Updated the README.md, added notebook and visualizations

.gitignore

@@ -1,5 +1,7 @@
 .DS_Store
 
+environ.yaml
+
 # ignore data folder by default
 data/
 

analysis/db/mitch/README.md

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+-What does this figure show?
+
+This figure shows the changes in NO2 concentrations for Milan, Italy and Wuhan, China.
+
+-Why did you choose to look at the values shown in the figure?
+
+Largely because NO2 is a commonly used substance to measure air quality and it was
+the most available data I had out of my dataset.
+
+-Why should someone care about these results?
+
+Air pollution, especially from gases like nitrogen dioxide, are major sources of respiratory illnesses
+and deaths in numerous cities. Although rooted in unfortunate circumstances, having cleaner air in cities will 
+help save lives. Levels of air quality can also be an indicator of economic activity and how different governmets
+reacted to the pandemic.
+
+-What are the limitations/caveats to your data analysis?
+
+	-What decisions did you make in the data processing that could under/overestimate your results
+
+	I did not go back very far in my start date for my visualizations. In order to get a fuller view of 
+	air quality changes from COVID-19, I should have at least started a full year back to accomodate other 
+	potential factors such as seasonal changes.
+
+	-What external factors could affect your results?
+
+	Recent NO2 changes may not have been solely affected by COVID-19 quarantines. There could be countless of other variables
+	that play a role in air quality. For example, there may be areas that have a high concentration of essential businesses 
+	that require fossil fuel consumption and emission.
+
+	-Another way to think about this is this: if you were to present your findings to the public and people are going to take your
+	word to make some actionable decision, what would you tell them could skew the results and affect the decision making?
+
+	I only measured the concentrations of one substance. There are many other pollutants to measure such as SO2, CO, CO2, and particulate matter.
+

analysis/db/somya/riskFactorsCount.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import pandas as pd\n",
+    "from nltk.stem import PorterStemmer\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pyprojroot import here\n",
+    "\n",
+    "# load the tab separated file\n",
+    "df = pd.read_csv(here(\"./data/db/final/kaggle/paper_text/comm_use_subset_pdf_json.tsv\"), sep=\"\\t\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# defining risk factor keywords\n",
+    "targets = {\"smoke\",  \"diabetes\", \"neonates\", \"pregnancy\",\n",
+    "                \"pregnant\", \"heart\", \"co-infection\", \"coinfection\", \"comorbidity\"}\n",
+    "\n",
+    "\n",
+    "\n",
+    "word_stemmer = PorterStemmer()\n",
+    "map = {}\n",
+    "# convert target terms into their stemmed versions for successfull matching\n",
+    "for term in targets:\n",
+    "    stemmedTerm = word_stemmer.stem(term)\n",
+    "    \n",
+    "    map[stemmedTerm] = 0\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'heart': 1054, 'smoke': 256, 'diabet': 706, 'neonat': 579, 'coinfect': 529, 'pregnant': 496, 'pregnanc': 407, 'co-infect': 835, 'comorbid': 412}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# searching for words in the text\n",
+    "text_data = df['text']\n",
+    "for text in text_data:\n",
+    "    for word in map:\n",
+    "        if word in text:\n",
+    "            map[word] = map[word]+1;\n",
+    "print(map)          "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "to_graph = {\"heart disease\": map['heart'], \"pregnancy\": map['pregnanc'],\n",
+    "            \"diabetes\": map[\"diabet\"], \"smoking\": map[\"smoke\"], \n",
+    "            \"coinfection\": map['coinfect']+map['co-infect']+map['comorbid']}\n",
+    "\n",
+    "keys = to_graph.keys()\n",
+    "values = to_graph.values()\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.bar(keys, values)\n",
+    "plt.title(\"Frequency of Risk Factors\")\n",
+    "plt.xlabel(\"Risk Factors\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}