Guiding research question.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "46ccfc43",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import plot_likert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "af56a3e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "PA = pd.read_csv('Files/CR-studentexperiences.csv', header = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "994d8579",
   "metadata": {},
   "source": [
    "**Q1** Will the time taken for the users to enter a set of characters using Character Recorder be roughly the same as the time taken using Excel?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "ea6f4085",
   "metadata": {},
   "outputs": [],
   "source": [
    "EX = pd.to_datetime(PA[\"end.1\"][9:]) - pd.to_datetime(PA[\"start.1\"][9:])\n",
    "CR = pd.to_datetime(PA[\"end.2\"][9:]) - pd.to_datetime(PA[\"start.2\"][9:])\n",
    "TU = pd.to_datetime(PA[\"end\"][9:]) - pd.to_datetime(PA[\"start\"][9:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b50705c1",
   "metadata": {},
   "source": [
    "Using paird t-test to analysis two paired data's relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "51a14999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=-2.4552412459617647, pvalue=0.02776240255818241)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_rel(EX, CR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "d7d56276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=4.652527341826689, pvalue=0.0003732899722582638)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_rel(TU, CR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b40fdd01",
   "metadata": {},
   "source": [
    "Use pearson correlation to check if people who are slow with Excel were also slow with CR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8bf1ea9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.004478438896139775, 0.9873619874039541)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.pearsonr(EX.dt.seconds, CR.dt.seconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "855d46f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9      942\n",
       "10     519\n",
       "11     394\n",
       "12     270\n",
       "13     364\n",
       "14     416\n",
       "15     349\n",
       "16     500\n",
       "17    1534\n",
       "18     303\n",
       "19     250\n",
       "20     426\n",
       "21     459\n",
       "22     359\n",
       "23     225\n",
       "dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EX.dt.seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "6a825917",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'942,519,394,270,364,416,349,500,1534,303,250,426,459,359,225,'"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new = str()\n",
    "for i in EX.dt.seconds:\n",
    "    new += str(i)\n",
    "    new += str(\",\")\n",
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "78e28ab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "EX.mean()\n",
    "EX.astype('timedelta64[s]').plot.hist()\n",
    "\n",
    "plt.title(\"Participant's Excel behavior distribution\")\n",
    "plt.xlabel(\"Times (seconds)\") \n",
    "plt.savefig(os.path.join('Pics/Guilding Analysis/Q1/EX-behavior.png'), dpi=300, format='png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "c0a89d13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "CR.mean()\n",
    "CR.astype('timedelta64[s]').plot.hist()\n",
    "\n",
    "plt.title(\"Participant's Character recorder behavior distribution\")\n",
    "plt.xlabel(\"Times (seconds)\") \n",
    "plt.savefig(os.path.join('Pics/Guilding Analysis/Q1/CR-behavior.png'), dpi=300, format='png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "905a196e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "TU.mean()\n",
    "TU.astype('timedelta64[s]').plot.hist()\n",
    "\n",
    "plt.title(\"Participant's practice CR behavior distribution\")\n",
    "plt.xlabel(\"Times (seconds)\") \n",
    "plt.savefig(os.path.join('Pics/Guilding Analysis/Q1/TU-behavior.png'), dpi=300, format='png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe0a862d",
   "metadata": {},
   "source": [
    "We find out that the main value can not represent the user's time spent on the software based on the graph. So we will keep using the Box chart to explore more information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "7d55f92c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_1 = EX\n",
    "data_2 = CR\n",
    "data = [data_1.dt.seconds, data_2.dt.seconds]\n",
    "\n",
    "plt.boxplot(data)\n",
    "plt.xticks([1, 2], [\"Excel\", \"Character Recorder\"])\n",
    "plt.title(\"Boxplot for Excel and Character Recorder\")\n",
    "plt.ylabel(\"time spent(seconds)\")\n",
    "plt.savefig(os.path.join('Pics/Guilding Analysis/Q1/Boxplot.png'), dpi=300, format='png', bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "c7fcbe03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_1 = CR\n",
    "data_2 = TU\n",
    "data = [data_1.dt.seconds, data_2.dt.seconds]\n",
    "\n",
    "plt.boxplot(data)\n",
    "plt.xticks([1, 2], [\"Turtorial\", \"Character Recorder\"])\n",
    "plt.title(\"Boxplot for Excel and Character Recorder\")\n",
    "plt.ylabel(\"time spent(seconds)\")\n",
    "plt.savefig(os.path.join('Pics/Guilding Analysis/Q1/Turtorial and Task Boxplot.png'), dpi=300, format='png', bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89cda448",
   "metadata": {},
   "source": [
    "After we look through Boxplot, we find out that there are two outliers in EX and one in CR. So, in the next step, We will remove those outliers and get their mean and median time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "de018cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_remove(col):\n",
    "\n",
    "    mid = col.quantile(0.75) - col.quantile(0.25)\n",
    "\n",
    "    top = col.quantile(0.75) + 1.5 * mid\n",
    "    bot = col.quantile(0.25) - 1.5 * mid\n",
    "\n",
    "    def remove(x):\n",
    "        if x <= top and x >= bot:\n",
    "            return x\n",
    "\n",
    "    return col.map(remove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "bc4d362c",
   "metadata": {},
   "outputs": [],
   "source": [
    "New_CR = outlier_remove(CR).dropna()\n",
    "New_EX = outlier_remove(EX).dropna()\n",
    "New_TU = outlier_remove(TU).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "8e59308b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_1 = New_EX\n",
    "data_2 = New_CR\n",
    "data_3 = New_TU\n",
    "data = [data_1.dt.seconds, data_2.dt.seconds, data_3.dt.seconds]\n",
    "\n",
    "plt.boxplot(data)\n",
    "plt.xticks([1, 2, 3], [\"New Excel\", \"New Character Recorder\", \"CR turtorial\"])\n",
    "plt.title(\"Boxplot for Excel and Character Recorder\")\n",
    "plt.ylabel(\"time spent(seconds)\")\n",
    "plt.savefig(os.path.join('Pics/Guilding Analysis/Q1/New-Boxplot.png'), dpi=300, format='png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "5bfdc107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_1 = New_EX\n",
    "data_2 = New_CR\n",
    "data = [data_1.dt.seconds, data_2.dt.seconds]\n",
    "\n",
    "plt.boxplot(data)\n",
    "plt.xticks([1, 2], [\"New Excel\", \"New Character Recorder\"])\n",
    "plt.title(\"Boxplot for Excel and Character Recorder\")\n",
    "plt.ylabel(\"time spent(seconds)\")\n",
    "plt.savefig(os.path.join('Pics/Guilding Analysis/Q1/New-Boxplot.png'), dpi=300, format='png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "98ba5508",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excel Median time spent : 0 days 00:06:04\n",
      "Excel Mean time spent : 0 days 00:06:11.846153846\n"
     ]
    }
   ],
   "source": [
    "print('Excel Median time spent : ' + str(New_EX.median()))\n",
    "print('Excel Mean time spent : ' +  str(New_EX.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "657bda61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Character Recorder Median time spent : 0 days 00:11:14.500000\n",
      "Character Recorder Mean time spent : 0 days 00:11:50.571428571\n"
     ]
    }
   ],
   "source": [
    "print('Character Recorder Median time spent : ' + str(New_CR.median()))\n",
    "print('Character Recorder Mean time spent : ' +  str(New_CR.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "5cbb297c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Character Recorder Median time spent : 0 days 00:18:13.500000\n",
      "Character Recorder Mean time spent : 0 days 00:17:59.071428571\n"
     ]
    }
   ],
   "source": [
    "print('Character Recorder Median time spent : ' + str(New_TU.median()))\n",
    "print('Character Recorder Mean time spent : ' +  str(New_TU.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "490e33fe",
   "metadata": {},
   "source": [
    "After we delete outlier, we will get the t-test result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "914cefb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "New_CR = outlier_remove(New_CR).dropna()\n",
    "len(New_CR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30517e7a",
   "metadata": {},
   "source": [
    "Compare two different software (Excel and Character Recorder); Whether there is a significant difference in the time spent by users. Through the comparative analysis of the two sets of data, it is judged whether character Recorder is a software that is more suitable for entering such information than excel. \n",
    "\n",
    "Firstly, we set the null hypothesis that the character recorder didn't have many differences between excel in input terms. After using the paired t-test, we got a p-value of less than 0.05 (1.3398e-07). So we reject the null hypothesis that character recorders have many differences between Excel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "2f63aaca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=-10.944205270380335, pvalue=1.3398628969046098e-07)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_rel(New_EX, New_CR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ce50a99",
   "metadata": {},
   "source": [
    "Drop the maximum value in Turtorial and calculate the paired t-test between CR and Turtorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "e0d1a689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=-5.164801306872204, pvalue=0.00023516214443738585)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_rel(New_CR, New_TU.drop(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb2bb09c",
   "metadata": {},
   "source": [
    "Drop the maximum value in Turtorial and calculate the paired t-test between CR and Turtorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "6fbe3805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=-7.1378633714077635, pvalue=1.1839376367330832e-05)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_rel(New_CR, New_TU.drop(23))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b436d4f",
   "metadata": {},
   "source": [
    "**Q2** Will the data entered using Character Recorder be more consistent than the data entered using Excel? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "186eb59b",
   "metadata": {},
   "source": [
    "In this question, we go throught character recorder and Excel files and count words. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "be066c20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count excel words\n",
    "def excel_word_detach():\n",
    "    # read excel files and fill NA values as \"of\", trans interger to string\n",
    "    folder = os.listdir(\"Experiment Files\")\n",
    "    folder.remove('.DS_Store')\n",
    "    d={}\n",
    "\n",
    "    for i in folder:\n",
    "        test = pd.read_excel(\"Experiment Files/\" + i + \"/\" + i + \".xlsx\").fillna(\"of\").astype(str)\n",
    "        # remove stopwords (e.g., of, the, or) and create empty list to store values\n",
    "        remove = [\"of\", \"the\", \"or\", \"_\", \"…\"]\n",
    "        store = []\n",
    "\n",
    "        # store non-na value into store\n",
    "        for i in test:\n",
    "            for p in test[i]:\n",
    "                if p not in remove:\n",
    "                    store.append(p.split())\n",
    "\n",
    "        # store non-stopwords value into word_count\n",
    "        word_count = []\n",
    "        for q in store:\n",
    "            for c in q:\n",
    "                if c not in remove:\n",
    "                    word_count.append(c)\n",
    "        \n",
    "        # store and count value, store those value and words into dictionary d\n",
    "        for x in word_count:\n",
    "            if not x in d:\n",
    "                d[x]=1\n",
    "            else:\n",
    "                d[x]=d[x]+1\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "e5bcd0c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "orders = excel_word_detach()\n",
    "last_sort_orders_excel = {}\n",
    "\n",
    "# Store words as dictionary, and set key as lower key.\n",
    "alphabet = dict()\n",
    "# alphabet =  {k.lower(): v for k, v in last_orders.items()}\n",
    "\n",
    "for p, q in orders.items():\n",
    "    if p.lower() in alphabet:\n",
    "       alphabet[p.lower()] = alphabet[p.lower()] + q\n",
    "    else:\n",
    "       alphabet[p.lower()] = q\n",
    "\n",
    "\n",
    "sort_orders = sorted(alphabet.items(), key=lambda x: x[1], reverse=False)\n",
    "\n",
    "punctuations = \";:.,\"\n",
    "key_list = ()\n",
    "store = []\n",
    "\n",
    "for i in sort_orders:\n",
    "    key_list = i[0]\n",
    "\n",
    "    for p in punctuations:\n",
    "\t    key_list = key_list.replace(p, \"\")    \n",
    "    \n",
    "    last_sort_orders_excel[key_list] = i[1]\n",
    "\n",
    "# remove punctuation from orders and save those value into dictionary\n",
    "last_sort_orders = sorted(last_sort_orders_excel.items(), key=lambda x: x[1], reverse=False)\n",
    "last_orders = {}\n",
    "\n",
    "# last_sort_orders will be the output dictionary for future useage\n",
    "for i in last_sort_orders:\n",
    "\tlast_orders[i[0].replace(\";\", \"\")] = i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "695f0c12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'leaf': 1,\n",
       " 'mean=700': 1,\n",
       " 'range=(40-)': 1,\n",
       " '55-80(-100)': 1,\n",
       " 'mean=75': 1,\n",
       " 'range=(5-)': 1,\n",
       " '625-875(-10)': 1,\n",
       " 'description': 1,\n",
       " '(40-)55-85(-100)': 1,\n",
       " 'long': 1,\n",
       " '(5-)625-875(-10)': 1,\n",
       " 'reddish-brown': 1,\n",
       " 'vegitative': 1,\n",
       " 'clums': 1,\n",
       " 'stems': 1,\n",
       " 'not': 1,\n",
       " 'applicable': 1,\n",
       " 'visability': 1,\n",
       " 'lanceiloid': 1,\n",
       " 'colum': 1,\n",
       " 'maroom': 1,\n",
       " 'inflorescene': 1,\n",
       " 'charater': 1,\n",
       " 'conspicous': 1,\n",
       " '40cm': 2,\n",
       " 'conspicious': 2,\n",
       " '100cm': 2,\n",
       " 'carex': 2,\n",
       " 'muskingumensis': 2,\n",
       " '(cm)': 2,\n",
       " 'range-percentile': 2,\n",
       " 'sumits': 2,\n",
       " 'unit': 3,\n",
       " 'summrary': 3,\n",
       " 'red-brown': 3,\n",
       " 'colour': 3,\n",
       " 'reddish': 4,\n",
       " 'culm': 5,\n",
       " 'lancehead-shaped': 5,\n",
       " 'gutter': 5,\n",
       " 'prominence': 5,\n",
       " 'summit': 5,\n",
       " 'units': 5,\n",
       " 'count': 5,\n",
       " 'vegetative': 6,\n",
       " 'coloration': 6,\n",
       " 'u-shaped': 6,\n",
       " 'red': 6,\n",
       " 'culms': 7,\n",
       " 'gutter-shaped': 7,\n",
       " 'lanceloid': 7,\n",
       " 'lanceoloid': 8,\n",
       " 'maroon': 8,\n",
       " 'color': 8,\n",
       " 'brown': 9,\n",
       " 'lanciform': 9,\n",
       " 'visibility': 10,\n",
       " 'brown-red': 10,\n",
       " 'inflorescence': 10,\n",
       " 'conspicuous': 11,\n",
       " 'number': 11,\n",
       " 'u': 12,\n",
       " '100': 13,\n",
       " '5': 14,\n",
       " '10': 14,\n",
       " '40': 14,\n",
       " 'stem': 15,\n",
       " 'character': 16,\n",
       " 'length': 16,\n",
       " '2': 16,\n",
       " 'shaped': 17,\n",
       " '1': 17,\n",
       " 'summits': 18,\n",
       " 'spikes': 18,\n",
       " 'cm': 23,\n",
       " 'sheath': 26,\n",
       " 'shape': 28,\n",
       " 'sample': 33}"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "9fd772b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2880x2880 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw bar chart\n",
    "data = last_orders\n",
    "names = list(data.keys())\n",
    "values = list(data.values())\n",
    "\n",
    "plt.figure(figsize=(40, 40))\n",
    "plt.barh(range(len(data)), values, tick_label=names)\n",
    "plt.title(\"Excel word distribution\")\n",
    "plt.xlabel(\"Terms\")\n",
    "plt.ylabel(\"Times\")\n",
    "\n",
    "# show how many times those term been used in the experiment\n",
    "for i, v in enumerate(values):\n",
    "    plt.text(v - 0.8, i - 0.03 , str(v), color='black', fontweight='bold')\n",
    "\n",
    "plt.savefig(os.path.join('Pics/Guilding Analysis/Q2/Ex_words_consistency.png'), dpi=300, format='png', bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c58118e",
   "metadata": {},
   "source": [
    "Read character recorder export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "95f872ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count cr words\n",
    "def cr_word_detach():\n",
    "    # read cr files and fill NA values as \"of\", trans interger to string\n",
    "    folder = os.listdir(\"Experiment Files\")\n",
    "    folder.remove('.DS_Store')\n",
    "    d={}\n",
    "\n",
    "    for i in folder:\n",
    "        test = pd.read_csv(\"Experiment Files/\" + i + \"/Carex muskingumensis/Carex muskingumensis.csv\").astype(str)\n",
    "        test = test.drop(columns=['Summary'])\n",
    "        # remove stopwords (e.g., of, the, or) and create empty list to store values\n",
    "        remove = [\"of\", \"the\", \"or\"]\n",
    "        store = []\n",
    "\n",
    "        # store non-na value into store\n",
    "        for i in test:\n",
    "            for p in test[i]:\n",
    "                if p not in remove:\n",
    "                    store.append(p.split())\n",
    "\n",
    "        # store non-stopwords value into word_count\n",
    "        word_count = []\n",
    "        for q in store:\n",
    "            for c in q:\n",
    "                if c not in remove:\n",
    "                    word_count.append(c)\n",
    "\n",
    "        # store and count value, store those value and words into dictionary d\n",
    "        for x in word_count:\n",
    "            if not x in d:\n",
    "                d[x]=1\n",
    "            else:\n",
    "                d[x]=d[x]+1\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "1eb97c3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "orders = cr_word_detach()\n",
    "last_sort_orders = {}\n",
    "\n",
    "\n",
    "# Store words as dictionary, and set key as lower key.\n",
    "alphabet = dict()\n",
    "# alphabet =  {k.lower(): v for k, v in last_orders.items()}\n",
    "\n",
    "for p, q in orders.items():\n",
    "    if p.lower() in alphabet:\n",
    "       alphabet[p.lower()] = alphabet[p.lower()] + q\n",
    "    else:\n",
    "       alphabet[p.lower()] = q\n",
    "\n",
    "# remove punctuation from orders and save those value into dictionary\n",
    "sort_orders = sorted(alphabet.items(), key=lambda x: x[1], reverse=False)\n",
    "\n",
    "# last_sort_orders will be the output dictionary for future useage\n",
    "for i in sort_orders:\n",
    "\tlast_sort_orders[i[0].replace(\";\", \"\")] = i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "5704c6b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw bar chart\n",
    "data = last_sort_orders\n",
    "names = list(data.keys())\n",
    "values = list(data.values())\n",
    "\n",
    "plt.figure(figsize=(20, 20))\n",
    "plt.barh(range(len(data)), values, tick_label=names)\n",
    "plt.title(\"Character Recorder word distribution\")\n",
    "plt.xlabel(\"Terms\")\n",
    "plt.ylabel(\"Times\")\n",
    "\n",
    "# show how many times those term been used in the experiment\n",
    "for i, v in enumerate(values):\n",
    "    plt.text(v - 0.8, i - 0.03 , str(v), color='black', fontweight='bold')\n",
    "\n",
    "plt.savefig(os.path.join('Pics/Guilding Analysis/Q2/Cr_words_consistency.png'), dpi=300, format='png', bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1af63dd2",
   "metadata": {},
   "source": [
    "**Q3** Will the user add new terms to the ontology while using Character Recorder? If so, how often?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "13f3a907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[9,7])\n",
    "PA[\"use my terms\"][1:].dropna().value_counts().plot.pie(explode = (0, 0.1), autopct='%1.1f%%')\n",
    "plt.title(\"How many new term add by participants in research\")\n",
    "\n",
    "plt.legend(['add nothing', 'add one term'])\n",
    "\n",
    "plt.savefig(os.path.join('Pics/Guilding Analysis/Q3/CR_new_term.png'), dpi=300, format='png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34425ef3",
   "metadata": {},
   "source": [
    "**Q4** Will the terms added by different users to the ontology create conflicts in the ontology? If so, what kinds of conflict? and what are their relative frequency? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7d7d38f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "277ed38a",
   "metadata": {},
   "source": [
    "**Q5** Will the user find the software intuitive to use and instructions clear and easy to follow?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae8ee3c5",
   "metadata": {},
   "source": [
    "As for answering this question. firstly, we should draw bar chart for each question. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "f4771e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_fill(question):\n",
    "\n",
    "    if \"almost always\" not in question.keys():\n",
    "        fill = pd.Series([0], index = [\"almost always\"])\n",
    "        question = question.add(fill, fill_value=0)\n",
    "    \n",
    "    if \"sometimes\" not in question.keys():\n",
    "        fill = pd.Series([0], index = [\"sometimes\"])\n",
    "        question = question.add(fill, fill_value=0)\n",
    "    \n",
    "    if \"rarely\" not in question.keys():\n",
    "        fill = pd.Series([0], index = [\"rarely\"])\n",
    "        question = question.add(fill, fill_value=0)\n",
    "    \n",
    "    else:\n",
    "        question = question\n",
    "\n",
    "    return question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "fa523eec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_fill_2(question):\n",
    "\n",
    "    if \"good\" not in question.keys():\n",
    "        fill = pd.Series([0], index = [\"good\"])\n",
    "        question = question.add(fill, fill_value=0)\n",
    "    \n",
    "    if \"fair\" not in question.keys():\n",
    "        fill = pd.Series([0], index = [\"fair\"])\n",
    "        question = question.add(fill, fill_value=0)\n",
    "    \n",
    "    if \"poor\" not in question.keys():\n",
    "        fill = pd.Series([0], index = [\"poor\"])\n",
    "        question = question.add(fill, fill_value=0)\n",
    "    \n",
    "    else:\n",
    "        question = question\n",
    "\n",
    "    return question"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34c3ccce",
   "metadata": {},
   "source": [
    "Q2#1 - Your experience with the main components of Character Recorder: - Did what I wanted it to do"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "98e278e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "SA = pd.read_csv('Files/CR-shortanswer.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "7271081a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw(value, group, Width):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_axes([0,0,1,1])\n",
    "   \n",
    "    barWidth = Width\n",
    "    br = np.arange(3)\n",
    "    \n",
    "    for i in range(1, 6):\n",
    "        q = value[\"Q2#\" + str(group) + \"_\" + str(i)][18:34].value_counts()\n",
    "\n",
    "        ax.bar(br, missing_fill(q)[['almost always', 'sometimes', 'rarely']], width = 0.25, label = value[\"Q2#\" + str(group) + \"_\" + str(i)][0].split(\"-\")[2])\n",
    "        br = [x + barWidth for x in br]\n",
    "        ax.legend()\n",
    "\n",
    "    plt.xticks([r + barWidth for r in range(3)],\n",
    "        ['almost always', 'sometimes', 'rarely'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "3e8dc552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"User's experiences - Did what I wanted it to do\")"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sZOPGjaxatQoz45hjjomt81FHHZVwfUtaN4B+/folJURBQbq3EY2quoL/GrGtqisQkUpKTU1l6dKl7N69O2Gv9IgjjojddnfuuOMOrr766r3mmTNnDm+88Qbz58/n8MMPp1evXgl/luHuDBkyhAceeKDMug477DAAateuTWFhYez5zz777D7h+N1335Gbm8sJJ5xAv3792Lx5M+PHj6dTp05lb4AEyyxaFsDkyZPZtGkTixYtom7duqSkpMTWLX7bfPjhh4wZM4aFCxdy9NFHM3To0Ar9NKWkdXv33Xf3Wk5U+o5URCTJ2rRpQ3Z2Nvfee28sPAoKCnj55X1PgDrzzDOZOHEi27dvB+CTTz7hs88+Y9u2bRx99NEcfvjhrFmzhnfeeSf2nLp167Jr1y4A+vbty/Tp0/nss88A2Lp1Kx99VOJAJQmX/8gjj8TqXLJkCQD16tXj+9//PtOmTaNr16706NGDMWPG0LNnz33a6NOnD9OmTWPLli2xGkqzbds2WrRoQd26dZk9e3aJ9X755ZccccQRNGrUiE8//ZRXX30VgLZt27Jx40YWLlwIwFdffUVhYSFHHnkkX331VZnrlmwKUhGR/WDChAl8+umnnHjiiXTo0IGhQ4cmPOTav39/LrnkErp27UpaWhoXXHABX331FWeddRaFhYWccsop3H777Xsd7rzqqqtIT09n0KBBtG/fnvvuu4/+/fuTnp5Ov3792LhxY7nrHD58OLt27SI9PZ3U1FSGDx8em9ajRw9atGhBgwYN6NGjB+vXr6dHjx77tJGamspdd93F6aefTkZGBr/61a9KXeagQYPIzc0lLS2NJ598knbt2iWcLyMjg6ysLNq1a8cll1xC9+7dgSDkp06dyg033EBGRgb9+vXjm2++oXfv3qxatSp2slFp65ZMVpTUB0J2drZX6/FIdWhXpMZavXo1p5xySlWXIQeBRPuSmS1y9+xE86tHKiIiEoGCVEREJAIFqYiISAQKUhERkQgUpCIiIhEoSEVERCJQkIqI7AcaRq38yhr+LDc3lxtvvPEAVlQxukSgiByckv278Ar+tlvDqJVfacOfFRYWkp2dTXZ2wp9wVgvqkYqIJNmhNoxaQUEB7dq1Y+jQoZx88skMGjSIN954g+7du3PSSSexYMECABYsWEDXrl3JysqiW7durF27lu+++26f4c9GjBjB4MGD6d69O4MHD2bOnDmcc845ANx0002xWl577TV69uzJnj17kvCqVZ6CVEQkycozjNp1113HypUrWbt2bWwYtby8PBYtWkROTg4AEydOZNGiReTm5vLwww+zZcsWRo8eTYMGDcjLy2Py5Ml7DaOWl5dH7dq1mTx5csLlFg2jdu2118ZGRCkaRm3BggXMnj2bW2+9lR07dsSGUdu2bds+w6glut7u+++/zy233MKaNWtYs2YNzzzzDG+99RZjxoyJHYpu164dc+fOZcmSJYwcOZI777yTevXqMXLkSAYOHEheXh4DBw4EYNWqVbzxxhtMmTJlr+U88MADTJ06ldmzZ3PjjTfyl7/8pcI9+mQr89CumU0EzgE+c/cOxabdAowBmrv75v1ToojIweVgG0YNgh53WloaEFx7t2/fvpgZaWlpFBQUAMHF6ocMGUJ+fj5mFrvwfiIDBgygQYMG+zx++OGHM378eHr27MnYsWNp06ZNiW0cKOX5jnQS8CjwZPyDZvZ9oD/wcfLLEhGpuQ7FYdTih0urVatW7H6tWrViyxo+fDi9e/dmxowZFBQU0KtXrxJrLW2Ys+XLl9O0aVM2bNhQ5jofCGX2h909B0g0Js5YYBhw4K56LyJSAxxqw6iV17Zt2zj22GMBmDRpUuzx4sOfleajjz7i//7v/1iyZAmvvvpq0s6EjqJSB5bN7FzgE3dfmuR6REQOCofSMGrlNWzYMO644w6ysrJivVRgn+HPSuLu/PznP2fMmDG0bNmSJ554giuuuKJCg33vD+UaRs3MUoCX3L2DmR0OzAb6u/s2MysAskv6jtTMrgKuAmjVqlWninxSKkvK7ft+uouioP4lSW0PIK11q6S3WRnLhywvcdpj18w6gJWU7heP96nqEqSG0jBqkiwHYhi1NkBrYGkYoscBi83s/yWa2d3HuXu2u2c3b968EosTERGpvip8QQZ3Xw7Ejk+U1SMVERE5mJXZIzWzKcB8oK2ZrTezn+//skRERGqGMnuk7n5xGdNTklaNiIhIDaMrG4mIiESgIBUREYlAQSoish+kpKSweXNyz8H84osv+OMf/1jhaclUUFBAhw4dypznmWee2e+1VBcaRk1EDkppf01Lanul/Rb7QCkKy+uuu65C0w60oiC95JLk/za/OlKPVERkPyooKOCUU07hyiuvJDU1lf79+7Nz504AevXqxU033URmZiYdOnSIDTc2YsSI2EXkATp06EBBQQG33347H3zwAZmZmdx66617LSfRtETDs5V3yLOiocy6du3KSSedtM/QaUVt9ejRg44dO9KxY0fmzZsXq2Xu3LlkZmYyduxYdu/eza233hqr5c9//nOSt3LVUo9URGQ/y8/PZ8qUKYwfP56LLrqIZ599lksvvRSAr7/+mry8PHJycrj88stZsWJFie2MHj2aFStWkJeXV+a0mTNnxoZnc3cGDBhATk4OrVq14v3332fatGlMnDiRzp07x4Y8e/HFF7n//vt5/vnnAVi2bBnvvPMOO3bsICsri7PPPnuvZbZo0YLXX3+d+vXrk5+fz8UXX0xubi6jR49mzJgxvPTSSwCMGzeORo0asXDhQr799lu6d+9O//79ad26dfSNWw0oSEVE9rPWrVuTmZkJBEOYFQ0rBnDxxcEvDHv27MmXX37JF198kZRlljQ8W6tWrco15BnAueeeS4MGDWjQoAG9e/dmwYIFsfUA2LVrF9dff31sHNT33nuvxFqWLVvG9OnTgeDi9fn5+QpSEREpn/ghxmrXrh07tAtgZnvNa2bUqVOHPXv2xB6rzEXZSxqeraCgoFxDnpVUW7yxY8fyve99j6VLl7Jnzx7q169fYi2PPPIIZ555ZoXXoybQd6QiIlWoaLSTt956i0aNGtGoUSNSUlJYvHgxAIsXL+bDDz8ESh9urPi0koZnq4gXXniBb775hi1btjBnzpzY4OFFtm3bxjHHHEOtWrV46qmn2L17d4m1/OlPf4oN/fbee++xY8eOCtVSnalHKiJSherXr09WVha7du1i4sSJAJx//vk8+eSTpKamcuqpp3LyyScD0LRpU7p3706HDh344Q9/yIMPPhhrJ9G01atX07VrVwAaNmzI008/nXCg8ZKkp6fTu3dvNm/ezPDhw2nZsuVeh36vu+66WK1nnXVWbDDu9PR0ateuTUZGBkOHDuWmm26ioKCAjh074u40b9489j3swaBcw6glS3Z2tufm5iatPQ2jVn4aRk0OdjVxGLVevXoxZswYsrMTjs5VpUaMGEHDhg359a9/XdWlHHAHYhg1ERERCenQrohIFZkzZ05Vl1CiESNGVHUJNYaCtAb6+wOFZc9UzOoHSjnk1euxCNWIiBzadGhXREQkAgWpiIhIBApSERGRCBSkIiL7QUpKCmlpaWRmZpKZmcmNN964X5c3Z86c2EXji/v2228544wzyMzMZOrUqVxxxRWsWrUqVmeyh3s71OhkIxE5KK1ul9zflJ6yZnWFnzN79myaNWuW1DoSKSwsZM6cOTRs2JBu3brtM33JkiUAsQvaDxw4cL/XdChRj1RE5AApLCykc+fOsZ+93HHHHdx1111A0DMcNmwYaWlpdOnShffffx8Iro3bp08f0tPT6du3Lx9//DEAQ4cO5ZprruHUU0/loosu4vHHH2fs2LFkZmYyd+7c2DI/++wzLr30UhYuXEhmZiYffPABvXr1ItHFcZ5++mm6dOlCZmYmV199deySf1I6BamIyH7Su3fv2KHdsWPHUqdOHSZNmsS1117LG2+8wT//+c/YOKEAjRo1Yvny5Vx//fX88pe/BOCGG25gyJAhLFu2jEGDBu11iHj9+vXMmzeP5557jmuuuYabb76ZvLw8evToEZunRYsWTJgwgR49epCXl0ebNm0S1rp69WqmTp3K22+/HRvNZfLkyftnwxxkdGhXRGQ/SXRoNzU1lcGDB3POOecwf/586tWrF5tWNKTaxRdfzM033wzA/Pnzee655wAYPHgww4YNi81/4YUXVujauaV58803WbRoUezC9Dt37qRFixZJaftgpyAVETnAli9fTuPGjfcZjSV+mLLiQ5YlUnSR+GRwd4YMGcIDDzyQtDYPFTq0KyJyAD333HNs3bqVnJwcbrjhhr0G8i4aUm3q1KmxUVu6devG3/72NwAmT56812HbeKUNsVYeffv2Zfr06bFw37p1Kx999FGl2zuUKEhFRPaT+O9IL7vsMjZv3sztt9/OhAkTOPnkk7n++uu56aabYvN//vnnpKen89BDDzF27FgAHnnkEf7yl7+Qnp7OU089xUMPPZRwWT/+8Y+ZMWPGPicblVf79u2577776N+/P+np6fTr14+NGzdWbsUPMRpGLU5NGUatMtfaLc2sanStXQ2jJpVVE4dRi5eSkkJubu4B+bmMlE7DqImIiBxAOtlIRKQaKCgoqOoSpJLK7JGa2UQz+8zMVsQ99qCZrTGzZWY2w8wa79cqRUREqqnyHNqdBJxV7LHXgQ7ung68B9yR5LpERERqhDKD1N1zgK3FHpvp7kVnvLwDHLcfahMREan2knGy0eXAqyVNNLOrzCzXzHI3bdqUhMWJiIhUH5GC1MzuAgqBEi/I6O7j3D3b3bObN28eZXEiIjXG9u3bufbaa2nTpg0dO3akU6dOjB8/vsrqycvL45VXXonUxtChQ5k+fXqSKtpX/PBuJZk0aRIbNmyo0HP2t0qftWtmQ4FzgL5+IH+MKiJSDo9dMyup7VX0N85XXHEFJ5xwAvn5+dSqVYtNmzYxceLEpNZUXGFhIXXqJH5bz8vLIzc3lx/96Ef7tYbK2r17NxMmTChzvkmTJtGhQwdatmwJUK7n7G+V6pGa2VnAMGCAu3+d3JJERGq2Dz74gAULFnDfffdRq1bwNtu8eXNuu+222DwPPvggnTt3Jj09PTYCTEFBAaeccgpXXnklqamp9O/fn507d8baPOuss+jUqRM9evRgzZo1wN7DqQ0bNowFCxbQtWtXsrKy6NatG2vXruW7777jnnvuYerUqbHBvXfs2MHll19Oly5dyMrK4oUXXthnPdyd66+/nrZt23LGGWfsdW3gRYsWcfrpp9OpUyfOPPPM2FWQHn74Ydq3b096ejo//elPgaB3/rOf/Yy0tDTS09N59tlnAWjYsCG33HILGRkZzJ8/f6/h3Ro2bMjNN99Mamoqffv2ZdOmTUyfPp3c3FwGDRpEZmYmO3fu3Os5U6ZMIS0tjQ4dOuy1rRs2bMhdd91FRkYGp512Gp9++mkSXuX/Ks/PX6YA84G2ZrbezH4OPAocCbxuZnlm9nhSqxIRqcFWrlxJRkZGLESLmzlzJvn5+SxYsIC8vDwWLVpETk4OAPn5+fziF79g5cqVNG7cOBY6V111FY888giLFi1izJgxXHfddbH2ioZT+/3vf0+7du2YO3cuS5YsYeTIkdx5553Uq1ePkSNHMnDgQPLy8hg4cCCjRo2iT58+LFiwgNmzZ3PrrbeyY8eOveqcMWMGa9euZdWqVTz55JPMmzcPgF27dnHDDTcwffp0Fi1axOWXXx4bV3X06NEsWbKEZcuW8fjjQTT87//+b2yIuGXLltGnT9C737FjB6eeeipLly7lBz/4wV7L3rFjB9nZ2axcuZLTTz+d3/zmN1xwwQVkZ2czefJk8vLyaNCgQWz+DRs2cNtttzFr1izy8vJYuHAhzz//fKyt0047jaVLl9KzZ8+kH2Iv89Cuu1+c4OEnklqFiMhBbNSoUUybNo3PPvuMDRs2MHPmTGbOnElWVhYQ9Njy8/Np1aoVrVu3JjMzE4BOnTpRUFDA9u3bmTdvHhdeeGGszW+//TZ2O344tW3btjFkyBDy8/MxM3bt2pWwppkzZ/Liiy8yZswYAL755hs+/vjjvS6Nl5OTw8UXX0zt2rVp2bJlLADXrl3LihUr6NevHxAclj3mmGMASE9PZ9CgQZx33nmcd955ALzxxhuxC+8DHH300QDUrl2b888/P2F9tWrVYuDAgQBceuml/OQnPyl1Gy9cuJBevXpRdC7OoEGDyMnJ4bzzzqNevXqcc845sW36+uuvl9pWRenKRiIiSda+fXuWLl3Knj17qFWrFnfddRd33XUXDRs2BIJDpnfccQdXX331Xs8rKCjgsMMOi92vXbs2O3fuZM+ePTRu3Ji8vLyEy4sfTm348OH07t2bGTNmUFBQQK9evRI+x9159tlnadu2bYXXz91JTU1l/vz5+0x7+eWXycnJ4R//+AejRo1i+fLlJbZTv379co+nWp5h5UpSt27d2PNr165NYWFyr1eua+2KiCTZiSeeSHZ2NnfffTe7d+8Ggh5f0XmZZ555JhMnTmT79u0AfPLJJ/uMTRrvqKOOonXr1kybNg0Igmzp0qUJ5922bRvHHnssEJyYU6T4MGtnnnkmjzzySKymJUuW7NNWz549mTp1Krt372bjxo3Mnj0bgLZt27Jp06ZYkO7atYuVK1eyZ88e1q1bR+/evfntb3/Ltm3b2L59O/369eOxx/47OMbnn39eytYL7NmzJ3aG8DPPPBM79FvScHFdunThX//6F5s3b2b37t1MmTKF008/vczlJIOCVERkP5gwYQJbtmyJhWq/fv343e9+B0D//v255JJL6Nq1K2lpaVxwwQVljiU6efJknnjiCTIyMkhNTU14chDAsGHDuOOOO8jKytqr59W7d29WrVoVO9lo+PDh7Nq1i/T0dFJTUxk+fPg+bf3P//wPJ510Eu3bt+eyyy6LjZFar149pk+fzm233UZGRgaZmZnMmzeP3bt3c+mll5KWlkZWVhY33ngjjRs35u677+bzzz+nQ4cOZGRkxAK5NEcccQQLFiygQ4cOzJo1i3vuuQf478lVRScbFTnmmGMYPXo0vXv3JiMjg06dOnHuueeWuZxk0DBqcTSMWtXTMGpSWTV9GDXZW8OGDWM99gNNw6iJiIgcQApSERGpdqqqN1oZClIREZEIFKQictDQ1UolqsrsQwpSETko1K9fny1btihMpdLcnS1btlC/fv0KPU8XZBCRg8Jxxx3H+vXr0XCNEkX9+vU57riKDbGtIBWRg0LdunVp3bp1VZchhyAd2hUREYlAQSoiIhKBglRERCQCBamIiEgEClIREZEIFKQiIiIRKEhFREQiUJCKiIhEoCAVERGJQEEqIiISgYJUREQkAgWpiIhIBApSERGRCBSkIiIiEShIRUREIlCQioiIRFBmkJrZRDP7zMxWxD3WxMxeN7P88N+j92+ZIiIi1VN5eqSTgLOKPXY78Ka7nwS8Gd4XERE55JQZpO6eA2wt9vC5wF/D238FzktuWSIiIjVDZb8j/Z67bwxv/wf4XpLqERERqVEin2zk7g54SdPN7CozyzWz3E2bNkVdnIiISLVS2SD91MyOAQj//aykGd19nLtnu3t28+bNK7k4ERGR6qmyQfoiMCS8PQR4ITnliIiI1Czl+fnLFGA+0NbM1pvZz4HRQD8zywfOCO+LiIgccuqUNYO7X1zCpL5JrkVERKTG0ZWNREREIlCQioiIRKAgFRERiUBBKiIiEoGCVEREJAIFqYiISAQKUhERkQgUpCIiIhEoSEVERCJQkIqIiESgIBUREYlAQSoiIhKBglRERCQCBamIiEgEClIREZEIFKQiIiIRKEhFREQiUJCKiIhEoCAVERGJQEEqIiISgYJUREQkAgWpiIhIBApSERGRCBSkIiIiEShIRUREIlCQioiIRKAgFRERiUBBKiIiEkGkIDWzm81spZmtMLMpZlY/WYWJiIjUBJUOUjM7FrgRyHb3DkBt4KfJKkxERKQmiHpotw7QwMzqAIcDG6KXJCIiUnNUOkjd/RNgDPAxsBHY5u4zk1WYiIhITRDl0O7RwLlAa6AlcISZXZpgvqvMLNfMcjdt2lT5SkVERKqhKId2zwA+dPdN7r4LeA7oVnwmdx/n7tnunt28efMIixMREal+ogTpx8BpZna4mRnQF1idnLJERERqhijfkb4LTAcWA8vDtsYlqS4REZEaoU6UJ7v7vcC9SapFRESkxtGVjURERCJQkIqIiESgIBUREYlAQSoiIhKBglRERCQCBamIiEgEClIREZEIFKQiIiIRKEhFREQiUJCKiIhEoCAVERGJQEEqIiISgYJUREQkAgWpiIhIBApSERGRCBSkIiIiEShIRUREIlCQioiIRKAgFRERiUBBKiIiEoGCVEREJAIFqYiISAQKUhERkQgUpCIiIhEoSEVERCJQkIqIiESgIBUREYkgUpCaWWMzm25ma8xstZl1TVZhIiIiNUGdiM9/CPinu19gZvWAw5NQk4iISI1R6SA1s0ZAT2AogLt/B3yXnLJERERqhiiHdlsDm4C/mNkSM5tgZkckqS4REZEaIUqQ1gE6An9y9yxgB3B78ZnM7CozyzWz3E2bNkVYnIiISPUTJUjXA+vd/d3w/nSCYN2Lu49z92x3z27evHmExYmIiFQ/lQ5Sd/8PsM7M2oYP9QVWJaUqERGRGiLqWbs3AJPDM3b/DfwsekkiIiI1R6Qgdfc8IDs5pYiIiNQ8urKRiIhIBApSERGRCBSkIiIiEShIRUREIlCQioiIRKAgFRERiUBBKiIiEoGCVEREJAIFqYiISAQKUhERkQgUpCIiIhEoSEVERCJQkIqIiESgIBUREYlAQSoiIhKBglRERCQCBamIiEgEClIREZEIFKQiIiIRKEhFREQiUJCKiIhEoCAVERGJQEEqIiISgYJUREQkAgWpiIhIBApSERGRCBSkIiIiEShIRUREIogcpGZW28yWmNlLyShIRESkJklGj/QmYHUS2hEREalxIgWpmR0HnA1MSE45IiIiNUvUHukfgGHAnuiliIiI1DyVDlIzOwf4zN0XlTHfVWaWa2a5mzZtquziREREqqUoPdLuwAAzKwD+BvQxs6eLz+Tu49w9292zmzdvHmFxIiIi1U+lg9Td73D349w9BfgpMMvdL01aZSIiIjWAfkcqIiISQZ1kNOLuc4A5yWhLRESkJlGPVEREJAIFqYiISAQKUhERkQgUpCIiIhEoSEVERCJQkIqIiESgIBUREYlAQSoiIhKBglRERCQCBamIiEgEClIREZEIFKQiIiIRKEhFREQiSMroLyJSvaxud0pVlxBzyprVVV2CyH6lHqmIiEgEClIREZEIFKQiIiIRKEhFREQiUJCKiIhEoCAVERGJQEEqIiISgYJUREQkAgWpiIhIBApSERGRCBSkIiIiEShIRUREIlCQioiIRKAgFRERiaDSQWpm3zez2Wa2ysxWmtlNySxMRESkJogyHmkhcIu7LzazI4FFZva6u69KUm0iIiLVXqV7pO6+0d0Xh7e/AlYDxyarMBERkZogSo80xsxSgCzg3QTTrgKuAmjVqlUyFidSbim3v1zVJcQUjD67qkuIZFavxyr3vGtmJbkS+MXjfZLepkhlRT7ZyMwaAs8Cv3T3L4tPd/dx7p7t7tnNmzePujgREZFqJVKQmlldghCd7O7PJackERGRmiPKWbsGPAGsdvffJ68kERGRmiNKj7Q7MBjoY2Z54d+PklSXiIhIjVDpk43c/S3AkliLiIhIjaMrG4mIiESgIBUREYlAQSoiIhKBglRERCQCBamIiEgEClIREZEIFKQiIiIRKEhFREQiUJCKiIhEoCAVERGJQEEqIiISgYJUREQkAgWpiIhIBJUe/UXkUFVQ/5LKPXFEyZPSWreqXJsl+HtSW5PqIu2vaVVdAgDLhyyv6hKqFfVIRUREIlCQioiIRKAgFRERiUBBKiIiEoGCVEREJAIFqYiISAQKUhERkQgUpCIiIhEoSEVERCJQkIqIiESgIBUREYlAQSoiIhKBglRERCSCSEFqZmeZ2Voze9/Mbk9WUSIiIjVFpYPUzGoDjwE/BNoDF5tZ+2QVJiIiUhNE6ZF2Ad5393+7+3fA34Bzk1OWiIhIzRAlSI8F1sXdXx8+JiIicsgwd6/cE80uAM5y9yvC+4OBU939+mLzXQVcFd5tC6ytfLk1QjNgc1UXIdWe9hMpD+0n1cfx7t480YQ6ERr9BPh+3P3jwsf24u7jgHERllOjmFmuu2dXdR1SvWk/kfLQflIzRDm0uxA4ycxam1k94KfAi8kpS0REpGaodI/U3QvN7HrgNaA2MNHdVyatMhERkRogyqFd3P0V4JUk1XKwOGQOY0sk2k+kPLSf1ACVPtlIREREdIlAERGRSA76IDWzAjNrtp/aTjGzSyrxvO37ox6pGcyssZldF3e/pZlNr8qapGYxszlmprN5q4mDPkj3sxSgwkEqh7zGQCxI3X2Du19QdeXIgWSBMt97w8uwSg1w0ASpmT1vZovMbGV4EYji01PMbI2ZTTKz98xsspmdYWZvm1m+mXUJ52sStrXMzN4xs/Tw8dPNLC/8W2JmRwKjgR7hYzcXW15DM3vTzBab2XIz2+fyiWb2mJkNCG/PMLOJ4e3LzWxUSesVTv9DXDtXmtlYMzvCzF42s6VmtsLMBiZp8x4SEm0/M+sbvt7LzWyimR0WzltgZg+Er32umXU0s9fM7AMzuyauzVvNbGG4P/0mfHg00CZ87oPhvrkinH9o+Jq/Hi7jejP7VVjDO2bWJJyvjZn9M9w35ppZu/DxC8Pal5pZzoHdglKS8DVea2ZPAiuAJ8L9ZmXcflG0X/3WzBYDF5pZfzObH76PTDOzhsXaTfhecKDWS0LuflD8AU3CfxsQ7KhNw/sFBFcHSQEKgTSCDxCLgImAEVwj+Plw/keAe8PbfYC88PY/gO7h7YYEZzz3Al4qoZ46wFHh7WbA+/z35K7t4b8/BR4Mby8A3glv/wU4s6T1Cpf/AVA3nDYvXK/zgfFxNTSq6telJv0l2n4El8E8Obz/JPDLuP3q2vD2WGAZcCTQHPg0fLw/wVmXFu5zLwE9w31xRdxyYveBoeG+UtTWNuCauOUULf9N4KTw9qnArPD2cuDY8Hbjqt6m+tvrNd4DnBbeL/p/XRuYA6TH7VfDwtvNgBzgiPD+bcA94e05QHZJ7wVVvb6H2t9B0yMFbjSzpcA7BFdcOinBPB+6+3J33wOsBN70YO9bTrCjA/wAeArA3WcBTc3sKOBt4PdmdiPBG1RhGfUYcL+ZLQPeILgO8feKzTOXoEfbHlgFfGpmxwBdCf5DJFwvd98OzALOCXsidd19ebge/cJPtD3cfVsZNcre9tp+BPvEh+7+Xjj9rwRBWOTFuOe96+5fufsm4Fsza0wQpP2BJcBioB2J98viZse1tY3gQ1zRclLCXkk3YJqZ5QF/Bo4J53kbmGRmVxK8SUv18ZG7vxPevijsdS4BUglG0CoyNfz3tPDxt8PXeQhwfHyDpbwXyAEU6Xek1YWZ9QLOALq6+9dmNgeon2DWb+Nu74m7v4cytoW7jzazl4EfEezYZ5ZR1iCCHkUnd99lZgXFa3L3T8I33LMIPnk2AS4i6LF+VcZ6TQDuBNYQ9GBx9/fMrGNY431m9qa7jyyjTgkV334Eb1Clid9/iu9bdQg+TD3g7n+Of5KZpZSz3eJtF7VbC/jC3TMTrMM1ZnYqcDawyMw6ufuWMpYnB8YOADNrDfwa6Ozun5vZJPZ+b9gR/mvA6+5+cRnt7vNeIAfWwdIjbQR8HoZNO4JPcpU1lyAEiwJ6s7t/aWZtwt7sbwkuj9gO+IrgEFxJNX0Whmhvin2SjPMO8EuCIJ1L8B9sblnr5e7vEvRQLwGmhPW2BL5296eBB4GOFV77Q1iC7deVoAd4YjjLYOBfFWjyNeDyou+1zOxYM2tB6ftNmdz9S+BDM7swbNfMLCO83cbd33X3e4BN7H09bKkejiIIy21m9j2CMZ0TeQfoXrT/hd/hn1x8pkTvBXJgHRQ9UuCfwDVmtppgdJl3ypi/NCOAieEh2a8JDqcA/DIMxKLDwq+Gt3eHh14nuXv8l/yTgX+Y2XIgl+DTYiJzgf7u/r6ZfUTQKy0K0rLW6+9Aprt/Ht5PAx40sz3ALuDaCq/9oS3R9mtEcAi1DsEHqMfL25i7zzSzU4D5ZgawHbjU3T+w4CS3FQT70WOVqHUQ8CczuxuoSzAe8NKw/pMIejNvho9JNeLuS81sCcF7wjqCw/GJ5ttkZkOBKRae5AbcDbyXYPbi7wVyAOnKRjWYmb0EjHX3N6u6FhGpOnovqFoHy6HdQ4oFP+h/D9ip/zgihy69F1QP6pGKiIhEoB6piIhIBApSERGRCBSkIiIiEShIRUREIlCQioiIRKAgFRERieD/A13RFTFwaJjrAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw(SA, 1, 0.15)\n",
    "\n",
    "plt.savefig(os.path.join('Pics/Guilding Analysis/Q5/Did what I wanted it to do.png'), dpi=300, format='png', bbox_inches='tight')\n",
    "plt.title(\"User's experiences - Did what I wanted it to do\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dee4ffd5",
   "metadata": {},
   "source": [
    "Q2#2 - Your experience with the main components of Character Recorder: - Responded quickly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "02acf138",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"User's experiences - Responded quickly\")"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw(SA, 2, 0.15)\n",
    "\n",
    "plt.savefig(os.path.join('Pics/Guilding Analysis/Q5/Responded quickly.png'), dpi=300, format='png', bbox_inches='tight')\n",
    "plt.title(\"User's experiences - Responded quickly\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45508a81",
   "metadata": {},
   "source": [
    "Q2#3 - Your experience with the main components of Character Recorder: - My experience with it was"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "c7b28844",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw2(value, group, Width):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "    barWidth = Width\n",
    "    br = np.arange(3)\n",
    "    \n",
    "    for i in range(1, 6):\n",
    "        q = value[\"Q2#\" + str(group) + \"_\" +str(i)][18:34].value_counts()\n",
    "\n",
    "        ax.bar(br, missing_fill_2(q)[['good', 'fair', 'poor']], width = 0.25, label = value[\"Q2#\" + str(group) + \"_\" +str(i)][0].split(\"-\")[2])\n",
    "        br = [x + barWidth for x in br]\n",
    "        ax.legend()\n",
    "\n",
    "    plt.xticks([r + barWidth for r in range(3)],\n",
    "        ['good', 'fair', 'poor'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "d1a3237d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"User's experiences - My experience with it was\")"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw2(SA, 3, 0.15)\n",
    "\n",
    "plt.savefig(os.path.join('Pics/Guilding Analysis/Q5/My experience with it was.png'), dpi=300, format='png', bbox_inches='tight')\n",
    "plt.title(\"User's experiences - My experience with it was\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72180f74",
   "metadata": {},
   "source": [
    "Q3 - Your experience with features of Character Recorder (CR):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "7009b500",
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_fill_3(question):\n",
    "\n",
    "    if \"Strongly agree\" not in question.keys():\n",
    "        fill = pd.Series([0], index = [\"Strongly agree\"])\n",
    "        question = question.add(fill, fill_value=0)\n",
    "    \n",
    "    if \"Agree\" not in question.keys():\n",
    "        fill = pd.Series([0], index = [\"Agree\"])\n",
    "        question = question.add(fill, fill_value=0)\n",
    "    \n",
    "    if \"Somewhat agree\" not in question.keys():\n",
    "        fill = pd.Series([0], index = [\"Somewhat agree\"])\n",
    "        question = question.add(fill, fill_value=0)\n",
    "    \n",
    "    if \"Neither agree nor disagree\" not in question.keys():\n",
    "        fill = pd.Series([0], index = [\"Neither agree nor disagree\"])\n",
    "        question = question.add(fill, fill_value=0)\n",
    "    \n",
    "    if \"Somewhat disagree\" not in question.keys():\n",
    "        fill = pd.Series([0], index = [\"Somewhat disagree\"])\n",
    "        question = question.add(fill, fill_value=0)\n",
    "    \n",
    "    if \"Disagree\" not in question.keys():\n",
    "        fill = pd.Series([0], index = [\"Disagree\"])\n",
    "        question = question.add(fill, fill_value=0)\n",
    "    \n",
    "    if \"Strongly disagree\" not in question.keys():\n",
    "        fill = pd.Series([0], index = [\"Strongly disagree\"])\n",
    "        question = question.add(fill, fill_value=0)\n",
    "\n",
    "    else:\n",
    "        question = question\n",
    "\n",
    "    return question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "7fef5f51",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw3(value, Width, xlabel, start, end):\n",
    "    fig = plt.figure(figsize=[15, 15])\n",
    "    ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "    barWidth = Width\n",
    "    br = np.arange(7)\n",
    "    \n",
    "    for i in range(start, end):\n",
    "        q = value[\"Q3_\" + str(i)][18:34].value_counts()\n",
    "\n",
    "        ax.bar(br, missing_fill_3(q), width = 0.1, label = value[\"Q3_\" + str(i)][0].split(\"-\")[1])\n",
    "        ax.margins(x=0.1)\n",
    "        br = [x + barWidth for x in br]\n",
    "        ax.legend()\n",
    "        #ax.legend(title='Columns', bbox_to_anchor=(1, 1.02), loc='upper left')\n",
    "    \n",
    "    plt.xticks([r + barWidth for r in range(7)],\n",
    "        xlabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "4d31d7a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"User's experience with features of Character Recorder (CR) Part one\")"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = ['Strongly agree','Agree', 'Somewhat agree', 'Neither agree nor disagree',\n",
    "       'Somewhat disagree', 'Disagree', 'Strongly disagree']\n",
    "\n",
    "\n",
    "draw3(SA, 0.1, a, 1, 9)\n",
    "\n",
    "plt.title(\"User's experience with features of Character Recorder (CR) Part one\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "51d87b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"User's experience with features of Character Recorder (CR) Part two\")"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw3(SA, 0.1, a, 9, 17)\n",
    "plt.title(\"User's experience with features of Character Recorder (CR) Part two\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcccb48f",
   "metadata": {},
   "source": [
    "Q6 - Your perceived task load with Character Recorder (CR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "9ab54e2e",
   "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>Mental Demand</th>\n",
       "      <th>Performance</th>\n",
       "      <th>Effort</th>\n",
       "      <th>Frustration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>Much stronger than Excel\\n9</td>\n",
       "      <td>4</td>\n",
       "      <td>Much stronger than Excel\\n9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Same as Excel\\n5</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Same as Excel\\n5</td>\n",
       "      <td>Much stronger than Excel\\n9</td>\n",
       "      <td>Same as Excel\\n5</td>\n",
       "      <td>Same as Excel\\n5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Same as Excel\\n5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Same as Excel\\n5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8</td>\n",
       "      <td>Much stronger than Excel\\n9</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4</td>\n",
       "      <td>Same as Excel\\n5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>Much stronger than Excel\\n9</td>\n",
       "      <td>3</td>\n",
       "      <td>Same as Excel\\n5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>Same as Excel\\n5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>6</td>\n",
       "      <td>Much stronger than Excel\\n9</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>Same as Excel\\n5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Mental Demand                  Performance            Effort  \\\n",
       "18                 3                            8                 3   \n",
       "19                 3  Much stronger than Excel\\n9                 4   \n",
       "20  Same as Excel\\n5                            8                 3   \n",
       "21  Same as Excel\\n5  Much stronger than Excel\\n9  Same as Excel\\n5   \n",
       "22  Same as Excel\\n5                            4                 6   \n",
       "23                 6                            7                 7   \n",
       "24  Same as Excel\\n5                            6                 4   \n",
       "25                 8                            6                 7   \n",
       "26                 8                            6                 7   \n",
       "27                 8  Much stronger than Excel\\n9                 8   \n",
       "28                 4             Same as Excel\\n5                 2   \n",
       "29                 3  Much stronger than Excel\\n9                 3   \n",
       "30                 7                            7  Same as Excel\\n5   \n",
       "31                 6  Much stronger than Excel\\n9                 4   \n",
       "32                 6                            8                 6   \n",
       "33                 6                            6                 7   \n",
       "\n",
       "                    Frustration  \n",
       "18                            3  \n",
       "19  Much stronger than Excel\\n9  \n",
       "20                            2  \n",
       "21             Same as Excel\\n5  \n",
       "22                            4  \n",
       "23                            4  \n",
       "24                            4  \n",
       "25                            6  \n",
       "26                            8  \n",
       "27                            7  \n",
       "28                            3  \n",
       "29             Same as Excel\\n5  \n",
       "30                            6  \n",
       "31                            2  \n",
       "32                            4  \n",
       "33             Same as Excel\\n5  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(SA[[\"Q14_1\", \"Q14_2\", \"Q14_3\", \"Q14_4\"]][18:34], columns=[\"Q14_1\", \"Q14_2\", \"Q14_3\", \"Q14_4\"])\n",
    "df.rename(columns={'Q14_1': \"Mental Demand\", 'Q14_2': 'Performance', 'Q14_3': 'Effort', 'Q14_4': 'Frustration'}, inplace=True)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "27092b20",
   "metadata": {},
   "outputs": [],
   "source": [
    "scales = [\"Much weaker than Excel\\n1\", \"2\", \"3\",\"4\",\"Same as Excel\\n5\",\"6\",\"7\",\"8\",\"Much stronger than Excel\\n9\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "cfc100dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [i for i in range (9, 0, -1)]\n",
    "b = [p for p in range (0, 15)]\n",
    "c = a + b\n",
    "\n",
    "v = [d for d in range(0, 27)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "bc89bf09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"User's perceived task load with Character Recorder (compare with Excel)\")"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_likert.plot_likert(df, scales,figsize=(15,8), plot_percentage= True,\n",
    "                        bar_labels=True, bar_labels_color=\"black\", colors=plot_likert.colors.likert9)\n",
    "\n",
    "plt.savefig(os.path.join('Pics/Guilding Analysis/Q6/User perceived task (ratio).png'), dpi=300, format='png', bbox_inches='tight')\n",
    "plt.title(\"User's perceived task load with Character Recorder (compare with Excel)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "6d7c43f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"User's perceived task load with Character Recorder (compare with Excel)\")"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_likert.plot_likert(df, scales,figsize=(15,8), \n",
    "                        bar_labels=True, bar_labels_color=\"black\", colors=plot_likert.colors.likert9)\n",
    "\n",
    "plt.savefig(os.path.join('Pics/Guilding Analysis/Q6/User perceived task (times)).png'), dpi=300, format='png', bbox_inches='tight')\n",
    "plt.title(\"User's perceived task load with Character Recorder (compare with Excel)\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}