Evaluation Survey Responses.ipynb

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    "# Evaluation Survey Responses\n",
    "\n",
    "The full set of survey questions were: \n",
    "\n",
    "- eval-clear - Overall, how clear did you find the concept of the Data Hazards?\n",
    "- eval-easy - How easy was it to apply the Data Hazards to the projects?\n",
    "- eval-hazards-useful - How useful did you think the Data Hazards were for exploring potential downfalls of data science projects?\n",
    "- eval-group-useful - How useful was the reflective group activity for helping to decide on the relevant hazards?\n",
    "\n",
    "For ease of analysis we will use a version of the likert responses that have been turned from words to their corresponding values (`likert-values.txt`), where 1 is the most positive response and 5 is the most negative response."
   ]
  },
  {
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   "execution_count": 1,
   "id": "626e2f49-1ca2-485d-862a-0dd6064730ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plot_likert  # Plotting library based on matplotlib\n",
    "import matplotlib.pyplot as plt  # We import matplotlib to use the subplots feature\n",
    "from matplotlib import gridspec  # To control the subplot grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "71e4172e-c324-498e-9b3f-b6935187fa23",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data (note, not UTF8 encoded)\n",
    "\n",
    "data = pd.read_csv('likert-values.txt', sep='\\t', encoding = \"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08a5cbba-8e99-4d83-aef4-8e61a8b4cc52",
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eval-clear</th>\n",
       "      <th>eval-easy</th>\n",
       "      <th>eval-hazards-useful</th>\n",
       "      <th>eval-group-useful</th>\n",
       "      <th>eval-learned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   eval-clear  eval-easy  eval-hazards-useful  eval-group-useful  eval-learned\n",
       "0         1.0        1.0                  1.0                1.0           1.0\n",
       "1         1.0        2.0                  2.0                2.0           2.0\n",
       "2         1.0        1.0                  1.0                2.0           1.0\n",
       "3         2.0        1.0                  1.0                2.0           1.0\n",
       "4         1.0        2.0                  1.0                2.0           1.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "id": "4e524f9f-3c40-4b04-b336-59d25958495f",
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   "outputs": [],
   "source": [
    "# Drop those NA rows\n",
    "\n",
    "data = data.dropna()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bc542094",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change floats to ints (for plot_likert package)\n",
    "\n",
    "data = data.astype(int)\n",
    "data = data.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b704358c",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eval-clear</th>\n",
       "      <th>eval-easy</th>\n",
       "      <th>eval-hazards-useful</th>\n",
       "      <th>eval-group-useful</th>\n",
       "      <th>eval-learned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  eval-clear eval-easy eval-hazards-useful eval-group-useful eval-learned\n",
       "0          1         1                   1                 1            1\n",
       "1          1         2                   2                 2            2\n",
       "2          1         1                   1                 2            1\n",
       "3          2         1                   1                 2            1\n",
       "4          1         2                   1                 2            1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9cb924ca-d097-4ec5-88ba-34779de87026",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eval-clear</th>\n",
       "      <th>eval-easy</th>\n",
       "      <th>eval-hazards-useful</th>\n",
       "      <th>eval-group-useful</th>\n",
       "      <th>eval-learned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
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       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       eval-clear eval-easy eval-hazards-useful eval-group-useful eval-learned\n",
       "count          41        41                  41                41           41\n",
       "unique          3         4                   3                 3            3\n",
       "top             1         2                   1                 1            1\n",
       "freq           21        19                  24                22           25"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Brief overview of the data\n",
    "\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c37d88f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We're going to re-code eval-learned from 1,2,3 to 1,3,5 so that it can be plotted with the other questions. \n",
    "\n",
    "data = data.replace({'eval-learned': {'3': '5', '2': '3'}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c26b9a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up custom colours using PiYGn (https://colorbrewer2.org/#type=diverging&scheme=PiYG&n=5)\n",
    "\n",
    "customcolours5 = ['#ffffff00', '#4dac26', '#b8e186', 'silver', '#f1b6da', '#d01c8b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ca43af35",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ff18302/opt/anaconda3/envs/datahazardsanalysis/lib/python3.11/site-packages/plot_likert/plot_likert.py:251: FutureWarning: DataFrame.set_axis 'inplace' keyword is deprecated and will be removed in a future version. Use `obj = obj.set_axis(..., copy=False)` instead\n",
      "  df = df.set_axis(new_labels, axis=1, inplace=False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Learned from the group discussion'),\n",
       " Text(0, 1, 'Reflective group activity is useful'),\n",
       " Text(0, 2, 'Hazards are useful'),\n",
       " Text(0, 3, 'Hazards are easy to apply'),\n",
       " Text(0, 4, 'Concept is clear')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the figure for the four questions that had five response options. \n",
    "\n",
    "plt = plot_likert.plot_likert(\n",
    "    data, \n",
    "    plot_likert.scales.raw5, # Default scale is 1-5\n",
    "    plot_percentage=True,\n",
    "    bar_labels=True,\n",
    "    colors=customcolours5,\n",
    "    figsize=(8,4))\n",
    "\n",
    "plt.set_yticklabels([\"Learned from the group discussion\",\n",
    "                    \"Reflective group activity is useful\",\n",
    "                     \"Hazards are useful\",\n",
    "                     \"Hazards are easy to apply\",\n",
    "                     \"Concept is clear\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1c81be48",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Save the image out\n",
    "\n",
    "plt.figure.savefig('survey_results.pdf', dpi=330, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6464cb85",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,md:myst"
  },
  "kernelspec": {
   "display_name": "Python [conda env:datahazardsanalysis]",
   "language": "python",
   "name": "conda-env-datahazardsanalysis-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}