{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day 0: Installation test\n",
    "\n",
    "The goal of this exercise is to make sure that you can run it from your computer before we start class.\n",
    "\n",
    "The exercise will have you replicate a simple plot about wind installed capacity from the [Our World in Data](https://ourworldindata.org/renewable-energy).\n",
    "\n",
    "Make sure you have the [data file](cumulative-installed-wind-energy-capacity-gigawatts.csv) donwloaded in the same folder as this file. Otherwise, you can check the current directory `pwd()` and specify the path with the data file `cd(\"\")`.\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to load packages in Julia, similar to the import function in Python or the library functionality in R. \n",
    "\n",
    "```\n",
    "using Pkg\n",
    "Pkg.add(\"LibraryName\")\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    " # installing libraries\n",
    "using Pkg\n",
    "# uncomment the following line to install the packages for the first time\n",
    "#Pkg.add([\"DataFrames\",\"CSV\",\"Plots\"])\n",
    "\n",
    "# libraries that we will use\n",
    "using DataFrames\n",
    "using CSV\n",
    "using Plots\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the data using the DataFrames and CSV syntax in Julia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>Entity</th><th>Code</th><th>Year</th><th>WindCapacity</th></tr><tr><th></th><th>String31</th><th>String15?</th><th>Int64</th><th>Float64</th></tr></thead><tbody><p>1,403 rows × 4 columns</p><tr><th>1</th><td>Africa</td><td><em>missing</em></td><td>1997</td><td>0.006</td></tr><tr><th>2</th><td>Africa</td><td><em>missing</em></td><td>1998</td><td>0.01</td></tr><tr><th>3</th><td>Africa</td><td><em>missing</em></td><td>1999</td><td>0.064</td></tr><tr><th>4</th><td>Africa</td><td><em>missing</em></td><td>2000</td><td>0.1334</td></tr><tr><th>5</th><td>Africa</td><td><em>missing</em></td><td>2001</td><td>0.1334</td></tr><tr><th>6</th><td>Africa</td><td><em>missing</em></td><td>2002</td><td>0.13966</td></tr><tr><th>7</th><td>Africa</td><td><em>missing</em></td><td>2003</td><td>0.15024</td></tr><tr><th>8</th><td>Africa</td><td><em>missing</em></td><td>2004</td><td>0.22616</td></tr><tr><th>9</th><td>Africa</td><td><em>missing</em></td><td>2005</td><td>0.22638</td></tr><tr><th>10</th><td>Africa</td><td><em>missing</em></td><td>2006</td><td>0.31108</td></tr><tr><th>11</th><td>Africa</td><td><em>missing</em></td><td>2007</td><td>0.45192</td></tr><tr><th>12</th><td>Africa</td><td><em>missing</em></td><td>2008</td><td>0.53712</td></tr><tr><th>13</th><td>Africa</td><td><em>missing</em></td><td>2009</td><td>0.72405</td></tr><tr><th>14</th><td>Africa</td><td><em>missing</em></td><td>2010</td><td>0.846477</td></tr><tr><th>15</th><td>Africa</td><td><em>missing</em></td><td>2011</td><td>0.976682</td></tr><tr><th>16</th><td>Africa</td><td><em>missing</em></td><td>2012</td><td>1.10997</td></tr><tr><th>17</th><td>Africa</td><td><em>missing</em></td><td>2013</td><td>1.7242</td></tr><tr><th>18</th><td>Africa</td><td><em>missing</em></td><td>2014</td><td>2.38277</td></tr><tr><th>19</th><td>Africa</td><td><em>missing</em></td><td>2015</td><td>3.322</td></tr><tr><th>20</th><td>Africa</td><td><em>missing</em></td><td>2016</td><td>3.829</td></tr><tr><th>21</th><td>Africa</td><td><em>missing</em></td><td>2017</td><td>4.581</td></tr><tr><th>22</th><td>Africa</td><td><em>missing</em></td><td>2018</td><td>5.469</td></tr><tr><th>23</th><td>Africa</td><td><em>missing</em></td><td>2019</td><td>5.769</td></tr><tr><th>24</th><td>Africa</td><td><em>missing</em></td><td>2020</td><td>6.491</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& Entity & Code & Year & WindCapacity\\\\\n",
       "\t\\hline\n",
       "\t& String31 & String15? & Int64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & Africa & \\emph{missing} & 1997 & 0.006 \\\\\n",
       "\t2 & Africa & \\emph{missing} & 1998 & 0.01 \\\\\n",
       "\t3 & Africa & \\emph{missing} & 1999 & 0.064 \\\\\n",
       "\t4 & Africa & \\emph{missing} & 2000 & 0.1334 \\\\\n",
       "\t5 & Africa & \\emph{missing} & 2001 & 0.1334 \\\\\n",
       "\t6 & Africa & \\emph{missing} & 2002 & 0.13966 \\\\\n",
       "\t7 & Africa & \\emph{missing} & 2003 & 0.15024 \\\\\n",
       "\t8 & Africa & \\emph{missing} & 2004 & 0.22616 \\\\\n",
       "\t9 & Africa & \\emph{missing} & 2005 & 0.22638 \\\\\n",
       "\t10 & Africa & \\emph{missing} & 2006 & 0.31108 \\\\\n",
       "\t11 & Africa & \\emph{missing} & 2007 & 0.45192 \\\\\n",
       "\t12 & Africa & \\emph{missing} & 2008 & 0.53712 \\\\\n",
       "\t13 & Africa & \\emph{missing} & 2009 & 0.72405 \\\\\n",
       "\t14 & Africa & \\emph{missing} & 2010 & 0.846477 \\\\\n",
       "\t15 & Africa & \\emph{missing} & 2011 & 0.976682 \\\\\n",
       "\t16 & Africa & \\emph{missing} & 2012 & 1.10997 \\\\\n",
       "\t17 & Africa & \\emph{missing} & 2013 & 1.7242 \\\\\n",
       "\t18 & Africa & \\emph{missing} & 2014 & 2.38277 \\\\\n",
       "\t19 & Africa & \\emph{missing} & 2015 & 3.322 \\\\\n",
       "\t20 & Africa & \\emph{missing} & 2016 & 3.829 \\\\\n",
       "\t21 & Africa & \\emph{missing} & 2017 & 4.581 \\\\\n",
       "\t22 & Africa & \\emph{missing} & 2018 & 5.469 \\\\\n",
       "\t23 & Africa & \\emph{missing} & 2019 & 5.769 \\\\\n",
       "\t24 & Africa & \\emph{missing} & 2020 & 6.491 \\\\\n",
       "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m1403×4 DataFrame\u001b[0m\n",
       "\u001b[1m  Row \u001b[0m│\u001b[1m Entity   \u001b[0m\u001b[1m Code      \u001b[0m\u001b[1m Year  \u001b[0m\u001b[1m WindCapacity \u001b[0m\n",
       "\u001b[1m      \u001b[0m│\u001b[90m String31 \u001b[0m\u001b[90m String15? \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Float64      \u001b[0m\n",
       "──────┼──────────────────────────────────────────\n",
       "    1 │ Africa   \u001b[90m missing   \u001b[0m  1997       0.006\n",
       "    2 │ Africa   \u001b[90m missing   \u001b[0m  1998       0.01\n",
       "    3 │ Africa   \u001b[90m missing   \u001b[0m  1999       0.064\n",
       "    4 │ Africa   \u001b[90m missing   \u001b[0m  2000       0.1334\n",
       "    5 │ Africa   \u001b[90m missing   \u001b[0m  2001       0.1334\n",
       "    6 │ Africa   \u001b[90m missing   \u001b[0m  2002       0.13966\n",
       "    7 │ Africa   \u001b[90m missing   \u001b[0m  2003       0.15024\n",
       "    8 │ Africa   \u001b[90m missing   \u001b[0m  2004       0.22616\n",
       "  ⋮   │    ⋮          ⋮        ⋮         ⋮\n",
       " 1397 │ World     OWID_WRL    2014     349.671\n",
       " 1398 │ World     OWID_WRL    2015     416.245\n",
       " 1399 │ World     OWID_WRL    2016     466.861\n",
       " 1400 │ World     OWID_WRL    2017     514.374\n",
       " 1401 │ World     OWID_WRL    2018     563.829\n",
       " 1402 │ World     OWID_WRL    2019     622.249\n",
       " 1403 │ World     OWID_WRL    2020     733.276\n",
       "\u001b[36m                                1388 rows omitted\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df = CSV.read(\"cumulative-installed-wind-energy-capacity-gigawatts.csv\", DataFrame)\n",
    "first(df, 5)\n",
    "rename!(df,\"Wind Capacity\"=>\"WindCapacity\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be plotting the evolution of wind investment over time for a few select countries.\n",
    "\n",
    "1. We first filter the data with the list of countries.\n",
    "\n",
    "2. We then plot the evolution of installed capacities for each country. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>Entity</th><th>Year</th><th>WindCapacity</th></tr><tr><th></th><th>String31</th><th>Int64</th><th>Float64</th></tr></thead><tbody><p>60 rows × 3 columns</p><tr><th>1</th><td>Germany</td><td>2001</td><td>8.754</td></tr><tr><th>2</th><td>Germany</td><td>2002</td><td>12.001</td></tr><tr><th>3</th><td>Germany</td><td>2003</td><td>14.381</td></tr><tr><th>4</th><td>Germany</td><td>2004</td><td>16.419</td></tr><tr><th>5</th><td>Germany</td><td>2005</td><td>18.248</td></tr><tr><th>6</th><td>Germany</td><td>2006</td><td>20.474</td></tr><tr><th>7</th><td>Germany</td><td>2007</td><td>22.116</td></tr><tr><th>8</th><td>Germany</td><td>2008</td><td>22.794</td></tr><tr><th>9</th><td>Germany</td><td>2009</td><td>25.732</td></tr><tr><th>10</th><td>Germany</td><td>2010</td><td>26.903</td></tr><tr><th>11</th><td>Germany</td><td>2011</td><td>28.712</td></tr><tr><th>12</th><td>Germany</td><td>2012</td><td>30.979</td></tr><tr><th>13</th><td>Germany</td><td>2013</td><td>33.477</td></tr><tr><th>14</th><td>Germany</td><td>2014</td><td>38.614</td></tr><tr><th>15</th><td>Germany</td><td>2015</td><td>44.58</td></tr><tr><th>16</th><td>Germany</td><td>2016</td><td>49.435</td></tr><tr><th>17</th><td>Germany</td><td>2017</td><td>55.58</td></tr><tr><th>18</th><td>Germany</td><td>2018</td><td>58.721</td></tr><tr><th>19</th><td>Germany</td><td>2019</td><td>60.721</td></tr><tr><th>20</th><td>Germany</td><td>2020</td><td>62.184</td></tr><tr><th>21</th><td>Spain</td><td>2001</td><td>3.397</td></tr><tr><th>22</th><td>Spain</td><td>2002</td><td>4.891</td></tr><tr><th>23</th><td>Spain</td><td>2003</td><td>5.945</td></tr><tr><th>24</th><td>Spain</td><td>2004</td><td>8.317</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& Entity & Year & WindCapacity\\\\\n",
       "\t\\hline\n",
       "\t& String31 & Int64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & Germany & 2001 & 8.754 \\\\\n",
       "\t2 & Germany & 2002 & 12.001 \\\\\n",
       "\t3 & Germany & 2003 & 14.381 \\\\\n",
       "\t4 & Germany & 2004 & 16.419 \\\\\n",
       "\t5 & Germany & 2005 & 18.248 \\\\\n",
       "\t6 & Germany & 2006 & 20.474 \\\\\n",
       "\t7 & Germany & 2007 & 22.116 \\\\\n",
       "\t8 & Germany & 2008 & 22.794 \\\\\n",
       "\t9 & Germany & 2009 & 25.732 \\\\\n",
       "\t10 & Germany & 2010 & 26.903 \\\\\n",
       "\t11 & Germany & 2011 & 28.712 \\\\\n",
       "\t12 & Germany & 2012 & 30.979 \\\\\n",
       "\t13 & Germany & 2013 & 33.477 \\\\\n",
       "\t14 & Germany & 2014 & 38.614 \\\\\n",
       "\t15 & Germany & 2015 & 44.58 \\\\\n",
       "\t16 & Germany & 2016 & 49.435 \\\\\n",
       "\t17 & Germany & 2017 & 55.58 \\\\\n",
       "\t18 & Germany & 2018 & 58.721 \\\\\n",
       "\t19 & Germany & 2019 & 60.721 \\\\\n",
       "\t20 & Germany & 2020 & 62.184 \\\\\n",
       "\t21 & Spain & 2001 & 3.397 \\\\\n",
       "\t22 & Spain & 2002 & 4.891 \\\\\n",
       "\t23 & Spain & 2003 & 5.945 \\\\\n",
       "\t24 & Spain & 2004 & 8.317 \\\\\n",
       "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m60×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m Entity        \u001b[0m\u001b[1m Year  \u001b[0m\u001b[1m WindCapacity \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m String31      \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Float64      \u001b[0m\n",
       "─────┼────────────────────────────────────\n",
       "   1 │ Germany         2001        8.754\n",
       "   2 │ Germany         2002       12.001\n",
       "   3 │ Germany         2003       14.381\n",
       "   4 │ Germany         2004       16.419\n",
       "   5 │ Germany         2005       18.248\n",
       "   6 │ Germany         2006       20.474\n",
       "   7 │ Germany         2007       22.116\n",
       "   8 │ Germany         2008       22.794\n",
       "  ⋮  │       ⋮          ⋮         ⋮\n",
       "  54 │ United States   2014       64.232\n",
       "  55 │ United States   2015       72.573\n",
       "  56 │ United States   2016       81.286\n",
       "  57 │ United States   2017       87.597\n",
       "  58 │ United States   2018       94.4174\n",
       "  59 │ United States   2019      103.571\n",
       "  60 │ United States   2020      117.744\n",
       "\u001b[36m                           45 rows omitted\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# we clean up the data and select relevant entries\n",
    "country_list = [\"Germany\", \"Spain\", \"United States\"];\n",
    "df_sample = df[df.Year .> 2000, :];\n",
    "filter!(row -> row.Entity in country_list, df_sample);\n",
    "select!(df_sample, [\"Entity\", \"Year\", \"WindCapacity\"]);\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# we create a plot\n",
    "plot1 = plot(df_sample.Year, df_sample.WindCapacity, group = df_sample.Entity, \n",
    "\t\t\tseriestype = :line, linewidth = 3,\n",
    "\t\t\txlabel = \"Year\",\n",
    "\t\t\tylabel = \"Wind Capacity\";\n",
    ");\n",
    "\n",
    "# and save it\n",
    "#savefig(plot1,\"plot1.png\")"
   ]
  }
 ],
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