covid_vaccination_geo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Global Covid-19 Vaccination Data Exploration\n",
    "#### Kendall Dyke\n",
    "___\n",
    "## Geospatial Data Visualization\n",
    "\n",
    "### Choropleth Maps [Visualization Technique]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I used geospatial data visualization, specifically a choropleth map to explore global COVID-19 vaccination progress. A choropleth map is a map that represents patterns using coloration based on certain geographic boundaries. In my example, I am going to use country boundaries to explore how many total vaccinations have been given in each country. Green countries have the most vaccinations per 100 people and red countries have the least.\n",
    "\n",
    "A choropleth map can be used to show univariate data over a geographic region. In order to show higher dimensional data, some amount of interactivity needs to be added to the choropleth map. Additional information can be added via tooltip, interactive elements that allow the user to change which variables is represented, etc.\n",
    "\n",
    "This is not the most effective visualization for showing change over time. Other visualizations such as bar charts, line charts, etc. are better at showing change over time. However, if you want to show a snapshot in time, a choropleth map is great because the viewer can see patterns in which regions have more or less vaccinations.\n",
    "\n",
    "\n",
    "##### Example Choropleth Map\n",
    "![download.png](download.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "### Folium [Visualization Package]\n",
    "\n",
    "[Folium Documentation](https://python-visualization.github.io/folium/)\n",
    "\n",
    "Folium is an open source Python library that specializes in geospatial data visualization. It is easy to install using either pip install or conda install in your Python environment.\n",
    "\n",
    "In general, it is a declarative programming package. Folium has many built in classes (eg. Choropleth() used below) that help the user explain what features should be included on the map without having to tell Folium exactly how to plot the Polygon objects. This being said, Folium itself is somewhat limited in how much a map can be customized. However, it integrates with other packages like brewer (for color map creation) and Vega/VegaLite (for additional annotations on the plot). This allows for more customization, outside of Folium's limitations.\n",
    "\n",
    "Folium has a built-in class specifically for Choropleth maps. This class makes it very easy to specify which values should be included on the map without having to do too much overhead data manipulation. I also considered, Matplotlib, Bokeh, and Plotly. I decided on Folium because it seemed to have the most straight-forward process for creating choropleth maps. Further, I have used Matplotlib, Bokeh and Plotly in the past so I wanted to branch my learning into a new area."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "### Creating my own Choropleth Map [Demonstration]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Geopandas allows for geospatial data structures. It makes data manipulation seamless with other pandas dataframes. We need to install it to injest the country boundary objects (Polygons and MultiPolygons).\n",
    "\n",
    "[GeoPandas Documentation](https://geopandas.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#uncomment if the geopandas package needs to be installed in your environment\n",
    "\n",
    "#!pip install geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "from shapely.geometry import Polygon\n",
    "from shapely.geometry import MultiPolygon\n",
    "from datetime import datetime\n",
    "import folium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Pre-Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Data Source #1 -- COVID-19 Vaccination Data\n",
    "\n",
    "I used a [Kaggle dataset](https://www.kaggle.com/gpreda/covid-world-vaccination-progress) to gather Covid-19 immunization data from many countries. This dataset is created and manged by a Romanian data scientist named [Gabriel Preda](https://www.kaggle.com/gpreda). It gets updated about once a day and gathers each country's data from various goverment websites."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Source: https://www.kaggle.com/gpreda/covid-world-vaccination-progress\n",
    "\n",
    "#load csv data\n",
    "vaccinations = pd.read_csv(\"country_vaccinations.csv\")\n",
    "\n",
    "#drop rows that have no vaccination data for that day\n",
    "vaccinations.dropna(subset=['total_vaccinations',\n",
    "       'people_vaccinated', 'people_fully_vaccinated',\n",
    "       'daily_vaccinations_raw', 'daily_vaccinations',\n",
    "       'total_vaccinations_per_hundred', 'people_vaccinated_per_hundred',\n",
    "       'people_fully_vaccinated_per_hundred', 'daily_vaccinations_per_million',\n",
    "       'vaccines'], how=\"all\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Raw Sample of COVID-19 Vaccination Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>date</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>daily_vaccinations_raw</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>vaccines</th>\n",
       "      <th>source_name</th>\n",
       "      <th>source_website</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2021-02-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oxford/AstraZeneca</td>\n",
       "      <td>Government of Afghanistan</td>\n",
       "      <td>http://www.xinhuanet.com/english/asiapacific/2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2021-02-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Oxford/AstraZeneca</td>\n",
       "      <td>Government of Afghanistan</td>\n",
       "      <td>http://www.xinhuanet.com/english/asiapacific/2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2021-02-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Oxford/AstraZeneca</td>\n",
       "      <td>Government of Afghanistan</td>\n",
       "      <td>http://www.xinhuanet.com/english/asiapacific/2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       country iso_code        date  total_vaccinations  people_vaccinated  \\\n",
       "0  Afghanistan      AFG  2021-02-22                 0.0                0.0   \n",
       "1  Afghanistan      AFG  2021-02-23                 NaN                NaN   \n",
       "2  Afghanistan      AFG  2021-02-24                 NaN                NaN   \n",
       "\n",
       "   people_fully_vaccinated  daily_vaccinations_raw  daily_vaccinations  \\\n",
       "0                      NaN                     NaN                 NaN   \n",
       "1                      NaN                     NaN              1367.0   \n",
       "2                      NaN                     NaN              1367.0   \n",
       "\n",
       "   total_vaccinations_per_hundred  people_vaccinated_per_hundred  \\\n",
       "0                             0.0                            0.0   \n",
       "1                             NaN                            NaN   \n",
       "2                             NaN                            NaN   \n",
       "\n",
       "   people_fully_vaccinated_per_hundred  daily_vaccinations_per_million  \\\n",
       "0                                  NaN                             NaN   \n",
       "1                                  NaN                            35.0   \n",
       "2                                  NaN                            35.0   \n",
       "\n",
       "             vaccines                source_name  \\\n",
       "0  Oxford/AstraZeneca  Government of Afghanistan   \n",
       "1  Oxford/AstraZeneca  Government of Afghanistan   \n",
       "2  Oxford/AstraZeneca  Government of Afghanistan   \n",
       "\n",
       "                                      source_website  \n",
       "0  http://www.xinhuanet.com/english/asiapacific/2...  \n",
       "1  http://www.xinhuanet.com/english/asiapacific/2...  \n",
       "2  http://www.xinhuanet.com/english/asiapacific/2...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccinations.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "The vaccination dataset has a row for each date in which the dataset was updated. We are only interested in the most recent data for each country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter on most recent date for each country\n",
    "vaccinations = vaccinations.join(vaccinations.groupby('country').date.max().rename('MaxDate'), on=\"country\")\n",
    "vaccinations_recent = vaccinations[vaccinations[\"date\"]==vaccinations[\"MaxDate\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few of the ISO codes have a prefix \"OWID_\". We need to remove that prefix in order to properly match up each country with the geographic dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove the ISO code prefix from a few columns\n",
    "vaccinations_recent[\"iso_code\"] = vaccinations_recent[\"iso_code\"].str.replace(\"OWID_\", \"\")\n",
    "vaccinations_recent = vaccinations_recent.set_index(\"iso_code\") # set index to ISO code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>date</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>daily_vaccinations_raw</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>vaccines</th>\n",
       "      <th>source_name</th>\n",
       "      <th>source_website</th>\n",
       "      <th>MaxDate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iso_code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-03-16</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2862.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.0</td>\n",
       "      <td>Oxford/AstraZeneca</td>\n",
       "      <td>Government of Afghanistan</td>\n",
       "      <td>http://www.xinhuanet.com/english/asiapacific/2...</td>\n",
       "      <td>2021-03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALB</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2021-03-17</td>\n",
       "      <td>33369.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1679.0</td>\n",
       "      <td>1.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>583.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://shendetesia.gov.al/covid19-ministria-e...</td>\n",
       "      <td>2021-03-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DZA</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2021-02-19</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3748.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.0</td>\n",
       "      <td>Sputnik V</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://www.echoroukonline.com/%d9%84%d9%82%d8...</td>\n",
       "      <td>2021-02-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country        date  total_vaccinations  people_vaccinated  \\\n",
       "iso_code                                                                   \n",
       "AFG       Afghanistan  2021-03-16             54000.0            54000.0   \n",
       "ALB           Albania  2021-03-17             33369.0                NaN   \n",
       "DZA           Algeria  2021-02-19             75000.0                NaN   \n",
       "\n",
       "          people_fully_vaccinated  daily_vaccinations_raw  daily_vaccinations  \\\n",
       "iso_code                                                                        \n",
       "AFG                           NaN                     NaN              2862.0   \n",
       "ALB                           NaN                     NaN              1679.0   \n",
       "DZA                           NaN                     NaN              3748.0   \n",
       "\n",
       "          total_vaccinations_per_hundred  people_vaccinated_per_hundred  \\\n",
       "iso_code                                                                  \n",
       "AFG                                 0.14                           0.14   \n",
       "ALB                                 1.16                            NaN   \n",
       "DZA                                 0.17                            NaN   \n",
       "\n",
       "          people_fully_vaccinated_per_hundred  daily_vaccinations_per_million  \\\n",
       "iso_code                                                                        \n",
       "AFG                                       NaN                            74.0   \n",
       "ALB                                       NaN                           583.0   \n",
       "DZA                                       NaN                            85.0   \n",
       "\n",
       "                    vaccines                source_name  \\\n",
       "iso_code                                                  \n",
       "AFG       Oxford/AstraZeneca  Government of Afghanistan   \n",
       "ALB          Pfizer/BioNTech         Ministry of Health   \n",
       "DZA                Sputnik V         Ministry of Health   \n",
       "\n",
       "                                             source_website     MaxDate  \n",
       "iso_code                                                                 \n",
       "AFG       http://www.xinhuanet.com/english/asiapacific/2...  2021-03-16  \n",
       "ALB       https://shendetesia.gov.al/covid19-ministria-e...  2021-03-17  \n",
       "DZA       https://www.echoroukonline.com/%d9%84%d9%82%d8...  2021-02-19  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccinations_recent.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the columns have many null values. It's best if we focus on columns with little to no null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                                 0\n",
       "date                                    0\n",
       "total_vaccinations                      0\n",
       "people_vaccinated                      19\n",
       "people_fully_vaccinated                70\n",
       "daily_vaccinations_raw                 67\n",
       "daily_vaccinations                      4\n",
       "total_vaccinations_per_hundred          0\n",
       "people_vaccinated_per_hundred          19\n",
       "people_fully_vaccinated_per_hundred    70\n",
       "daily_vaccinations_per_million          4\n",
       "vaccines                                0\n",
       "source_name                             0\n",
       "source_website                          0\n",
       "MaxDate                                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccinations_recent.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Data Source #2 -- Geographic Data (Country Polygons)\n",
    "    \n",
    "The second data source comes from a [Folium GitHub repository](https://github.com/python-visualization/folium/blob/master/examples/data/world-countries.json). This dataset contains country names along with Shapely Polygon/MultiPolygon objects to aid in plotting the country shapes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Source: https://github.com/python-visualization/folium/blob/master/examples/data/world-countries.json\n",
    "\n",
    "world2 = geopandas.read_file(\"world_countries.json\") #read dataset\n",
    "world2 = world2.set_index('id') #set index to ISO code to match vaccinations_recent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Raw geographic data source sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>POLYGON ((61.21082 35.65007, 62.23065 35.27066...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGO</th>\n",
       "      <td>Angola</td>\n",
       "      <td>MULTIPOLYGON (((16.32653 -5.87747, 16.57318 -6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALB</th>\n",
       "      <td>Albania</td>\n",
       "      <td>POLYGON ((20.59025 41.85540, 20.46317 41.51509...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name                                           geometry\n",
       "id                                                                 \n",
       "AFG  Afghanistan  POLYGON ((61.21082 35.65007, 62.23065 35.27066...\n",
       "AGO       Angola  MULTIPOLYGON (((16.32653 -5.87747, 16.57318 -6...\n",
       "ALB      Albania  POLYGON ((20.59025 41.85540, 20.46317 41.51509..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world2.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't want to plot shapes on countries where we have no vaccination data. To eliminate these countries, we do an inner merge with the COVID-19 vaccination dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge the two datasets together so that they only include countries where we have vaccination data\n",
    "world2 = world2.merge(vaccinations_recent, how = \"inner\", left_index=True, right_index=True)\n",
    "vaccinations_recent = vaccinations_recent.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Choropleth Map with Folim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First step is initializing a Folium map. We want to center it on the global map (location=[0,0]) and initialize the zoom so that we see the whole map.\n",
    "\n",
    "\n",
    "<b> Add plot Title\n",
    "    \n",
    "    \n",
    "Add a title to map using an HTML object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = folium.Map(location=[0, 0], zoom_start=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/61928013/adding-a-title-or-text-to-a-folium-map\n",
    "loc = 'Global COVID-19 Vaccination Data -- Total Vaccinations per 100 People (as of March 21, 2021)'\n",
    "title_html = '''\n",
    "             <h3 align=\"center\" style=\"font-size:16px\"><b>{}</b></h3>\n",
    "             '''.format(loc)   \n",
    "\n",
    "m.get_root().html.add_child(folium.Element(title_html));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Preview\n",
    "\n",
    "Initialized Map in Folium (no annotation yet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=%3C%21DOCTYPE%20html%3E%0A%3Chead%3E%20%20%20%20%0A%20%20%20%20%3Cmeta%20http-equiv%3D%22content-type%22%20content%3D%22text/html%3B%20charset%3DUTF-8%22%20/%3E%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%3Cscript%3E%0A%20%20%20%20%20%20%20%20%20%20%20%20L_NO_TOUCH%20%3D%20false%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20L_DISABLE_3D%20%3D%20false%3B%0A%20%20%20%20%20%20%20%20%3C/script%3E%0A%20%20%20%20%0A%20%20%20%20%3Cstyle%3Ehtml%2C%20body%20%7Bwidth%3A%20100%25%3Bheight%3A%20100%25%3Bmargin%3A%200%3Bpadding%3A%200%3B%7D%3C/style%3E%0A%20%20%20%20%3Cstyle%3E%23map%20%7Bposition%3Aabsolute%3Btop%3A0%3Bbottom%3A0%3Bright%3A0%3Bleft%3A0%3B%7D%3C/style%3E%0A%20%20%20%20%3Cscript%20src%3D%22https%3A//cdn.jsdelivr.net/npm/leaflet%401.6.0/dist/leaflet.js%22%3E%3C/script%3E%0A%20%20%20%20%3Cscript%20src%3D%22https%3A//code.jquery.com/jquery-1.12.4.min.js%22%3E%3C/script%3E%0A%20%20%20%20%3Cscript%20src%3D%22https%3A//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js%22%3E%3C/script%3E%0A%20%20%20%20%3Cscript%20src%3D%22https%3A//cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js%22%3E%3C/script%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//cdn.jsdelivr.net/npm/leaflet%401.6.0/dist/leaflet.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css%22/%3E%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cmeta%20name%3D%22viewport%22%20content%3D%22width%3Ddevice-width%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20initial-scale%3D1.0%2C%20maximum-scale%3D1.0%2C%20user-scalable%3Dno%22%20/%3E%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cstyle%3E%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%23map_8c2f1fe2a77b418d8c659dd98c4e437a%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20position%3A%20relative%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20width%3A%20100.0%25%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20height%3A%20100.0%25%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20left%3A%200.0%25%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20top%3A%200.0%25%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C/style%3E%0A%20%20%20%20%20%20%20%20%0A%3C/head%3E%0A%3Cbody%3E%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%3Ch3%20align%3D%22center%22%20style%3D%22font-size%3A16px%22%3E%3Cb%3EGlobal%20COVID-19%20Vaccination%20Data%20--%20Total%20Vaccinations%20per%20100%20People%20%28as%20of%20March%2021%2C%202021%29%3C/b%3E%3C/h3%3E%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22folium-map%22%20id%3D%22map_8c2f1fe2a77b418d8c659dd98c4e437a%22%20%3E%3C/div%3E%0A%20%20%20%20%20%20%20%20%0A%3C/body%3E%0A%3Cscript%3E%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20map_8c2f1fe2a77b418d8c659dd98c4e437a%20%3D%20L.map%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%22map_8c2f1fe2a77b418d8c659dd98c4e437a%22%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20center%3A%20%5B0.0%2C%200.0%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20crs%3A%20L.CRS.EPSG3857%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20zoom%3A%202%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20zoomControl%3A%20true%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20preferCanvas%3A%20false%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29%3B%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20tile_layer_d818adfdf6484fc19364316ac56e992d%20%3D%20L.tileLayer%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%22https%3A//%7Bs%7D.tile.openstreetmap.org/%7Bz%7D/%7Bx%7D/%7By%7D.png%22%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22attribution%22%3A%20%22Data%20by%20%5Cu0026copy%3B%20%5Cu003ca%20href%3D%5C%22http%3A//openstreetmap.org%5C%22%5Cu003eOpenStreetMap%5Cu003c/a%5Cu003e%2C%20under%20%5Cu003ca%20href%3D%5C%22http%3A//www.openstreetmap.org/copyright%5C%22%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onload=\"this.contentDocument.open();this.contentDocument.write(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f5eb0ed6f28>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Adding Choropleth Map\n",
    "\n",
    "Use the Folium Choropleth() class to plot Choropleth shapes over each country with vaccination data.\n",
    "\n",
    "<b> Total Vaccinations per 100 People\n",
    "    \n",
    "I chose to plot total vaccinations per 100 people because it puts all countries on a more consistent scale. (Countries with bigger populations won't be heavily weighted vs smaller countries.)\n",
    "    \n",
    "    \n",
    "<b> Binning Color Scale\n",
    "    \n",
    "It's required to bin the color scale because the distribution of vaccinations per 100 people is very heavily right skewed. There are many more low values than high values. The default scaling method would result in mostly red on the map. Chaning how the color scale is binned, allows the viewer to see more granularity in the many countries with lower vaccination counts, to date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "choropleth = folium.Choropleth(\n",
    "    geo_data=world2, #geographic data\n",
    "    name=\"choropleth\",\n",
    "    data=vaccinations_recent, # vaccination data\n",
    "    columns=[\"iso_code\", \"total_vaccinations_per_hundred\"],\n",
    "    key_on=\"feature.id\",\n",
    "    fill_color = \"RdYlGn\", #specify red, yellow, green color scheme\n",
    "    fill_opacity=0.8,\n",
    "    line_opacity=.1,\n",
    "    legend_name=\"Total Vaccinations per 100 People\",# legend title (below legend)\n",
    "    bins = [0, 5, 15, 30, 60, vaccinations_recent[\"total_vaccinations_per_hundred\"].max()] # bin color scale\n",
    ").add_to(m)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Tooltip\n",
    "\n",
    "Add tooltip with country name, total vaccination count per 100 people, and available vaccines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://github.com/python-visualization/folium/issues/1074\n",
    "\n",
    "#add tooltip\n",
    "choropleth.geojson.add_child(\n",
    "    folium.features.GeoJsonTooltip(['country','total_vaccinations_per_hundred', 'vaccines'], \n",
    "                                   aliases = [\"Country\", \"Vaccinations per 100 people\", \"Available Vaccines\"])\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Tooltip Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![tooltip.png](tooltip.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Final Plot: View or Save HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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onload=\"this.contentDocument.open();this.contentDocument.write(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f5eb0ed6f28>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Uncomment this line to save to HTML\n",
    "\n",
    "# m.save('index.html')\n",
    "\n",
    "#show plot\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results & Conclusions\n",
    "\n",
    "#### Results\n",
    "The countries that are not included in the vaccination dataset are gray. It appears that most \"gray\" countries are in under-developed countries. The downfall with this dataset is that we don't know whether these countries don't have vaccines or have not published the number of vaccines available in publically available online form.\n",
    "\n",
    "It's also important to note why some countries (like Israel) have over 100 vaccines per 100 people. On the surface, this may seem like a data issue, however this is because many of the vaccines require two doses. In this case, they need 200 vaccines/100 people to be complete.\n",
    "\n",
    "\n",
    "#### Conclusions\n",
    "Folium is great for building Choropleth maps. It makes it very easy to overlay the shapes onto an existing map. However, if you want to customize further, it's going to require expertise on different Python packages like Altair or even some HTML."
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