visualize_covid_19.ipynb

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   "source": [
    "## 1. From epidemic to pandemic\n",
    "<p><img style=\"float: left; margin:5px 20px 5px 1px; width:40%\" src=\"https://www.nps.gov/aboutus/news/images/CDC-coronavirus-image-23311-for-web.jpg?maxwidth=650&autorotate=false\"></p>\n",
    "<p>In December 2019, COVID-19 coronavirus was first identified in the Wuhan region of China. By March 11, 2020, the World Health Organization (WHO) categorized the COVID-19 outbreak as a pandemic. A lot has happened in the months in between with major outbreaks in Iran, South Korea, and Italy. </p>\n",
    "<p>We know that COVID-19 spreads through respiratory droplets, such as through coughing, sneezing, or speaking. But, how quickly did the virus spread across the globe? And, can we see any effect from country-wide policies, like shutdowns and quarantines? </p>\n",
    "<p>Fortunately, organizations around the world have been collecting data so that governments can monitor and learn from this pandemic. Notably, the Johns Hopkins University Center for Systems Science and Engineering created a <a href=\"https://github.com/RamiKrispin/coronavirus\">publicly available data repository</a> to consolidate this data from sources like the WHO, the Centers for Disease Control and Prevention (CDC), and the Ministry of Health from multiple countries.</p>\n",
    "<p>In this notebook, data from the first several weeks of the outbreak will be visualized to see at what point this virus became a global pandemic.</p>\n",
    "<p><em>Please note that information and data regarding COVID-19 is frequently being updated. The data used in this project was pulled on March 17, 2020, and should not be considered to be the most up to date data available.</em></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "institutional-personal",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: 'dplyr'\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:stats':\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m56\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m2\u001b[39m\n",
      "\u001b[36m--\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m--------------------------------------------------------\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[32mdbl\u001b[39m  (1): cum_cases\n",
      "\u001b[34mdate\u001b[39m (1): date\n",
      "\n",
      "\u001b[36mi\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mi\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A spec_tbl_df: 56 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>date</th><th scope=col>cum_cases</th></tr>\n",
       "\t<tr><th scope=col>&lt;date&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>2020-01-22</td><td>   555</td></tr>\n",
       "\t<tr><td>2020-01-23</td><td>   653</td></tr>\n",
       "\t<tr><td>2020-01-24</td><td>   941</td></tr>\n",
       "\t<tr><td>2020-01-25</td><td>  1434</td></tr>\n",
       "\t<tr><td>2020-01-26</td><td>  2118</td></tr>\n",
       "\t<tr><td>2020-01-27</td><td>  2927</td></tr>\n",
       "\t<tr><td>2020-01-28</td><td>  5578</td></tr>\n",
       "\t<tr><td>2020-01-29</td><td>  6166</td></tr>\n",
       "\t<tr><td>2020-01-30</td><td>  8234</td></tr>\n",
       "\t<tr><td>2020-01-31</td><td>  9927</td></tr>\n",
       "\t<tr><td>2020-02-01</td><td> 12038</td></tr>\n",
       "\t<tr><td>2020-02-02</td><td> 16787</td></tr>\n",
       "\t<tr><td>2020-02-03</td><td> 19881</td></tr>\n",
       "\t<tr><td>2020-02-04</td><td> 23892</td></tr>\n",
       "\t<tr><td>2020-02-05</td><td> 27635</td></tr>\n",
       "\t<tr><td>2020-02-06</td><td> 30817</td></tr>\n",
       "\t<tr><td>2020-02-07</td><td> 34391</td></tr>\n",
       "\t<tr><td>2020-02-08</td><td> 37120</td></tr>\n",
       "\t<tr><td>2020-02-09</td><td> 40150</td></tr>\n",
       "\t<tr><td>2020-02-10</td><td> 42762</td></tr>\n",
       "\t<tr><td>2020-02-11</td><td> 44802</td></tr>\n",
       "\t<tr><td>2020-02-12</td><td> 45221</td></tr>\n",
       "\t<tr><td>2020-02-13</td><td> 60368</td></tr>\n",
       "\t<tr><td>2020-02-14</td><td> 66885</td></tr>\n",
       "\t<tr><td>2020-02-15</td><td> 69030</td></tr>\n",
       "\t<tr><td>2020-02-16</td><td> 71224</td></tr>\n",
       "\t<tr><td>2020-02-17</td><td> 73258</td></tr>\n",
       "\t<tr><td>2020-02-18</td><td> 75136</td></tr>\n",
       "\t<tr><td>2020-02-19</td><td> 75639</td></tr>\n",
       "\t<tr><td>2020-02-20</td><td> 76197</td></tr>\n",
       "\t<tr><td>2020-02-21</td><td> 76823</td></tr>\n",
       "\t<tr><td>2020-02-22</td><td> 78579</td></tr>\n",
       "\t<tr><td>2020-02-23</td><td> 78965</td></tr>\n",
       "\t<tr><td>2020-02-24</td><td> 79568</td></tr>\n",
       "\t<tr><td>2020-02-25</td><td> 80413</td></tr>\n",
       "\t<tr><td>2020-02-26</td><td> 81395</td></tr>\n",
       "\t<tr><td>2020-02-27</td><td> 82754</td></tr>\n",
       "\t<tr><td>2020-02-28</td><td> 84120</td></tr>\n",
       "\t<tr><td>2020-02-29</td><td> 86011</td></tr>\n",
       "\t<tr><td>2020-03-01</td><td> 88369</td></tr>\n",
       "\t<tr><td>2020-03-02</td><td> 90306</td></tr>\n",
       "\t<tr><td>2020-03-03</td><td> 92840</td></tr>\n",
       "\t<tr><td>2020-03-04</td><td> 95120</td></tr>\n",
       "\t<tr><td>2020-03-05</td><td> 97882</td></tr>\n",
       "\t<tr><td>2020-03-06</td><td>101784</td></tr>\n",
       "\t<tr><td>2020-03-07</td><td>105821</td></tr>\n",
       "\t<tr><td>2020-03-08</td><td>109795</td></tr>\n",
       "\t<tr><td>2020-03-09</td><td>113561</td></tr>\n",
       "\t<tr><td>2020-03-10</td><td>118592</td></tr>\n",
       "\t<tr><td>2020-03-11</td><td>125865</td></tr>\n",
       "\t<tr><td>2020-03-12</td><td>128343</td></tr>\n",
       "\t<tr><td>2020-03-13</td><td>145193</td></tr>\n",
       "\t<tr><td>2020-03-14</td><td>156097</td></tr>\n",
       "\t<tr><td>2020-03-15</td><td>167449</td></tr>\n",
       "\t<tr><td>2020-03-16</td><td>181531</td></tr>\n",
       "\t<tr><td>2020-03-17</td><td>197146</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A spec\\_tbl\\_df: 56 × 2\n",
       "\\begin{tabular}{ll}\n",
       " date & cum\\_cases\\\\\n",
       " <date> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 2020-01-22 &    555\\\\\n",
       "\t 2020-01-23 &    653\\\\\n",
       "\t 2020-01-24 &    941\\\\\n",
       "\t 2020-01-25 &   1434\\\\\n",
       "\t 2020-01-26 &   2118\\\\\n",
       "\t 2020-01-27 &   2927\\\\\n",
       "\t 2020-01-28 &   5578\\\\\n",
       "\t 2020-01-29 &   6166\\\\\n",
       "\t 2020-01-30 &   8234\\\\\n",
       "\t 2020-01-31 &   9927\\\\\n",
       "\t 2020-02-01 &  12038\\\\\n",
       "\t 2020-02-02 &  16787\\\\\n",
       "\t 2020-02-03 &  19881\\\\\n",
       "\t 2020-02-04 &  23892\\\\\n",
       "\t 2020-02-05 &  27635\\\\\n",
       "\t 2020-02-06 &  30817\\\\\n",
       "\t 2020-02-07 &  34391\\\\\n",
       "\t 2020-02-08 &  37120\\\\\n",
       "\t 2020-02-09 &  40150\\\\\n",
       "\t 2020-02-10 &  42762\\\\\n",
       "\t 2020-02-11 &  44802\\\\\n",
       "\t 2020-02-12 &  45221\\\\\n",
       "\t 2020-02-13 &  60368\\\\\n",
       "\t 2020-02-14 &  66885\\\\\n",
       "\t 2020-02-15 &  69030\\\\\n",
       "\t 2020-02-16 &  71224\\\\\n",
       "\t 2020-02-17 &  73258\\\\\n",
       "\t 2020-02-18 &  75136\\\\\n",
       "\t 2020-02-19 &  75639\\\\\n",
       "\t 2020-02-20 &  76197\\\\\n",
       "\t 2020-02-21 &  76823\\\\\n",
       "\t 2020-02-22 &  78579\\\\\n",
       "\t 2020-02-23 &  78965\\\\\n",
       "\t 2020-02-24 &  79568\\\\\n",
       "\t 2020-02-25 &  80413\\\\\n",
       "\t 2020-02-26 &  81395\\\\\n",
       "\t 2020-02-27 &  82754\\\\\n",
       "\t 2020-02-28 &  84120\\\\\n",
       "\t 2020-02-29 &  86011\\\\\n",
       "\t 2020-03-01 &  88369\\\\\n",
       "\t 2020-03-02 &  90306\\\\\n",
       "\t 2020-03-03 &  92840\\\\\n",
       "\t 2020-03-04 &  95120\\\\\n",
       "\t 2020-03-05 &  97882\\\\\n",
       "\t 2020-03-06 & 101784\\\\\n",
       "\t 2020-03-07 & 105821\\\\\n",
       "\t 2020-03-08 & 109795\\\\\n",
       "\t 2020-03-09 & 113561\\\\\n",
       "\t 2020-03-10 & 118592\\\\\n",
       "\t 2020-03-11 & 125865\\\\\n",
       "\t 2020-03-12 & 128343\\\\\n",
       "\t 2020-03-13 & 145193\\\\\n",
       "\t 2020-03-14 & 156097\\\\\n",
       "\t 2020-03-15 & 167449\\\\\n",
       "\t 2020-03-16 & 181531\\\\\n",
       "\t 2020-03-17 & 197146\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A spec_tbl_df: 56 × 2\n",
       "\n",
       "| date &lt;date&gt; | cum_cases &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| 2020-01-22 |    555 |\n",
       "| 2020-01-23 |    653 |\n",
       "| 2020-01-24 |    941 |\n",
       "| 2020-01-25 |   1434 |\n",
       "| 2020-01-26 |   2118 |\n",
       "| 2020-01-27 |   2927 |\n",
       "| 2020-01-28 |   5578 |\n",
       "| 2020-01-29 |   6166 |\n",
       "| 2020-01-30 |   8234 |\n",
       "| 2020-01-31 |   9927 |\n",
       "| 2020-02-01 |  12038 |\n",
       "| 2020-02-02 |  16787 |\n",
       "| 2020-02-03 |  19881 |\n",
       "| 2020-02-04 |  23892 |\n",
       "| 2020-02-05 |  27635 |\n",
       "| 2020-02-06 |  30817 |\n",
       "| 2020-02-07 |  34391 |\n",
       "| 2020-02-08 |  37120 |\n",
       "| 2020-02-09 |  40150 |\n",
       "| 2020-02-10 |  42762 |\n",
       "| 2020-02-11 |  44802 |\n",
       "| 2020-02-12 |  45221 |\n",
       "| 2020-02-13 |  60368 |\n",
       "| 2020-02-14 |  66885 |\n",
       "| 2020-02-15 |  69030 |\n",
       "| 2020-02-16 |  71224 |\n",
       "| 2020-02-17 |  73258 |\n",
       "| 2020-02-18 |  75136 |\n",
       "| 2020-02-19 |  75639 |\n",
       "| 2020-02-20 |  76197 |\n",
       "| 2020-02-21 |  76823 |\n",
       "| 2020-02-22 |  78579 |\n",
       "| 2020-02-23 |  78965 |\n",
       "| 2020-02-24 |  79568 |\n",
       "| 2020-02-25 |  80413 |\n",
       "| 2020-02-26 |  81395 |\n",
       "| 2020-02-27 |  82754 |\n",
       "| 2020-02-28 |  84120 |\n",
       "| 2020-02-29 |  86011 |\n",
       "| 2020-03-01 |  88369 |\n",
       "| 2020-03-02 |  90306 |\n",
       "| 2020-03-03 |  92840 |\n",
       "| 2020-03-04 |  95120 |\n",
       "| 2020-03-05 |  97882 |\n",
       "| 2020-03-06 | 101784 |\n",
       "| 2020-03-07 | 105821 |\n",
       "| 2020-03-08 | 109795 |\n",
       "| 2020-03-09 | 113561 |\n",
       "| 2020-03-10 | 118592 |\n",
       "| 2020-03-11 | 125865 |\n",
       "| 2020-03-12 | 128343 |\n",
       "| 2020-03-13 | 145193 |\n",
       "| 2020-03-14 | 156097 |\n",
       "| 2020-03-15 | 167449 |\n",
       "| 2020-03-16 | 181531 |\n",
       "| 2020-03-17 | 197146 |\n",
       "\n"
      ],
      "text/plain": [
       "   date       cum_cases\n",
       "1  2020-01-22    555   \n",
       "2  2020-01-23    653   \n",
       "3  2020-01-24    941   \n",
       "4  2020-01-25   1434   \n",
       "5  2020-01-26   2118   \n",
       "6  2020-01-27   2927   \n",
       "7  2020-01-28   5578   \n",
       "8  2020-01-29   6166   \n",
       "9  2020-01-30   8234   \n",
       "10 2020-01-31   9927   \n",
       "11 2020-02-01  12038   \n",
       "12 2020-02-02  16787   \n",
       "13 2020-02-03  19881   \n",
       "14 2020-02-04  23892   \n",
       "15 2020-02-05  27635   \n",
       "16 2020-02-06  30817   \n",
       "17 2020-02-07  34391   \n",
       "18 2020-02-08  37120   \n",
       "19 2020-02-09  40150   \n",
       "20 2020-02-10  42762   \n",
       "21 2020-02-11  44802   \n",
       "22 2020-02-12  45221   \n",
       "23 2020-02-13  60368   \n",
       "24 2020-02-14  66885   \n",
       "25 2020-02-15  69030   \n",
       "26 2020-02-16  71224   \n",
       "27 2020-02-17  73258   \n",
       "28 2020-02-18  75136   \n",
       "29 2020-02-19  75639   \n",
       "30 2020-02-20  76197   \n",
       "31 2020-02-21  76823   \n",
       "32 2020-02-22  78579   \n",
       "33 2020-02-23  78965   \n",
       "34 2020-02-24  79568   \n",
       "35 2020-02-25  80413   \n",
       "36 2020-02-26  81395   \n",
       "37 2020-02-27  82754   \n",
       "38 2020-02-28  84120   \n",
       "39 2020-02-29  86011   \n",
       "40 2020-03-01  88369   \n",
       "41 2020-03-02  90306   \n",
       "42 2020-03-03  92840   \n",
       "43 2020-03-04  95120   \n",
       "44 2020-03-05  97882   \n",
       "45 2020-03-06 101784   \n",
       "46 2020-03-07 105821   \n",
       "47 2020-03-08 109795   \n",
       "48 2020-03-09 113561   \n",
       "49 2020-03-10 118592   \n",
       "50 2020-03-11 125865   \n",
       "51 2020-03-12 128343   \n",
       "52 2020-03-13 145193   \n",
       "53 2020-03-14 156097   \n",
       "54 2020-03-15 167449   \n",
       "55 2020-03-16 181531   \n",
       "56 2020-03-17 197146   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the readr, ggplot2, and dplyr packages\n",
    "library(readr)\n",
    "library(ggplot2)\n",
    "library(dplyr)\n",
    "# library(pkgname)\n",
    "\n",
    "# Read datasets/confirmed_cases_worldwide.csv into confirmed_cases_worldwide\n",
    "confirmed_cases_worldwide <- read_csv(\"confirmed_cases_worldwide.csv\")\n",
    "\n",
    "# See the result\n",
    "confirmed_cases_worldwide"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compound-divorce",
   "metadata": {},
   "source": [
    "## 2. Confirmed cases throughout the world\n",
    "<p>The table above shows the cumulative confirmed cases of COVID-19 worldwide by date. Just reading numbers in a table makes it hard to get a sense of the scale and growth of the outbreak. Let's draw a line plot to visualize the confirmed cases worldwide.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "joint-honolulu",
   "metadata": {},
   "outputs": [
    {
     "data": {
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515b2LeYc+01VSHpMc6JKDqkOTYuTfL5x32TFtNdUhyrEMiqg5JjZ396S28w55p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\n/wFz9hXzTFG+jwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a line plot of cumulative cases vs. date\n",
    "# Label the y-axis\n",
    "ggplot(confirmed_cases_worldwide, aes(date, cum_cases)) +\n",
    "  geom_line() + \n",
    "  labs(y = \"Cumulative confirmed cases\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "breathing-navigation",
   "metadata": {},
   "source": [
    "## 3. China compared to the rest of the world\n",
    "<p>The y-axis in that plot is pretty scary, with the total number of confirmed cases around the world approaching 200,000. Beyond that, some weird things are happening: there is an odd jump in mid February, then the rate of new cases slows down for a while, then speeds up again in March. We need to dig deeper to see what is happening.</p>\n",
    "<p>Early on in the outbreak, the COVID-19 cases were primarily centered in China. Let's plot confirmed COVID-19 cases in China and the rest of the world separately to see if it gives us any insight.</p>\n",
    "<p><em>We'll build on this plot in future tasks. One thing that will be important for the following tasks is that aesthetics will be added within the line geometry of your ggplot, rather than making them global aesthetics.</em></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dominant-funeral",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m112\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m4\u001b[39m\n",
      "\u001b[36m--\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m--------------------------------------------------------\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m  (1): is_china\n",
      "\u001b[32mdbl\u001b[39m  (2): cases, cum_cases\n",
      "\u001b[34mdate\u001b[39m (1): date\n",
      "\n",
      "\u001b[36mi\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mi\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 112\n",
      "Columns: 4\n",
      "$ is_china  \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"China\", \"China\", \"China\", \"China\", \"China\", \"China\", \"China~\n",
      "$ date      \u001b[3m\u001b[90m<date>\u001b[39m\u001b[23m 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26,~\n",
      "$ cases     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 548, 95, 277, 486, 669, 802, 2632, 578, 2054, 1661, 2089, 47~\n",
      "$ cum_cases \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 548, 643, 920, 1406, 2075, 2877, 5509, 6087, 8141, 9802, 118~\n"
     ]
    },
    {
     "data": {
      "image/png": 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heI7JPkdhRu/XUmQrJwgcgsSXZH4dZfZ+q1ZHS09pC4QGS3JL+jcOuvMxGShQtE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\nJwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in datasets/confirmed_cases_china_vs_world.csv\n",
    "confirmed_cases_china_vs_world <- read_csv(\"confirmed_cases_china_vs_world.csv\")\n",
    "\n",
    "# See the result\n",
    "glimpse(confirmed_cases_china_vs_world)\n",
    "# Draw a line plot of cumulative cases vs. date, colored by is_china\n",
    "# Define aesthetics within the line geom\n",
    "plt_cum_confirmed_cases_china_vs_world <- ggplot(confirmed_cases_china_vs_world) +\n",
    "  geom_line(aes(date, cum_cases, color = is_china)) +\n",
    "  ylab(\"Cumulative confirmed cases\")\n",
    "\n",
    "# See the plot\n",
    "plt_cum_confirmed_cases_china_vs_world"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "established-chart",
   "metadata": {},
   "source": [
    "## 4. Let's annotate!\n",
    "<p>Wow! The two lines have very different shapes. In February, the majority of cases were in China. That changed in March when it really became a global outbreak: around March 14, the total number of cases outside China overtook the cases inside China. This was days after the WHO declared a pandemic.</p>\n",
    "<p>There were a couple of other landmark events that happened during the outbreak. For example, the huge jump in the China line on February 13, 2020 wasn't just a bad day regarding the outbreak; China changed the way it reported figures on that day (CT scans were accepted as evidence for COVID-19, rather than only lab tests).</p>\n",
    "<p>By annotating events like this, we can better interpret changes in the plot.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "posted-pulse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vW9qb/ZgEiFkhCp94iZBhBWn/jKIlKxpECRRj262V07NcLqE19ZYyJdeWRu+usl\nIZKkfpFMhSCSkxWJdPgzFNXt2pkKQSQniBRFWH3iK2ssBJGcrEgktc+Z79Yq0rVH5qa/XhIi\nSa62SM5bbvb4fD8SVp/4ytpqNiDSmVU3GyZF2py+zH0/EVaf+MoiUrEkYyL93xTRfngjxXls\nGvcxEiLNg0gdqxbpdTMn0g6Rpsg2FJNJiCQZ+Yt942x2x2OgeZH+15JlsNbZlh6AYe5yP4At\nkdycLTK3RTIUMrJnZ247opcUljK+QbrZLVL7wajKjpEMhSCSm5WJ9FJfs8FQCCK5WZlIm+br\n+O3sikSzoVBSWLNhZSLxPlJKCCJ1rF2kp8a5hj5nNsyxRaSOtYv0unnko+axIYh0wbJI5xZA\ntwSrxoSv/xOydkLGeg3mpr9eUqXNhotEqp/FQyS9EESawI5I3becIqkRVp/4yloKQSQ3Do+W\nF6mRF48bjOZycRe9DUEkvRBEcrOISNtxnCJ1FsmLkU4M7/T+ct80Cn//Mqw+8ZWl2VAsKajZ\nYHOLtOtEkr/SEOl8AvgmtXcXVp/4yiJSsaTbFCl2325wp+embX+/Pjapa4CH1Se+sohULOkm\nRYru5jnObKioa2dGpC1nNlwwK9Kwa9cML65WJDMho70Gc9NfL8les8FPpP77SNcirXXXzkwI\nIk1gR6TBmQ3DXbt24ZKYCV9/s8FMCCJNYEmkLNTf/jYTgkgTrE4kLcLqE19Zmg3FkkKaDS6P\nEGmOsPrEVxaRiiUhkmQo0tPxsOu+nmMkRCqUhEiS6zUbDtdW1LVDpEJJiCS5WrPhV/vtD+8j\nBYeM9xrMTX+9pJCU1YnEG7LRIYg0wepEemqe39seePOYmBtWn/jK2glBpAlWJ1L3huyfxNyw\n+sRX1k4IIk2wOpHOb8gmr4ASVp/4ytppNmxZjkuw9maDGmH1ia8sIhVLQiQJIjlBpIQURFIi\nrD7xlUWkYkkBIjk9QqQ5wuoTX1k7ITQb3CBSNGH1ia+snRBEcoNI0YTVJ76yZkIcHpmb/npJ\niCRBJKUQRJpgjSJ9fWqa3WPq+7HrazZscw7FZFKdzYam921AwvL6g5u+3x+ymuO5qwmE1Se+\nsohULKlSkZrhvG/6v4pckOtq8ZOXNu9bRefaIVKhpEpFulpyqxlciFtef+Ts7/O/JMLqE19Z\nRCqWVL1Ixx25bmnVtOX16xfJSAjNhgkWEulunOEx0mDB/JEDoojl9cd37V4q+oSskRBEcuP2\nqEizwUOky698mw/DZgPr2kWGIJIbYyKNLVO8mxdpet/u6ldfWNcuJsTlkbnpr5dUr0jHA6VA\nkWa6eYNfJb9/dCasPvGVtdJs2GYdiskk/2ZDHSINu3Yj2y1/kZr7H96uTBJWn/jKIlKxpJ92\niDoAABgDSURBVIpFOjbUTj+63ke6Filg126/X7f5krxft0Mk3aGYTKpZJNH+3olDn4Tl9Ye/\nen3ZNM1T6nkNiKQ7FJNJdYqUixHHfr00zf23xNyw+sRX1koIzYYJVirSfrMU+zfSL4TVJ76y\nVkIQaYKVivTreb9F+pqYG1af+MpaCUGkCdYo0uEY6bmmYyQbIYjkZsKjmxWpfTf2K1274JBt\n3qGYTPJuNqxRpOaJ95FiQhBpwNpF0tgYHQirT3xlEalYkjGRCiNFOnw09kxiblh94iuLSMWS\nEElSv0gmQpy9BnPTXy/JWLOhMKnCuAirT3xljYQg0gSIlEBYfeIrayQEkSZYo0jnXbrNJjE3\nrD7xlTUSgkgTrE6kTdNUeIxEs6FQEs0GiRTmq/ConlOELIi0zTwUk0m+Ik15dJsi7RRWDzoT\nVp/4yiJSsSREktTfbECkQkmIJBmK9FLdMZKFEHevwdz010vyTVmlSC/1NRsshCDSBKsUadP8\neWxe3x8rWkTfQggiTbBKkfZboi/Nj917RYvoWwhBpAnWKtKPtvVd0a4dzYZCSTQbJANhnppv\nr8397hciIVJSytpFag16bHsN9Syib0Ck854dInWsXaTdj/v2T1I0L6m5YfWJrywiFUvyFGnS\no9sVSYuw+sRX1kTIRK/B3PTXS/JMQaQkwuoTX1kTIYg0wQpFkid/19NsMBCCSBMgUhJh9Ymv\nrIkQRJpghSJpElaf+MrSbCiWRLNBgkhOECkhpSvFtEc3K1KFu3aIVCgJkSSI5ASRElLOpZjx\n6GZFOvL6+CU1N6w+8ZW1EDLVazA3/fWSfFLmPLpxkXbvTapJYfWJr6yFEERysnaRajr7u3wI\nIrmY9ejWRfrWsK4dIiWnzHt0syJ1vYbUs1bD6hNfWQvNhk4kmg0dh1Ig0oazvxEpKaUthYdH\nNyuSGmH1ia8sIhVLmhPJxyNEmiOsPvGVRaRiSYgkqV+k8iE0G8bw8uhmRXp/ru7MhuIhkx6Z\nm/56SdMpfh7drEhP9Z0iVDwEkcZYuUhN863MOCpmW3oAFrkrPYClGYh0z1+jCA65bJFoNnSM\nlSI4ZOTmZhmI83r/8q6SG1af+MoWF2m7RaQR1i7S7lt1x0jlRNpupUU5h2IyaSrlbu0iVdhs\nKCTSwKG8QzGZhEiSXM2GsPrEV7ZUyHSrzjclCGtJkyJphIzd3CxXWySl3LD6xFe2UIi3R+am\nv14SIkmG4jw9v6rkhtUnvrJlQvw9Mjf99ZImUrw9ulmRKlyzoUQIIk2nIFKFIhVoNrg8otlw\n4o72txZh9YmvbAmRnNsjRDqBSGqE1Se+sgVEcu/XIdKRO96QZdduNmTi+AiRjiBSjSItHBLQ\nZ5hIicFa0qRI6SHjNzcLC0QGhiDSfAoinWGBSFdIqEfmpr9eEiJJWCAyKCTYI3PTXy/JmRLg\n0a2LVNMCkUs2G2Y8otlwoBWJZgMLRE6EIJJPCiKxQOR0yNyOHSIdQCRFwuoTX9klRZo9QEKk\nlsMhEiIpEVaf+MouGRLeaRhLicZa0qRIqSGum5ulL9LXQ4/h1/Of5Nyw+sRXdsGQKI/MTX+9\nJESS9ER6bJqDQpvkXgMiuVLisZaESBIp0rdm8+Nw4dcm+RPnYfWJr+xyIXEemZv+ekmIJJEi\nPTY/Tpd+NI+JuWH1ia/sYs0GH49oNrydPVp1s0GczlDRmQ2IVCgJkSQukTizoR/itWOHSG+I\ntGt37c4Ln7w2z4m5YfWJr+xCIvkdICHSGyLt+drp89wdLcUSVp/4yi4UEtlpeLM3/fWSxlOC\nWg03KtJu0zz92n/79dTcp+aG1Se+ssuExHtkbvrrJSGSpCfS6+Z8rl3y4nZh9Ymv7DIhiOSd\ngkgHvrWLfz8pLFscVp/4yi4SkuCRuemvl4RIkvrPtcvfbPD2iGZD59Gqmw2ahNUnvrKIVCwJ\nkSSI5OQc4r9jh0iIpE1YfeIri0jFkhBJUr9IuUNSOg1v9qa/XtKkSEkhEzc3CyLNhSBSQEqo\nR4g0R2hBYyubPSTRI3PTXy8JkSSINBOCSCEpiKRNaEFjK5u72RDkEc2GTiSaDUqE1Se+splF\nCtserV6kywYJkZQIq098ZRGpWBIiSRDJyS74AAmRukuIpERYfeIrmzcktdPwZm/66yVNipQQ\nMnlzsyDSRIiCR+amv17SdUq4R4g0R3hJ4yqbNQSRwlIQSZ/wksZVNmeIhkfmpr9eEiJJ6hcp\nX7MhWKSVNxukRzQblAirT3xlM87e8A0SInUgkhJh9YmvbL7Zuw0PQaQORFIirD7xlUWkYknD\nlN4REiIpEVaf+MpmC9nm/NTOTSRNihQbMndzsyDSONucp3beRhIiSRBpHEQKTYnyCJHmiKpq\nRGUzhWw1Qg5Ym/56SYgkqV+kLEf4cSKtutnQF4lmgxJh9YmvbJ7Zu40LWbNIgw0SIikRVp/4\nymaZvdvIEETqQCQlwuoTX1lEKpaESJL6RdIP2WqEnLE2/fWSJkWKC5m/uVkQ6YrzOXaIFJAS\n6REizRFZV0SqJwmRJIg0pDvpG5ECUhApU25kXQ00GxJEWm+z4cojmg1KhNUnvrLqs/fyKSRE\n8k9BJEQagEgxKYiESH3Ex2IRyTvl+ggJkZQIq098ZbVDkkQaw9r010uaFCkixOvmZkGkHnKd\nBkTyTkEkROqDSDEp8R4h0hxLVVY5BJFiUhDpFkTSPMLvrcBFs8E3ZUQkmg1KhNUnvrKIVCyp\nSxnbICGSEmH1ia+s6uztLwmJSJ4piLRDpB6IFJWCSLtbEEkvZLBGMc0Gv5SEVgMizbJUZTVD\nECkqBZFaEOkCIsWkJHmESHMsVVnFkOFfn0AkrxREOlC/SGpH+OkirbLZMC4SzQYlwuoTX1m9\n2Xv155AQySfFsUFCJCXC6hNfWUQqloRIEkQ6g0gxKa4jJERSIqw+8ZVVC7n+Q5c0GzxS0loN\niDTLUpVVC0GkqBREOuEv0maPz/cjS1VWK2TkLy8j0nxKqkcrFGlz+jL3/cRSldUKQaSoFEQ6\nU79IKkf4Ix7RbPBIcYpEs2ESRMo/FCfWknZTGyREmsRLpP+1qA5yEo12yVYhY5evc2OWO+dv\nVleKgCd8bCbc5BZpbIPEFmk2ZeIIiS3SJBZ37RRCRj2i2TCbktxqQCRE8sXa9NdLQiRJ/V27\n9JBxjxBpLkXBI0RCJE+sTX+9JESS1H9mQ/IRvsMjmg0zTHpEs0GJsPrEVxaRiiUhkgSRXB4h\n0gyIJEEkRIrjbjIFkZQIq098ZZNDnB7RbJhmWiRfEGmGpSqbHIJIcdwVeccv02xVYO0iuT1C\npEkQqQ8iKYRMYWv6qyXdlTkHJdNsVaB+kZKO8Cc8otkwxZxINBuUCKtPfGURqUwSIg1Yt0hT\nHiHSBHdzKYikRFh94iuLSEWSEGlI/SIlhEx6RLPBzZ1KSkRIptmqACKlhsxgaPrrJSHSFWsW\nadojRHJyp5ISE5JptiqASIkhc9iZ/npJiHRN/SJFH+HPeESzwcWdRwrNBiXC6hNfWURaPgmR\nRkAkvZEg0gVEUiKsPvGVjZ+9cx4hkoM7nxREUiKsPvGVjQ/JIFK+FEtJXiL5gkgzLFXZ6JBZ\njxBpnLNHiNQDkVJCPDAy/fWSEGmUtYo07xEijdJ5hEg96hcp7gg/i0hraDZ4ikSzQYmw+sRX\nNnL2eniESKMg0jiIpDeSNYh08QiReiCS3kgQ6QIiKRFWn/jKxoX4eESzYQThEc2GHogUHeKH\nhemvl4RILlYpkpdHiHSN9AiReiBSbIgnBqa/XhIiOalfpPAjfD+PaDZc4y8SzQYlwuoTX1lE\nWjCp5xEi9UAkvZEg0gVEUiKsPvGVjZi9nh4h0pC+R4jUo36RgkPyiZQvxURSiEi+INIMS1U2\nPMTXI0QagkgTIFJUiD/Fp79a0sAjROqxOpG8PUKkAYg0Rf0iBR7h5xTpppsNQ49oNvRAJL2R\nINIFRFIirD7xlQ2dvf4eIVKPK48QqQci6Y0EkS4gkhJh9YmvbGBIgEc0G3oEiuQLIs2wVGUD\nQxApMunaI0TqgUjhIUEg0iSINMNSlQ0LCfEIkQQjHiFSj/pFCjnCzyzS7TYbgkWi2aBEWH3i\nKxs0e4M8QiQBIs2BSHojuV2RxjxCpB5rEinMI0S6gEiz1C+Sf0h2kfKllE0a9YhmQw9ECgwJ\nBZEmQaQZlqpsQEigR4h0ZtwjROqBSGEhwSDSJIg0w1KV9T/CD/WIZsOZGJFoNigRVp/4yiJS\n9iSHR4jUYzUiBXuESCcQyQdE0hvJbYrk8giRetQvkl9IuEc0G47EieQLIs2wVGV9QxApMsnp\nESL1WIlIER4h0gFE8gOR/EOiqFwkt0eI1KN+kXyO8GM8otnQEisSzQYlwuoTX1lEypuESJ6s\nQqQojxDpbdIjROqBSHojQaQLiKREWH3iK+sTEucRzYZpj2g29EAkz5BYEGkSRJphqcp6hER6\nhEjTHiFSD0TyC4kGkTRDMs1WBeoXae4IP9Yjmg3THtFs6IFIeiNBpAuIpERYfeIrOzt7oz1a\nvUgzHiFSD0TSG8ltiTTnESL1qF+k6ZB4j9bebEgTyRdEmmGpys6FIFJk0qxHiNTjxkVK8AiR\nFFK0QzLNVgUQaT4kiUpFmvcIkXrUL9LkEf6yIt1Os8HDI5oNPW5bpBSPECklBZGUCKtPfGUR\nKUeSj0eI1OOmRUryaMUieXmESD3qF2kiZGmR8qUsm6Qgki+IVAPb0gOok7vSA6iRW94ipW2Q\n1rtF8tsgsUXqgUgzIanUJ5KnR4jUo36R3Ef4i4t0E80GX49oNvS4YZESPVqpSN4eIVIPRNIb\nCSJdQCQlwuoTX1lE0k3y9wiRetQvkisk1aNVNhsCPKLZ0AORJkPSqUqkEI8QqQciTYakg0ia\nIZlmqwI3K1KyRysUKcgjROpRv0iOI/wSIlXebAjziGZDj1sVKd2j1YkU6BEi9UAkvZEg0gVE\nUiKsPvGVRSStpFCPEKlH/SKNhih4tLJmQ7BHNBt6IJI7RCGjGpHCPUKkHojkDlHIQCTdkEyz\nVYHbFEnDo1WJFOERIvWoX6Sxw9pCIlXbbIjxiGZDj5sUaVviP8s3RBIgkhJh9Ymv7NhLti2y\n1zE6lDpEivIIkXogkt5IahUpziNE6lG/SFdsyxwHZ03JmpRDpEwhmWarArcn0lYj5E0rpAKR\nIj1CpB43J9JWI+RNLcS+SLEeIVIPRHKCSAEpC4Vkmq0K1C9S/7B2GxeiMpIqmw3RHtFs6HFj\nIm0jQ1RGUqNI8R4hUo/bEul8RgMi+SYhkhKIpDeSCkVK8AiRetQvkqA7xY5mg19Sikc0G3rc\nkkiXU1URySspySNE6nFDIolTvhHJJynNI0TqcTsiyY9OIJJHUqJHiNSjfpFOh7W9jyDRbJhP\nSvWIZkOPWxGp/1E+RJpNSvYIkXrciEiDj8Qi0lxSukeI1OM2RBp+tByRZpIUPEKkHvWL9Day\nRAPNhukkDY9oNvS4BZGulzpBpElUPEKkHjcg0siSQYg0xZ2hZ4ZIMyxV2fGltxBpgjtLzwyR\nZliqsm/bbEf4N9psuFvkvwiaDUqE1Se+stt8s/c2RbpTS0IkCSJpjeStCpHu1JIQqUflIm0z\nzt6bFEkxCZEkdYvkWuObZoODO7Ukmg19qhbJuVY+Io1zp5aklYJIMyxSWUQK4/xGrKFnhkgz\nLFFZ9x9vQaQxuhMaDD0zRJphgcqePKLZ4MnlxCCaDfrUK9J5e4RIfogT7BBJH0RKH0mHZZHk\niaqIpE+1InUHSIjkg/QIkTJQqUjb6b8SS7Ohz13PI0vPDJFmyFjZ7XZGI0QacDf42dAzQ6QZ\nclV2XiKPEJWRLJqSlDT0yNIzQ6QZ8lTWy6K5EJWRLJySknTlkaVnhkgzZKnsqEY0G6a59ohm\nQwYQKWok41gUacQjRMpATSKN79ch0gR3Yx4hUgYQKWYkDsyJNKoRIuWgIpE8Gw3TIQEYOiSP\nSxrfHMUk5UtBpBkyVBaRwnBpZOqZIdIM+pUN8AiRJjZHwUlZUxBpBvXKhniESNOLqRp6Zog0\ng3plnSLRbLhmanMUlhSbQrNBibD6zFfWvUFCpCEzGiFSDhApdCQT2BBpTiNEykElIk0cISFS\nj9nNkXdSUgoiKRFWn9nKBrUaVtxs8NHI1DNDpBl0Kxvo0VpF8tPI1DNDpBl0K4tIHkm+Gpl6\nZog0g2ZlfT+FNBmiMpJiKbNJd/4amXpmiDSDXmXnNKLZELIxmkvSSqHZoERYfdyVnd8arV2k\nu6CN0VSSZgoiKRFWH1dlfXbqVizSXYRE40kxIJLEskh+x0brFCnWoeukaBBJYlek8BbDSEgK\nhg7Je0lJDvWS0qDZILEqUopGtytSukPnJAUQSWJSJL/F62ZCkjE03d7UHDpg6Jkh0gwJlU21\n6O3WRDo5tPRZe4ukINIMsYUN3xjddLNBboeqEolmgxJh9TlyWNQ7+J43KdLdifSkMRBJHzMi\nnTdF6xPpbgSd8bhAJH1MiCT/vsRaRJqWRmc8eZMQSVJapO3wj7TY6RMoTjfPbc4S49FLotkg\n0RNps+fy00QxtpLUyjrqXTDEd09tqfFkS0IkiZpIm+7LgaUqu1zImCAe0lib/npJiCRZrUi+\nXqRuVaxNf70kRJLYFClkgjfBSnh6YeF9JMtJNBskGUT6X8vELT1mecgD52qXRGBoKKVZXSls\nbpFCqLT9HY61JLZIEkRygkgJKYgUS23NhoVCzE1/vSSaDRJEyhtibvrrJSGSBJHyhpib/npJ\niCQpcGaDamWth5ib/npJiCQpfa5damVpNhRLotkgQSQniJSQgkhKhNUnvrKIVCwJkSSI5ASR\nElIQSYmw+sRX1nqIuemvl0SzQYJIeUPMTX+9JESSIFLeEHPTXy8JkSSIlDfE3PTXS0IkSf0i\n0WwolESzQYJIThApIQWRlAirT3xlEalYEiJJEMkJIiWkIJISYfWJr6z1EHPTXy+JZoMEkfKG\nmJv+ekmIJEGkvCHmpr9eEiJJEClviLnpr5eESJL6RaLZUCiJZoMEkZwgUkIKIikRVp/4yiJS\nsSREkiCSE0RKSEEkJcLqE19Z6yHmpr9eEs0GCSLlDTE3/fWSEEmCSHlDzE1/vSREkiBS3hBz\n018vCZEk9YtEs6FQEs0GCSI5QaSEFERSIqw+8ZVFpGJJiCRBJCeIlJCCSDfB1F/eXBhDQ+kw\nNCZDQ0kDkTJjaCgdhsZkaChpIFJmDA2lw9CYDA0lDUTKjKGhdBgak6GhpHGbIgEsDCIBKIBI\nAAogEoACiASgACIBKFC7SJsj4orhb0euLjGU84+D22Qf1PhYdt1YJn695FAKlUeP6kWaumJz\n+bKESNNXnIaw8Cw5P+rYwy5bnsmhlCqPHusQabPIFmnyivMQLIq0SHmmRSpUHj1uR6TTPsFm\nd9k3uPyXu6xII0Ppj2Y5NsIUMazhgJbZtXMPZVeoPHrcjEjn1+H0AvWu2y0r0thQuqsXPgYQ\ns7c3LDnO3eIiXQ1lV6g8elQv0qb7z+34Rf7HtrBIU0PpjWbRZkN/320jHr3gFmk4lF2h8uhR\nvUjdhdM0LijS1FAcl5YYlNgGXIbVDWNJkdxD2RUqjx63I5L8bmDXbmdPpM355+vxLS3ScCg7\nRCpMbSItv2vXzd7eHudgLAuLdDWUXaHy6HFbIp2PXw2INBiKHM2SE2Xw33/hXTvnUHaFyqPH\nzYg02nNe9syGyaF0U3bprp34t7kcowzGsrBI10MpVB49ahcJwASIBKAAIgEogEgACiASgAKI\nBKAAIgEogEgACiASgAKIVIamV/ivdb6bDxcQqQx9kRpehtrhFSwDIt0YvIKL8/rYPB3V+fXU\nNJuX1qPDj+/PTfP8Xnp4EAUiLc37Zq/NU2vOj+bAy1mk9hfNfenxQRSItDQvzePu/bE15775\nttv9aS8dPPqyV2r/26+lBwgxINLS3Dev+92741HR648vj51I94er9rt9UCGItDRHhQ5fH4/7\ndqefmub8I9QHL9vSXER6bu6//nhFpJuAl21pLrt2x1bdYNcO6oQXb2m+NI/vu9OR0a9T2+Eg\n0kvbbPjWPJYeIMSASEtzaX+/NJdjpM3pF03zp/QAIQZEWpzXp/Mbss9N8/irvfS1FWn3evi5\n9PAgCkQCUACRABRAJAAFEAlAAUQCUACRABRAJAAFEAlAAUQCUACRABRAJAAFEAlAgf8HO1YO\nr/5vwhEAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "who_events <- tribble(\n",
    "  ~ date, ~ event,\n",
    "  \"2020-01-30\", \"Global health\\nemergency declared\",\n",
    "  \"2020-03-11\", \"Pandemic\\ndeclared\",\n",
    "  \"2020-02-13\", \"China reporting\\nchange\"\n",
    ") %>%\n",
    "  mutate(date = as.Date(date))\n",
    "\n",
    "# Using who_events, add vertical dashed lines with an xintercept at date\n",
    "# and text at date, labeled by event, and at 100000 on the y-axis\n",
    "plt_cum_confirmed_cases_china_vs_world +\n",
    "  geom_vline(aes(xintercept = date), data = who_events, linetype = \"dashed\") +\n",
    "  geom_text(aes(date, label = event), data = who_events, y = 1e5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "powered-header",
   "metadata": {},
   "source": [
    "## 5. Adding a trend line to China\n",
    "<p>When trying to assess how big future problems are going to be, we need a measure of how fast the number of cases is growing. A good starting point is to see if the cases are growing faster or slower than linearly.</p>\n",
    "<p>There is a clear surge of cases around February 13, 2020, with the reporting change in China. However, a couple of days after, the growth of cases in China slows down. How can we describe COVID-19's growth in China after February 15, 2020?</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "invalid-former",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`geom_smooth()` using formula 'y ~ x'\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter for China, from Feb 15\n",
    "china_after_feb15 <- confirmed_cases_china_vs_world %>%\n",
    "  filter(is_china == \"China\" & date >= as.Date(\"2020-02-15\"))\n",
    "\n",
    "# Using china_after_feb15, draw a line plot cum_cases vs. date\n",
    "# Add a smooth trend line using linear regression, no error bars\n",
    "ggplot(china_after_feb15, aes(date, cum_cases)) +\n",
    "  geom_line() +\n",
    "  geom_smooth(method = \"lm\", se = FALSE) +\n",
    "  ylab(\"Cumulative confirmed cases\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "numerous-prophet",
   "metadata": {},
   "source": [
    "## 6. And the rest of the world?\n",
    "<p>From the plot above, the growth rate in China is slower than linear. That's great news because it indicates China has at least somewhat contained the virus in late February and early March.</p>\n",
    "<p>How does the rest of the world compare to linear growth?</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "close-girlfriend",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`geom_smooth()` using formula 'y ~ x'\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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+FZFOP8qLdPoRJtLm+Pev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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter confirmed_cases_china_vs_world for not China\n",
    "not_china <- confirmed_cases_china_vs_world %>%\n",
    "  filter(is_china == \"Not China\")\n",
    "\n",
    "# Using not_china, draw a line plot cum_cases vs. date\n",
    "# Add a smooth trend line using linear regression, no error bars\n",
    "plt_not_china_trend_lin <- ggplot(not_china, aes(date, cum_cases)) +\n",
    "  geom_line() +\n",
    "  geom_smooth(method = \"lm\", se = FALSE) +\n",
    "  ylab(\"Cumulative confirmed cases\")\n",
    "\n",
    "# See the result\n",
    "plt_not_china_trend_lin "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "related-attack",
   "metadata": {},
   "source": [
    "## 7. Adding a logarithmic scale\n",
    "<p>From the plot above, we can see a straight line does not fit well at all, and the rest of the world is growing much faster than linearly. What if we added a logarithmic scale to the y-axis?</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "modular-profit",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`geom_smooth()` using formula 'y ~ x'\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ByRIosrxfZrNOlhE1LjRhiOSJHFlWEzu6NpD22RGjfCcESKLK4Em9No4sOPpMaN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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Modify the plot to use a logarithmic scale on the y-axis\n",
    "plt_not_china_trend_lin + \n",
    "  scale_y_log10()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "colored-stomach",
   "metadata": {},
   "source": [
    "## 8. Which countries outside of China have been hit hardest?\n",
    "<p>With the logarithmic scale, we get a much closer fit to the data. From a data science point of view, a good fit is great news. Unfortunately, from a public health point of view, that means that cases of COVID-19 in the rest of the world are growing at an exponential rate, which is terrible news.</p>\n",
    "<p>Not all countries are being affected by COVID-19 equally, and it would be helpful to know where in the world the problems are greatest. Let's find the countries outside of China with the most confirmed cases in our dataset.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "outdoor-interest",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m13272\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m5\u001b[39m\n",
      "\u001b[36m--\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m--------------------------------------------------------\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m  (2): country, province\n",
      "\u001b[32mdbl\u001b[39m  (2): cases, cum_cases\n",
      "\u001b[34mdate\u001b[39m (1): date\n",
      "\n",
      "\u001b[36mi\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mi\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 13,272\n",
      "Columns: 5\n",
      "$ country   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Afghanistan\", \"Albania\", \"Algeria\", \"Andorra\", \"Antigua and~\n",
      "$ province  \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ~\n",
      "$ date      \u001b[3m\u001b[90m<date>\u001b[39m\u001b[23m 2020-01-22, 2020-01-22, 2020-01-22, 2020-01-22, 2020-01-22,~\n",
      "$ cases     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~\n",
      "$ cum_cases \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 7 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>country</th><th scope=col>total_cases</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>France      </td><td> 7699</td></tr>\n",
       "\t<tr><td>Germany     </td><td> 9257</td></tr>\n",
       "\t<tr><td>Iran        </td><td>16169</td></tr>\n",
       "\t<tr><td>Italy       </td><td>31506</td></tr>\n",
       "\t<tr><td>Korea, South</td><td> 8320</td></tr>\n",
       "\t<tr><td>Spain       </td><td>11748</td></tr>\n",
       "\t<tr><td>US          </td><td> 6421</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 7 × 2\n",
       "\\begin{tabular}{ll}\n",
       " country & total\\_cases\\\\\n",
       " <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t France       &  7699\\\\\n",
       "\t Germany      &  9257\\\\\n",
       "\t Iran         & 16169\\\\\n",
       "\t Italy        & 31506\\\\\n",
       "\t Korea, South &  8320\\\\\n",
       "\t Spain        & 11748\\\\\n",
       "\t US           &  6421\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 7 × 2\n",
       "\n",
       "| country &lt;chr&gt; | total_cases &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| France       |  7699 |\n",
       "| Germany      |  9257 |\n",
       "| Iran         | 16169 |\n",
       "| Italy        | 31506 |\n",
       "| Korea, South |  8320 |\n",
       "| Spain        | 11748 |\n",
       "| US           |  6421 |\n",
       "\n"
      ],
      "text/plain": [
       "  country      total_cases\n",
       "1 France        7699      \n",
       "2 Germany       9257      \n",
       "3 Iran         16169      \n",
       "4 Italy        31506      \n",
       "5 Korea, South  8320      \n",
       "6 Spain        11748      \n",
       "7 US            6421      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this to get the data for each country\n",
    "confirmed_cases_by_country <- read_csv(\"confirmed_cases_by_country.csv\")\n",
    "glimpse(confirmed_cases_by_country)\n",
    "# confirmed_cases_by_country\n",
    "\n",
    "# Group by country, summarize to calculate total cases, find the top 7\n",
    "top_countries_by_total_cases <- confirmed_cases_by_country %>%\n",
    "  group_by(country) %>%\n",
    "  summarize(total_cases = max(cum_cases)) %>%\n",
    "  top_n(7, total_cases)\n",
    "\n",
    "# See the result\n",
    "top_countries_by_total_cases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blessed-checkout",
   "metadata": {},
   "source": [
    "## 9. Plotting hardest hit countries as of Mid-March 2020\n",
    "<p>Even though the outbreak was first identified in China, there is only one country from East Asia (South Korea) in the above table. Four of the listed countries (France, Germany, Italy, and Spain) are in Europe and share borders. To get more context, we can plot these countries' confirmed cases over time.</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "commercial-guess",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m2030\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m3\u001b[39m\n",
      "\u001b[36m--\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m--------------------------------------------------------\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m  (1): country\n",
      "\u001b[32mdbl\u001b[39m  (1): cum_cases\n",
      "\u001b[34mdate\u001b[39m (1): date\n",
      "\n",
      "\u001b[36mi\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mi\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 2,030\n",
      "Columns: 3\n",
      "$ country   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Germany\", \"Iran\", \"Italy\", \"Korea, South\", \"Spain\", \"US\", \"~\n",
      "$ date      \u001b[3m\u001b[90m<date>\u001b[39m\u001b[23m 2020-02-18, 2020-02-18, 2020-02-18, 2020-02-18, 2020-02-18,~\n",
      "$ cum_cases \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 16, 0, 3, 31, 2, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,~\n"
     ]
    },
    {
     "data": {
      "image/png": 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dI5WuHQ7u88SL+6tz3wbLhAQltQhGgN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\n5KD/D6r5fcRUnDK7AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in the dataset from datasets/confirmed_cases_top7_outside_china.csv\n",
    "confirmed_cases_top7_outside_china <- read_csv(\"confirmed_cases_top7_outside_china.csv\")\n",
    "\n",
    "# Glimpse at the contents of confirmed_cases_top7_outside_china\n",
    "glimpse(confirmed_cases_top7_outside_china)\n",
    "\n",
    "# Using confirmed_cases_top7_outside_china, draw a line plot of\n",
    "# cum_cases vs. date, colored by country\n",
    "ggplot(confirmed_cases_top7_outside_china, aes(date, cum_cases, color = country)) + \n",
    "    geom_line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fundamental-library",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.1.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}