├── 210124_venn.ipynb
├── 210219_Medium_Squarify_Matplotlib.ipynb
├── 210630_Medium_ColabwithR.ipynb
├── 210630_Medium_Exercise_Visualization.ipynb
├── 220219_Medium_PoissonDistribution.ipynb
├── 220220_Medium_ChiSquareDistribution.ipynb
├── 220220_Medium_GeometricDistribution.ipynb
├── 220225_Medium_PDF.ipynb
└── README.md


/210124_venn.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |   "nbformat": 4,
   3 |   "nbformat_minor": 0,
   4 |   "metadata": {
   5 |     "colab": {
   6 |       "name": "Medium_210124Venn.ipynb",
   7 |       "provenance": [],
   8 |       "collapsed_sections": []
   9 |     },
  10 |     "kernelspec": {
  11 |       "name": "python3",
  12 |       "display_name": "Python 3"
  13 |     }
  14 |   },
  15 |   "cells": [
  16 |     {
  17 |       "cell_type": "markdown",
  18 |       "metadata": {
  19 |         "id": "m5FstR3BXWxM"
  20 |       },
  21 |       "source": [
  22 |         "# Venn Diagrams\r\n",
  23 |         "\r\n",
  24 |         "## Kate Wall\r\n",
  25 |         "## Last Edit: Jan. 24, 2021\r\n",
  26 |         "## How to easily make a Venn Diagrams from pandas DataFrames\r\n"
  27 |       ]
  28 |     },
  29 |     {
  30 |       "cell_type": "code",
  31 |       "metadata": {
  32 |         "id": "mSJrqTGenhbe"
  33 |       },
  34 |       "source": [
  35 |         "from collections import Counter\n",
  36 |         "from matplotlib_venn import venn2, venn3\n",
  37 |         "import matplotlib.pyplot as plt\n",
  38 |         "import pandas as pd\n",
  39 |         "from IPython.core.display import HTML\n",
  40 |         "def multi_table(table_list): #for visualizing tables in the same cell\n",
  41 |         "  return HTML(\n",
  42 |         "      '<table><tr style=\"background-color:white;\">' +\n",
  43 |         "      ''.join(['<td>' + table._repr_html_() + '</td>' for table in table_list]) +\n",
  44 |         "      '</tr></table>'\n",
  45 |         "  ) #https://github.com/epmoyer/ipy_table/issues/24"
  46 |       ],
  47 |       "execution_count": 1,
  48 |       "outputs": []
  49 |     },
  50 |     {
  51 |       "cell_type": "markdown",
  52 |       "metadata": {
  53 |         "id": "zVJb4ddcV9MH"
  54 |       },
  55 |       "source": [
  56 |         "# Setup"
  57 |       ]
  58 |     },
  59 |     {
  60 |       "cell_type": "code",
  61 |       "metadata": {
  62 |         "colab": {
  63 |           "base_uri": "https://localhost:8080/",
  64 |           "height": 260
  65 |         },
  66 |         "id": "08njqMO1Vz9r",
  67 |         "outputId": "11a878f9-0f36-46f5-89d2-3ed9ed7c7f7c"
  68 |       },
  69 |       "source": [
  70 |         "groupA = ['person1','person2','person3','person4','person5','person6','person7']\r\n",
  71 |         "groupB = ['person1','person2','person3',                                        'person8','person9']\r\n",
  72 |         "groupC = ['person1','person2',          'person4',                                        'person9','person10']\r\n",
  73 |         "dfA = pd.DataFrame(groupA,columns=['A'])\r\n",
  74 |         "dfB = pd.DataFrame(groupB,columns=['B'])\r\n",
  75 |         "dfC = pd.DataFrame(groupC,columns=['C'])\r\n",
  76 |         "multi_table([dfA,dfB,dfC])"
  77 |       ],
  78 |       "execution_count": 2,
  79 |       "outputs": [
  80 |         {
  81 |           "output_type": "execute_result",
  82 |           "data": {
  83 |             "text/html": [
  84 |               "<table><tr style=\"background-color:white;\"><td><div>\n",
  85 |               "<style scoped>\n",
  86 |               "    .dataframe tbody tr th:only-of-type {\n",
  87 |               "        vertical-align: middle;\n",
  88 |               "    }\n",
  89 |               "\n",
  90 |               "    .dataframe tbody tr th {\n",
  91 |               "        vertical-align: top;\n",
  92 |               "    }\n",
  93 |               "\n",
  94 |               "    .dataframe thead th {\n",
  95 |               "        text-align: right;\n",
  96 |               "    }\n",
  97 |               "</style>\n",
  98 |               "<table border=\"1\" class=\"dataframe\">\n",
  99 |               "  <thead>\n",
 100 |               "    <tr style=\"text-align: right;\">\n",
 101 |               "      <th></th>\n",
 102 |               "      <th>A</th>\n",
 103 |               "    </tr>\n",
 104 |               "  </thead>\n",
 105 |               "  <tbody>\n",
 106 |               "    <tr>\n",
 107 |               "      <th>0</th>\n",
 108 |               "      <td>person1</td>\n",
 109 |               "    </tr>\n",
 110 |               "    <tr>\n",
 111 |               "      <th>1</th>\n",
 112 |               "      <td>person2</td>\n",
 113 |               "    </tr>\n",
 114 |               "    <tr>\n",
 115 |               "      <th>2</th>\n",
 116 |               "      <td>person3</td>\n",
 117 |               "    </tr>\n",
 118 |               "    <tr>\n",
 119 |               "      <th>3</th>\n",
 120 |               "      <td>person4</td>\n",
 121 |               "    </tr>\n",
 122 |               "    <tr>\n",
 123 |               "      <th>4</th>\n",
 124 |               "      <td>person5</td>\n",
 125 |               "    </tr>\n",
 126 |               "    <tr>\n",
 127 |               "      <th>5</th>\n",
 128 |               "      <td>person6</td>\n",
 129 |               "    </tr>\n",
 130 |               "    <tr>\n",
 131 |               "      <th>6</th>\n",
 132 |               "      <td>person7</td>\n",
 133 |               "    </tr>\n",
 134 |               "  </tbody>\n",
 135 |               "</table>\n",
 136 |               "</div></td><td><div>\n",
 137 |               "<style scoped>\n",
 138 |               "    .dataframe tbody tr th:only-of-type {\n",
 139 |               "        vertical-align: middle;\n",
 140 |               "    }\n",
 141 |               "\n",
 142 |               "    .dataframe tbody tr th {\n",
 143 |               "        vertical-align: top;\n",
 144 |               "    }\n",
 145 |               "\n",
 146 |               "    .dataframe thead th {\n",
 147 |               "        text-align: right;\n",
 148 |               "    }\n",
 149 |               "</style>\n",
 150 |               "<table border=\"1\" class=\"dataframe\">\n",
 151 |               "  <thead>\n",
 152 |               "    <tr style=\"text-align: right;\">\n",
 153 |               "      <th></th>\n",
 154 |               "      <th>B</th>\n",
 155 |               "    </tr>\n",
 156 |               "  </thead>\n",
 157 |               "  <tbody>\n",
 158 |               "    <tr>\n",
 159 |               "      <th>0</th>\n",
 160 |               "      <td>person1</td>\n",
 161 |               "    </tr>\n",
 162 |               "    <tr>\n",
 163 |               "      <th>1</th>\n",
 164 |               "      <td>person2</td>\n",
 165 |               "    </tr>\n",
 166 |               "    <tr>\n",
 167 |               "      <th>2</th>\n",
 168 |               "      <td>person3</td>\n",
 169 |               "    </tr>\n",
 170 |               "    <tr>\n",
 171 |               "      <th>3</th>\n",
 172 |               "      <td>person8</td>\n",
 173 |               "    </tr>\n",
 174 |               "    <tr>\n",
 175 |               "      <th>4</th>\n",
 176 |               "      <td>person9</td>\n",
 177 |               "    </tr>\n",
 178 |               "  </tbody>\n",
 179 |               "</table>\n",
 180 |               "</div></td><td><div>\n",
 181 |               "<style scoped>\n",
 182 |               "    .dataframe tbody tr th:only-of-type {\n",
 183 |               "        vertical-align: middle;\n",
 184 |               "    }\n",
 185 |               "\n",
 186 |               "    .dataframe tbody tr th {\n",
 187 |               "        vertical-align: top;\n",
 188 |               "    }\n",
 189 |               "\n",
 190 |               "    .dataframe thead th {\n",
 191 |               "        text-align: right;\n",
 192 |               "    }\n",
 193 |               "</style>\n",
 194 |               "<table border=\"1\" class=\"dataframe\">\n",
 195 |               "  <thead>\n",
 196 |               "    <tr style=\"text-align: right;\">\n",
 197 |               "      <th></th>\n",
 198 |               "      <th>C</th>\n",
 199 |               "    </tr>\n",
 200 |               "  </thead>\n",
 201 |               "  <tbody>\n",
 202 |               "    <tr>\n",
 203 |               "      <th>0</th>\n",
 204 |               "      <td>person1</td>\n",
 205 |               "    </tr>\n",
 206 |               "    <tr>\n",
 207 |               "      <th>1</th>\n",
 208 |               "      <td>person2</td>\n",
 209 |               "    </tr>\n",
 210 |               "    <tr>\n",
 211 |               "      <th>2</th>\n",
 212 |               "      <td>person4</td>\n",
 213 |               "    </tr>\n",
 214 |               "    <tr>\n",
 215 |               "      <th>3</th>\n",
 216 |               "      <td>person9</td>\n",
 217 |               "    </tr>\n",
 218 |               "    <tr>\n",
 219 |               "      <th>4</th>\n",
 220 |               "      <td>person10</td>\n",
 221 |               "    </tr>\n",
 222 |               "  </tbody>\n",
 223 |               "</table>\n",
 224 |               "</div></td></tr></table>"
 225 |             ],
 226 |             "text/plain": [
 227 |               "<IPython.core.display.HTML object>"
 228 |             ]
 229 |           },
 230 |           "metadata": {
 231 |             "tags": []
 232 |           },
 233 |           "execution_count": 2
 234 |         }
 235 |       ]
 236 |     },
 237 |     {
 238 |       "cell_type": "code",
 239 |       "metadata": {
 240 |         "colab": {
 241 |           "base_uri": "https://localhost:8080/",
 242 |           "height": 345
 243 |         },
 244 |         "id": "D3FzGXlItoZh",
 245 |         "outputId": "0587d28b-82d3-4330-a781-5d7a28ff3c21"
 246 |       },
 247 |       "source": [
 248 |         "people = ['person1','person2','person3','person4','person5','person6','person7','person8','person9','person10']\r\n",
 249 |         "names =  ['angela ','nick   ','harry  ','nicole ','britney','haseem ','anna   ','sarah  ','taylor ','jim     ']\r\n",
 250 |         "roster = pd.DataFrame(zip(people,names),columns=['people','name'])\r\n",
 251 |         "roster"
 252 |       ],
 253 |       "execution_count": 3,
 254 |       "outputs": [
 255 |         {
 256 |           "output_type": "execute_result",
 257 |           "data": {
 258 |             "text/html": [
 259 |               "<div>\n",
 260 |               "<style scoped>\n",
 261 |               "    .dataframe tbody tr th:only-of-type {\n",
 262 |               "        vertical-align: middle;\n",
 263 |               "    }\n",
 264 |               "\n",
 265 |               "    .dataframe tbody tr th {\n",
 266 |               "        vertical-align: top;\n",
 267 |               "    }\n",
 268 |               "\n",
 269 |               "    .dataframe thead th {\n",
 270 |               "        text-align: right;\n",
 271 |               "    }\n",
 272 |               "</style>\n",
 273 |               "<table border=\"1\" class=\"dataframe\">\n",
 274 |               "  <thead>\n",
 275 |               "    <tr style=\"text-align: right;\">\n",
 276 |               "      <th></th>\n",
 277 |               "      <th>people</th>\n",
 278 |               "      <th>name</th>\n",
 279 |               "    </tr>\n",
 280 |               "  </thead>\n",
 281 |               "  <tbody>\n",
 282 |               "    <tr>\n",
 283 |               "      <th>0</th>\n",
 284 |               "      <td>person1</td>\n",
 285 |               "      <td>angela</td>\n",
 286 |               "    </tr>\n",
 287 |               "    <tr>\n",
 288 |               "      <th>1</th>\n",
 289 |               "      <td>person2</td>\n",
 290 |               "      <td>nick</td>\n",
 291 |               "    </tr>\n",
 292 |               "    <tr>\n",
 293 |               "      <th>2</th>\n",
 294 |               "      <td>person3</td>\n",
 295 |               "      <td>harry</td>\n",
 296 |               "    </tr>\n",
 297 |               "    <tr>\n",
 298 |               "      <th>3</th>\n",
 299 |               "      <td>person4</td>\n",
 300 |               "      <td>nicole</td>\n",
 301 |               "    </tr>\n",
 302 |               "    <tr>\n",
 303 |               "      <th>4</th>\n",
 304 |               "      <td>person5</td>\n",
 305 |               "      <td>britney</td>\n",
 306 |               "    </tr>\n",
 307 |               "    <tr>\n",
 308 |               "      <th>5</th>\n",
 309 |               "      <td>person6</td>\n",
 310 |               "      <td>haseem</td>\n",
 311 |               "    </tr>\n",
 312 |               "    <tr>\n",
 313 |               "      <th>6</th>\n",
 314 |               "      <td>person7</td>\n",
 315 |               "      <td>anna</td>\n",
 316 |               "    </tr>\n",
 317 |               "    <tr>\n",
 318 |               "      <th>7</th>\n",
 319 |               "      <td>person8</td>\n",
 320 |               "      <td>sarah</td>\n",
 321 |               "    </tr>\n",
 322 |               "    <tr>\n",
 323 |               "      <th>8</th>\n",
 324 |               "      <td>person9</td>\n",
 325 |               "      <td>taylor</td>\n",
 326 |               "    </tr>\n",
 327 |               "    <tr>\n",
 328 |               "      <th>9</th>\n",
 329 |               "      <td>person10</td>\n",
 330 |               "      <td>jim</td>\n",
 331 |               "    </tr>\n",
 332 |               "  </tbody>\n",
 333 |               "</table>\n",
 334 |               "</div>"
 335 |             ],
 336 |             "text/plain": [
 337 |               "     people      name\n",
 338 |               "0   person1   angela \n",
 339 |               "1   person2   nick   \n",
 340 |               "2   person3   harry  \n",
 341 |               "3   person4   nicole \n",
 342 |               "4   person5   britney\n",
 343 |               "5   person6   haseem \n",
 344 |               "6   person7   anna   \n",
 345 |               "7   person8   sarah  \n",
 346 |               "8   person9   taylor \n",
 347 |               "9  person10  jim     "
 348 |             ]
 349 |           },
 350 |           "metadata": {
 351 |             "tags": []
 352 |           },
 353 |           "execution_count": 3
 354 |         }
 355 |       ]
 356 |     },
 357 |     {
 358 |       "cell_type": "code",
 359 |       "metadata": {
 360 |         "colab": {
 361 |           "base_uri": "https://localhost:8080/"
 362 |         },
 363 |         "id": "FXD2XRNYvtsB",
 364 |         "outputId": "a42560d1-2c05-4a51-b07f-93a3bb0c2826"
 365 |       },
 366 |       "source": [
 367 |         "mapRoster = roster.set_index('people').squeeze().to_dict()\r\n",
 368 |         "mapRoster"
 369 |       ],
 370 |       "execution_count": 4,
 371 |       "outputs": [
 372 |         {
 373 |           "output_type": "execute_result",
 374 |           "data": {
 375 |             "text/plain": [
 376 |               "{'person1': 'angela ',\n",
 377 |               " 'person10': 'jim     ',\n",
 378 |               " 'person2': 'nick   ',\n",
 379 |               " 'person3': 'harry  ',\n",
 380 |               " 'person4': 'nicole ',\n",
 381 |               " 'person5': 'britney',\n",
 382 |               " 'person6': 'haseem ',\n",
 383 |               " 'person7': 'anna   ',\n",
 384 |               " 'person8': 'sarah  ',\n",
 385 |               " 'person9': 'taylor '}"
 386 |             ]
 387 |           },
 388 |           "metadata": {
 389 |             "tags": []
 390 |           },
 391 |           "execution_count": 4
 392 |         }
 393 |       ]
 394 |     },
 395 |     {
 396 |       "cell_type": "code",
 397 |       "metadata": {
 398 |         "colab": {
 399 |           "base_uri": "https://localhost:8080/",
 400 |           "height": 260
 401 |         },
 402 |         "id": "6r9XoI2Q32s8",
 403 |         "outputId": "bb7d358f-2fbc-4d1e-fdf7-2497f6c58446"
 404 |       },
 405 |       "source": [
 406 |         "dfA['ANames'] = dfA.A.map(mapRoster)\n",
 407 |         "dfB['BNames'] = dfB.B.map(mapRoster)\n",
 408 |         "dfC['CNames'] = dfC.C.map(mapRoster)\n",
 409 |         "multi_table([dfA,dfB,dfC])"
 410 |       ],
 411 |       "execution_count": 5,
 412 |       "outputs": [
 413 |         {
 414 |           "output_type": "execute_result",
 415 |           "data": {
 416 |             "text/html": [
 417 |               "<table><tr style=\"background-color:white;\"><td><div>\n",
 418 |               "<style scoped>\n",
 419 |               "    .dataframe tbody tr th:only-of-type {\n",
 420 |               "        vertical-align: middle;\n",
 421 |               "    }\n",
 422 |               "\n",
 423 |               "    .dataframe tbody tr th {\n",
 424 |               "        vertical-align: top;\n",
 425 |               "    }\n",
 426 |               "\n",
 427 |               "    .dataframe thead th {\n",
 428 |               "        text-align: right;\n",
 429 |               "    }\n",
 430 |               "</style>\n",
 431 |               "<table border=\"1\" class=\"dataframe\">\n",
 432 |               "  <thead>\n",
 433 |               "    <tr style=\"text-align: right;\">\n",
 434 |               "      <th></th>\n",
 435 |               "      <th>A</th>\n",
 436 |               "      <th>ANames</th>\n",
 437 |               "    </tr>\n",
 438 |               "  </thead>\n",
 439 |               "  <tbody>\n",
 440 |               "    <tr>\n",
 441 |               "      <th>0</th>\n",
 442 |               "      <td>person1</td>\n",
 443 |               "      <td>angela</td>\n",
 444 |               "    </tr>\n",
 445 |               "    <tr>\n",
 446 |               "      <th>1</th>\n",
 447 |               "      <td>person2</td>\n",
 448 |               "      <td>nick</td>\n",
 449 |               "    </tr>\n",
 450 |               "    <tr>\n",
 451 |               "      <th>2</th>\n",
 452 |               "      <td>person3</td>\n",
 453 |               "      <td>harry</td>\n",
 454 |               "    </tr>\n",
 455 |               "    <tr>\n",
 456 |               "      <th>3</th>\n",
 457 |               "      <td>person4</td>\n",
 458 |               "      <td>nicole</td>\n",
 459 |               "    </tr>\n",
 460 |               "    <tr>\n",
 461 |               "      <th>4</th>\n",
 462 |               "      <td>person5</td>\n",
 463 |               "      <td>britney</td>\n",
 464 |               "    </tr>\n",
 465 |               "    <tr>\n",
 466 |               "      <th>5</th>\n",
 467 |               "      <td>person6</td>\n",
 468 |               "      <td>haseem</td>\n",
 469 |               "    </tr>\n",
 470 |               "    <tr>\n",
 471 |               "      <th>6</th>\n",
 472 |               "      <td>person7</td>\n",
 473 |               "      <td>anna</td>\n",
 474 |               "    </tr>\n",
 475 |               "  </tbody>\n",
 476 |               "</table>\n",
 477 |               "</div></td><td><div>\n",
 478 |               "<style scoped>\n",
 479 |               "    .dataframe tbody tr th:only-of-type {\n",
 480 |               "        vertical-align: middle;\n",
 481 |               "    }\n",
 482 |               "\n",
 483 |               "    .dataframe tbody tr th {\n",
 484 |               "        vertical-align: top;\n",
 485 |               "    }\n",
 486 |               "\n",
 487 |               "    .dataframe thead th {\n",
 488 |               "        text-align: right;\n",
 489 |               "    }\n",
 490 |               "</style>\n",
 491 |               "<table border=\"1\" class=\"dataframe\">\n",
 492 |               "  <thead>\n",
 493 |               "    <tr style=\"text-align: right;\">\n",
 494 |               "      <th></th>\n",
 495 |               "      <th>B</th>\n",
 496 |               "      <th>BNames</th>\n",
 497 |               "    </tr>\n",
 498 |               "  </thead>\n",
 499 |               "  <tbody>\n",
 500 |               "    <tr>\n",
 501 |               "      <th>0</th>\n",
 502 |               "      <td>person1</td>\n",
 503 |               "      <td>angela</td>\n",
 504 |               "    </tr>\n",
 505 |               "    <tr>\n",
 506 |               "      <th>1</th>\n",
 507 |               "      <td>person2</td>\n",
 508 |               "      <td>nick</td>\n",
 509 |               "    </tr>\n",
 510 |               "    <tr>\n",
 511 |               "      <th>2</th>\n",
 512 |               "      <td>person3</td>\n",
 513 |               "      <td>harry</td>\n",
 514 |               "    </tr>\n",
 515 |               "    <tr>\n",
 516 |               "      <th>3</th>\n",
 517 |               "      <td>person8</td>\n",
 518 |               "      <td>sarah</td>\n",
 519 |               "    </tr>\n",
 520 |               "    <tr>\n",
 521 |               "      <th>4</th>\n",
 522 |               "      <td>person9</td>\n",
 523 |               "      <td>taylor</td>\n",
 524 |               "    </tr>\n",
 525 |               "  </tbody>\n",
 526 |               "</table>\n",
 527 |               "</div></td><td><div>\n",
 528 |               "<style scoped>\n",
 529 |               "    .dataframe tbody tr th:only-of-type {\n",
 530 |               "        vertical-align: middle;\n",
 531 |               "    }\n",
 532 |               "\n",
 533 |               "    .dataframe tbody tr th {\n",
 534 |               "        vertical-align: top;\n",
 535 |               "    }\n",
 536 |               "\n",
 537 |               "    .dataframe thead th {\n",
 538 |               "        text-align: right;\n",
 539 |               "    }\n",
 540 |               "</style>\n",
 541 |               "<table border=\"1\" class=\"dataframe\">\n",
 542 |               "  <thead>\n",
 543 |               "    <tr style=\"text-align: right;\">\n",
 544 |               "      <th></th>\n",
 545 |               "      <th>C</th>\n",
 546 |               "      <th>CNames</th>\n",
 547 |               "    </tr>\n",
 548 |               "  </thead>\n",
 549 |               "  <tbody>\n",
 550 |               "    <tr>\n",
 551 |               "      <th>0</th>\n",
 552 |               "      <td>person1</td>\n",
 553 |               "      <td>angela</td>\n",
 554 |               "    </tr>\n",
 555 |               "    <tr>\n",
 556 |               "      <th>1</th>\n",
 557 |               "      <td>person2</td>\n",
 558 |               "      <td>nick</td>\n",
 559 |               "    </tr>\n",
 560 |               "    <tr>\n",
 561 |               "      <th>2</th>\n",
 562 |               "      <td>person4</td>\n",
 563 |               "      <td>nicole</td>\n",
 564 |               "    </tr>\n",
 565 |               "    <tr>\n",
 566 |               "      <th>3</th>\n",
 567 |               "      <td>person9</td>\n",
 568 |               "      <td>taylor</td>\n",
 569 |               "    </tr>\n",
 570 |               "    <tr>\n",
 571 |               "      <th>4</th>\n",
 572 |               "      <td>person10</td>\n",
 573 |               "      <td>jim</td>\n",
 574 |               "    </tr>\n",
 575 |               "  </tbody>\n",
 576 |               "</table>\n",
 577 |               "</div></td></tr></table>"
 578 |             ],
 579 |             "text/plain": [
 580 |               "<IPython.core.display.HTML object>"
 581 |             ]
 582 |           },
 583 |           "metadata": {
 584 |             "tags": []
 585 |           },
 586 |           "execution_count": 5
 587 |         }
 588 |       ]
 589 |     },
 590 |     {
 591 |       "cell_type": "code",
 592 |       "metadata": {
 593 |         "colab": {
 594 |           "base_uri": "https://localhost:8080/",
 595 |           "height": 260
 596 |         },
 597 |         "id": "aU_lAO0yDJSC",
 598 |         "outputId": "79833585-ae71-4bdd-fd66-9020184d3066"
 599 |       },
 600 |       "source": [
 601 |         "del dfA['A'],dfB['B'],dfC['C']\r\n",
 602 |         "multi_table([dfA,dfB,dfC])"
 603 |       ],
 604 |       "execution_count": 6,
 605 |       "outputs": [
 606 |         {
 607 |           "output_type": "execute_result",
 608 |           "data": {
 609 |             "text/html": [
 610 |               "<table><tr style=\"background-color:white;\"><td><div>\n",
 611 |               "<style scoped>\n",
 612 |               "    .dataframe tbody tr th:only-of-type {\n",
 613 |               "        vertical-align: middle;\n",
 614 |               "    }\n",
 615 |               "\n",
 616 |               "    .dataframe tbody tr th {\n",
 617 |               "        vertical-align: top;\n",
 618 |               "    }\n",
 619 |               "\n",
 620 |               "    .dataframe thead th {\n",
 621 |               "        text-align: right;\n",
 622 |               "    }\n",
 623 |               "</style>\n",
 624 |               "<table border=\"1\" class=\"dataframe\">\n",
 625 |               "  <thead>\n",
 626 |               "    <tr style=\"text-align: right;\">\n",
 627 |               "      <th></th>\n",
 628 |               "      <th>ANames</th>\n",
 629 |               "    </tr>\n",
 630 |               "  </thead>\n",
 631 |               "  <tbody>\n",
 632 |               "    <tr>\n",
 633 |               "      <th>0</th>\n",
 634 |               "      <td>angela</td>\n",
 635 |               "    </tr>\n",
 636 |               "    <tr>\n",
 637 |               "      <th>1</th>\n",
 638 |               "      <td>nick</td>\n",
 639 |               "    </tr>\n",
 640 |               "    <tr>\n",
 641 |               "      <th>2</th>\n",
 642 |               "      <td>harry</td>\n",
 643 |               "    </tr>\n",
 644 |               "    <tr>\n",
 645 |               "      <th>3</th>\n",
 646 |               "      <td>nicole</td>\n",
 647 |               "    </tr>\n",
 648 |               "    <tr>\n",
 649 |               "      <th>4</th>\n",
 650 |               "      <td>britney</td>\n",
 651 |               "    </tr>\n",
 652 |               "    <tr>\n",
 653 |               "      <th>5</th>\n",
 654 |               "      <td>haseem</td>\n",
 655 |               "    </tr>\n",
 656 |               "    <tr>\n",
 657 |               "      <th>6</th>\n",
 658 |               "      <td>anna</td>\n",
 659 |               "    </tr>\n",
 660 |               "  </tbody>\n",
 661 |               "</table>\n",
 662 |               "</div></td><td><div>\n",
 663 |               "<style scoped>\n",
 664 |               "    .dataframe tbody tr th:only-of-type {\n",
 665 |               "        vertical-align: middle;\n",
 666 |               "    }\n",
 667 |               "\n",
 668 |               "    .dataframe tbody tr th {\n",
 669 |               "        vertical-align: top;\n",
 670 |               "    }\n",
 671 |               "\n",
 672 |               "    .dataframe thead th {\n",
 673 |               "        text-align: right;\n",
 674 |               "    }\n",
 675 |               "</style>\n",
 676 |               "<table border=\"1\" class=\"dataframe\">\n",
 677 |               "  <thead>\n",
 678 |               "    <tr style=\"text-align: right;\">\n",
 679 |               "      <th></th>\n",
 680 |               "      <th>BNames</th>\n",
 681 |               "    </tr>\n",
 682 |               "  </thead>\n",
 683 |               "  <tbody>\n",
 684 |               "    <tr>\n",
 685 |               "      <th>0</th>\n",
 686 |               "      <td>angela</td>\n",
 687 |               "    </tr>\n",
 688 |               "    <tr>\n",
 689 |               "      <th>1</th>\n",
 690 |               "      <td>nick</td>\n",
 691 |               "    </tr>\n",
 692 |               "    <tr>\n",
 693 |               "      <th>2</th>\n",
 694 |               "      <td>harry</td>\n",
 695 |               "    </tr>\n",
 696 |               "    <tr>\n",
 697 |               "      <th>3</th>\n",
 698 |               "      <td>sarah</td>\n",
 699 |               "    </tr>\n",
 700 |               "    <tr>\n",
 701 |               "      <th>4</th>\n",
 702 |               "      <td>taylor</td>\n",
 703 |               "    </tr>\n",
 704 |               "  </tbody>\n",
 705 |               "</table>\n",
 706 |               "</div></td><td><div>\n",
 707 |               "<style scoped>\n",
 708 |               "    .dataframe tbody tr th:only-of-type {\n",
 709 |               "        vertical-align: middle;\n",
 710 |               "    }\n",
 711 |               "\n",
 712 |               "    .dataframe tbody tr th {\n",
 713 |               "        vertical-align: top;\n",
 714 |               "    }\n",
 715 |               "\n",
 716 |               "    .dataframe thead th {\n",
 717 |               "        text-align: right;\n",
 718 |               "    }\n",
 719 |               "</style>\n",
 720 |               "<table border=\"1\" class=\"dataframe\">\n",
 721 |               "  <thead>\n",
 722 |               "    <tr style=\"text-align: right;\">\n",
 723 |               "      <th></th>\n",
 724 |               "      <th>CNames</th>\n",
 725 |               "    </tr>\n",
 726 |               "  </thead>\n",
 727 |               "  <tbody>\n",
 728 |               "    <tr>\n",
 729 |               "      <th>0</th>\n",
 730 |               "      <td>angela</td>\n",
 731 |               "    </tr>\n",
 732 |               "    <tr>\n",
 733 |               "      <th>1</th>\n",
 734 |               "      <td>nick</td>\n",
 735 |               "    </tr>\n",
 736 |               "    <tr>\n",
 737 |               "      <th>2</th>\n",
 738 |               "      <td>nicole</td>\n",
 739 |               "    </tr>\n",
 740 |               "    <tr>\n",
 741 |               "      <th>3</th>\n",
 742 |               "      <td>taylor</td>\n",
 743 |               "    </tr>\n",
 744 |               "    <tr>\n",
 745 |               "      <th>4</th>\n",
 746 |               "      <td>jim</td>\n",
 747 |               "    </tr>\n",
 748 |               "  </tbody>\n",
 749 |               "</table>\n",
 750 |               "</div></td></tr></table>"
 751 |             ],
 752 |             "text/plain": [
 753 |               "<IPython.core.display.HTML object>"
 754 |             ]
 755 |           },
 756 |           "metadata": {
 757 |             "tags": []
 758 |           },
 759 |           "execution_count": 6
 760 |         }
 761 |       ]
 762 |     },
 763 |     {
 764 |       "cell_type": "markdown",
 765 |       "metadata": {
 766 |         "id": "JKdtDSyUja-x"
 767 |       },
 768 |       "source": [
 769 |         "# Three-Way Venn Diagram"
 770 |       ]
 771 |     },
 772 |     {
 773 |       "cell_type": "code",
 774 |       "metadata": {
 775 |         "id": "iLXyA-VJ40kn"
 776 |       },
 777 |       "source": [
 778 |         "A = set(dfA.ANames)\r\n",
 779 |         "B = set(dfB.BNames)\r\n",
 780 |         "C = set(dfC.CNames)\r\n",
 781 |         "\r\n",
 782 |         "AB_overlap = A & B\r\n",
 783 |         "AC_overlap = A & C\r\n",
 784 |         "BC_overlap = B & C\r\n",
 785 |         "ABC_overlap = A & B & C\r\n",
 786 |         "\r\n",
 787 |         "A_rest = A - AB_overlap - AC_overlap\r\n",
 788 |         "B_rest = B - AB_overlap - BC_overlap\r\n",
 789 |         "C_rest = C - AC_overlap - BC_overlap\r\n",
 790 |         "\r\n",
 791 |         "AB_only = AB_overlap - ABC_overlap\r\n",
 792 |         "AC_only = AC_overlap - ABC_overlap\r\n",
 793 |         "BC_only = BC_overlap - ABC_overlap"
 794 |       ],
 795 |       "execution_count": 7,
 796 |       "outputs": []
 797 |     },
 798 |     {
 799 |       "cell_type": "code",
 800 |       "metadata": {
 801 |         "colab": {
 802 |           "base_uri": "https://localhost:8080/",
 803 |           "height": 392
 804 |         },
 805 |         "id": "nFixjxK570H0",
 806 |         "outputId": "c35ac279-c8cc-48ba-bb4e-83fe0c00627c"
 807 |       },
 808 |       "source": [
 809 |         "sets = Counter()               #set order A, B, C  \r\n",
 810 |         "sets['100'] = len(A_rest)      #100 denotes A on, B off, C off\r\n",
 811 |         "sets['001'] = len(C_rest)      #001 denotes A off, B off, C on\r\n",
 812 |         "sets['010'] = len(B_rest)      #010 denotes A off, B on, C off\r\n",
 813 |         "sets['101'] = len(AC_only)     #101 denotes A on, B off, C on\r\n",
 814 |         "sets['110'] = len(AB_only)     #110 denotes A on, B on, C off\r\n",
 815 |         "sets['011'] = len(BC_only)     #011 denotes A off, B on, C on\r\n",
 816 |         "sets['111'] = len(ABC_overlap) #011 denotes A on, B on, C on\r\n",
 817 |         "labels = ('Group A', 'Group B', 'Group C')\r\n",
 818 |         "\r\n",
 819 |         "plt.figure(figsize=(7,7))\r\n",
 820 |         "ax = plt.gca()\r\n",
 821 |         "venn3(subsets=sets, set_labels=labels, ax=ax,set_colors=('darkviolet','deepskyblue','blue'),alpha=0.7)   \r\n",
 822 |         "plt.show()"
 823 |       ],
 824 |       "execution_count": 8,
 825 |       "outputs": [
 826 |         {
 827 |           "output_type": "display_data",
 828 |           "data": {
 829 |             "image/png": 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\n",
 830 |             "text/plain": [
 831 |               "<Figure size 504x504 with 1 Axes>"
 832 |             ]
 833 |           },
 834 |           "metadata": {
 835 |             "tags": []
 836 |           }
 837 |         }
 838 |       ]
 839 |     },
 840 |     {
 841 |       "cell_type": "markdown",
 842 |       "metadata": {
 843 |         "id": "g01YsUjIMepu"
 844 |       },
 845 |       "source": [
 846 |         "## Set the \"set label\" color (e.g. Group A) "
 847 |       ]
 848 |     },
 849 |     {
 850 |       "cell_type": "code",
 851 |       "metadata": {
 852 |         "colab": {
 853 |           "base_uri": "https://localhost:8080/",
 854 |           "height": 392
 855 |         },
 856 |         "id": "sc2Xd8fmJ-z4",
 857 |         "outputId": "128f303f-2ac7-4dfc-a213-6f63c25f2cce"
 858 |       },
 859 |       "source": [
 860 |         "plt.figure(figsize=(7,7))\r\n",
 861 |         "ax = plt.gca()\r\n",
 862 |         "colors = ['darkviolet','deepskyblue','blue']\r\n",
 863 |         "v = venn3(subsets=sets, set_labels=labels, ax=ax,set_colors=('darkviolet','deepskyblue','blue'),alpha=0.7)   \r\n",
 864 |         "i = 0\r\n",
 865 |         "for text in v.set_labels:\r\n",
 866 |         "  text.set_color(colors[i])\r\n",
 867 |         "  i+=1\r\n",
 868 |         "plt.show()"
 869 |       ],
 870 |       "execution_count": 9,
 871 |       "outputs": [
 872 |         {
 873 |           "output_type": "display_data",
 874 |           "data": {
 875 |             "image/png": 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\n",
 876 |             "text/plain": [
 877 |               "<Figure size 504x504 with 1 Axes>"
 878 |             ]
 879 |           },
 880 |           "metadata": {
 881 |             "tags": []
 882 |           }
 883 |         }
 884 |       ]
 885 |     },
 886 |     {
 887 |       "cell_type": "markdown",
 888 |       "metadata": {
 889 |         "id": "XoX45JzEMp_R"
 890 |       },
 891 |       "source": [
 892 |         "## Set the \"subset label\" color (e.g. 1,2,3)"
 893 |       ]
 894 |     },
 895 |     {
 896 |       "cell_type": "code",
 897 |       "metadata": {
 898 |         "colab": {
 899 |           "base_uri": "https://localhost:8080/",
 900 |           "height": 392
 901 |         },
 902 |         "id": "OIIEYLgGL1Ha",
 903 |         "outputId": "90895949-1bab-40a4-e94c-00e498574833"
 904 |       },
 905 |       "source": [
 906 |         "plt.figure(figsize=(7,7))\r\n",
 907 |         "ax = plt.gca()\r\n",
 908 |         "colors = ['darkviolet','deepskyblue','blue']\r\n",
 909 |         "v = venn3(subsets=sets, set_labels=labels, ax=ax,set_colors=('darkviolet','deepskyblue','blue'),alpha=0.7)   \r\n",
 910 |         "i = 0\r\n",
 911 |         "for text in v.set_labels:\r\n",
 912 |         "  text.set_color(colors[i])\r\n",
 913 |         "  i+=1\r\n",
 914 |         "\r\n",
 915 |         "for text in v.subset_labels:\r\n",
 916 |         "  text.set_color('white')\r\n",
 917 |         "  text.set_fontsize(16)\r\n",
 918 |         "  text.set_fontweight('bold')\r\n",
 919 |         "plt.show()"
 920 |       ],
 921 |       "execution_count": 10,
 922 |       "outputs": [
 923 |         {
 924 |           "output_type": "display_data",
 925 |           "data": {
 926 |             "image/png": 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\n",
 927 |             "text/plain": [
 928 |               "<Figure size 504x504 with 1 Axes>"
 929 |             ]
 930 |           },
 931 |           "metadata": {
 932 |             "tags": []
 933 |           }
 934 |         }
 935 |       ]
 936 |     },
 937 |     {
 938 |       "cell_type": "markdown",
 939 |       "metadata": {
 940 |         "id": "p8MzwD3TNM-y"
 941 |       },
 942 |       "source": [
 943 |         "## Replace the labels with a legend"
 944 |       ]
 945 |     },
 946 |     {
 947 |       "cell_type": "code",
 948 |       "metadata": {
 949 |         "colab": {
 950 |           "base_uri": "https://localhost:8080/",
 951 |           "height": 408
 952 |         },
 953 |         "id": "zUnC9s3a1CN_",
 954 |         "outputId": "dcdd09bc-35d6-4e06-b86f-1888d7309740"
 955 |       },
 956 |       "source": [
 957 |         "plt.figure(figsize=(7,7))\r\n",
 958 |         "ax = plt.gca()\r\n",
 959 |         "v = venn3(subsets=sets, set_labels=('','',''), ax=ax,set_colors=('darkviolet','deepskyblue','blue'),alpha=0.7)  \r\n",
 960 |         "for text in v.subset_labels:\r\n",
 961 |         "  text.set_color('white')\r\n",
 962 |         "  text.set_fontsize(16)\r\n",
 963 |         "  text.set_fontweight('bold')\r\n",
 964 |         "\r\n",
 965 |         "h = [] \r\n",
 966 |         "for i in sets:\r\n",
 967 |         "    h.append(v.get_patch_by_id(i)) #get color patches \r\n",
 968 |         "l = ['A only','C only','B only','A & C shared','A & B shared','B & C shared','A & B & C shared']\r\n",
 969 |         "ax.legend(handles=h, labels=l, title=\"Legend\",loc='lower left')  #write labels for color patches\r\n",
 970 |         "plt.title('Membership across Three Groups')\r\n",
 971 |         "plt.show()"
 972 |       ],
 973 |       "execution_count": 11,
 974 |       "outputs": [
 975 |         {
 976 |           "output_type": "display_data",
 977 |           "data": {
 978 |             "image/png": 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\n",
 979 |             "text/plain": [
 980 |               "<Figure size 504x504 with 1 Axes>"
 981 |             ]
 982 |           },
 983 |           "metadata": {
 984 |             "tags": []
 985 |           }
 986 |         }
 987 |       ]
 988 |     },
 989 |     {
 990 |       "cell_type": "markdown",
 991 |       "metadata": {
 992 |         "id": "hrKHhSqWNWI1"
 993 |       },
 994 |       "source": [
 995 |         "## Customize the legend contents"
 996 |       ]
 997 |     },
 998 |     {
 999 |       "cell_type": "code",
1000 |       "metadata": {
1001 |         "colab": {
1002 |           "base_uri": "https://localhost:8080/",
1003 |           "height": 408
1004 |         },
1005 |         "id": "gpE1_2oG-8zE",
1006 |         "outputId": "3c56b033-478d-40cd-ece5-dabbf236de12"
1007 |       },
1008 |       "source": [
1009 |         "plt.figure(figsize=(7,7))\r\n",
1010 |         "ax = plt.gca()\r\n",
1011 |         "v = venn3(subsets=sets, set_labels=('','',''), ax=ax,set_colors=('darkviolet','deepskyblue','blue'),alpha=0.7)\r\n",
1012 |         "for text in v.subset_labels:\r\n",
1013 |         "  text.set_color('white')\r\n",
1014 |         "  text.set_fontsize(16)\r\n",
1015 |         "  text.set_fontweight('bold')\r\n",
1016 |         "\r\n",
1017 |         "ax.legend(handles=h[0:3], labels=l[0:3], title=\"Legend\",loc='lower left') #create tailored legend from handles and labels    \r\n",
1018 |         "plt.title('Membership across Three Groups')\r\n",
1019 |         "plt.show()"
1020 |       ],
1021 |       "execution_count": 12,
1022 |       "outputs": [
1023 |         {
1024 |           "output_type": "display_data",
1025 |           "data": {
1026 |             "image/png": 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\n",
1027 |             "text/plain": [
1028 |               "<Figure size 504x504 with 1 Axes>"
1029 |             ]
1030 |           },
1031 |           "metadata": {
1032 |             "tags": []
1033 |           }
1034 |         }
1035 |       ]
1036 |     },
1037 |     {
1038 |       "cell_type": "markdown",
1039 |       "metadata": {
1040 |         "id": "6KepmYEvAV8X"
1041 |       },
1042 |       "source": [
1043 |         "# Two-Way Venn Diagram "
1044 |       ]
1045 |     },
1046 |     {
1047 |       "cell_type": "code",
1048 |       "metadata": {
1049 |         "colab": {
1050 |           "base_uri": "https://localhost:8080/",
1051 |           "height": 260
1052 |         },
1053 |         "id": "V9WKdqteBn9G",
1054 |         "outputId": "e53d79ee-08ff-433a-debb-1b78b19ea41e"
1055 |       },
1056 |       "source": [
1057 |         "multi_table([dfA,dfB])"
1058 |       ],
1059 |       "execution_count": 13,
1060 |       "outputs": [
1061 |         {
1062 |           "output_type": "execute_result",
1063 |           "data": {
1064 |             "text/html": [
1065 |               "<table><tr style=\"background-color:white;\"><td><div>\n",
1066 |               "<style scoped>\n",
1067 |               "    .dataframe tbody tr th:only-of-type {\n",
1068 |               "        vertical-align: middle;\n",
1069 |               "    }\n",
1070 |               "\n",
1071 |               "    .dataframe tbody tr th {\n",
1072 |               "        vertical-align: top;\n",
1073 |               "    }\n",
1074 |               "\n",
1075 |               "    .dataframe thead th {\n",
1076 |               "        text-align: right;\n",
1077 |               "    }\n",
1078 |               "</style>\n",
1079 |               "<table border=\"1\" class=\"dataframe\">\n",
1080 |               "  <thead>\n",
1081 |               "    <tr style=\"text-align: right;\">\n",
1082 |               "      <th></th>\n",
1083 |               "      <th>ANames</th>\n",
1084 |               "    </tr>\n",
1085 |               "  </thead>\n",
1086 |               "  <tbody>\n",
1087 |               "    <tr>\n",
1088 |               "      <th>0</th>\n",
1089 |               "      <td>angela</td>\n",
1090 |               "    </tr>\n",
1091 |               "    <tr>\n",
1092 |               "      <th>1</th>\n",
1093 |               "      <td>nick</td>\n",
1094 |               "    </tr>\n",
1095 |               "    <tr>\n",
1096 |               "      <th>2</th>\n",
1097 |               "      <td>harry</td>\n",
1098 |               "    </tr>\n",
1099 |               "    <tr>\n",
1100 |               "      <th>3</th>\n",
1101 |               "      <td>nicole</td>\n",
1102 |               "    </tr>\n",
1103 |               "    <tr>\n",
1104 |               "      <th>4</th>\n",
1105 |               "      <td>britney</td>\n",
1106 |               "    </tr>\n",
1107 |               "    <tr>\n",
1108 |               "      <th>5</th>\n",
1109 |               "      <td>haseem</td>\n",
1110 |               "    </tr>\n",
1111 |               "    <tr>\n",
1112 |               "      <th>6</th>\n",
1113 |               "      <td>anna</td>\n",
1114 |               "    </tr>\n",
1115 |               "  </tbody>\n",
1116 |               "</table>\n",
1117 |               "</div></td><td><div>\n",
1118 |               "<style scoped>\n",
1119 |               "    .dataframe tbody tr th:only-of-type {\n",
1120 |               "        vertical-align: middle;\n",
1121 |               "    }\n",
1122 |               "\n",
1123 |               "    .dataframe tbody tr th {\n",
1124 |               "        vertical-align: top;\n",
1125 |               "    }\n",
1126 |               "\n",
1127 |               "    .dataframe thead th {\n",
1128 |               "        text-align: right;\n",
1129 |               "    }\n",
1130 |               "</style>\n",
1131 |               "<table border=\"1\" class=\"dataframe\">\n",
1132 |               "  <thead>\n",
1133 |               "    <tr style=\"text-align: right;\">\n",
1134 |               "      <th></th>\n",
1135 |               "      <th>BNames</th>\n",
1136 |               "    </tr>\n",
1137 |               "  </thead>\n",
1138 |               "  <tbody>\n",
1139 |               "    <tr>\n",
1140 |               "      <th>0</th>\n",
1141 |               "      <td>angela</td>\n",
1142 |               "    </tr>\n",
1143 |               "    <tr>\n",
1144 |               "      <th>1</th>\n",
1145 |               "      <td>nick</td>\n",
1146 |               "    </tr>\n",
1147 |               "    <tr>\n",
1148 |               "      <th>2</th>\n",
1149 |               "      <td>harry</td>\n",
1150 |               "    </tr>\n",
1151 |               "    <tr>\n",
1152 |               "      <th>3</th>\n",
1153 |               "      <td>sarah</td>\n",
1154 |               "    </tr>\n",
1155 |               "    <tr>\n",
1156 |               "      <th>4</th>\n",
1157 |               "      <td>taylor</td>\n",
1158 |               "    </tr>\n",
1159 |               "  </tbody>\n",
1160 |               "</table>\n",
1161 |               "</div></td></tr></table>"
1162 |             ],
1163 |             "text/plain": [
1164 |               "<IPython.core.display.HTML object>"
1165 |             ]
1166 |           },
1167 |           "metadata": {
1168 |             "tags": []
1169 |           },
1170 |           "execution_count": 13
1171 |         }
1172 |       ]
1173 |     },
1174 |     {
1175 |       "cell_type": "code",
1176 |       "metadata": {
1177 |         "colab": {
1178 |           "base_uri": "https://localhost:8080/",
1179 |           "height": 362
1180 |         },
1181 |         "id": "zGQxYula_xUb",
1182 |         "outputId": "2f4972c7-110c-4338-bf92-10cfbae3ca05"
1183 |       },
1184 |       "source": [
1185 |         "sets = Counter()               #set order A, B  \r\n",
1186 |         "sets['10'] = len(A-AB_overlap) #10 denotes A on, B off\r\n",
1187 |         "sets['01'] = len(B-AB_overlap) #01 denotes A off, B on\r\n",
1188 |         "sets['11'] = len(AB_overlap)   #11 denotes A on, B on\r\n",
1189 |         "labels = ('Group A', 'Group B')\r\n",
1190 |         "\r\n",
1191 |         "plt.figure(figsize=(7,7))\r\n",
1192 |         "ax = plt.gca()\r\n",
1193 |         "v= venn2(subsets=sets, set_labels=labels, ax=ax,set_colors=('darkviolet','deepskyblue'),alpha=0.7)\r\n",
1194 |         "plt.title('Membership across Two Groups')\r\n",
1195 |         "plt.show()"
1196 |       ],
1197 |       "execution_count": 14,
1198 |       "outputs": [
1199 |         {
1200 |           "output_type": "display_data",
1201 |           "data": {
1202 |             "image/png": 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wOE65PGTijFjOo6agheKRhCTzgQ+hFymqSQxX53e1nLG0q+fw8iUmFsnqtUfDUTpvOxzTVsKibN6PJQPA/TjL0yR1QN9ftFA8kJDkIuCzwGG2syj/SEdlrPO4qqb2UxbnjSzOr/H6aGTa+4bRB9fR9PRyVi/UfVrQAvweuN3E6bUdRmmhzFtCkhuBfwAW286i/CEdk9GW1y3d337S4upsH40czAvVNNy9kbVIqLdAGARuA27RYrFLC2UeEpI8FfgouhWvwimS5jNr9nWcuCjrp7Vmo6mUxl8ezop0JPTjeoPA7cAvtFjs0EKZA3ctrkuBt9nOouxL5UdGWs5aur/jhEVLTSziy+tBegpouulIKofzKLSdZQEM4RTLzVosC0sLZZbcJVQ+ig6+57xUfmSk+fU1+ztPqFpq8vxZJJmG8ui8+QgiPYVU2M6yQMaL5RcmTo/tMLlAC2UW3E2wrgJOtJ1F2WPAtMaXNLaesaQ6CEWSaUzov30rA03lLLGdZQENAb/CKZZu22HCTAtlhhKSLMAZfN9mO4uyZ2B1Udued6xJj1XGltrOMldpGP3tJtp2L2K57SwLrB/4jonzO9tBwkoLZQYSkiwB/hldcj5npQojQ3vfuqq5d3PZmoWc/pstaRi9cxNtDblXKuCsHfbfJs4B20HCRgvlIBKSLAc+D2ywnUXZ0XbK4j3Nr6+p9uuA+1ylYfR3h9G6a3FObj09DPwIuNXESdsOExZaKNNwL1j8Vwj1xWFqCoPLCzv2XLpmdHRRfhgWXZyUgbHfH0rLS9U5WSoAO4Bv6EKU3tBCmUJCklXAlyEnTwnkNAPmwHnLd3ectGg1ImG/dgMDqbsOpenFalbazmLJGHAzcIOJM2o7TJBpoUzCHTP5N5wtVVUOGS3L69915fqekSUFOfVEwkDq7o007ViSs6UCsBf4ponzrO0gQaWFMoG7yOO/AFttZ1ELq7u2fP/et66qDNtYyUwZGLtjM22NVTm97YIB7gC+q6saz54WSgb3osXPAsfbzqIWjomSanzr6saeI8rXhmEG13ykhKFf1NLfVprza9O9BHzRxGmxHSRItFBc7nIqH0N3WMwpw9X5XbuuXD86Vh7LpQv9pjUaoe/GozC9hZTZzmJZL/AfJs7jtoMERcR2AB95L1omOaXjhKrGHR85tFjL5JViaUrf8jRjBaMM2s5iWRnwOannYqkP9WrNntEjFCAhyYtxdllUOWLfBSt2d/7VojBtl+u53nyabziaRakIMdtZfOBh4D9NnCHbQfws549QEpI8GXin7RxqYZgIqV1XrNujZXJwZSPUvPlpWjC6KyLwV8CXpZ5q20H8LKePUBKSXAN8BXJiSe+clyqIDL/0wQ0duTYleL62L6Hhno06hd7VAfyribPDdhA/ytlCSUiyFPgqeuFiThipjPW+9MENI6nSvFyfvTRrBkz9BvZtX8oq21l8YgT4ionzgO0gfpOTp7zcGV1/h5ZJThhYVdS246OHipbJ3AjI6S+xdFE/nbaz+EQ+8PdSzwW2g/hNThYK8G7gGNshVPZ115bv3/n+Q8pNfqTUdpYgi0D+Bc9A/hjDtrP4hABXSj3n2w7iJzlXKO4+8G+xnUNlX/cR5fsb37G6hqjk284SBgUpqs5/hjYdpH+F90o959oO4Rc5VSgJSa4DPmI7h8q+ni1lBxrftnpJLizuuJCqB1h56i52287hMx+Qes62HcIPcqZQ3O17PwkU2M6isqv30NKmPZeuqSYiev1EFmxtZu3qTpps5/ARAeqknjNtB7EtZwoFuAzQaw9Cru+Qkpbdl61dpGWSPQJy1gsU63jKKwjwYanP7dU2cqJQEpI8EnRGRtj1ry1ubfibdeU6ZpJ9+WnKz9lOs+0cPiPAx6Se02wHsSX0heLubfIx0LV4wmxgZVF7w3vWlRIVvUh1gazoYc3mZvbazuEzAnxC6nmN7SA2hL5QgL8FXS4hzIZqCjp3vW99kcmLFNnOkmtO3cXikmH6bOfwmQjwSannJNtBFlqoCyUhydOA023nUNkzVhwd3Pn+QyK5uimWbVFD0XnP0aNTiV8lClwl9WyxHWQhhbZQEpJcjHN0okLKREm99MEN3enCaIXtLLmsapAVx+5lj+0cPpSHc0X9IttBFkpoCwWnTPTq6BBruHzdvtFF+bm8Xa1vHLOPmuIR+m3n8KFFwKeknpy4HiqUhZKQ5Ik4y02rkGo6p2Z3/4bSNbZzKEfUUPj67brW1xS2AlfYDrEQQlcoCUkWAu+3nUNlT8+msqa2U6tX286hXmlZH6vWdnDAdg6fOj8XphOHrlCAS9BZXaE1UhnrbbxkdTkiYfzbDbwzXqQwkiZlO4dPfVjqw72vTKj+UyYkuRp4k+0cKjvSURnb+YFDhnRGl38VpKg6uYFG2zl8qgD4jNRTYjtItoSqUID34cysUCG09+2r9o2Vx5bYzqGmt7WZleVDdNvO4VPLcS58DOWF1qEpFHcgfpvtHCo7eg8rberZWq6D8AEQgdjrt+vFjtM4Hnib7RDZEIpCSUgyD3iP7RwqO1L5kZHGd6wuRiSUz+rCqHqAlau6dK2vaVwi9eHb5C8UhQK8HtDrEUJqz6VrmtIF0XLbOdTsxF8K52kdj4yvThyq5YICXygJSeYT0sNH5ey62H+oXm8SRKUjLN3UootHTmMx8E7bIbwU+EIB3gC5s7RBLhkrjg7ufcsqPTIJsJN2UyKGtO0cPnae1HOI7RBeCXShuBcxvtV2DpUdu9+1tt3kR3T5nAArHKOq9oAepUwjAvy/sMz6CnShAOcBujBgCHUcV7V3cG3xKts51Pwdv5cKvdhxWocC59oO4YXAFkpCkkXARbZzKO+l8iMjB85bXmk7h/JGfoqKo/azz3YOn7ssDKsSB7ZQcLb0LbMdQnmv6Y3L9uuprnDZtp8y3TNlWsXAlbZDzFcgCyUhyWLgQts5lPdGy/P6Oo+tWmk7h/JWQYqqQ9vYbzuHz50a9GtTAlkowJkQ3vVwctnei1d1EJGY7RzKeyc06rJIM/ABqSffdoi5ClyhJCQpOIPxKmQGVhe19a8v0WXpQ6psmJrl3bTYzuFzywnwdXWBKxTgOJwfugqZvRevGtPlVcLtpN2M2s4QABdJPUtth5iLIBaKLk8fQp1HV+4bWVygy+eE3JJ+VlQM0mU7h8/lEdDr6wJVKAlJrkJXFA4dEyV14LzlOiaWAwTkpN26tP0MnCn1BG6rhkAVCs7RiZ4SCZnWU5fsTRdF9bqTHLG6i2XRtJ76Oog84GLbIWYrMIWSkGQJcIbtHMpbBkzba6p1va4cEjUUbG2myXaOADhT6oO1nXlgCgV4HVBoO4TyVve2iv3p4miV7RxqYdU2oVPDDy5wYylBKxQVMs1n1ei1CTmofIiaikEdS5mBM6U+OOsVBqJQ3MH40CzxrBz964tbRqvya2znUAtPQI7ep7O9ZiCfAF13F4hCAU63HUB578Ablo/YzqDs2dDOIl3fa0beGJSr57VQlBXD1fldQysLdc2uHBZLU7auQwfnZ6AMZ7kp3/N9oSQkeRh6ZXzoHDh3ebdeFa+OOsCY7QwBcWEQNuHyfaGgRyehM1YcHew7rHSF7RzKvqV9LBGjm2/NwHKcZad8zdeF4i4EeartHMpb7SctbtUVhRVA1FC4tpNW2zkCIm47wMH4ulCAIwG9RiFkuo6t1OuJ1Ms2tzBsO0NAnCD1FNgOMR2/F8pJtgMobw0vzu8ercwP5EqqKjtWdrNYZ3vNSCFwgu0Q0/F7oQR69zL1au2nLO60nUH5SyxN6bJe2mznCAhfjyn7tlASklyGzu4Kne4jKwJz1a9aOFub6bedISCOkXr/7lbr20JBj05CZ2B1UVuqOE/HxNSrrOkKzvIilsXw8VCAFopaMG2nVuuzUDWpwjGqSobps50jIE6zHWAqviyUhCTzcGZ4qZAwYHoPK1tsO4fyr/UdurbXDB3p1wUjfVkowGagyHYI5Z3eTWXNJj9SajuH8q+1naRtZwiIKHCK7RCT8WuhHG07gPJW97aKIdsZlL8t7UM3Wps5X5720kJRC6L/kBI9OlHTKkhRqeMoM7bVj7s5+q5QEpKMAett51DeGSuODo6V5un4iTqotZ3odUozI/jwibfvCgWnTHQXvxDp2VLeoSsLq5lYp+Mos7HFdoCJ/Fgoh9oOoLzVU1uuS5SrGdFxlFnZbDvARFooKusG1hT7coqj8p+CMSrzUuhOnjOzSurx1dikForKqpHKWG+6KFppO4cKBgFZ0qfXo8yQ4LOjFF8VSkKShcBq2zmUd3oOL9dBVjUry3sZtJ0hQHw1juKrQgE2gv+3uVQz17O1XH+falZqevUxYBb0CGUaerorZIZrCnx1jlf536IBdAO2mTtM6v3zOO6bIK51tgMo75goqVRRVGftqFkpGaFSN9yasUJ89Ljpt0LR/U9CZKimsAuRqO0cKlgikF85SLftHAHim9NeWigqawbWFOvgqpqTmj5dgmUWfDMw75tCcWd46fTSEBlYU6wXNKo5qRokZTtDgGihTGKZ7QDKW0PLCmO2M6hgqtBj29mokXrybYcAfxWKnu4KmdGqWJntDCqYyoZ1Pb9Z8sXiq34qFD1CCZFUfmQkXaAzvNTcFI/q1OFZ8sVS9looKisGVxbpLB01ZwVjlNjOEDBaKBPoKa8QGV5WoDs0qjmLGgrzx9C/oZnTQplgke0AyjujFTHd10LNS8UQA7YzBIgWygQ6gBsio+UxvdJZzUv5kB6hzMIS2wHAX4Wiaz6FyFhZnl4hr+alaFR3b5wFneU1LiHJfPDHPGrljbGSPF/8bangKhzTQpkFPULJoEcnIZMqiuoTBDUvhXqEMhtlfri4UQtFZUW6MFJgO4MKtoIx3RdllqwPzGuhqKxIxyJFtjOoYCvQ1bxmSwvFpTO8QiSVHxkhIrqOl5qXgjF0YsfsWF+Zwi+FokcoIZIqiep0TzVv+SktlFmy/vPyywJsOfFsNsUY/8r5p5WyeOgTXP8n23myJR2L6MmKKYyO9EVuShx/cjo1EkmnU5E1h52z/7QLv/2C7Vx+lJfyzRNex57nC/mXS46mt70ABM68ZDdXfnGX7VgZrD+eWw/g8tcfTpbcxjcPWczq3mEGQl2gJqJjqVPJixWn3/yBBx4sKKpKpcaG5GffPPqUPdt/3bJm0xu6bGfzG8Fng/J5McPf/uezHHNGNz3tUd5z1GmcfH4rW0/0y2Zg1o9Q/PJAbv0HkW3NNBRu58GlJ3PRHttZss1ERa+Sn4JIhIKiqhRAamw4YtJjgvjrcdMvxPisUFZsGOaYM5xFT8sXp1i+vo+mXX5aFdn6AYL1AC6/FFvW3MDnD7+Qjz83SK9ffubZE9HrB6aTTo1yw9e2nNbfs69k45Fvb1hz2Dl6dDIJ3x2hZNqVLKJxeznHnuWn3531J+ahfyD3g4e4ZWkJlSObODEnlnQ3ET1CmU4kGuOST774h0uvavhd24EnK5t236+zHCfnz0Lp7YzyTxcdxxX/8gwV1X7a5tr647n1AK5QPwC9xGOLdvBIzaeJv+5HXH1MI88sTvCBo23nyhZJGX8+EPhMcWnN2LK1J7XtevYWXyyb4UP+e1wYGRKuOvs4XvPmfbzp/U2240xgfTKMX06/hPoUyaV8/vlL4XmAJ/jd4rv4wYY6vv247VzZImnjlycqvtPX1ZgfyctPF5fWjI0M90YONNy35MhTPvai7Vx+ZPxWKOk0/NNFR7FyYx/v//JO23EmYf1oSQtFeU6PUKbW29VQcM9NVxxt0inBGNZsPnf/5mP/psV2Lj8y4rNCefD2RTz861UsX9/LpRtPA+Cyf3qesy/zy+9Pj1Bc1n8QC2UbZ7Vv46x22zmyym+zc3xk+bpTey/5xI4/2M4RBGm/Fcop53dwj7nddoxpWD9C8cupiUHbAZR3okMpvzxRUQE2Gs2dJ5oesf7z8kuh+OXCIOWBvP6xIozx17NLFTgjWiizNWo7gBaK8pykiErKDNvOoYJtOM9np7z8r8N2AL8USq/tAMpbkZG0LhCp5kULZdZabQfwS6HoEUrIRIbSeoSi5mVQC2U2UkCn7RB+KhT94wmR6EDK+owTFWzDeb55fNG13SAAABdVSURBVAqCDhO3/xjqi19Ynak1wIDtHMo7eX1jWihqXoZi/nh8Cog22wHAJ4Xi0tNeIRLrtT7hRAVcf75vrpMLAi2UCXpsB1DeiXVroaj56S7ET0vD+50WygTWZygo78S6Rq0vpa2Cy4DpLUBXYZ45LZQJDtgOoLxTtG+wxHYGFVxjEfrTEfv7ewSIFsoEflsKWs1DQetwOWmjA/NqToby6LedIWC0UCbQQgkRSRPN6x/LiQ3FlPf6CxixnSFgtFAm0FNeIZPfNqJTwdWc9BToOl6zMIYPLmoEfxVKKz5Yfll5p2j/oO5zo+akq0i3QJiFl/xwUSP4qFDqTG0anekVKsV7BmK2M6hgai8m33aGAHnedoBxvikUl572CpGixkGd9qnmpLmMCtsZAkQLZQr7bAdQ3snvGi1Dl7FXszQaoW8oRrHtHAHynO0A4/xWKC/ZDqC8FesZ7bKdQQVLT4GumjELrSaOb7YU91uhvGA7gPJWUeOg7ouiZqWtVKcMz4JvTneB/wplL6APQCFS8XS3DsyrWWkq00UhZ8E3p7vAZ4XiLmP/ou0cyjtlL/QuwRi9pkDNWFMZumzPzOkRykHssB1AeScyamKxzlGdDq5mJA2jnUVU2s4REMPATtshMmmhqKwr3dGnpzHVjPQV0I7oRY0ztMPE/bWigBaKyrqKZHeR7QwqGPaX6xjqLPjqdBf4sFDqTG0T0Gs7h/JOya7+alJGZ+6og9q5WDfVmoVnbQeYyHeF4vLVzAU1P5ImWtA6rOMoaloGUvsqWGw7R0AMAE/aDjGRXwvlMdsBlLfKtvfqwp9qWr0FtKUi6DTzmXnIxP13vY4WiloQVY926swdNa395QzazhAgf7AdYDK+LJQ6U3sA3XArVAraRyryukZbbOdQ/tWwSMdPZqgXeMJ2iMn4slBcepQSMlWPdeozUDUpA6m9On4yU/f7bbrwOC0UtWAWP9iuV82rSXUV0TIW1fGTGfLl6S7wd6E8he7gGCp5/anigpbhZts5lP+8WO2/AWaf6gCStkNMxbeFUmdqB/HhhTtqfhY93KFHKOoVDJjnl+jprhm6zy/b/U7Gt4Xi+rPtAMpblY911ehFjipTfz6t/QWU2s4REPfZDjAdvxfK/bYDKG9FR9L5xY0DetpLvWznIp0uPENNJs522yGm4+tCcacP66ZbIbP4gfao7QzKP56rodx2hoDw9dEJ+LxQXPfaDqC8VZ7sWR4ZTOnWwIqhPDo7i6mynSMADHCX7RAHE4RCuQ9I2w6hvCMg1fe3ddvOoexrqEL/DmbmURNnn+0QB+P7QqkztZ04U4hViFT/oW2FjKUHbOdQ9hgwj6/UzbRm6GbbAWbC94Xi0tNeIRMZM7GKJ7p1KZYc1lNAS7fuzjgTO0zcv9eeZApKoTwAeuFT2Cz7bdNS0mbUdg5lR3IZw7YzBMQvbAeYqUAUSp2pHQAetZ1DeSuvP1Vc+mLffts51MJLw8hzNSy3nSMAWgjQ5ROBKBTXnbYDKO8tu6OpHGN8e+Wvyo59FTTp2l0z8nMTD86kpCAVymPg/1kOanYKW4arCg8M6VFKjnl8JUW2MwRAG/A72yFmIzCFUmdqDXC77RzKe8t/dUCfqeaQoTw691ewxHaOAPiZiQdrgdzAFIrr9zh7KasQKdk1sLSocWCv7RxqYTy9jB7bGQKgjQCe5g9UodSZ2iGcUlEhs+rGvWWkTaCejanZGxMGnljJCts5AiBwRycQsEJx3Q7+Xb5ZzU1B+0hFebJHx8hCbscSWlMRHYw/iGYCNnYyLnCF4i4YqVOIQ2jlLftq9Or58ErD6J9W69jJDHzLxAnk9VmBKxTXbbYDKO9Fh9KF1fe2tdnOobJjbyX7B/Mptp3D5/5o4sHdByqQhVJnah8HdtrOoby39J6WldGBsU7bOZS3DJgH1+oy9QcxAFxrO8R8BLJQXD+yHUB5T9JEl//ygJ72Cpn2YvbrMvUH9UMTp8N2iPkIbKHUmdo/oZtvhVLlU90r81uHD9jOobxz/3rybWfwuRcJwXV2gS0U1w9tB1DZsfb/dhfr3vPh0FbM/gPlOhg/jTSQMPHgz14NdKG4YynP2s6hvFfQMVKx9K6WJts51PwYMPduoMB2Dp/7lYnzou0QXgh0obj0KCWklta3rilo1nW+gqyllL2tpSy2ncPHOgjRY1jgC6XO1D6N7ugYWuu+11AuY+kh2znU7BlI3b2RMts5fO5aEw/PclKBLxTX9bYDqOyI9YyVLrujSa9NCaDdVezVHRmn9aiJ80fbIbwUikKpM7XP4+zqqEJo8UMdqwr3DurikQGShpF7D9GB+Gm0A/9lO4TXQlEoru+g2wSH1rr/a1gko7osS1A8W6NXxU9jDPiSidNtO4jXQlModaa2hQDtvaxmJ68/Vbzy5n16BX0ADOXR+cA6VtvO4WPXmjjbbYfIhtAUiutnQKvtECo7Kp/sXlnxRFeD7RxqendvZDgdIWo7h0/dZeLcYTtEtoSqUOpM7TBwne0cKntW3bh3rV5F719NZTTuqWKZ7Rw+tRO4xnaIbApVoQDUmdoHgEds51DZISCH/M/OyshwSnf985mUMHTnYSyyncOneoEvmni4x3lDVyiubwPDtkOo7MgbSBWtu65hVJdm8ZcnVtA8kE+J7Rw+ZICvmDjNtoNkWygLxR2g/4ntHCp7ivcNLl55875WjAn8+kdh0B+j9ZHVrLGdw6d+HOQ9TmYjlIXi+gXwvO0QKnuqHu9auejBjt22c+Q6A6nfbkIQxHYWH/oT8FPbIRZKaAulztSmga8CumxHiK24/cC6kp19e2znyGXJZTS2lFFtO4cPNQJfDcMqwjMV2kKBl/efD/QOaOrg1n2vYWVB09A+2zlyUU8BTfevY63tHD60F/iMidNvO8hCCnWhANSZ2juBh23nUNkjKaIbEi/V6HTihZUShm/bSrGe6nqV/cBnTZwu20EWWugLxfVNyL1fbi6JpEzexm++WJ3fPqx7qCyQh9bQ3Fuo+8RPcADnyCTQW/nOVU4USp2p7Qa+YTuHyq7ImIlt+OZLi2KdI6GfnmlbSwl7n16hs7omaME5Mmm3HcSWnCgUgDpT+wjwG9s5VHZFR9L5G7/xYmVez6guwZMloxH6frVFN82aoBX4tInn9tJPOVMormtxlj9QIRYdThds/PqLZXm9o7qPiscMpO7YzMBwjCLbWXykHefIpMV2ENtyqlDqTO0I8AVAl+0IubzBVOHGb7xYHO0fy8lz2dnyyGr2Hqhgqe0cPtKBM2aiE0LIsUKBl6+i/zKQsp1FZVdef6r4sK+8UKwD9d5orGDPY6t0inCGLpwjk/22g/hFzhUKQJ2pfQr4ru0cKvuiQ+nCQ/9rx5LiXf2NtrMEWV8+rb/ezArbOXykGWfMRHcSzZCThQJQZ2p/CdxlO4fKPkkRPeTaXasrH+1ssJ0liMaEgVtqKUxHyLOdxSeeAz6uZfJqOVsormuAHbZDqIWx6uZ962ruaNqDMXq6c4bcdbp6+goos53FJ+7BOc2l47CTkFxfrDUhyWrga0Cl7SxqYfRsKTuw55I1VUSl0HYWv3tgLXue0utNwFmC/kcmnjsLPc5FzhcKQEKSG4AvgU6FzBWDyws7dr1vfSxdENVn3lN4ehkN969nne0cPjAI/JeJ84DtIH6nheJKSPII4HNAzHYWtTDGSqIDu65c3zVcU6iDzRM0VLH7N5t1RhfOIo9fNHF0UscMaKFkSEjyRODT6NhSzjBgms+p2dN2avUqRKK28/hBSwl7bz6ClbroIw/gHJkM2g4SFFooEyQkeSbwEds51MIaWFPU2vDudbF0UTSnx9J6Cmi6YRvVOT6jKw38wMS5yXaQoNFCmURCkm8GrrCdQy2sVH5kZPe71zYPrC9ZbTuLDYN5tP/kaEpG8sjlyQp7gG+YONttBwkiLZQpJCR5OfAW2znUwms7eXFj0xuW1RCVfNtZFspgHh03HkXhYD7FtrNYMgbcCPzMxBmzHSaotFCmkZDk3wLn2s6hFt5wdX5XwxXrRkYr80O/btVgHu0/3UbRUCxny2Q7zlGJbiU9T1ooB5GQ5BXAm23nUAvPgGk7tbqx5ayl1SYvEsoH24EYbT89ipIcXT14CLgeuC2X9n3PJi2UGUhI8u3ApbZzKDvGiqODey9e1dJ3aOkaREIz86k/RuuN2ygbzs0xk8eB/9Yl572lhTJDCUleAFxpO4eyp39dcWvjO1bLWFms2naW+eqP0frTbZTl4AB8L3CdiXO37SBhpIUyCwlJvh74f5Dz8/NzlgHT8rqlja3xJUuDunRLbwHNPzuSihwrEwPci1Mm3bbDhJUWyiwlJHka8HFAL4LLYWMl0YG9b13V2ndoaaAuiGwupfHWw1meQ9eZGOB+4Cc66J59WihzkJDk8cBVkFPP8NQkRipjvfsvWNHRd5j/i2XHYhruOpS1OXIFvAH+iFMkumzKAtFCmaOEJNcD/wgssZ1F2TdSGevdf/6Kjr7DSlcSEV89+zeQfnQVjX9enRNrcxngPuAGLZKFp4UyDwlJVgCfBbbYzqL8YaQi1nfgTcs7ejeXLSci1hcaTcPI3YfS+mI1K21nyTItEh/QQpmnhCTzgDrgTNtZlH+Mluf1HThveXvPlvIaW4P3oxH6bt/KYHNZqI+iDfAHnCLRHRQt00LxSEKSFwJ/g65UrDKkYzLaccKipvaTF+ePVuXXLNT9dhfQdEstZYP5lCzUfS6wFpzdE+8ycQ7YDqMcWigeSkjyWODvILT/idU8DNUUdLacsbSnd0vZkmxdeW/A7Khm9z0bWWMkdE9uBnBmbN0NPKNXt/uPForHEpJciVMqG2xnUf5koqQ6j6k60Paa6uhIdf4yr66+TwlD9Rto37EkVOMlaZyr2u8GHjJxRiznUdPQQskCd1zlMuBC9CJINY2Rylhv53FVnT215bHh6oIlc50hNhCj7dbDiXUXUeF1Rkt24pzSutfE6bQdRs2MFkoWJSS5DfgYsMh2FuV/qfzISM/h5a1dR1emBtYWLzaxyIxOnTZWsOc3m1meigR6++ox4AXgCeBBE6fBbhw1F1ooWZaQZDnODpAn2M6igsOAGVhb3N51TGV/76ay4rGyvEUTL5wcEwbu3UBnQE9xGWA38KT78rSJM2Q3kpovLZQFkpDkG3F2gcyZTZuUd1L5kZGBtcUdvZvKhgbWF8ea1hYO3XqELA/QhlhDOEcgz4+/mDi9diMpr2mhLKCEJNcAHwY22c6iAqsX+N86U1sv9VQCh7gv692Xldibum6ADqDZfWly/20Adpk4aUu51ALRQllgCUkKcA7wbnR6sZqdB4Fr6kxt11Q3kHqiQAVQCVRN8m/m66UHuT8DpHDGNzL/7eTVpdEEtJg4o3P83lQIaKFYkpBkJfBe4DTbWZTvtQHfqTO1f/Tyi0o9eUC5+2ZmYaSAMb3OQ82WFopl7kywDwLLbWdRvjMC3Az8vM7UDtsOo9TBaKH4QEKS+cDFwFsgZ/apUNN7AOeoRLeoVYGhheIjCUkux7kg8jW2syhrGoBr60ztU7aDKDVbWig+lJDkJuByoNZyFLVweoEfAb+uM7U6G0oFkhaKjyUkeRzwLpxpoSqceoBfALfXmVq9sE8FmhaKz7nTjE8CLgXWWI6jvNONM+B+hxaJCgstlIBwi+UUnAUn9cLI4OoCbsI5taUzt1SoaKEEUEKSW3CK5SR0NeOgaANuwSkSXYJdhZIWSoAlJLkMOB84C7CyzayalsFZ+PAO4GEdbFdhp4USAglJlgBnA+dBqPcPD4p+4Pc4RyP7bIdRaqFooYSIO85yBHAGzniLHrUsrJeAXwH36mktlYu0UEIqIckC4GSccjkKHWvJlmbgPuC+OlO703YYpWzSQskBCUkuBuLAa4G1dtOEQgtwP06J7LAdRim/0ELJMe5A/vHAcTinx4K8bexCauMvJbLddpiFICJvx9nCuhZnXGgX8H/At4zlBw4RuRr4LDA+9boR+AdjzE3WQiktlFzmnhY7ir8UTLXdRL4yACRx9jh/ss7U7rGcZ0GJyCeAq4A64LdAH7AN+CRwhTHmVdfQiEjUGJNaoHxXAxuNMe903z4bZ1r2OmNM80JkUK+mhaJelpDkOpyC2ey+5FLBjAHP4UzzfQLYkavTfEWkAtgPXDbdM34R+T4wiHMa9XTgAmAf8C2c8tkHfNoY80v39vXAD40x17lvXw5caYx5jfu2AT4CfBRnn5bvAZ8yxrzq9zCxUNz3tQAXGmMemPt3r+ZDl0pXL6sztQ04q93eCi+PvWzGuTJ/M7AByLcUz0tpYC/OrKyd7st2vXL9ZScBBbh/BwdxCXAuzpT1EuBx4LvA63FWzb5VRI4zxsz0NOGbcY6WS3GmXm8HrpvuE0RE3Az5wLMzvB+VBVooakp1prYdZ9zgfoCEJPNw9i1fB6wAVuHsYb4c//4tDeA8U84sj106rXda1UCbMWZs/B0i8gCwFadozjbG/MH90K3GmPvd22zDKYJ/c48q7haR24F3AFfP8L6/bIzpADpE5L/cz52qUC4WkfNwxgELcY6GptweWWWfXx8ElA/VmdoxYIf78rKEJCPAUpxyWYlTNpU4e5tX4Jy+KMf7qcsG59x+y1Qvdaa2z+P7zAXtQLWI5I2XijHmZAAR2QtEMm7bmPH6CqBxwimq3Th/EzOV+fV2u19zKjdmjKGsA24XkW5jzP/M4v6Uh7RQ1Ly5Yw1N7sufJ7uNe9FlGX8pmVKcv78IEJ3iX3Bm8Qxl/DuAUyJ9QH+ujnNk2YM4P+8LcBaynE7mIOx+YLWIRDJKZQ3wgvt6P1Cccftlk3y91cAzGZ+7fyaBjTENIvJr4E2AFoolWihqQdSZWoOz90cPr3wWqnzGGNMlIp8DrnHHJ36LUwZH4oyTTOVhnMK/SkS+grNaw5twZhGCM9nhIhG5DufI4z04F4Zm+jsReRjnCcdHgK/OJLOIrALOwVk3TVkSOfhNlFK5xhjz78DHcaYON7sv/wN8Cme/+8k+ZwSnQN6Ac93ONTgzxZ53b/I1YMT9Wv+Hs0PlRLfiHOU+gbOMzXemifk2EekTkT7gEZyxvs/N/LtUXtNpw0opX3CnDR9qjHnRdhY1N3qEopRSyhNaKEoppTyhp7yUUkp5Qo9QlFJKeUILRSmllCe0UJRSSnlCC0UppZQntFCUUkp5QgtFKaWUJ7RQlFJKeUILJaRE5O0i8rCI9ItIi/v6B93F/nxBRC4XESMib7OdRSk1f1ooIeTuB/514D9wlgivAT6As/rrpDsuikh0wQL+xbuBDuAyC/etlPKYFkrIuPuBfx74oDHm58aYXuN43BhzqTFm2L3d90XkWyJyh4j0A68VkS0iUi8iXSLyjIicn/F160Xkyoy3LxeRP2a8bUTkwyKyU0TaROQ/RGTKvy8RGd+H/H3A2SIy2d4YSqkA0UIJn9nuB/4FnI2vHgZuA+7E2X3xQ8CPRGTTLO57fD/wY3A2Z7pimtteBjxqjLkJeA64dBb3o5TyIS2U8Jl0P3D3qGNQRE7LuO2txpj73d31MvcDHzHG3A2M7wc+U182xnQYY/YA4/uBT+Uy4Mfu6z9GT3spFXhaKOHz8n7g4+8wxpxsjKl0P2Z9P3AROQVYD9zgvuvHwBEism0W96WU8hktlPDJ3A/8YCbdDzzjfWuAfe7rM90PPPNzp9oP/N2AAE+ISBPO6bbx9yulAkoLJWSMMV0426BeIyJ/LSJlIhJxn/3PdD/wmIjEcbZzHT+KGN8PvFhENuLsBz7R34lIlYisxtkP/KcTbyAihcDFOIPx2zJePgRcknlkpZQKFi2UEPL5fuAXAoPAD4wxTeMvwHeBPOCc2X6/Sil/0A22lCd0P3CllB6hKKWU8oQWilJKKU/oKS+llFKe0CMUpZRSntBCUUop5QktFKWUUp7QQlFKKeUJLRSllFKe0EJRSinlCS0UpZRSnvj/xHsq3PiiQOIAAAAASUVORK5CYII=\n",
1203 |             "text/plain": [
1204 |               "<Figure size 504x504 with 1 Axes>"
1205 |             ]
1206 |           },
1207 |           "metadata": {
1208 |             "tags": []
1209 |           }
1210 |         }
1211 |       ]
1212 |     }
1213 |   ]
1214 | }
1215 | 


--------------------------------------------------------------------------------
/210630_Medium_ColabwithR.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "Medium_ColabwithR.ipynb",
  7 |       "provenance": [],
  8 |       "collapsed_sections": [],
  9 |       "authorship_tag": "ABX9TyMqsBhxGjsK57FPugbyamcU"
 10 |     },
 11 |     "kernelspec": {
 12 |       "name": "ir",
 13 |       "display_name": "R"
 14 |     }
 15 |   },
 16 |   "cells": [
 17 |     {
 18 |       "cell_type": "markdown",
 19 |       "metadata": {
 20 |         "id": "3VcL-Nj-MBVy"
 21 |       },
 22 |       "source": [
 23 |         "# I. Initialization"
 24 |       ]
 25 |     },
 26 |     {
 27 |       "cell_type": "code",
 28 |       "metadata": {
 29 |         "colab": {
 30 |           "base_uri": "https://localhost:8080/",
 31 |           "height": 34
 32 |         },
 33 |         "id": "sGEHnVLpgFdV",
 34 |         "outputId": "d108dabf-f15a-4b47-a1dc-560a626286c4"
 35 |       },
 36 |       "source": [
 37 |         "R.version.string"
 38 |       ],
 39 |       "execution_count": null,
 40 |       "outputs": [
 41 |         {
 42 |           "output_type": "display_data",
 43 |           "data": {
 44 |             "text/plain": [
 45 |               "[1] \"R version 4.1.0 (2021-05-18)\""
 46 |             ],
 47 |             "text/latex": "'R version 4.1.0 (2021-05-18)'",
 48 |             "text/markdown": "'R version 4.1.0 (2021-05-18)'",
 49 |             "text/html": [
 50 |               "'R version 4.1.0 (2021-05-18)'"
 51 |             ]
 52 |           },
 53 |           "metadata": {
 54 |             "tags": []
 55 |           }
 56 |         }
 57 |       ]
 58 |     },
 59 |     {
 60 |       "cell_type": "code",
 61 |       "metadata": {
 62 |         "id": "VNMyewZ4vRko"
 63 |       },
 64 |       "source": [
 65 |         "#https://towardsdatascience.com/how-to-use-r-in-google-colab-b6e02d736497 method 2, runtime is R\n",
 66 |         "#This should work, but it did not for me. More info at the link below. Did not work in full but got close. \n",
 67 |         "#https://towardsdatascience.com/how-to-use-r-in-google-colab-b6e02d736497\n",
 68 |         "#install.packages(\"googledrive\")\n",
 69 |         "#library(\"googledrive\")\n",
 70 |         "#drive_auth(\n",
 71 |         "#  email = gargle::gargle_oauth_email(),\n",
 72 |         "#  path = NULL,\n",
 73 |         "#  scopes = \"https://www.googleapis.com/auth/drive\",\n",
 74 |         "#  cache = gargle::gargle_oauth_cache(),\n",
 75 |         "#  use_oob = gargle::gargle_oob_default(),\n",
 76 |         "#  token = NULL\n",
 77 |         "#)"
 78 |       ],
 79 |       "execution_count": null,
 80 |       "outputs": []
 81 |     },
 82 |     {
 83 |       "cell_type": "code",
 84 |       "metadata": {
 85 |         "colab": {
 86 |           "base_uri": "https://localhost:8080/"
 87 |         },
 88 |         "id": "y99Ep9_hgx2k",
 89 |         "outputId": "277d1cc2-65c7-4fcb-a2ef-5773b30610c2"
 90 |       },
 91 |       "source": [
 92 |         "install.packages(\"googledrive\") #only need to install occasionally\n",
 93 |         "install.packages(\"httpuv\")\n",
 94 |         "library(\"googledrive\")\n",
 95 |         "library(\"httpuv\")"
 96 |       ],
 97 |       "execution_count": null,
 98 |       "outputs": [
 99 |         {
100 |           "output_type": "stream",
101 |           "text": [
102 |             "Installing package into ‘/usr/local/lib/R/site-library’\n",
103 |             "(as ‘lib’ is unspecified)\n",
104 |             "\n",
105 |             "Installing package into ‘/usr/local/lib/R/site-library’\n",
106 |             "(as ‘lib’ is unspecified)\n",
107 |             "\n"
108 |           ],
109 |           "name": "stderr"
110 |         }
111 |       ]
112 |     },
113 |     {
114 |       "cell_type": "code",
115 |       "metadata": {
116 |         "id": "628_cnx5NZe2",
117 |         "colab": {
118 |           "base_uri": "https://localhost:8080/"
119 |         },
120 |         "outputId": "619c7bd9-f465-485b-e34e-ab0e52e3bd2a"
121 |       },
122 |       "source": [
123 |         "if (file.exists(\"/usr/local/lib/python3.7/dist-packages/google/colab/_ipython.py\")) { #may need to update python version occasionally\n",
124 |         "  install.packages(\"R.utils\")\n",
125 |         "  library(\"R.utils\")\n",
126 |         "  library(\"httr\")\n",
127 |         "  my_check <- function() {return(TRUE)}\n",
128 |         "  reassignInPackage(\"is_interactive\", pkgName = \"httr\", my_check) \n",
129 |         "  options(rlang_interactive=TRUE)\n",
130 |         "}"
131 |       ],
132 |       "execution_count": null,
133 |       "outputs": [
134 |         {
135 |           "output_type": "stream",
136 |           "text": [
137 |             "Installing package into ‘/usr/local/lib/R/site-library’\n",
138 |             "(as ‘lib’ is unspecified)\n",
139 |             "\n",
140 |             "Loading required package: R.oo\n",
141 |             "\n",
142 |             "Loading required package: R.methodsS3\n",
143 |             "\n",
144 |             "R.methodsS3 v1.8.1 (2020-08-26 16:20:06 UTC) successfully loaded. See ?R.methodsS3 for help.\n",
145 |             "\n",
146 |             "R.oo v1.24.0 (2020-08-26 16:11:58 UTC) successfully loaded. See ?R.oo for help.\n",
147 |             "\n",
148 |             "\n",
149 |             "Attaching package: ‘R.oo’\n",
150 |             "\n",
151 |             "\n",
152 |             "The following object is masked from ‘package:R.methodsS3’:\n",
153 |             "\n",
154 |             "    throw\n",
155 |             "\n",
156 |             "\n",
157 |             "The following objects are masked from ‘package:methods’:\n",
158 |             "\n",
159 |             "    getClasses, getMethods\n",
160 |             "\n",
161 |             "\n",
162 |             "The following objects are masked from ‘package:base’:\n",
163 |             "\n",
164 |             "    attach, detach, load, save\n",
165 |             "\n",
166 |             "\n",
167 |             "R.utils v2.10.1 (2020-08-26 22:50:31 UTC) successfully loaded. See ?R.utils for help.\n",
168 |             "\n",
169 |             "\n",
170 |             "Attaching package: ‘R.utils’\n",
171 |             "\n",
172 |             "\n",
173 |             "The following object is masked from ‘package:utils’:\n",
174 |             "\n",
175 |             "    timestamp\n",
176 |             "\n",
177 |             "\n",
178 |             "The following objects are masked from ‘package:base’:\n",
179 |             "\n",
180 |             "    cat, commandArgs, getOption, inherits, isOpen, nullfile, parse,\n",
181 |             "    warnings\n",
182 |             "\n",
183 |             "\n"
184 |           ],
185 |           "name": "stderr"
186 |         }
187 |       ]
188 |     },
189 |     {
190 |       "cell_type": "code",
191 |       "metadata": {
192 |         "id": "aKSqnB81a5nH"
193 |       },
194 |       "source": [
195 |         "drive_auth(use_oob = TRUE, cache = FALSE)"
196 |       ],
197 |       "execution_count": null,
198 |       "outputs": []
199 |     },
200 |     {
201 |       "cell_type": "markdown",
202 |       "metadata": {
203 |         "id": "5RO6C0OKLTbC"
204 |       },
205 |       "source": [
206 |         "# II. Method to Download Direct from Web to Colab Session"
207 |       ]
208 |     },
209 |     {
210 |       "cell_type": "code",
211 |       "metadata": {
212 |         "colab": {
213 |           "base_uri": "https://localhost:8080/",
214 |           "height": 34
215 |         },
216 |         "id": "tGGCu4Mp56nq",
217 |         "outputId": "9d4ce75a-09cf-42d2-fba8-bbdb3a29c4f7"
218 |       },
219 |       "source": [
220 |         "getwd()"
221 |       ],
222 |       "execution_count": null,
223 |       "outputs": [
224 |         {
225 |           "output_type": "display_data",
226 |           "data": {
227 |             "text/plain": [
228 |               "[1] \"/content\""
229 |             ],
230 |             "text/latex": "'/content'",
231 |             "text/markdown": "'/content'",
232 |             "text/html": [
233 |               "'/content'"
234 |             ]
235 |           },
236 |           "metadata": {
237 |             "tags": []
238 |           }
239 |         }
240 |       ]
241 |     },
242 |     {
243 |       "cell_type": "markdown",
244 |       "metadata": {
245 |         "id": "RQe3jHzYmMdT"
246 |       },
247 |       "source": [
248 |         "That only gets you part of the way to the problem. Next step is to load your file. Create a session download into your temporary 'content' folder, and proceed from there."
249 |       ]
250 |     },
251 |     {
252 |       "cell_type": "code",
253 |       "metadata": {
254 |         "id": "-45f7m5S3PxM",
255 |         "colab": {
256 |           "base_uri": "https://localhost:8080/"
257 |         },
258 |         "outputId": "a5e07d11-e020-4e93-f6af-8bd87ce930c5"
259 |       },
260 |       "source": [
261 |         "download.file('https://data.cdc.gov/api/views/vba9-s8jp/rows.csv?accessType=DOWNLOAD','/content/dnld_direct_from_web.csv')\n",
262 |         "unzip(zipfile=\"/content/downloaded.zip\", exdir=\".\") "
263 |       ],
264 |       "execution_count": null,
265 |       "outputs": [
266 |         {
267 |           "output_type": "stream",
268 |           "text": [
269 |             "Warning message in unzip(zipfile = \"/content/downloaded.zip\", exdir = \".\"):\n",
270 |             "“error 1 in extracting from zip file”\n"
271 |           ],
272 |           "name": "stderr"
273 |         }
274 |       ]
275 |     },
276 |     {
277 |       "cell_type": "code",
278 |       "metadata": {
279 |         "colab": {
280 |           "base_uri": "https://localhost:8080/",
281 |           "height": 881
282 |         },
283 |         "id": "ARXril-X6fXa",
284 |         "outputId": "b9969961-d823-40ce-ea31-b2dafc5d10f2"
285 |       },
286 |       "source": [
287 |         "library(readr)\n",
288 |         "a <- read.csv('/content/dnld_direct_from_web.csv')\n",
289 |         "head(a)"
290 |       ],
291 |       "execution_count": null,
292 |       "outputs": [
293 |         {
294 |           "output_type": "display_data",
295 |           "data": {
296 |             "text/plain": [
297 |               "  YearStart YearEnd LocationAbbr LocationDesc \n",
298 |               "1 2019      2019    AL           Alabama      \n",
299 |               "2 2019      2019    ID           Idaho        \n",
300 |               "3 2019      2019    IA           Iowa         \n",
301 |               "4 2019      2019    LA           Louisiana    \n",
302 |               "5 2019      2019    ME           Maine        \n",
303 |               "6 2019      2019    MA           Massachusetts\n",
304 |               "  Datasource                              Class                  \n",
305 |               "1 Youth Risk Behavior Surveillance System Sugar Drinks           \n",
306 |               "2 Youth Risk Behavior Surveillance System Sugar Drinks           \n",
307 |               "3 Youth Risk Behavior Surveillance System Obesity / Weight Status\n",
308 |               "4 Youth Risk Behavior Surveillance System Obesity / Weight Status\n",
309 |               "5 Youth Risk Behavior Surveillance System Obesity / Weight Status\n",
310 |               "6 Youth Risk Behavior Surveillance System Obesity / Weight Status\n",
311 |               "  Topic                  \n",
312 |               "1 Sugar Drinks - Behavior\n",
313 |               "2 Sugar Drinks - Behavior\n",
314 |               "3 Obesity / Weight Status\n",
315 |               "4 Obesity / Weight Status\n",
316 |               "5 Obesity / Weight Status\n",
317 |               "6 Obesity / Weight Status\n",
318 |               "  Question                                                                               \n",
319 |               "1 Percent of students in grades 9-12 who drank regular soda/pop at least one time per day\n",
320 |               "2 Percent of students in grades 9-12 who drank regular soda/pop at least one time per day\n",
321 |               "3 Percent of students in grades 9-12 who have obesity                                    \n",
322 |               "4 Percent of students in grades 9-12 who have obesity                                    \n",
323 |               "5 Percent of students in grades 9-12 who have an overweight classification               \n",
324 |               "6 Percent of students in grades 9-12 who have an overweight classification               \n",
325 |               "  Data_Value_Unit Data_Value_Type ⋯ GeoLocation                      ClassID\n",
326 |               "1 NA              Value           ⋯ (32.840571122, -86.631860762)    SD     \n",
327 |               "2 NA              Value           ⋯ (43.682630005, -114.363730042)   SD     \n",
328 |               "3 NA              Value           ⋯ (42.469400913, -93.816490556)    OWS    \n",
329 |               "4 NA              Value           ⋯ (31.312660644, -92.445680071)    OWS    \n",
330 |               "5 NA              Value           ⋯ (45.254228894001, -68.985031336) OWS    \n",
331 |               "6 NA              Value           ⋯ (42.27687047, -72.082690675)     OWS    \n",
332 |               "  TopicID QuestionID DataValueTypeID LocationID StratificationCategory1\n",
333 |               "1 SD1     Q058       VALUE            1         Race/Ethnicity         \n",
334 |               "2 SD1     Q058       VALUE           16         Race/Ethnicity         \n",
335 |               "3 OWS1    Q038       VALUE           19         Grade                  \n",
336 |               "4 OWS1    Q038       VALUE           22         Gender                 \n",
337 |               "5 OWS1    Q039       VALUE           23         Race/Ethnicity         \n",
338 |               "6 OWS1    Q039       VALUE           25         Total                  \n",
339 |               "  Stratification1 StratificationCategoryId1 StratificationID1\n",
340 |               "1 Hispanic        RACE                      RACEHIS          \n",
341 |               "2 Hispanic        RACE                      RACEHIS          \n",
342 |               "3 11th            GRADE                     GRADE11          \n",
343 |               "4 Female          GEN                       FEMALE           \n",
344 |               "5 Hispanic        RACE                      RACEHIS          \n",
345 |               "6 Total           OVR                       OVERALL          "
346 |             ],
347 |             "text/latex": "A data.frame: 6 × 31\n\\begin{tabular}{r|lllllllllllllllllllll}\n  & YearStart & YearEnd & LocationAbbr & LocationDesc & Datasource & Class & Topic & Question & Data\\_Value\\_Unit & Data\\_Value\\_Type & ⋯ & GeoLocation & ClassID & TopicID & QuestionID & DataValueTypeID & LocationID & StratificationCategory1 & Stratification1 & StratificationCategoryId1 & StratificationID1\\\\\n  & <int> & <int> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <lgl> & <chr> & ⋯ & <chr> & <chr> & <chr> & <chr> & <chr> & <int> & <chr> & <chr> & <chr> & <chr>\\\\\n\\hline\n\t1 & 2019 & 2019 & AL & Alabama       & Youth Risk Behavior Surveillance System & Sugar Drinks            & Sugar Drinks - Behavior & Percent of students in grades 9-12 who drank regular soda/pop at least one time per day & NA & Value & ⋯ & (32.840571122, -86.631860762)    & SD  & SD1  & Q058 & VALUE &  1 & Race/Ethnicity & Hispanic & RACE  & RACEHIS\\\\\n\t2 & 2019 & 2019 & ID & Idaho         & Youth Risk Behavior Surveillance System & Sugar Drinks            & Sugar Drinks - Behavior & Percent of students in grades 9-12 who drank regular soda/pop at least one time per day & NA & Value & ⋯ & (43.682630005, -114.363730042)   & SD  & SD1  & Q058 & VALUE & 16 & Race/Ethnicity & Hispanic & RACE  & RACEHIS\\\\\n\t3 & 2019 & 2019 & IA & Iowa          & Youth Risk Behavior Surveillance System & Obesity / Weight Status & Obesity / Weight Status & Percent of students in grades 9-12 who have obesity                                     & NA & Value & ⋯ & (42.469400913, -93.816490556)    & OWS & OWS1 & Q038 & VALUE & 19 & Grade          & 11th     & GRADE & GRADE11\\\\\n\t4 & 2019 & 2019 & LA & Louisiana     & Youth Risk Behavior Surveillance System & Obesity / Weight Status & Obesity / Weight Status & Percent of students in grades 9-12 who have obesity                                     & NA & Value & ⋯ & (31.312660644, -92.445680071)    & OWS & OWS1 & Q038 & VALUE & 22 & Gender         & Female   & GEN   & FEMALE \\\\\n\t5 & 2019 & 2019 & ME & Maine         & Youth Risk Behavior Surveillance System & Obesity / Weight Status & Obesity / Weight Status & Percent of students in grades 9-12 who have an overweight classification                & NA & Value & ⋯ & (45.254228894001, -68.985031336) & OWS & OWS1 & Q039 & VALUE & 23 & Race/Ethnicity & Hispanic & RACE  & RACEHIS\\\\\n\t6 & 2019 & 2019 & MA & Massachusetts & Youth Risk Behavior Surveillance System & Obesity / Weight Status & Obesity / Weight Status & Percent of students in grades 9-12 who have an overweight classification                & NA & Value & ⋯ & (42.27687047, -72.082690675)     & OWS & OWS1 & Q039 & VALUE & 25 & Total          & Total    & OVR   & OVERALL\\\\\n\\end{tabular}\n",
348 |             "text/markdown": "\nA data.frame: 6 × 31\n\n| <!--/--> | YearStart &lt;int&gt; | YearEnd &lt;int&gt; | LocationAbbr &lt;chr&gt; | LocationDesc &lt;chr&gt; | Datasource &lt;chr&gt; | Class &lt;chr&gt; | Topic &lt;chr&gt; | Question &lt;chr&gt; | Data_Value_Unit &lt;lgl&gt; | Data_Value_Type &lt;chr&gt; | ⋯ ⋯ | GeoLocation &lt;chr&gt; | ClassID &lt;chr&gt; | TopicID &lt;chr&gt; | QuestionID &lt;chr&gt; | DataValueTypeID &lt;chr&gt; | LocationID &lt;int&gt; | StratificationCategory1 &lt;chr&gt; | Stratification1 &lt;chr&gt; | StratificationCategoryId1 &lt;chr&gt; | StratificationID1 &lt;chr&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 2019 | 2019 | AL | Alabama       | Youth Risk Behavior Surveillance System | Sugar Drinks            | Sugar Drinks - Behavior | Percent of students in grades 9-12 who drank regular soda/pop at least one time per day | NA | Value | ⋯ | (32.840571122, -86.631860762)    | SD  | SD1  | Q058 | VALUE |  1 | Race/Ethnicity | Hispanic | RACE  | RACEHIS |\n| 2 | 2019 | 2019 | ID | Idaho         | Youth Risk Behavior Surveillance System | Sugar Drinks            | Sugar Drinks - Behavior | Percent of students in grades 9-12 who drank regular soda/pop at least one time per day | NA | Value | ⋯ | (43.682630005, -114.363730042)   | SD  | SD1  | Q058 | VALUE | 16 | Race/Ethnicity | Hispanic | RACE  | RACEHIS |\n| 3 | 2019 | 2019 | IA | Iowa          | Youth Risk Behavior Surveillance System | Obesity / Weight Status | Obesity / Weight Status | Percent of students in grades 9-12 who have obesity                                     | NA | Value | ⋯ | (42.469400913, -93.816490556)    | OWS | OWS1 | Q038 | VALUE | 19 | Grade          | 11th     | GRADE | GRADE11 |\n| 4 | 2019 | 2019 | LA | Louisiana     | Youth Risk Behavior Surveillance System | Obesity / Weight Status | Obesity / Weight Status | Percent of students in grades 9-12 who have obesity                                     | NA | Value | ⋯ | (31.312660644, -92.445680071)    | OWS | OWS1 | Q038 | VALUE | 22 | Gender         | Female   | GEN   | FEMALE  |\n| 5 | 2019 | 2019 | ME | Maine         | Youth Risk Behavior Surveillance System | Obesity / Weight Status | Obesity / Weight Status | Percent of students in grades 9-12 who have an overweight classification                | NA | Value | ⋯ | (45.254228894001, -68.985031336) | OWS | OWS1 | Q039 | VALUE | 23 | Race/Ethnicity | Hispanic | RACE  | RACEHIS |\n| 6 | 2019 | 2019 | MA | Massachusetts | Youth Risk Behavior Surveillance System | Obesity / Weight Status | Obesity / Weight Status | Percent of students in grades 9-12 who have an overweight classification                | NA | Value | ⋯ | (42.27687047, -72.082690675)     | OWS | OWS1 | Q039 | VALUE | 25 | Total          | Total    | OVR   | OVERALL |\n\n",
349 |             "text/html": [
350 |               "<table class=\"dataframe\">\n",
351 |               "<caption>A data.frame: 6 × 31</caption>\n",
352 |               "<thead>\n",
353 |               "\t<tr><th></th><th scope=col>YearStart</th><th scope=col>YearEnd</th><th scope=col>LocationAbbr</th><th scope=col>LocationDesc</th><th scope=col>Datasource</th><th scope=col>Class</th><th scope=col>Topic</th><th scope=col>Question</th><th scope=col>Data_Value_Unit</th><th scope=col>Data_Value_Type</th><th scope=col>⋯</th><th scope=col>GeoLocation</th><th scope=col>ClassID</th><th scope=col>TopicID</th><th scope=col>QuestionID</th><th scope=col>DataValueTypeID</th><th scope=col>LocationID</th><th scope=col>StratificationCategory1</th><th scope=col>Stratification1</th><th scope=col>StratificationCategoryId1</th><th scope=col>StratificationID1</th></tr>\n",
354 |               "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
355 |               "</thead>\n",
356 |               "<tbody>\n",
357 |               "\t<tr><th scope=row>1</th><td>2019</td><td>2019</td><td>AL</td><td>Alabama      </td><td>Youth Risk Behavior Surveillance System</td><td>Sugar Drinks           </td><td>Sugar Drinks - Behavior</td><td>Percent of students in grades 9-12 who drank regular soda/pop at least one time per day</td><td>NA</td><td>Value</td><td>⋯</td><td>(32.840571122, -86.631860762)   </td><td>SD </td><td>SD1 </td><td>Q058</td><td>VALUE</td><td> 1</td><td>Race/Ethnicity</td><td>Hispanic</td><td>RACE </td><td>RACEHIS</td></tr>\n",
358 |               "\t<tr><th scope=row>2</th><td>2019</td><td>2019</td><td>ID</td><td>Idaho        </td><td>Youth Risk Behavior Surveillance System</td><td>Sugar Drinks           </td><td>Sugar Drinks - Behavior</td><td>Percent of students in grades 9-12 who drank regular soda/pop at least one time per day</td><td>NA</td><td>Value</td><td>⋯</td><td>(43.682630005, -114.363730042)  </td><td>SD </td><td>SD1 </td><td>Q058</td><td>VALUE</td><td>16</td><td>Race/Ethnicity</td><td>Hispanic</td><td>RACE </td><td>RACEHIS</td></tr>\n",
359 |               "\t<tr><th scope=row>3</th><td>2019</td><td>2019</td><td>IA</td><td>Iowa         </td><td>Youth Risk Behavior Surveillance System</td><td>Obesity / Weight Status</td><td>Obesity / Weight Status</td><td>Percent of students in grades 9-12 who have obesity                                    </td><td>NA</td><td>Value</td><td>⋯</td><td>(42.469400913, -93.816490556)   </td><td>OWS</td><td>OWS1</td><td>Q038</td><td>VALUE</td><td>19</td><td>Grade         </td><td>11th    </td><td>GRADE</td><td>GRADE11</td></tr>\n",
360 |               "\t<tr><th scope=row>4</th><td>2019</td><td>2019</td><td>LA</td><td>Louisiana    </td><td>Youth Risk Behavior Surveillance System</td><td>Obesity / Weight Status</td><td>Obesity / Weight Status</td><td>Percent of students in grades 9-12 who have obesity                                    </td><td>NA</td><td>Value</td><td>⋯</td><td>(31.312660644, -92.445680071)   </td><td>OWS</td><td>OWS1</td><td>Q038</td><td>VALUE</td><td>22</td><td>Gender        </td><td>Female  </td><td>GEN  </td><td>FEMALE </td></tr>\n",
361 |               "\t<tr><th scope=row>5</th><td>2019</td><td>2019</td><td>ME</td><td>Maine        </td><td>Youth Risk Behavior Surveillance System</td><td>Obesity / Weight Status</td><td>Obesity / Weight Status</td><td>Percent of students in grades 9-12 who have an overweight classification               </td><td>NA</td><td>Value</td><td>⋯</td><td>(45.254228894001, -68.985031336)</td><td>OWS</td><td>OWS1</td><td>Q039</td><td>VALUE</td><td>23</td><td>Race/Ethnicity</td><td>Hispanic</td><td>RACE </td><td>RACEHIS</td></tr>\n",
362 |               "\t<tr><th scope=row>6</th><td>2019</td><td>2019</td><td>MA</td><td>Massachusetts</td><td>Youth Risk Behavior Surveillance System</td><td>Obesity / Weight Status</td><td>Obesity / Weight Status</td><td>Percent of students in grades 9-12 who have an overweight classification               </td><td>NA</td><td>Value</td><td>⋯</td><td>(42.27687047, -72.082690675)    </td><td>OWS</td><td>OWS1</td><td>Q039</td><td>VALUE</td><td>25</td><td>Total         </td><td>Total   </td><td>OVR  </td><td>OVERALL</td></tr>\n",
363 |               "</tbody>\n",
364 |               "</table>\n"
365 |             ]
366 |           },
367 |           "metadata": {
368 |             "tags": []
369 |           }
370 |         }
371 |       ]
372 |     },
373 |     {
374 |       "cell_type": "markdown",
375 |       "metadata": {
376 |         "id": "YDcXvN4hLlSF"
377 |       },
378 |       "source": [
379 |         "# III. Method to Download from Your Google Drive to Colab Session"
380 |       ]
381 |     },
382 |     {
383 |       "cell_type": "code",
384 |       "metadata": {
385 |         "colab": {
386 |           "base_uri": "https://localhost:8080/",
387 |           "height": 966
388 |         },
389 |         "id": "qecatv0iqCyv",
390 |         "outputId": "c480ad68-4c6d-494d-bf7e-cfa0f2debd7f"
391 |       },
392 |       "source": [
393 |         "x <- drive_get(\"~/Folder1/Folder2/Nutrition__Physical_Activity__and_Obesity_-_Youth_Risk_Behavior_Surveillance_System.csv\")\n",
394 |         "drive_download(x)\n",
395 |         "z <- read.csv(\"/content/Nutrition__Physical_Activity__and_Obesity_-_Youth_Risk_Behavior_Surveillance_System.csv\")\n",
396 |         "head(z)"
397 |       ],
398 |       "execution_count": null,
399 |       "outputs": [
400 |         {
401 |           "output_type": "stream",
402 |           "text": [
403 |             "File downloaded:\n",
404 |             "  * Nutrition__Physical_Activity__and_Obesity_-_Youth_Risk_Behavior_Surveillance_System.csv\n",
405 |             "Saved locally as:\n",
406 |             "  * Nutrition__Physical_Activity__and_Obesity_-_Youth_Risk_Behavior_Surveillance_System.csv\n",
407 |             "\n"
408 |           ],
409 |           "name": "stderr"
410 |         },
411 |         {
412 |           "output_type": "display_data",
413 |           "data": {
414 |             "text/plain": [
415 |               "  YearStart YearEnd LocationAbbr LocationDesc \n",
416 |               "1 2019      2019    AL           Alabama      \n",
417 |               "2 2019      2019    ID           Idaho        \n",
418 |               "3 2019      2019    IA           Iowa         \n",
419 |               "4 2019      2019    LA           Louisiana    \n",
420 |               "5 2019      2019    ME           Maine        \n",
421 |               "6 2019      2019    MA           Massachusetts\n",
422 |               "  Datasource                              Class                  \n",
423 |               "1 Youth Risk Behavior Surveillance System Sugar Drinks           \n",
424 |               "2 Youth Risk Behavior Surveillance System Sugar Drinks           \n",
425 |               "3 Youth Risk Behavior Surveillance System Obesity / Weight Status\n",
426 |               "4 Youth Risk Behavior Surveillance System Obesity / Weight Status\n",
427 |               "5 Youth Risk Behavior Surveillance System Obesity / Weight Status\n",
428 |               "6 Youth Risk Behavior Surveillance System Obesity / Weight Status\n",
429 |               "  Topic                  \n",
430 |               "1 Sugar Drinks - Behavior\n",
431 |               "2 Sugar Drinks - Behavior\n",
432 |               "3 Obesity / Weight Status\n",
433 |               "4 Obesity / Weight Status\n",
434 |               "5 Obesity / Weight Status\n",
435 |               "6 Obesity / Weight Status\n",
436 |               "  Question                                                                               \n",
437 |               "1 Percent of students in grades 9-12 who drank regular soda/pop at least one time per day\n",
438 |               "2 Percent of students in grades 9-12 who drank regular soda/pop at least one time per day\n",
439 |               "3 Percent of students in grades 9-12 who have obesity                                    \n",
440 |               "4 Percent of students in grades 9-12 who have obesity                                    \n",
441 |               "5 Percent of students in grades 9-12 who have an overweight classification               \n",
442 |               "6 Percent of students in grades 9-12 who have an overweight classification               \n",
443 |               "  Data_Value_Unit Data_Value_Type ⋯ GeoLocation                      ClassID\n",
444 |               "1 NA              Value           ⋯ (32.840571122, -86.631860762)    SD     \n",
445 |               "2 NA              Value           ⋯ (43.682630005, -114.363730042)   SD     \n",
446 |               "3 NA              Value           ⋯ (42.469400913, -93.816490556)    OWS    \n",
447 |               "4 NA              Value           ⋯ (31.312660644, -92.445680071)    OWS    \n",
448 |               "5 NA              Value           ⋯ (45.254228894001, -68.985031336) OWS    \n",
449 |               "6 NA              Value           ⋯ (42.27687047, -72.082690675)     OWS    \n",
450 |               "  TopicID QuestionID DataValueTypeID LocationID StratificationCategory1\n",
451 |               "1 SD1     Q058       VALUE            1         Race/Ethnicity         \n",
452 |               "2 SD1     Q058       VALUE           16         Race/Ethnicity         \n",
453 |               "3 OWS1    Q038       VALUE           19         Grade                  \n",
454 |               "4 OWS1    Q038       VALUE           22         Gender                 \n",
455 |               "5 OWS1    Q039       VALUE           23         Race/Ethnicity         \n",
456 |               "6 OWS1    Q039       VALUE           25         Total                  \n",
457 |               "  Stratification1 StratificationCategoryId1 StratificationID1\n",
458 |               "1 Hispanic        RACE                      RACEHIS          \n",
459 |               "2 Hispanic        RACE                      RACEHIS          \n",
460 |               "3 11th            GRADE                     GRADE11          \n",
461 |               "4 Female          GEN                       FEMALE           \n",
462 |               "5 Hispanic        RACE                      RACEHIS          \n",
463 |               "6 Total           OVR                       OVERALL          "
464 |             ],
465 |             "text/latex": "A data.frame: 6 × 31\n\\begin{tabular}{r|lllllllllllllllllllll}\n  & YearStart & YearEnd & LocationAbbr & LocationDesc & Datasource & Class & Topic & Question & Data\\_Value\\_Unit & Data\\_Value\\_Type & ⋯ & GeoLocation & ClassID & TopicID & QuestionID & DataValueTypeID & LocationID & StratificationCategory1 & Stratification1 & StratificationCategoryId1 & StratificationID1\\\\\n  & <int> & <int> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <lgl> & <chr> & ⋯ & <chr> & <chr> & <chr> & <chr> & <chr> & <int> & <chr> & <chr> & <chr> & <chr>\\\\\n\\hline\n\t1 & 2019 & 2019 & AL & Alabama       & Youth Risk Behavior Surveillance System & Sugar Drinks            & Sugar Drinks - Behavior & Percent of students in grades 9-12 who drank regular soda/pop at least one time per day & NA & Value & ⋯ & (32.840571122, -86.631860762)    & SD  & SD1  & Q058 & VALUE &  1 & Race/Ethnicity & Hispanic & RACE  & RACEHIS\\\\\n\t2 & 2019 & 2019 & ID & Idaho         & Youth Risk Behavior Surveillance System & Sugar Drinks            & Sugar Drinks - Behavior & Percent of students in grades 9-12 who drank regular soda/pop at least one time per day & NA & Value & ⋯ & (43.682630005, -114.363730042)   & SD  & SD1  & Q058 & VALUE & 16 & Race/Ethnicity & Hispanic & RACE  & RACEHIS\\\\\n\t3 & 2019 & 2019 & IA & Iowa          & Youth Risk Behavior Surveillance System & Obesity / Weight Status & Obesity / Weight Status & Percent of students in grades 9-12 who have obesity                                     & NA & Value & ⋯ & (42.469400913, -93.816490556)    & OWS & OWS1 & Q038 & VALUE & 19 & Grade          & 11th     & GRADE & GRADE11\\\\\n\t4 & 2019 & 2019 & LA & Louisiana     & Youth Risk Behavior Surveillance System & Obesity / Weight Status & Obesity / Weight Status & Percent of students in grades 9-12 who have obesity                                     & NA & Value & ⋯ & (31.312660644, -92.445680071)    & OWS & OWS1 & Q038 & VALUE & 22 & Gender         & Female   & GEN   & FEMALE \\\\\n\t5 & 2019 & 2019 & ME & Maine         & Youth Risk Behavior Surveillance System & Obesity / Weight Status & Obesity / Weight Status & Percent of students in grades 9-12 who have an overweight classification                & NA & Value & ⋯ & (45.254228894001, -68.985031336) & OWS & OWS1 & Q039 & VALUE & 23 & Race/Ethnicity & Hispanic & RACE  & RACEHIS\\\\\n\t6 & 2019 & 2019 & MA & Massachusetts & Youth Risk Behavior Surveillance System & Obesity / Weight Status & Obesity / Weight Status & Percent of students in grades 9-12 who have an overweight classification                & NA & Value & ⋯ & (42.27687047, -72.082690675)     & OWS & OWS1 & Q039 & VALUE & 25 & Total          & Total    & OVR   & OVERALL\\\\\n\\end{tabular}\n",
466 |             "text/markdown": "\nA data.frame: 6 × 31\n\n| <!--/--> | YearStart &lt;int&gt; | YearEnd &lt;int&gt; | LocationAbbr &lt;chr&gt; | LocationDesc &lt;chr&gt; | Datasource &lt;chr&gt; | Class &lt;chr&gt; | Topic &lt;chr&gt; | Question &lt;chr&gt; | Data_Value_Unit &lt;lgl&gt; | Data_Value_Type &lt;chr&gt; | ⋯ ⋯ | GeoLocation &lt;chr&gt; | ClassID &lt;chr&gt; | TopicID &lt;chr&gt; | QuestionID &lt;chr&gt; | DataValueTypeID &lt;chr&gt; | LocationID &lt;int&gt; | StratificationCategory1 &lt;chr&gt; | Stratification1 &lt;chr&gt; | StratificationCategoryId1 &lt;chr&gt; | StratificationID1 &lt;chr&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 2019 | 2019 | AL | Alabama       | Youth Risk Behavior Surveillance System | Sugar Drinks            | Sugar Drinks - Behavior | Percent of students in grades 9-12 who drank regular soda/pop at least one time per day | NA | Value | ⋯ | (32.840571122, -86.631860762)    | SD  | SD1  | Q058 | VALUE |  1 | Race/Ethnicity | Hispanic | RACE  | RACEHIS |\n| 2 | 2019 | 2019 | ID | Idaho         | Youth Risk Behavior Surveillance System | Sugar Drinks            | Sugar Drinks - Behavior | Percent of students in grades 9-12 who drank regular soda/pop at least one time per day | NA | Value | ⋯ | (43.682630005, -114.363730042)   | SD  | SD1  | Q058 | VALUE | 16 | Race/Ethnicity | Hispanic | RACE  | RACEHIS |\n| 3 | 2019 | 2019 | IA | Iowa          | Youth Risk Behavior Surveillance System | Obesity / Weight Status | Obesity / Weight Status | Percent of students in grades 9-12 who have obesity                                     | NA | Value | ⋯ | (42.469400913, -93.816490556)    | OWS | OWS1 | Q038 | VALUE | 19 | Grade          | 11th     | GRADE | GRADE11 |\n| 4 | 2019 | 2019 | LA | Louisiana     | Youth Risk Behavior Surveillance System | Obesity / Weight Status | Obesity / Weight Status | Percent of students in grades 9-12 who have obesity                                     | NA | Value | ⋯ | (31.312660644, -92.445680071)    | OWS | OWS1 | Q038 | VALUE | 22 | Gender         | Female   | GEN   | FEMALE  |\n| 5 | 2019 | 2019 | ME | Maine         | Youth Risk Behavior Surveillance System | Obesity / Weight Status | Obesity / Weight Status | Percent of students in grades 9-12 who have an overweight classification                | NA | Value | ⋯ | (45.254228894001, -68.985031336) | OWS | OWS1 | Q039 | VALUE | 23 | Race/Ethnicity | Hispanic | RACE  | RACEHIS |\n| 6 | 2019 | 2019 | MA | Massachusetts | Youth Risk Behavior Surveillance System | Obesity / Weight Status | Obesity / Weight Status | Percent of students in grades 9-12 who have an overweight classification                | NA | Value | ⋯ | (42.27687047, -72.082690675)     | OWS | OWS1 | Q039 | VALUE | 25 | Total          | Total    | OVR   | OVERALL |\n\n",
467 |             "text/html": [
468 |               "<table class=\"dataframe\">\n",
469 |               "<caption>A data.frame: 6 × 31</caption>\n",
470 |               "<thead>\n",
471 |               "\t<tr><th></th><th scope=col>YearStart</th><th scope=col>YearEnd</th><th scope=col>LocationAbbr</th><th scope=col>LocationDesc</th><th scope=col>Datasource</th><th scope=col>Class</th><th scope=col>Topic</th><th scope=col>Question</th><th scope=col>Data_Value_Unit</th><th scope=col>Data_Value_Type</th><th scope=col>⋯</th><th scope=col>GeoLocation</th><th scope=col>ClassID</th><th scope=col>TopicID</th><th scope=col>QuestionID</th><th scope=col>DataValueTypeID</th><th scope=col>LocationID</th><th scope=col>StratificationCategory1</th><th scope=col>Stratification1</th><th scope=col>StratificationCategoryId1</th><th scope=col>StratificationID1</th></tr>\n",
472 |               "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
473 |               "</thead>\n",
474 |               "<tbody>\n",
475 |               "\t<tr><th scope=row>1</th><td>2019</td><td>2019</td><td>AL</td><td>Alabama      </td><td>Youth Risk Behavior Surveillance System</td><td>Sugar Drinks           </td><td>Sugar Drinks - Behavior</td><td>Percent of students in grades 9-12 who drank regular soda/pop at least one time per day</td><td>NA</td><td>Value</td><td>⋯</td><td>(32.840571122, -86.631860762)   </td><td>SD </td><td>SD1 </td><td>Q058</td><td>VALUE</td><td> 1</td><td>Race/Ethnicity</td><td>Hispanic</td><td>RACE </td><td>RACEHIS</td></tr>\n",
476 |               "\t<tr><th scope=row>2</th><td>2019</td><td>2019</td><td>ID</td><td>Idaho        </td><td>Youth Risk Behavior Surveillance System</td><td>Sugar Drinks           </td><td>Sugar Drinks - Behavior</td><td>Percent of students in grades 9-12 who drank regular soda/pop at least one time per day</td><td>NA</td><td>Value</td><td>⋯</td><td>(43.682630005, -114.363730042)  </td><td>SD </td><td>SD1 </td><td>Q058</td><td>VALUE</td><td>16</td><td>Race/Ethnicity</td><td>Hispanic</td><td>RACE </td><td>RACEHIS</td></tr>\n",
477 |               "\t<tr><th scope=row>3</th><td>2019</td><td>2019</td><td>IA</td><td>Iowa         </td><td>Youth Risk Behavior Surveillance System</td><td>Obesity / Weight Status</td><td>Obesity / Weight Status</td><td>Percent of students in grades 9-12 who have obesity                                    </td><td>NA</td><td>Value</td><td>⋯</td><td>(42.469400913, -93.816490556)   </td><td>OWS</td><td>OWS1</td><td>Q038</td><td>VALUE</td><td>19</td><td>Grade         </td><td>11th    </td><td>GRADE</td><td>GRADE11</td></tr>\n",
478 |               "\t<tr><th scope=row>4</th><td>2019</td><td>2019</td><td>LA</td><td>Louisiana    </td><td>Youth Risk Behavior Surveillance System</td><td>Obesity / Weight Status</td><td>Obesity / Weight Status</td><td>Percent of students in grades 9-12 who have obesity                                    </td><td>NA</td><td>Value</td><td>⋯</td><td>(31.312660644, -92.445680071)   </td><td>OWS</td><td>OWS1</td><td>Q038</td><td>VALUE</td><td>22</td><td>Gender        </td><td>Female  </td><td>GEN  </td><td>FEMALE </td></tr>\n",
479 |               "\t<tr><th scope=row>5</th><td>2019</td><td>2019</td><td>ME</td><td>Maine        </td><td>Youth Risk Behavior Surveillance System</td><td>Obesity / Weight Status</td><td>Obesity / Weight Status</td><td>Percent of students in grades 9-12 who have an overweight classification               </td><td>NA</td><td>Value</td><td>⋯</td><td>(45.254228894001, -68.985031336)</td><td>OWS</td><td>OWS1</td><td>Q039</td><td>VALUE</td><td>23</td><td>Race/Ethnicity</td><td>Hispanic</td><td>RACE </td><td>RACEHIS</td></tr>\n",
480 |               "\t<tr><th scope=row>6</th><td>2019</td><td>2019</td><td>MA</td><td>Massachusetts</td><td>Youth Risk Behavior Surveillance System</td><td>Obesity / Weight Status</td><td>Obesity / Weight Status</td><td>Percent of students in grades 9-12 who have an overweight classification               </td><td>NA</td><td>Value</td><td>⋯</td><td>(42.27687047, -72.082690675)    </td><td>OWS</td><td>OWS1</td><td>Q039</td><td>VALUE</td><td>25</td><td>Total         </td><td>Total   </td><td>OVR  </td><td>OVERALL</td></tr>\n",
481 |               "</tbody>\n",
482 |               "</table>\n"
483 |             ]
484 |           },
485 |           "metadata": {
486 |             "tags": []
487 |           }
488 |         }
489 |       ]
490 |     }
491 |   ]
492 | }


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/README.md:
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1 | # medium_tutorials
2 | ## reference files to support posts authored at https://medium.com/@k8wall
3 | 


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