-full'): [-0.6748273697426239, 0.23673546319856742, 0.01538461538461533, 0.3257142857142856, -0.22131887985546517] [-0.4280397022332506, -0.10526315789473684, -0.24260551678298437, -0.24818511796733211, -0.21476833976833976]\n",
      "KruskalResult(statistic=1.3200000000000003, pvalue=0.2505920506856808)\n",
      "\n",
      "For ('priming', 'd-full'): [-0.2944130571249215, 0.6596927716520591, 0.5107692307692309, 0.8195918367346939, 0.48690153568202343] [0.09553349875930528, 0.4412955465587045, 0.16085078099036232, 0.45235934664246824, 0.5390926640926641]\n",
      "KruskalResult(statistic=1.3200000000000003, pvalue=0.2505920506856808)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "columns = [('establishment', 'd-full'), ('priming', 'd-full')]\n",
    "for c in columns:\n",
    "    p_col = (c[0], 'p')\n",
    "\n",
    "    print('For {}:'.format(c), end=' ')\n",
    "    \n",
    "    # # Exclude teams that are not significant.\n",
    "    # exclude = result_df[result_df[p_col]!= '$< .05$'].index\n",
    "\n",
    "    test = result_df.loc[positive_teams, c].tolist() #.astype(float).tolist()\n",
    "    control = result_df.loc[nonpositive_teams, c].tolist()\n",
    "\n",
    "    print(test, control)\n",
    "    print(kruskal(test, control))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RQ2: How do the interlocutors follow up the use of task-specific referents with actions? Does this link to task success?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## H2.1: Instructions are more likely to be followed by a corresponding action early in the dialogue for more successful teams."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute mean of medians and SD (and mean)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### for matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "combined mean = 62.36% , combined SD = 21.9%\n",
      "mean of the medians = 62.6%\n"
     ]
    }
   ],
   "source": [
    "df = all_corpus_df.copy()\n",
    "df = df[df.match.notnull()]\n",
    "of = 'scaled_end'\n",
    "compute_combined_mean_median_sd(df, of=of)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### for mismatches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "combined mean = 62.70% , combined SD = 26.0%\n",
      "mean of the medians = 67.6%\n"
     ]
    }
   ],
   "source": [
    "df = all_corpus_df.copy()\n",
    "df = df[df.mismatch.notnull()]\n",
    "of = 'scaled_end'\n",
    "compute_combined_mean_median_sd(df, of=of)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot (mis)match time distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### in absolute time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../figures/h2_1_time_absolute_plot.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_matches_absolute(df, x,\n",
    "                          order,\n",
    "                          seps=[],\n",
    "                          y='team_no',\n",
    "                          save=True,\n",
    "                          save_filename=None,\n",
    "                          xlabel=None,\n",
    "                          show_ylabel=True,\n",
    "                          show_yticks=True,\n",
    "                          show_perf_groups=False,\n",
    "                          show_learn_groups=False,\n",
    "                          fig=None, ax=None):\n",
    "    '''Creates box plots of matches and mismatches in a figure'''\n",
    "    # Create a new figure if not supplied.\n",
    "    if fig is None:\n",
    "        fig, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "    # dff = df.copy()\n",
    "    offset1, offset2 = -0.2, -0.1\n",
    "    # Plot match and mismatch times.\n",
    "    dff = df.copy()\n",
    "    dff = dff[dff.is_match | dff.is_mismatch]\n",
    "    bp = sns.boxplot(x=x, y=y,\n",
    "                     order=order,\n",
    "                     hue='is_match',\n",
    "                     palette={True: colors[0], False: colors[1]},\n",
    "                     hue_order=[True, False],\n",
    "                     #boxprops=dict(alpha=.15),\n",
    "                     boxprops=dict(alpha=.2),\n",
    "                     width=0.3,\n",
    "                     showfliers=True, \n",
    "                     fliersize=3,\n",
    "                     whis=0,\n",
    "                     data=dff, ax=ax)\n",
    "\n",
    "    # # Plot match times, with a \"plus\".\n",
    "    # dff = df.copy()\n",
    "    # dff = dff[dff.is_match]\n",
    "    # for team_no in order:\n",
    "    #     index = order.index(str(team_no))\n",
    "    #     xs = dff[dff.team_no == str(team_no)][x]\n",
    "    #     ys = index + offset1\n",
    "    #     sns.scatterplot(x=xs, y=ys,\n",
    "    #                     color=colors[0],\n",
    "    #                     marker='P', \n",
    "    #                     linewidth=0,\n",
    "    #                     marker='.',\n",
    "    #                     ax=ax)\n",
    "\n",
    "    # # Plot mismatch times, with a \"cross\".\n",
    "    # dff = df.copy()\n",
    "    # dff = dff[dff.is_mismatch]\n",
    "    # for team_no in order:\n",
    "    #     index = order.index(str(team_no))\n",
    "    #     xs = dff[dff.team_no == str(team_no)][x]\n",
    "    #     ys = index - offset1\n",
    "    #     sns.scatterplot(x=xs, y=ys,\n",
    "    #                     color=colors[1],\n",
    "    #                     marker='X', \n",
    "    #                     linewidth=0,\n",
    "    #                     ax=ax)\n",
    "\n",
    "    # Set x-axis properties.\n",
    "    if xlabel is not None:\n",
    "        ax.set_xlabel(xlabel, fontsize=18)\n",
    "\n",
    "    # Set y-axis properties.\n",
    "    if show_ylabel:\n",
    "        ax.set_ylabel('team no', fontsize=18)\n",
    "    else:\n",
    "        ax.set_ylabel('')\n",
    "    if not show_yticks:\n",
    "        ax.set_yticks([], [])\n",
    "\n",
    "    ax.xaxis.set_minor_locator(AutoMinorLocator(5))\n",
    "    ax.xaxis.grid(True, alpha=0.2, which='minor')\n",
    "\n",
    "    legend = ax.get_legend()\n",
    "    if legend is not None:\n",
    "        legend.remove()\n",
    "\n",
    "    # Annotate by task performance.\n",
    "    if show_perf_groups:\n",
    "        l = [0] + seps + [1]\n",
    "        x = 1.02\n",
    "        offset = 0.02\n",
    "        labels = [('well-performing', 'g'),\n",
    "                  ('badly-performing', 'r')]\n",
    "        for i, (previous, current) in enumerate(zip(l, l[1:])):\n",
    "            if isinstance(previous, str):\n",
    "                previous = (order.index(previous) + 1) / len(order)\n",
    "            if isinstance(current, str):\n",
    "                current = (order.index(current) + 1) / len(order)\n",
    "            previous += offset\n",
    "            current -= offset\n",
    "            previous, current = 1 - previous, 1 - current\n",
    "            s, c = labels[i]\n",
    "            ax.annotate('',\n",
    "                        xy=(x, previous),\n",
    "                        xytext=(x, current),\n",
    "                        xycoords='axes fraction',\n",
    "                        arrowprops=dict(arrowstyle='-', linewidth=3, color=c))\n",
    "            t = ax.annotate(s,\n",
    "                            xy=(x+0.01, (previous+current)/2),\n",
    "                            fontsize=16,\n",
    "                            color=c,\n",
    "                            rotation=90,\n",
    "                            xycoords='axes fraction',\n",
    "                            ha='left', va='center')\n",
    "            \n",
    "            s = '(increasing error)'\n",
    "            x = 1.02\n",
    "            style = '<-,head_length=0.8,head_width=0.5'\n",
    "            props = dict(arrowstyle=style, linewidth=3, color='r')\n",
    "            ax.annotate('',\n",
    "                        xy=(x, 0.1),\n",
    "                        xytext=(x, 0.01),\n",
    "                        xycoords='axes fraction',\n",
    "                        arrowprops=props)\n",
    "            t = ax.annotate(s,\n",
    "                            xy=(x+0.01, 0.06),\n",
    "                            fontsize=12,\n",
    "                            rotation=90,\n",
    "                            xycoords='axes fraction',\n",
    "                            ha='left', va='bottom')\n",
    "\n",
    "    # Annotate the groups of learning outcomes.\n",
    "    if show_learn_groups:\n",
    "        l = [0] + seps + [1]\n",
    "        x, offset = 1.02, 0.02\n",
    "        labels = [r'learn $> 0$', r'learn $ \\leq 0$']\n",
    "        color_list = [colors[2], colors[3]]\n",
    "        for i, (previous, current) in enumerate(zip(l, l[1:])):\n",
    "            if isinstance(previous, str):\n",
    "                previous = (order.index(previous)+1) / len(order)\n",
    "            if isinstance(current, str):\n",
    "                current = (order.index(current)+1) / len(order)\n",
    "            previous += offset\n",
    "            current -= offset\n",
    "            previous, current = 1 - previous, 1 - current\n",
    "            style = '-'\n",
    "            if i == 0:\n",
    "                # show the increasing direction\n",
    "                style = '->,head_length=0.8,head_width=0.5'\n",
    "            ax.annotate('',\n",
    "                        xy=(x, previous),\n",
    "                        xytext=(x, current),\n",
    "                        xycoords='axes fraction',\n",
    "                        arrowprops=dict(arrowstyle=style, linewidth=3, color=color_list[i]))\n",
    "            s = labels[i]\n",
    "            t = ax.annotate(s,\n",
    "                            xy=(x+0.01, (previous+current)/2),\n",
    "                            fontsize=16,\n",
    "                            rotation=90,\n",
    "                            xycoords='axes fraction',\n",
    "                            color=color_list[i],\n",
    "                            ha='left', va='center')\n",
    "\n",
    "    # Set x-axis properties.\n",
    "    ax.set_xticks(np.arange(0, 2400+1, 300))\n",
    "    ax.set_xlim([0, 37*60])\n",
    "    ax.xaxis.set_major_formatter(\n",
    "        FuncFormatter(lambda x, _: '{:.0f}'.format(x/60)))\n",
    "    if xlabel is not None:\n",
    "        ax.set_xlabel(xlabel, fontsize=18)\n",
    "\n",
    "    if fig is not None and save:\n",
    "        if save_filename is None:\n",
    "            save_filename = 'h2_1_{}_absolute_plot'.format(xlabel.split()[-2])\n",
    "            # if show_perf_groups:\n",
    "            #     save_filename += '_by-perf'\n",
    "            # elif show_learn_groups:\n",
    "            #     save_filename += '_by-learn'\n",
    "            save_filename += '.png'\n",
    "\n",
    "        # Export as pdf - vector graphics.\n",
    "        export_file = plots_dir.joinpath(\n",
    "            pl.Path(save_filename).with_suffix('.pdf'))\n",
    "        fig.savefig(export_file, bbox_inches='tight')\n",
    "        print(export_file)\n",
    "\n",
    "    return fig, ax\n",
    "\n",
    "\n",
    "# Order teams by performance.\n",
    "order, seps = perf_order, perf_seps\n",
    "# order, seps = learn_order, learn_seps\n",
    "\n",
    "figsize = (12, 12)\n",
    "fig, ax = plt.subplots(figsize=figsize)\n",
    "\n",
    "palette = sns.color_palette()\n",
    "colors = list(palette)\n",
    "cs = [colors[0], colors[3]]\n",
    "df = all_corpus_df.copy()\n",
    "\n",
    "# Annotate the end of interaction times on the plot.\n",
    "for team_no, row in counts_df.iterrows():\n",
    "    if perf_df.T.loc[int(team_no), 'error (%)'] == 0:\n",
    "        c = 'g'\n",
    "    else:\n",
    "        c = 'r'\n",
    "    pos = row['duration (secs)']\n",
    "    y = order.index(str(team_no))\n",
    "    props = dict(arrowstyle='-', alpha=1.0, linewidth=4, color=c)\n",
    "    ax.annotate('',\n",
    "                xy=(pos, y+0.5),\n",
    "                xytext=(pos, y-0.5),\n",
    "                xycoords='data',\n",
    "                color='green', arrowprops=props)\n",
    "\n",
    "# Annotate the learning outcomes on the plot.\n",
    "text_pos, offset = 1, 0\n",
    "t = ax.annotate('learn',\n",
    "                xy=(text_pos, 1-0.01),\n",
    "                backgroundcolor='w',\n",
    "                fontsize=20,\n",
    "                xycoords='axes fraction',\n",
    "                ha='center', va='center')\n",
    "for team_no, row in learn_df.T.iterrows():\n",
    "    value = row['learn (%)'] \n",
    "    if value > 0:\n",
    "        s = '$+$'\n",
    "    else:\n",
    "        s = '$-$'\n",
    "    y = (order.index(str(team_no)) + .5) / len(order)\n",
    "    t = ax.annotate(s,\n",
    "                    xy=(text_pos, y+offset),\n",
    "                    backgroundcolor='w',\n",
    "                    fontsize=20,\n",
    "                    xycoords='axes fraction',\n",
    "                    ha='center', va='center')\n",
    "\n",
    "# Annotate the submission costs on the plot.\n",
    "for team_no, _ in routine_dfs.items():\n",
    "    df = all_corpus_df.copy()\n",
    "    df = df[df.team_no == str(team_no)]\n",
    "    df = df[df.subject == 'T']\n",
    "    y = order.index(str(team_no))\n",
    "\n",
    "    for i, row in df.iterrows():\n",
    "        arrow_start, arrow_end = 0, 0\n",
    "        # text_pos = row['scaled_end']\n",
    "        text_pos = row['end']\n",
    "\n",
    "        val = row['error']\n",
    "        s = '{:2.0%}'.format(val).strip('%')\n",
    "        offset = 0\n",
    "        props = dict(arrowstyle='-', alpha=0.6, linewidth=2, color='b')\n",
    "        width = 0.3\n",
    "        ax.annotate('',\n",
    "                    xy=(text_pos, y+width/2),\n",
    "                    xytext=(text_pos, y-width/2),\n",
    "                    xycoords='data',\n",
    "                    color='green', arrowprops=props)\n",
    "        t = ax.annotate(s,\n",
    "                        xy=(text_pos, y+offset),\n",
    "                        backgroundcolor='w',\n",
    "                        fontsize=10,\n",
    "                        xycoords='data',\n",
    "                        ha='center', va='center')\n",
    "        t.set_bbox(dict(fc='w', alpha=0.9, ec='none',\n",
    "                        boxstyle='square,pad=0.05'))\n",
    "\n",
    "\n",
    "# Mark the common duration with a vertical line.\n",
    "ax.axvline(common_duration, ls='--', color='green')\n",
    "\n",
    "\n",
    "# Create box plots of matches and mismatches.\n",
    "x, xlabel = 'end', 'time (mins)'\n",
    "fig, ax = plot_matches_absolute(\n",
    "    all_corpus_df, x=x, xlabel=xlabel,\n",
    "    seps=seps, order=order,\n",
    "    fig=fig, ax=ax,\n",
    "    show_perf_groups=True)\n",
    "#     show_learn_groups=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### in normalised time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../figures/h2_1_time_plot.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_matches(df, x,\n",
    "                 order,\n",
    "                 seps=[],\n",
    "                 y='team_no',\n",
    "                 save=True,\n",
    "                 save_filename=None,\n",
    "                 xlabel=None,\n",
    "                 show_ylabel=True,\n",
    "                 show_yticks=True,\n",
    "                 show_perf_groups=False,\n",
    "                 show_learn_groups=False,\n",
    "                 fig=None, ax=None):\n",
    "    '''Creates box plots of matches and mismatches in a figure'''\n",
    "    # Create a new figure if not supplied.\n",
    "    if fig is None:\n",
    "        fig, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "    # # Plot establishment times.\n",
    "    # bp = sns.boxplot(x='scaled_establish_time', y=y,\n",
    "    #              order=order,\n",
    "    #              width=0.4,\n",
    "    #              boxprops=dict(alpha=.6),\n",
    "    #              data=all_routines_df, ax=ax)\n",
    "\n",
    "    # # Plot instruction times.\n",
    "    # dff = df.copy()\n",
    "    offset1, offset2 = -0.2, -0.1\n",
    "    # dff = dff[dff['n_instructions'] > 0]\n",
    "    # for team_no in order:\n",
    "    #     index = order.index(str(team_no))\n",
    "    #     xs = dff[dff.team_no == str(team_no)][x]\n",
    "    #     ys = index + offset1 + 2 * offset2\n",
    "    #     sns.scatterplot(x=xs, y=ys,\n",
    "    #                     color=colors[1],\n",
    "    #                     marker='^', linewidth=0,\n",
    "    #                     ax=ax)\n",
    "    # bp = sns.boxplot(x=x, y=y,\n",
    "    #                  order=order,\n",
    "    #                  boxprops=dict(alpha=.2),\n",
    "    #                  data=dff, ax=ax)\n",
    "\n",
    "    # Plot match and mismatch times.\n",
    "    dff = df.copy()\n",
    "    dff = dff[dff.is_match | dff.is_mismatch]\n",
    "    bp = sns.boxplot(x=x, y=y,\n",
    "                     order=order,\n",
    "                     hue='is_match',\n",
    "                     hue_order=[True, False],\n",
    "                     palette={True: colors[0], False: colors[1]},\n",
    "                     boxprops=dict(alpha=.15),\n",
    "                     #showfliers=False,\n",
    "                     showfliers=True, \n",
    "                     fliersize=3,\n",
    "                     # whis=0,\n",
    "                     data=dff, ax=ax)\n",
    "\n",
    "    # # Plot match times, with a \"plus\".\n",
    "    # dff = df.copy()\n",
    "    # dff = dff[dff.is_match]\n",
    "    # for team_no in order:\n",
    "    #     index = order.index(str(team_no))\n",
    "    #     xs = dff[dff.team_no == str(team_no)][x]\n",
    "    #     ys = index + offset1\n",
    "    #     sns.scatterplot(x=xs, y=ys,\n",
    "    #                     color=colors[0],\n",
    "    #                     marker='P', \n",
    "    #                     linewidth=0,\n",
    "    #                     ax=ax)\n",
    "\n",
    "    # # Plot mismatch times, with a \"cross\".\n",
    "    # dff = df.copy()\n",
    "    # dff = dff[dff.is_mismatch]\n",
    "    # for team_no in order:\n",
    "    #     index = order.index(str(team_no))\n",
    "    #     xs = dff[dff.team_no == str(team_no)][x]\n",
    "    #     ys = index - offset1\n",
    "    #     sns.scatterplot(x=xs, y=ys,\n",
    "    #                     color=colors[1],\n",
    "    #                     marker='X', \n",
    "    #                     linewidth=0,\n",
    "    #                     ax=ax)\n",
    "\n",
    "    # Set x-axis properties.\n",
    "    if xlabel is not None:\n",
    "        ax.set_xlabel(xlabel, fontsize=18)\n",
    "\n",
    "    # Set y-axis properties.\n",
    "    if show_ylabel:\n",
    "        ax.set_ylabel('team no', fontsize=18)\n",
    "    else:\n",
    "        ax.set_ylabel('')\n",
    "    if not show_yticks:\n",
    "        ax.set_yticks([], [])\n",
    "\n",
    "    ax.xaxis.set_minor_locator(AutoMinorLocator(5))\n",
    "    ax.xaxis.grid(True, alpha=0.2, which='minor')\n",
    "\n",
    "    legend = ax.get_legend()\n",
    "    if legend is not None:\n",
    "        legend.remove()\n",
    "\n",
    "    # Annotate by task performance.\n",
    "    if show_perf_groups:\n",
    "        l = [0] + seps + [1]\n",
    "        x = 1.02\n",
    "        offset = 0.02\n",
    "        labels = [('well-performing', 'g'),\n",
    "                  ('badly-performing', 'r')]\n",
    "        for i, (previous, current) in enumerate(zip(l, l[1:])):\n",
    "            if isinstance(previous, str):\n",
    "                previous = (order.index(previous) + 1) / len(order)\n",
    "            if isinstance(current, str):\n",
    "                current = (order.index(current) + 1) / len(order)\n",
    "            previous += offset\n",
    "            current -= offset\n",
    "            previous, current = 1 - previous, 1 - current\n",
    "            s, c = labels[i]\n",
    "            ax.annotate('',\n",
    "                        xy=(x, previous),\n",
    "                        xytext=(x, current),\n",
    "                        xycoords='axes fraction',\n",
    "                        arrowprops=dict(arrowstyle='-', linewidth=3, color=c))\n",
    "            t = ax.annotate(s,\n",
    "                            xy=(x+0.01, (previous+current)/2),\n",
    "                            fontsize=16,\n",
    "                            color=c,\n",
    "                            rotation=90,\n",
    "                            xycoords='axes fraction',\n",
    "                            ha='left', va='center')\n",
    "\n",
    "        s = '(increasing error)'\n",
    "        x = 1.02\n",
    "        style = '<-,head_length=0.8,head_width=0.5'\n",
    "        props = dict(arrowstyle=style, linewidth=3, color='r')\n",
    "        ax.annotate('',\n",
    "                    xy=(x, 0.1),\n",
    "                    xytext=(x, 0.01),\n",
    "                    xycoords='axes fraction',\n",
    "                    arrowprops=props)\n",
    "        t = ax.annotate(s,\n",
    "                        xy=(x+0.01, 0.06),\n",
    "                        fontsize=12,\n",
    "                        rotation=90,\n",
    "                        xycoords='axes fraction',\n",
    "                        ha='left', va='bottom')\n",
    "\n",
    "        \n",
    "    # Annotate the groups of learning outcomes.\n",
    "    if show_learn_groups:\n",
    "        l = [0] + seps + [1]\n",
    "        x, offset = 1.02, 0.02\n",
    "        labels = [r'learn $> 0$', r'learn $ \\leq 0$']\n",
    "        color_list = [colors[2], colors[3]]\n",
    "        for i, (previous, current) in enumerate(zip(l, l[1:])):\n",
    "            if isinstance(previous, str):\n",
    "                previous = (order.index(previous)+1) / len(order)\n",
    "            if isinstance(current, str):\n",
    "                current = (order.index(current)+1) / len(order)\n",
    "            previous += offset\n",
    "            current -= offset\n",
    "            previous, current = 1 - previous, 1 - current\n",
    "            style = '-'\n",
    "            if i == 0:\n",
    "                # show the increasing direction\n",
    "                style = '->,head_length=0.8,head_width=0.5'\n",
    "            ax.annotate('',\n",
    "                        xy=(x, previous),\n",
    "                        xytext=(x, current),\n",
    "                        xycoords='axes fraction',\n",
    "                        arrowprops=dict(arrowstyle=style, linewidth=3, color=color_list[i]))\n",
    "            s = labels[i]\n",
    "            t = ax.annotate(s,\n",
    "                            xy=(x+0.01, (previous+current)/2),\n",
    "                            fontsize=16,\n",
    "                            rotation=90,\n",
    "                            xycoords='axes fraction',\n",
    "                            color=color_list[i],\n",
    "                            ha='left', va='center')\n",
    "\n",
    "    # Set x-axis properties.\n",
    "    ax.set_xticks(np.arange(0, 1.001, 0.1))\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.xaxis.set_major_formatter(\n",
    "        FuncFormatter(lambda x, _: '{:.0%}'.format(x)))\n",
    "    if xlabel is not None:\n",
    "        ax.set_xlabel(xlabel, fontsize=18)\n",
    "\n",
    "    if fig is not None and save:\n",
    "        if save_filename is None:\n",
    "            save_filename = 'h2_1_{}_plot'.format(xlabel.split()[-2])\n",
    "            # if show_perf_groups:\n",
    "            #     save_filename += '_by-perf'\n",
    "            # elif show_learn_groups:\n",
    "            #     save_filename += '_by-learn'\n",
    "            save_filename += '.png'\n",
    "\n",
    "        export_file = plots_dir.joinpath(\n",
    "            pl.Path(save_filename).with_suffix('.png'))\n",
    "\n",
    "        # # Export as png - bitmap.\n",
    "        # fig.savefig(export_file, bbox_inches='tight')\n",
    "        # print(export_file)\n",
    "\n",
    "        # Export as pdf - vector graphics.\n",
    "        export_file = export_file.with_suffix('.pdf')\n",
    "        fig.savefig(export_file, bbox_inches='tight')\n",
    "        print(export_file)\n",
    "\n",
    "    return fig, ax\n",
    "\n",
    "\n",
    "# Order teams by performance.\n",
    "order, seps = perf_order, perf_seps\n",
    "# order, seps = learn_order, learn_seps\n",
    "\n",
    "figsize = (12, 12)\n",
    "fig, ax = plt.subplots(figsize=figsize)\n",
    "\n",
    "palette = sns.color_palette()\n",
    "colors = list(palette)\n",
    "cs = [colors[0], colors[3]]\n",
    "df = all_corpus_df.copy()\n",
    "\n",
    "\n",
    "# Annotate the submission costs on the plot.\n",
    "for team_no, _ in routine_dfs.items():\n",
    "    df = all_corpus_df.copy()\n",
    "    df = df[df.team_no == str(team_no)]\n",
    "    df = df[df.subject == 'T']\n",
    "    y = order.index(str(team_no))\n",
    "\n",
    "    for i, row in df.iterrows():\n",
    "        arrow_start, arrow_end = 0, 0\n",
    "        text_pos = row['scaled_end']\n",
    "\n",
    "        val = row['error']\n",
    "        s = '{:2.0%}'.format(val).strip('%')\n",
    "        offset = 0\n",
    "        props = dict(arrowstyle='-', alpha=0.6, linewidth=2, color='b')\n",
    "        ax.annotate('',\n",
    "                    xy=(text_pos, y+0.3),\n",
    "                    xytext=(text_pos, y-0.3),\n",
    "                    xycoords='data',\n",
    "                    color='green', arrowprops=props)\n",
    "        t = ax.annotate(s,\n",
    "                        xy=(text_pos, y+offset),\n",
    "                        backgroundcolor='w',\n",
    "                        fontsize=10,\n",
    "                        xycoords='data',\n",
    "                        ha='center', va='center')\n",
    "        t.set_bbox(dict(fc='w', alpha=0.9, ec='none',\n",
    "                        boxstyle='square,pad=0.05'))\n",
    "        \n",
    "\n",
    "# Create box plots of matches and mismatches.\n",
    "x, xlabel = 'scaled_end', 'time (%)'\n",
    "fig, ax = plot_matches(\n",
    "    all_corpus_df, x=x, xlabel=xlabel,\n",
    "    seps=seps, order=order,\n",
    "    fig=fig, ax=ax,\n",
    "    show_perf_groups=True)\n",
    "#     show_learn_groups=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Spearman's correlation with task performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtering for match\n",
      "For median: 10 values\n",
      "SpearmanrResult(correlation=0.5847903880706299, pvalue=0.07578674815760061)\n",
      "For scaled_median: 10 values\n",
      "SpearmanrResult(correlation=0.540295467239169, pvalue=0.10689161725875573)\n",
      "For in_common_median: Dropped teams [18]\n",
      "9 values\n",
      "SpearmanrResult(correlation=0.445435403187374, pvalue=0.22953161162393249)\n",
      "\n",
      "Filtering for mismatch\n",
      "For median: 10 values\n",
      "SpearmanrResult(correlation=0.699205898780101, pvalue=0.02443752559540314)\n",
      "For scaled_median: 10 values\n",
      "SpearmanrResult(correlation=0.5530083017624435, pvalue=0.09730464963395251)\n",
      "For in_common_median: Dropped teams [7, 18]\n",
      "8 values\n",
      "SpearmanrResult(correlation=0.629940788348712, pvalue=0.09413276656581646)\n",
      "\n",
      "Filtering for match_or_mismatch\n",
      "For median: 10 values\n",
      "SpearmanrResult(correlation=0.6928494815184638, pvalue=0.026342919713052852)\n",
      "For scaled_median: 10 values\n",
      "SpearmanrResult(correlation=0.6165724743788163, pvalue=0.05762241559176481)\n",
      "For in_common_median: Dropped teams [18]\n",
      "9 values\n",
      "SpearmanrResult(correlation=0.5077963596336064, pvalue=0.16283414497095547)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "filters = {\n",
    "    'match': lambda df: df[df.is_match],\n",
    "    'mismatch': lambda df: df[df.is_mismatch],\n",
    "    'match_or_mismatch': lambda df: df[df.is_match | df.is_mismatch],\n",
    "}\n",
    "\n",
    "for k, func in filters.items():\n",
    "    print('Filtering for {}'.format(k))\n",
    "    l = list()\n",
    "\n",
    "    # For scaled and absolute time: (columns 'end' and 'scaled_end').\n",
    "    for prefix in ['', 'scaled_']:\n",
    "        col = '{}end'.format(prefix)\n",
    "\n",
    "        # Match or mismatch statistics.\n",
    "        df = func(all_corpus_df).copy()\n",
    "        grouped = df.groupby('team_no')[col]\n",
    "        df = compute_quartiles(grouped, prefix)\n",
    "        l.append(df)\n",
    "\n",
    "    # In common time establishments.\n",
    "    df = func(all_corpus_df).copy()\n",
    "    df = df[df.in_common_time]\n",
    "    grouped = df.groupby('team_no')['end']\n",
    "    df = compute_quartiles(grouped, 'in_common_')\n",
    "    l.append(df)\n",
    "\n",
    "    result_df = pd.concat([*l, perf_df.T, learn_df.T], axis=1)\n",
    "    result_df.sort_values('error (%)')\n",
    "\n",
    "    for prefix in ['', 'scaled_', 'in_common_']:\n",
    "        c = '{}median'.format(prefix)\n",
    "        print('For {}:'.format(c), end=' ')\n",
    "        df = result_df[[c, 'error (%)']].copy()\n",
    "        dropped = df.dropna()\n",
    "        omitted_teams = set(df.index) - set(dropped.index)\n",
    "        if len(omitted_teams) > 0:\n",
    "            print('Dropped teams {}'.format(sorted(omitted_teams)))\n",
    "        df = dropped\n",
    "        print('{} values'.format(len(df)))\n",
    "        print(spearmanr(df[c], df['error (%)']))\n",
    "        \n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Kruskal-Wallis of Cliff's deltas for the learning outcomes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtering for match\n",
      "For median: [1036.073, 1294.0655000000002, 932.3215, 942.8885, 657.676] [1025.1685, 1276.336, 671.3225, 419.914, 669.0495]\n",
      "KruskalResult(statistic=0.5345454545454515, pvalue=0.4647020999404661)\n",
      "\n",
      "For scaled_median: [0.6551440677912514, 0.5989297103159726, 0.6731781899050872, 0.5945050018726283, 0.6108137576958191] [0.6527976857190707, 0.6952391379778988, 0.5804210224163138, 0.6266559416911536, 0.5675456611417766]\n",
      "KruskalResult(statistic=0.09818181818181415, pvalue=0.7540225300620795)\n",
      "\n",
      "Filtering for mismatch\n",
      "For median: [1260.7089999999998, 1516.372, 903.8910000000001, 610.3025, 747.0625] [1355.997, 1375.994, 488.8245, 493.316, 891.93]\n",
      "KruskalResult(statistic=0.2727272727272734, pvalue=0.6015081344405895)\n",
      "\n",
      "For scaled_median: [0.797189022936647, 0.7018193767558536, 0.6526500861038808, 0.38480466025979726, 0.6938310853043639] [0.8634597175410703, 0.7495243277810552, 0.42263445076270106, 0.7361969415911664, 0.7566121812245354]\n",
      "KruskalResult(statistic=1.3200000000000003, pvalue=0.2505920506856808)\n",
      "\n",
      "Filtering for match_or_mismatch\n",
      "For median: [1056.446, 1372.476, 929.199, 792.8489999999999, 685.345] [1046.724, 1308.3825000000002, 671.3225, 430.331, 746.24]\n",
      "KruskalResult(statistic=0.8836363636363629, pvalue=0.34720763934942456)\n",
      "\n",
      "For scaled_median: [0.6680266060806491, 0.6352202829730218, 0.6709236040160149, 0.4999029007456466, 0.6365112224986789] [0.666523605423507, 0.7126953415443646, 0.5804210224163138, 0.6422016842589097, 0.6330253205038483]\n",
      "KruskalResult(statistic=0.010909090909095198, pvalue=0.9168149485280722)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for k, func in filters.items():\n",
    "    print('Filtering for {}'.format(k))\n",
    "    l = list()\n",
    "\n",
    "    # For scaled and absolute time: (columns 'end' and 'scaled_end').\n",
    "    for prefix in ['', 'scaled_']:\n",
    "        col = '{}end'.format(prefix)\n",
    "\n",
    "        # Match or mismatch statistics.\n",
    "        df = func(all_corpus_df).copy()\n",
    "        grouped = df.groupby('team_no')[col]\n",
    "        df = compute_quartiles(grouped, prefix)\n",
    "        l.append(df)\n",
    "    result_df = pd.concat([*l, perf_df.T, learn_df.T], axis=1)\n",
    "    result_df.sort_values('error (%)')\n",
    "    \n",
    "    for prefix in ['', 'scaled_']:\n",
    "        c = '{}median'.format(prefix)\n",
    "        print('For {}:'.format(c), end=' ')\n",
    "\n",
    "        test = result_df.loc[positive_teams, c].astype(float).tolist()\n",
    "        control = result_df.loc[nonpositive_teams, c].astype(float).tolist()\n",
    "\n",
    "        print(test, control)\n",
    "        print(kruskal(test, control))\n",
    "        print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## H2.2: When instructions are followed by a corresponding or a different action, the action is more likely to be in the vicinity of information management phenomena for more successful teams."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Report oh counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "oh 333 33.3 19.26\n"
     ]
    }
   ],
   "source": [
    "for marker in ['oh']:\n",
    "    counts = list()\n",
    "    for team_no, df in tokens_dfs.items():\n",
    "        count = df.token.apply(lambda x: x.strip() == marker).sum()\n",
    "        counts.append(count)\n",
    "\n",
    "    print(marker, np.sum(counts), np.mean(counts), '{:.2f}'.format(np.std(counts)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform Mann-Whitney U tests and compute Cliff's Deltas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### for matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U is the Mann–Whitney U test statistic, where p \\< 0.05 indicates that the groups are statistically significantly different\n",
      "Cliff's delta \\> 0 indicates info marker (oh) times larger than match times. \\< otherwise.\n",
      "oh times vs.\\ match times (sorted by decreasing task performance (i.e.\\ increasing error):\n",
      "For Team 10: $U =  177.0$, $p < .05$, $\\text{Cliff's delta} = -0.45$ \\\\\n",
      "For Team 11: $U =  243.0$, $p = .79$, $\\text{Cliff's delta} = -0.05$ \\\\\n",
      "For Team 17: $U =  231.0$, $p = .98$, $\\text{Cliff's delta} = 0.00$ \\\\\n",
      "For Team 09: $U =  331.0$, $p = .23$, $\\text{Cliff's delta} = -0.18$ \\\\\n",
      "For Team 07: $U =  670.0$, $p < .05$, $\\text{Cliff's delta} = -0.39$ \\\\\n",
      "For Team 28: $U =  219.0$, $p = .83$, $\\text{Cliff's delta} = -0.04$ \\\\\n",
      "For Team 08: $U =  423.0$, $p < .05$, $\\text{Cliff's delta} = -0.49$ \\\\\n",
      "For Team 47: $U =  316.0$, $p = .15$, $\\text{Cliff's delta} = -0.22$ \\\\\n",
      "For Team 20: $U =  530.0$, $p < .05$, $\\text{Cliff's delta} = -0.32$ \\\\\n",
      "For Team 18: $U =  132.0$, $p = .56$, $\\text{Cliff's delta} = -0.12$ \\\\\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">N</th>\n",
       "      <th colspan=\"2\" halign=\"left\">median (%)</th>\n",
       "      <th colspan=\"4\" halign=\"left\">match</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>oh</th>\n",
       "      <th>match</th>\n",
       "      <th>oh</th>\n",
       "      <th>match</th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>d</th>\n",
       "      <th>d-full</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</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>10</th>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>47.6</td>\n",
       "      <td>67.3</td>\n",
       "      <td>177.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.453704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>57.3</td>\n",
       "      <td>59.5</td>\n",
       "      <td>243.0</td>\n",
       "      <td>$.79$</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.047059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>61.1</td>\n",
       "      <td>61.1</td>\n",
       "      <td>231.0</td>\n",
       "      <td>$.98$</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.004348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>42.9</td>\n",
       "      <td>59.9</td>\n",
       "      <td>331.0</td>\n",
       "      <td>$.23$</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.184729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>51</td>\n",
       "      <td>43</td>\n",
       "      <td>49.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>670.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.388965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>61.7</td>\n",
       "      <td>62.7</td>\n",
       "      <td>219.0</td>\n",
       "      <td>$.83$</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.039474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>55</td>\n",
       "      <td>30</td>\n",
       "      <td>36.3</td>\n",
       "      <td>65.3</td>\n",
       "      <td>423.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.487273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>53.4</td>\n",
       "      <td>56.8</td>\n",
       "      <td>316.0</td>\n",
       "      <td>$.15$</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.221675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>65</td>\n",
       "      <td>24</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.320513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>69.6</td>\n",
       "      <td>69.5</td>\n",
       "      <td>132.0</td>\n",
       "      <td>$.56$</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.120000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       N       median (%)        match                          \n",
       "      oh match         oh match      U        p      d    d-full\n",
       "Team                                                            \n",
       "10    18    36       47.6  67.3  177.0  $< .05$  -0.45 -0.453704\n",
       "11    15    34       57.3  59.5  243.0    $.79$  -0.05 -0.047059\n",
       "17    20    23       61.1  61.1  231.0    $.98$   0.00  0.004348\n",
       "9     29    28       42.9  59.9  331.0    $.23$  -0.18 -0.184729\n",
       "7     51    43       49.7  65.5  670.0  $< .05$  -0.39 -0.388965\n",
       "28    24    19       61.7  62.7  219.0    $.83$  -0.04 -0.039474\n",
       "8     55    30       36.3  65.3  423.0  $< .05$  -0.49 -0.487273\n",
       "47    29    28       53.4  56.8  316.0    $.15$  -0.22 -0.221675\n",
       "20    65    24       35.0  58.0  530.0  $< .05$  -0.32 -0.320513\n",
       "18    12    25       69.6  69.5  132.0    $.56$  -0.12 -0.120000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def test_marker_and_matches(match_col,\n",
    "                            n1='oh',\n",
    "                            n2='match',\n",
    "                            verbose=True):\n",
    "    n1_col = 'N_{}'.format(n1)\n",
    "    n2_col = 'N_{}'.format(n2)\n",
    "    m1_col = 'median (%)_{}'.format(n1)\n",
    "    m2_col = 'median (%)_{}'.format(n2)\n",
    "    u_col = '{}_U'.format(n2)\n",
    "    p_col = '{}_p'.format(n2)\n",
    "    d_col = '{}_d'.format(n2)\n",
    "    d_col_full = '{}_d-full'.format(n2)\n",
    "    data = {\n",
    "        'Team': [],\n",
    "        n1_col: [], n2_col: [],\n",
    "        m1_col: [], m2_col: [],\n",
    "        u_col: [],\n",
    "        p_col: [],\n",
    "        d_col: [], d_col_full: [],\n",
    "    }\n",
    "\n",
    "    order = [int(t) for t in learn_order]\n",
    "    # order = [int(t) for t in perf_order]\n",
    "\n",
    "    for team_no in order:\n",
    "        routine_df = routine_dfs[team_no]\n",
    "\n",
    "        # Filter for the markers of this team.\n",
    "        df = corpus_dfs[team_no].copy()\n",
    "        df = df[df.subject.isin(['A', 'B'])]\n",
    "        marker_df = df[df.n_info_markers > 0]\n",
    "        x = marker_df['scaled_end'].values\n",
    "\n",
    "        # Filter for the (mis)matches of this team.\n",
    "        df = corpus_dfs[team_no].copy()\n",
    "        df = df[df[match_col]]\n",
    "        y = df['scaled_end'].values\n",
    "\n",
    "        u, pvalue = mannwhitneyu(x=x, y=y,\n",
    "                                 use_continuity=False,\n",
    "                                 alternative='two-sided')\n",
    "        thres = 0.05\n",
    "        if pvalue < thres:\n",
    "            ps = 'p < ' + '{:.2f}'.format(thres).lstrip('0')\n",
    "        else:\n",
    "            ps = 'p = ' + '{:.2f}'.format(pvalue).lstrip('0')\n",
    "\n",
    "        d = two_group_difference(control=y,\n",
    "                                 test=x,\n",
    "                                 effect_size='cliffs_delta')\n",
    "\n",
    "        if verbose:\n",
    "            print('For Team {:02d}: '.format(team_no), end='')\n",
    "            s = '$U = {:6.1f}$, ${}$'.format(u, ps)\n",
    "            s += r\", $\\text{Cliff's delta}\"\n",
    "            s += \" = {:.2f}$\".format(d).lstrip('0')\n",
    "            s += r' \\\\'\n",
    "            print(s)\n",
    "\n",
    "        data['Team'].append(team_no)\n",
    "        data[n1_col].append(len(x))\n",
    "        data[n2_col].append(len(y))\n",
    "        data[m1_col].append('{:.1f}'.format(100*np.median(x)))\n",
    "        data[m2_col].append('{:.1f}'.format(100*np.median(y)))\n",
    "        data[u_col].append(u)\n",
    "        data[p_col].append('< .05' if pvalue <\n",
    "                           0.05 else '{:.2f}'.format(pvalue).lstrip('0'))\n",
    "        data[d_col].append('{:.2f}'.format(d))\n",
    "        data[d_col_full].append(d)\n",
    "\n",
    "    data_df = pd.DataFrame.from_dict(data)\n",
    "    data_df.set_index('Team', inplace=True)\n",
    "\n",
    "    data_df[p_col] = [r'${}$'.format(p) for p in data_df[p_col]]\n",
    "\n",
    "    idx = data_df.columns.str.split('_', expand=True)\n",
    "    idx = list(idx)\n",
    "    for i, (level0, level1) in enumerate(idx):\n",
    "        if type(level1) is not str and math.isnan(level1):\n",
    "            idx[i] = (level0, '.')\n",
    "    idx = pd.MultiIndex.from_tuples(idx)\n",
    "    data_df.columns = idx\n",
    "\n",
    "    data_df = data_df.loc[order]\n",
    "    return data_df\n",
    "\n",
    "\n",
    "print(\"U is the Mann–Whitney U test statistic, where p \\< 0.05 indicates that the groups are statistically significantly different\")\n",
    "print(\"Cliff's delta \\> 0 indicates info marker (oh) times larger than match times. \\< otherwise.\")\n",
    "print('oh times vs.\\ match times (sorted by decreasing task performance (i.e.\\ increasing error):')\n",
    "\n",
    "match_df = test_marker_and_matches('is_match', n2='match')\n",
    "\n",
    "display(match_df)\n",
    "# print(match_df.to_latex(escape=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### for mismatches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U is the Mann–Whitney U test statistic, where p \\< 0.05 indicates that the groups are statistically significantly different\n",
      "Cliff's delta \\> 0 indicates info marker (oh) times larger than mismatch times. \\< otherwise.\n",
      "oh times vs.\\ mismatch times (sorted by decreasing task performance (i.e.\\ increasing error):\n",
      "For Team 10: $U =   87.0$, $p = .08$, $\\text{Cliff's delta} = -0.36$ \\\\\n",
      "For Team 11: $U =  184.0$, $p = .08$, $\\text{Cliff's delta} = 0.36$ \\\\\n",
      "For Team 17: $U =   87.0$, $p = .57$, $\\text{Cliff's delta} = -0.13$ \\\\\n",
      "For Team 09: $U =   98.0$, $p = .26$, $\\text{Cliff's delta} = -0.25$ \\\\\n",
      "For Team 07: $U =  171.0$, $p < .05$, $\\text{Cliff's delta} = -0.48$ \\\\\n",
      "For Team 28: $U =  125.0$, $p = .32$, $\\text{Cliff's delta} = -0.20$ \\\\\n",
      "For Team 08: $U =   99.0$, $p < .05$, $\\text{Cliff's delta} = -0.67$ \\\\\n",
      "For Team 47: $U =  172.0$, $p < .05$, $\\text{Cliff's delta} = -0.44$ \\\\\n",
      "For Team 20: $U =  185.0$, $p = .84$, $\\text{Cliff's delta} = -0.05$ \\\\\n",
      "For Team 18: $U =   25.0$, $p = .15$, $\\text{Cliff's delta} = -0.40$ \\\\\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">N</th>\n",
       "      <th colspan=\"2\" halign=\"left\">median (%)</th>\n",
       "      <th colspan=\"4\" halign=\"left\">mismatch</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>oh</th>\n",
       "      <th>mismatch</th>\n",
       "      <th>oh</th>\n",
       "      <th>mismatch</th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>d</th>\n",
       "      <th>d-full</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</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>10</th>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>47.6</td>\n",
       "      <td>65.3</td>\n",
       "      <td>87.0</td>\n",
       "      <td>$.08$</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.355556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>57.3</td>\n",
       "      <td>38.5</td>\n",
       "      <td>184.0</td>\n",
       "      <td>$.08$</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.362963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>61.1</td>\n",
       "      <td>69.4</td>\n",
       "      <td>87.0</td>\n",
       "      <td>$.57$</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>29</td>\n",
       "      <td>9</td>\n",
       "      <td>42.9</td>\n",
       "      <td>70.2</td>\n",
       "      <td>98.0</td>\n",
       "      <td>$.26$</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.249042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>51</td>\n",
       "      <td>13</td>\n",
       "      <td>49.7</td>\n",
       "      <td>79.7</td>\n",
       "      <td>171.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.484163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>61.7</td>\n",
       "      <td>73.6</td>\n",
       "      <td>125.0</td>\n",
       "      <td>$.32$</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.198718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>55</td>\n",
       "      <td>11</td>\n",
       "      <td>36.3</td>\n",
       "      <td>86.3</td>\n",
       "      <td>99.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.672727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "      <td>53.4</td>\n",
       "      <td>75.7</td>\n",
       "      <td>172.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.435140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>65</td>\n",
       "      <td>6</td>\n",
       "      <td>35.0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>185.0</td>\n",
       "      <td>$.84$</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.051282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>69.6</td>\n",
       "      <td>75.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>$.15$</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.404762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       N          median (%)          mismatch                          \n",
       "      oh mismatch         oh mismatch        U        p      d    d-full\n",
       "Team                                                                    \n",
       "10    18       15       47.6     65.3     87.0    $.08$  -0.36 -0.355556\n",
       "11    15       18       57.3     38.5    184.0    $.08$   0.36  0.362963\n",
       "17    20       10       61.1     69.4     87.0    $.57$  -0.13 -0.130000\n",
       "9     29        9       42.9     70.2     98.0    $.26$  -0.25 -0.249042\n",
       "7     51       13       49.7     79.7    171.0  $< .05$  -0.48 -0.484163\n",
       "28    24       13       61.7     73.6    125.0    $.32$  -0.20 -0.198718\n",
       "8     55       11       36.3     86.3     99.0  $< .05$  -0.67 -0.672727\n",
       "47    29       21       53.4     75.7    172.0  $< .05$  -0.44 -0.435140\n",
       "20    65        6       35.0     42.3    185.0    $.84$  -0.05 -0.051282\n",
       "18    12        7       69.6     75.0     25.0    $.15$  -0.40 -0.404762"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"U is the Mann–Whitney U test statistic, where p \\< 0.05 indicates that the groups are statistically significantly different\")\n",
    "print(\"Cliff's delta \\> 0 indicates info marker (oh) times larger than mismatch times. \\< otherwise.\")\n",
    "print('oh times vs.\\ mismatch times (sorted by decreasing task performance (i.e.\\ increasing error):')\n",
    "\n",
    "mismatch_df = test_marker_and_matches('is_mismatch', n2='mismatch')\n",
    "\n",
    "display(mismatch_df)\n",
    "# print(mismatch_df.to_latex(escape=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### for match or mismatch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U is the Mann–Whitney U test statistic, where p \\< 0.05 indicates that the groups are statistically significantly different\n",
      "Cliff's delta \\> 0 indicates info marker (oh) times larger than mismatch times. \\< otherwise.\n",
      "oh times vs.\\ match or mismatch times (sorted by decreasing task performance (i.e.\\ increasing error):\n",
      "For Team 10: $U =  264.0$, $p < .05$, $\\text{Cliff's delta} = -0.42$ \\\\\n",
      "For Team 11: $U =  427.0$, $p = .58$, $\\text{Cliff's delta} = 0.09$ \\\\\n",
      "For Team 17: $U =  318.0$, $p = .83$, $\\text{Cliff's delta} = -0.04$ \\\\\n",
      "For Team 09: $U =  429.0$, $p = .16$, $\\text{Cliff's delta} = -0.20$ \\\\\n",
      "For Team 07: $U =  841.0$, $p < .05$, $\\text{Cliff's delta} = -0.41$ \\\\\n",
      "For Team 28: $U =  344.0$, $p = .51$, $\\text{Cliff's delta} = -0.10$ \\\\\n",
      "For Team 08: $U =  522.0$, $p < .05$, $\\text{Cliff's delta} = -0.54$ \\\\\n",
      "For Team 47: $U =  488.0$, $p < .05$, $\\text{Cliff's delta} = -0.31$ \\\\\n",
      "For Team 20: $U =  715.0$, $p < .05$, $\\text{Cliff's delta} = -0.27$ \\\\\n",
      "For Team 18: $U =  157.0$, $p = .36$, $\\text{Cliff's delta} = -0.18$ \\\\\n"
     ]
    },
    {
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       "  <thead>\n",
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       "      <th colspan=\"2\" halign=\"left\">N</th>\n",
       "      <th colspan=\"2\" halign=\"left\">median (%)</th>\n",
       "      <th colspan=\"4\" halign=\"left\">match/mismatch</th>\n",
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       "      <th></th>\n",
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       "      <th>d-full</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18</td>\n",
       "      <td>51</td>\n",
       "      <td>47.6</td>\n",
       "      <td>67.1</td>\n",
       "      <td>264.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.424837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>52</td>\n",
       "      <td>57.3</td>\n",
       "      <td>50.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>$.58$</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.094872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20</td>\n",
       "      <td>33</td>\n",
       "      <td>61.1</td>\n",
       "      <td>63.7</td>\n",
       "      <td>318.0</td>\n",
       "      <td>$.83$</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.036364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>29</td>\n",
       "      <td>37</td>\n",
       "      <td>42.9</td>\n",
       "      <td>63.5</td>\n",
       "      <td>429.0</td>\n",
       "      <td>$.16$</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.200373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>51</td>\n",
       "      <td>56</td>\n",
       "      <td>49.7</td>\n",
       "      <td>66.8</td>\n",
       "      <td>841.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.411064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>61.7</td>\n",
       "      <td>64.2</td>\n",
       "      <td>344.0</td>\n",
       "      <td>$.51$</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.104167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>55</td>\n",
       "      <td>41</td>\n",
       "      <td>36.3</td>\n",
       "      <td>66.7</td>\n",
       "      <td>522.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.537029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>29</td>\n",
       "      <td>49</td>\n",
       "      <td>53.4</td>\n",
       "      <td>63.3</td>\n",
       "      <td>488.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.313160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>65</td>\n",
       "      <td>30</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>715.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>69.6</td>\n",
       "      <td>71.3</td>\n",
       "      <td>157.0</td>\n",
       "      <td>$.36$</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.182292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       N                median (%)                match/mismatch           \\\n",
       "      oh match/mismatch         oh match/mismatch              U        p   \n",
       "Team                                                                        \n",
       "10    18             51       47.6           67.1          264.0  $< .05$   \n",
       "11    15             52       57.3           50.0          427.0    $.58$   \n",
       "17    20             33       61.1           63.7          318.0    $.83$   \n",
       "9     29             37       42.9           63.5          429.0    $.16$   \n",
       "7     51             56       49.7           66.8          841.0  $< .05$   \n",
       "28    24             32       61.7           64.2          344.0    $.51$   \n",
       "8     55             41       36.3           66.7          522.0  $< .05$   \n",
       "47    29             49       53.4           63.3          488.0  $< .05$   \n",
       "20    65             30       35.0           58.0          715.0  $< .05$   \n",
       "18    12             32       69.6           71.3          157.0    $.36$   \n",
       "\n",
       "                       \n",
       "          d    d-full  \n",
       "Team                   \n",
       "10    -0.42 -0.424837  \n",
       "11     0.09  0.094872  \n",
       "17    -0.04 -0.036364  \n",
       "9     -0.20 -0.200373  \n",
       "7     -0.41 -0.411064  \n",
       "28    -0.10 -0.104167  \n",
       "8     -0.54 -0.537029  \n",
       "47    -0.31 -0.313160  \n",
       "20    -0.27 -0.266667  \n",
       "18    -0.18 -0.182292  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrllrllr}\n",
      "\\toprule\n",
      "{} & \\multicolumn{2}{l}{N} & \\multicolumn{2}{l}{median (%)} & \\multicolumn{4}{l}{match/mismatch} \\\\\n",
      "{} &  oh & match/mismatch &         oh & match/mismatch &              U &        p &      d &    d-full \\\\\n",
      "Team &     &                &            &                &                &          &        &           \\\\\n",
      "\\midrule\n",
      "10   &  18 &             51 &       47.6 &           67.1 &          264.0 &  $< .05$ &  -0.42 & -0.424837 \\\\\n",
      "11   &  15 &             52 &       57.3 &           50.0 &          427.0 &    $.58$ &   0.09 &  0.094872 \\\\\n",
      "17   &  20 &             33 &       61.1 &           63.7 &          318.0 &    $.83$ &  -0.04 & -0.036364 \\\\\n",
      "9    &  29 &             37 &       42.9 &           63.5 &          429.0 &    $.16$ &  -0.20 & -0.200373 \\\\\n",
      "7    &  51 &             56 &       49.7 &           66.8 &          841.0 &  $< .05$ &  -0.41 & -0.411064 \\\\\n",
      "28   &  24 &             32 &       61.7 &           64.2 &          344.0 &    $.51$ &  -0.10 & -0.104167 \\\\\n",
      "8    &  55 &             41 &       36.3 &           66.7 &          522.0 &  $< .05$ &  -0.54 & -0.537029 \\\\\n",
      "47   &  29 &             49 &       53.4 &           63.3 &          488.0 &  $< .05$ &  -0.31 & -0.313160 \\\\\n",
      "20   &  65 &             30 &       35.0 &           58.0 &          715.0 &  $< .05$ &  -0.27 & -0.266667 \\\\\n",
      "18   &  12 &             32 &       69.6 &           71.3 &          157.0 &    $.36$ &  -0.18 & -0.182292 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"U is the Mann–Whitney U test statistic, where p \\< 0.05 indicates that the groups are statistically significantly different\")\n",
    "print(\"Cliff's delta \\> 0 indicates info marker (oh) times larger than mismatch times. \\< otherwise.\")\n",
    "print('oh times vs.\\ match or mismatch times (sorted by decreasing task performance (i.e.\\ increasing error):')\n",
    "\n",
    "match_mismatch_df = test_marker_and_matches('is_match_or_mismatch', n2='match/mismatch')\n",
    "\n",
    "display(match_mismatch_df)\n",
    "print(match_mismatch_df.to_latex(escape=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### merge match and mismatch results into a single table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">N</th>\n",
       "      <th colspan=\"3\" halign=\"left\">median (%)</th>\n",
       "      <th colspan=\"4\" halign=\"left\">match/mismatch</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>oh</th>\n",
       "      <th>match</th>\n",
       "      <th>mismatch</th>\n",
       "      <th>match/mismatch</th>\n",
       "      <th>oh</th>\n",
       "      <th>match</th>\n",
       "      <th>mismatch</th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>d</th>\n",
       "      <th>d-full</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</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>10</th>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>15</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>47.6</td>\n",
       "      <td>67.3</td>\n",
       "      <td>65.3</td>\n",
       "      <td>264.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.424837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>1.888889</td>\n",
       "      <td>57.3</td>\n",
       "      <td>59.5</td>\n",
       "      <td>38.5</td>\n",
       "      <td>427.0</td>\n",
       "      <td>$.58$</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.094872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>61.1</td>\n",
       "      <td>61.1</td>\n",
       "      <td>69.4</td>\n",
       "      <td>318.0</td>\n",
       "      <td>$.83$</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.036364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>3.111111</td>\n",
       "      <td>42.9</td>\n",
       "      <td>59.9</td>\n",
       "      <td>70.2</td>\n",
       "      <td>429.0</td>\n",
       "      <td>$.16$</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.200373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>51</td>\n",
       "      <td>43</td>\n",
       "      <td>13</td>\n",
       "      <td>3.307692</td>\n",
       "      <td>49.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>79.7</td>\n",
       "      <td>841.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.411064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>1.461538</td>\n",
       "      <td>61.7</td>\n",
       "      <td>62.7</td>\n",
       "      <td>73.6</td>\n",
       "      <td>344.0</td>\n",
       "      <td>$.51$</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.104167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>55</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>2.727273</td>\n",
       "      <td>36.3</td>\n",
       "      <td>65.3</td>\n",
       "      <td>86.3</td>\n",
       "      <td>522.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.537029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>21</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>53.4</td>\n",
       "      <td>56.8</td>\n",
       "      <td>75.7</td>\n",
       "      <td>488.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.313160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>65</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>715.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>3.571429</td>\n",
       "      <td>69.6</td>\n",
       "      <td>69.5</td>\n",
       "      <td>75.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>$.36$</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.182292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       N                               median (%)                 \\\n",
       "      oh match mismatch match/mismatch         oh match mismatch   \n",
       "Team                                                               \n",
       "10    18    36       15       2.400000       47.6  67.3     65.3   \n",
       "11    15    34       18       1.888889       57.3  59.5     38.5   \n",
       "17    20    23       10       2.300000       61.1  61.1     69.4   \n",
       "9     29    28        9       3.111111       42.9  59.9     70.2   \n",
       "7     51    43       13       3.307692       49.7  65.5     79.7   \n",
       "28    24    19       13       1.461538       61.7  62.7     73.6   \n",
       "8     55    30       11       2.727273       36.3  65.3     86.3   \n",
       "47    29    28       21       1.333333       53.4  56.8     75.7   \n",
       "20    65    24        6       4.000000       35.0  58.0     42.3   \n",
       "18    12    25        7       3.571429       69.6  69.5     75.0   \n",
       "\n",
       "     match/mismatch                            \n",
       "                  U        p      d    d-full  \n",
       "Team                                           \n",
       "10            264.0  $< .05$  -0.42 -0.424837  \n",
       "11            427.0    $.58$   0.09  0.094872  \n",
       "17            318.0    $.83$  -0.04 -0.036364  \n",
       "9             429.0    $.16$  -0.20 -0.200373  \n",
       "7             841.0  $< .05$  -0.41 -0.411064  \n",
       "28            344.0    $.51$  -0.10 -0.104167  \n",
       "8             522.0  $< .05$  -0.54 -0.537029  \n",
       "47            488.0  $< .05$  -0.31 -0.313160  \n",
       "20            715.0  $< .05$  -0.27 -0.266667  \n",
       "18            157.0    $.36$  -0.18 -0.182292  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrlllrllr}\n",
      "\\toprule\n",
      "{} & \\multicolumn{4}{l}{N} & \\multicolumn{3}{l}{median (%)} & \\multicolumn{4}{l}{match/mismatch} \\\\\n",
      "{} &  oh & match & mismatch & match/mismatch &         oh & match & mismatch &              U &        p &      d &    d-full \\\\\n",
      "Team &     &       &          &                &            &       &          &                &          &        &           \\\\\n",
      "\\midrule\n",
      "10   &  18 &    36 &       15 &       2.400000 &       47.6 &  67.3 &     65.3 &          264.0 &  $< .05$ &  -0.42 & -0.424837 \\\\\n",
      "11   &  15 &    34 &       18 &       1.888889 &       57.3 &  59.5 &     38.5 &          427.0 &    $.58$ &   0.09 &  0.094872 \\\\\n",
      "17   &  20 &    23 &       10 &       2.300000 &       61.1 &  61.1 &     69.4 &          318.0 &    $.83$ &  -0.04 & -0.036364 \\\\\n",
      "9    &  29 &    28 &        9 &       3.111111 &       42.9 &  59.9 &     70.2 &          429.0 &    $.16$ &  -0.20 & -0.200373 \\\\\n",
      "7    &  51 &    43 &       13 &       3.307692 &       49.7 &  65.5 &     79.7 &          841.0 &  $< .05$ &  -0.41 & -0.411064 \\\\\n",
      "28   &  24 &    19 &       13 &       1.461538 &       61.7 &  62.7 &     73.6 &          344.0 &    $.51$ &  -0.10 & -0.104167 \\\\\n",
      "8    &  55 &    30 &       11 &       2.727273 &       36.3 &  65.3 &     86.3 &          522.0 &  $< .05$ &  -0.54 & -0.537029 \\\\\n",
      "47   &  29 &    28 &       21 &       1.333333 &       53.4 &  56.8 &     75.7 &          488.0 &  $< .05$ &  -0.31 & -0.313160 \\\\\n",
      "20   &  65 &    24 &        6 &       4.000000 &       35.0 &  58.0 &     42.3 &          715.0 &  $< .05$ &  -0.27 & -0.266667 \\\\\n",
      "18   &  12 &    25 &        7 &       3.571429 &       69.6 &  69.5 &     75.0 &          157.0 &    $.36$ &  -0.18 & -0.182292 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "col_list = [('N', 'oh'),\n",
    "            ('N', 'match'),\n",
    "            ('N', 'mismatch'),\n",
    "            ('N', 'match/mismatch'),\n",
    "            ('median (%)', 'oh'),\n",
    "            ('median (%)', 'match'),\n",
    "            ('median (%)', 'mismatch'),\n",
    "            # ('match', 'U'),\n",
    "            # ('match', 'p'),\n",
    "            # ('match', 'd'),\n",
    "            # ('mismatch', 'U'),\n",
    "            # ('mismatch', 'p'),\n",
    "            # ('mismatch', 'd'),\n",
    "            ('match/mismatch', 'U'),\n",
    "            ('match/mismatch', 'p'),\n",
    "            ('match/mismatch', 'd'),\n",
    "            ('match/mismatch', 'd-full'),\n",
    "           ]\n",
    "columns = pd.Index(col_list)\n",
    "\n",
    "df = pd.concat([match_df, mismatch_df, match_mismatch_df], axis=1)\n",
    "\n",
    "\n",
    "df = df.loc[:, columns]\n",
    "df = df.loc[:, ~df.columns.duplicated()]\n",
    "df[('N', 'match/mismatch')] = df.loc[:,('N', 'match')] / df.loc[:,('N', 'mismatch')]\n",
    "\n",
    "result_df = df.copy()\n",
    "display(result_df)\n",
    "print(result_df.to_latex(escape=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
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       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>1.461538</td>\n",
       "      <td>61.7</td>\n",
       "      <td>62.7</td>\n",
       "      <td>73.6</td>\n",
       "      <td>344.0</td>\n",
       "      <td>$.51$</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.104167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>61.1</td>\n",
       "      <td>61.1</td>\n",
       "      <td>69.4</td>\n",
       "      <td>318.0</td>\n",
       "      <td>$.83$</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.036364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>65</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>715.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>1.888889</td>\n",
       "      <td>57.3</td>\n",
       "      <td>59.5</td>\n",
       "      <td>38.5</td>\n",
       "      <td>427.0</td>\n",
       "      <td>$.58$</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.094872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>3.111111</td>\n",
       "      <td>42.9</td>\n",
       "      <td>59.9</td>\n",
       "      <td>70.2</td>\n",
       "      <td>429.0</td>\n",
       "      <td>$.16$</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.200373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>21</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>53.4</td>\n",
       "      <td>56.8</td>\n",
       "      <td>75.7</td>\n",
       "      <td>488.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.313160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>15</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>47.6</td>\n",
       "      <td>67.3</td>\n",
       "      <td>65.3</td>\n",
       "      <td>264.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.424837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>55</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>2.727273</td>\n",
       "      <td>36.3</td>\n",
       "      <td>65.3</td>\n",
       "      <td>86.3</td>\n",
       "      <td>522.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.537029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>51</td>\n",
       "      <td>43</td>\n",
       "      <td>13</td>\n",
       "      <td>3.307692</td>\n",
       "      <td>49.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>79.7</td>\n",
       "      <td>841.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.411064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>3.571429</td>\n",
       "      <td>69.6</td>\n",
       "      <td>69.5</td>\n",
       "      <td>75.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>$.36$</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.182292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       N                               median (%)                 \\\n",
       "      oh match mismatch match/mismatch         oh match mismatch   \n",
       "Team                                                               \n",
       "28    24    19       13       1.461538       61.7  62.7     73.6   \n",
       "17    20    23       10       2.300000       61.1  61.1     69.4   \n",
       "20    65    24        6       4.000000       35.0  58.0     42.3   \n",
       "11    15    34       18       1.888889       57.3  59.5     38.5   \n",
       "9     29    28        9       3.111111       42.9  59.9     70.2   \n",
       "47    29    28       21       1.333333       53.4  56.8     75.7   \n",
       "10    18    36       15       2.400000       47.6  67.3     65.3   \n",
       "8     55    30       11       2.727273       36.3  65.3     86.3   \n",
       "7     51    43       13       3.307692       49.7  65.5     79.7   \n",
       "18    12    25        7       3.571429       69.6  69.5     75.0   \n",
       "\n",
       "     match/mismatch                            \n",
       "                  U        p      d    d-full  \n",
       "Team                                           \n",
       "28            344.0    $.51$  -0.10 -0.104167  \n",
       "17            318.0    $.83$  -0.04 -0.036364  \n",
       "20            715.0  $< .05$  -0.27 -0.266667  \n",
       "11            427.0    $.58$   0.09  0.094872  \n",
       "9             429.0    $.16$  -0.20 -0.200373  \n",
       "47            488.0  $< .05$  -0.31 -0.313160  \n",
       "10            264.0  $< .05$  -0.42 -0.424837  \n",
       "8             522.0  $< .05$  -0.54 -0.537029  \n",
       "7             841.0  $< .05$  -0.41 -0.411064  \n",
       "18            157.0    $.36$  -0.18 -0.182292  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrlllrllr}\n",
      "\\toprule\n",
      "{} & \\multicolumn{4}{l}{N} & \\multicolumn{3}{l}{median (%)} & \\multicolumn{4}{l}{match/mismatch} \\\\\n",
      "{} &  oh & match & mismatch & match/mismatch &         oh & match & mismatch &              U &        p &      d &    d-full \\\\\n",
      "Team &     &       &          &                &            &       &          &                &          &        &           \\\\\n",
      "\\midrule\n",
      "28   &  24 &    19 &       13 &       1.461538 &       61.7 &  62.7 &     73.6 &          344.0 &    $.51$ &  -0.10 & -0.104167 \\\\\n",
      "17   &  20 &    23 &       10 &       2.300000 &       61.1 &  61.1 &     69.4 &          318.0 &    $.83$ &  -0.04 & -0.036364 \\\\\n",
      "20   &  65 &    24 &        6 &       4.000000 &       35.0 &  58.0 &     42.3 &          715.0 &  $< .05$ &  -0.27 & -0.266667 \\\\\n",
      "11   &  15 &    34 &       18 &       1.888889 &       57.3 &  59.5 &     38.5 &          427.0 &    $.58$ &   0.09 &  0.094872 \\\\\n",
      "9    &  29 &    28 &        9 &       3.111111 &       42.9 &  59.9 &     70.2 &          429.0 &    $.16$ &  -0.20 & -0.200373 \\\\\n",
      "47   &  29 &    28 &       21 &       1.333333 &       53.4 &  56.8 &     75.7 &          488.0 &  $< .05$ &  -0.31 & -0.313160 \\\\\n",
      "10   &  18 &    36 &       15 &       2.400000 &       47.6 &  67.3 &     65.3 &          264.0 &  $< .05$ &  -0.42 & -0.424837 \\\\\n",
      "8    &  55 &    30 &       11 &       2.727273 &       36.3 &  65.3 &     86.3 &          522.0 &  $< .05$ &  -0.54 & -0.537029 \\\\\n",
      "7    &  51 &    43 &       13 &       3.307692 &       49.7 &  65.5 &     79.7 &          841.0 &  $< .05$ &  -0.41 & -0.411064 \\\\\n",
      "18   &  12 &    25 &        7 &       3.571429 &       69.6 &  69.5 &     75.0 &          157.0 &    $.36$ &  -0.18 & -0.182292 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = result_df.copy()\n",
    "df = df.loc[[int(t) for t in perf_order],:]\n",
    "display(df)\n",
    "print(df.to_latex(escape=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Spearman's correlation with task performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Team\n",
       "10    13.636364\n",
       "11     4.545455\n",
       "17     0.000000\n",
       "9      4.545455\n",
       "7     13.636364\n",
       "28     0.000000\n",
       "8     13.636364\n",
       "47    13.636364\n",
       "20     0.000000\n",
       "18    18.181818\n",
       "Name: error (%), dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df['error (%)'] = [perf_df.T.loc[int(i), 'error (%)']\n",
    "                          for i in result_df.index]\n",
    "result_df['error (%)'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df = result_df.sort_values('error (%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For ('match/mismatch', 'd-full'): 10\n",
      "[-0.036363636363636376, -0.10416666666666663, -0.2666666666666667, 0.09487179487179498, -0.20037278657968316, -0.4248366013071896, -0.4110644257703081, -0.537028824833703, -0.31315974665728363, -0.18229166666666663]\n",
      "SpearmanrResult(correlation=-0.5275826327158943, pvalue=0.11705311022708657)\n"
     ]
    }
   ],
   "source": [
    "columns = [('match/mismatch', 'd-full')]\n",
    "for c in columns:\n",
    "    print('For {}:'.format(c), end=' ')\n",
    "    print(len(result_df))\n",
    "    values = result_df[c].tolist()\n",
    "    print(values)\n",
    "    print(spearmanr(values, result_df['error (%)']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Kruskal-Wallis of Cliff's deltas for the learning outcomes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For ('match/mismatch', 'd-full'): [-0.4110644257703081, -0.20037278657968316, -0.4248366013071896, 0.09487179487179498, -0.036363636363636376] [-0.537028824833703, -0.18229166666666663, -0.2666666666666667, -0.10416666666666663, -0.31315974665728363]\n",
      "KruskalResult(statistic=0.2727272727272734, pvalue=0.6015081344405895)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "columns = [('match/mismatch', 'd-full')]\n",
    "for c in columns:\n",
    "    p_col = (c[0], 'p')\n",
    "\n",
    "    print('For {}:'.format(c), end=' ')\n",
    "    \n",
    "    # # Exclude teams that are not significant.\n",
    "    # exclude = result_df[result_df[p_col]!= '$< .05$'].index\n",
    "\n",
    "    test = result_df.loc[positive_teams, c].tolist() #.astype(float).tolist()\n",
    "    control = result_df.loc[nonpositive_teams, c].tolist()\n",
    "\n",
    "    print(test, control)\n",
    "    print(kruskal(test, control))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print table in latex in performance order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">N</th>\n",
       "      <th colspan=\"3\" halign=\"left\">median (%)</th>\n",
       "      <th colspan=\"3\" halign=\"left\">match/mismatch</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>oh</th>\n",
       "      <th>match</th>\n",
       "      <th>mismatch</th>\n",
       "      <th>match/mismatch</th>\n",
       "      <th>oh</th>\n",
       "      <th>match</th>\n",
       "      <th>mismatch</th>\n",
       "      <th>U</th>\n",
       "      <th>p</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</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>28</th>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>1.461538</td>\n",
       "      <td>61.7</td>\n",
       "      <td>62.7</td>\n",
       "      <td>73.6</td>\n",
       "      <td>344.0</td>\n",
       "      <td>$.51$</td>\n",
       "      <td>-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>61.1</td>\n",
       "      <td>61.1</td>\n",
       "      <td>69.4</td>\n",
       "      <td>318.0</td>\n",
       "      <td>$.83$</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>65</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>715.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>1.888889</td>\n",
       "      <td>57.3</td>\n",
       "      <td>59.5</td>\n",
       "      <td>38.5</td>\n",
       "      <td>427.0</td>\n",
       "      <td>$.58$</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>3.111111</td>\n",
       "      <td>42.9</td>\n",
       "      <td>59.9</td>\n",
       "      <td>70.2</td>\n",
       "      <td>429.0</td>\n",
       "      <td>$.16$</td>\n",
       "      <td>-0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>21</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>53.4</td>\n",
       "      <td>56.8</td>\n",
       "      <td>75.7</td>\n",
       "      <td>488.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>15</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>47.6</td>\n",
       "      <td>67.3</td>\n",
       "      <td>65.3</td>\n",
       "      <td>264.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>55</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>2.727273</td>\n",
       "      <td>36.3</td>\n",
       "      <td>65.3</td>\n",
       "      <td>86.3</td>\n",
       "      <td>522.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>51</td>\n",
       "      <td>43</td>\n",
       "      <td>13</td>\n",
       "      <td>3.307692</td>\n",
       "      <td>49.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>79.7</td>\n",
       "      <td>841.0</td>\n",
       "      <td>$&lt; .05$</td>\n",
       "      <td>-0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>3.571429</td>\n",
       "      <td>69.6</td>\n",
       "      <td>69.5</td>\n",
       "      <td>75.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>$.36$</td>\n",
       "      <td>-0.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       N                               median (%)                 \\\n",
       "      oh match mismatch match/mismatch         oh match mismatch   \n",
       "Team                                                               \n",
       "28    24    19       13       1.461538       61.7  62.7     73.6   \n",
       "17    20    23       10       2.300000       61.1  61.1     69.4   \n",
       "20    65    24        6       4.000000       35.0  58.0     42.3   \n",
       "11    15    34       18       1.888889       57.3  59.5     38.5   \n",
       "9     29    28        9       3.111111       42.9  59.9     70.2   \n",
       "47    29    28       21       1.333333       53.4  56.8     75.7   \n",
       "10    18    36       15       2.400000       47.6  67.3     65.3   \n",
       "8     55    30       11       2.727273       36.3  65.3     86.3   \n",
       "7     51    43       13       3.307692       49.7  65.5     79.7   \n",
       "18    12    25        7       3.571429       69.6  69.5     75.0   \n",
       "\n",
       "     match/mismatch                  \n",
       "                  U        p      d  \n",
       "Team                                 \n",
       "28            344.0    $.51$  -0.10  \n",
       "17            318.0    $.83$  -0.04  \n",
       "20            715.0  $< .05$  -0.27  \n",
       "11            427.0    $.58$   0.09  \n",
       "9             429.0    $.16$  -0.20  \n",
       "47            488.0  $< .05$  -0.31  \n",
       "10            264.0  $< .05$  -0.42  \n",
       "8             522.0  $< .05$  -0.54  \n",
       "7             841.0  $< .05$  -0.41  \n",
       "18            157.0    $.36$  -0.18  "
      ]
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     "text": [
      "\\begin{tabular}{lrrrrlllrll}\n",
      "\\toprule\n",
      "{} & \\multicolumn{4}{l}{N} & \\multicolumn{3}{l}{median (%)} & \\multicolumn{3}{l}{match/mismatch} \\\\\n",
      "{} &  oh & match & mismatch & match/mismatch &         oh & match & mismatch &              U &        p &      d \\\\\n",
      "Team &     &       &          &                &            &       &          &                &          &        \\\\\n",
      "\\midrule\n",
      "28   &  24 &    19 &       13 &       1.461538 &       61.7 &  62.7 &     73.6 &          344.0 &    $.51$ &  -0.10 \\\\\n",
      "17   &  20 &    23 &       10 &       2.300000 &       61.1 &  61.1 &     69.4 &          318.0 &    $.83$ &  -0.04 \\\\\n",
      "20   &  65 &    24 &        6 &       4.000000 &       35.0 &  58.0 &     42.3 &          715.0 &  $< .05$ &  -0.27 \\\\\n",
      "11   &  15 &    34 &       18 &       1.888889 &       57.3 &  59.5 &     38.5 &          427.0 &    $.58$ &   0.09 \\\\\n",
      "9    &  29 &    28 &        9 &       3.111111 &       42.9 &  59.9 &     70.2 &          429.0 &    $.16$ &  -0.20 \\\\\n",
      "47   &  29 &    28 &       21 &       1.333333 &       53.4 &  56.8 &     75.7 &          488.0 &  $< .05$ &  -0.31 \\\\\n",
      "10   &  18 &    36 &       15 &       2.400000 &       47.6 &  67.3 &     65.3 &          264.0 &  $< .05$ &  -0.42 \\\\\n",
      "8    &  55 &    30 &       11 &       2.727273 &       36.3 &  65.3 &     86.3 &          522.0 &  $< .05$ &  -0.54 \\\\\n",
      "7    &  51 &    43 &       13 &       3.307692 &       49.7 &  65.5 &     79.7 &          841.0 &  $< .05$ &  -0.41 \\\\\n",
      "18   &  12 &    25 &        7 &       3.571429 &       69.6 &  69.5 &     75.0 &          157.0 &    $.36$ &  -0.18 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "col_list = [('N', 'oh'),\n",
    "            ('N', 'match'),\n",
    "            ('N', 'mismatch'),\n",
    "            ('N', 'match/mismatch'),\n",
    "            ('median (%)', 'oh'),\n",
    "            ('median (%)', 'match'),\n",
    "            ('median (%)', 'mismatch'),\n",
    "            # ('match', 'U'),\n",
    "            # ('match', 'p'),\n",
    "            # ('match', 'd'),\n",
    "            # ('mismatch', 'U'),\n",
    "            # ('mismatch', 'p'),\n",
    "            # ('mismatch', 'd'),\n",
    "            ('match/mismatch', 'U'),\n",
    "            ('match/mismatch', 'p'),\n",
    "            ('match/mismatch', 'd'),\n",
    "           ]\n",
    "columns = pd.Index(col_list)\n",
    "\n",
    "df = result_df.copy()\n",
    "# df.sort_values('error (%)', inplace=True)\n",
    "df = df.loc[[int(t) for t in perf_order], :]\n",
    "\n",
    "df = df[columns]\n",
    "\n",
    "display(df)\n",
    "print(df.to_latex(escape=False))"
   ]
  }
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