world-happiness-report-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# World Happiness Report Analysis and Prediction\n",
    "\n",
    "Analysed the world happiness dataset and created a prediction model using Multiple Linear Regression using the World Happiness Report of the last 15 years. <br> \n",
    "Dataset Source: The World Happiness Report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The World Happiness Report is a publication from the United Nations that contains articles and rankings of national happiness, based on respondent ratings of their own lives, which the report also correlates with various life factors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'plyr' was built under R version 4.0.5\"\n",
      "\n",
      "Attaching package: 'plyr'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:corrgram':\n",
      "\n",
      "    baseball\n",
      "\n",
      "\n",
      "Warning message:\n",
      "\"package 'dplyr' was built under R version 4.0.5\"\n",
      "\n",
      "Attaching package: 'dplyr'\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:plyr':\n",
      "\n",
      "    arrange, count, desc, failwith, id, mutate, rename, summarise,\n",
      "    summarize\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"
     ]
    }
   ],
   "source": [
    "#importing libraries\n",
    "library(plyr)\n",
    "library(dplyr)\n",
    "library(tidyverse)\n",
    "library(caTools)\n",
    "library(ggplot2)\n",
    "library(ggthemes)\n",
    "#library(reshape2)\n",
    "#library(data.table)\n",
    "library(rwa)\n",
    "library(ggpubr)\n",
    "library(tidyr)\n",
    "library(corrgram)       \n",
    "library(corrplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t1949 obs. of  11 variables:\n",
      " $ ï..Country.name                 : chr  \"Afghanistan\" \"Afghanistan\" \"Afghanistan\" \"Afghanistan\" ...\n",
      " $ year                            : int  2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 ...\n",
      " $ Life.Ladder                     : num  3.72 4.4 4.76 3.83 3.78 ...\n",
      " $ Log.GDP.per.capita              : num  7.37 7.54 7.65 7.62 7.71 ...\n",
      " $ Social.support                  : num  0.451 0.552 0.539 0.521 0.521 0.484 0.526 0.529 0.559 0.491 ...\n",
      " $ Healthy.life.expectancy.at.birth: num  50.8 51.2 51.6 51.9 52.2 ...\n",
      " $ Freedom.to.make.life.choices    : num  0.718 0.679 0.6 0.496 0.531 0.578 0.509 0.389 0.523 0.427 ...\n",
      " $ Generosity                      : num  0.168 0.19 0.121 0.162 0.236 0.061 0.104 0.08 0.042 -0.121 ...\n",
      " $ Perceptions.of.corruption       : num  0.882 0.85 0.707 0.731 0.776 0.823 0.871 0.881 0.793 0.954 ...\n",
      " $ Positive.affect                 : num  0.518 0.584 0.618 0.611 0.71 0.621 0.532 0.554 0.565 0.496 ...\n",
      " $ Negative.affect                 : num  0.258 0.237 0.275 0.267 0.268 0.273 0.375 0.339 0.348 0.371 ...\n"
     ]
    }
   ],
   "source": [
    "#Loading dataset\n",
    "happ_all <- read.csv(\"world-happiness-report.csv\")\n",
    "str(happ_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>1949</li><li>11</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1949\n",
       "\\item 11\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1949\n",
       "2. 11\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1949   11"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(happ_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Renaming columns for convenience\n",
    "happ_all <- happ_all %>% rename(c(\"Country\" = \"ï..Country.name\" ,\n",
    "                      \"Score\" = \"Life.Ladder\",\n",
    "                      \"GDP\" = \"Log.GDP.per.capita\", \n",
    "                      \"Family\" = \"Social.support\",\n",
    "                      \"Health\" = \"Healthy.life.expectancy.at.birth\", \n",
    "                      \"Freedom\" = \"Freedom.to.make.life.choices\",\n",
    "                      \"Corruption\" = \"Perceptions.of.corruption\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Country</th><th scope=col>year</th><th scope=col>Score</th><th scope=col>GDP</th><th scope=col>Family</th><th scope=col>Health</th><th scope=col>Freedom</th><th scope=col>Generosity</th><th scope=col>Corruption</th><th scope=col>Positive.affect</th><th scope=col>Negative.affect</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Afghanistan</td><td>2008</td><td>3.724</td><td>7.370</td><td>0.451</td><td>50.80</td><td>0.718</td><td>0.168</td><td>0.882</td><td>0.518</td><td>0.258</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Afghanistan</td><td>2009</td><td>4.402</td><td>7.540</td><td>0.552</td><td>51.20</td><td>0.679</td><td>0.190</td><td>0.850</td><td>0.584</td><td>0.237</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Afghanistan</td><td>2010</td><td>4.758</td><td>7.647</td><td>0.539</td><td>51.60</td><td>0.600</td><td>0.121</td><td>0.707</td><td>0.618</td><td>0.275</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Afghanistan</td><td>2011</td><td>3.832</td><td>7.620</td><td>0.521</td><td>51.92</td><td>0.496</td><td>0.162</td><td>0.731</td><td>0.611</td><td>0.267</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Afghanistan</td><td>2012</td><td>3.783</td><td>7.705</td><td>0.521</td><td>52.24</td><td>0.531</td><td>0.236</td><td>0.776</td><td>0.710</td><td>0.268</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Afghanistan</td><td>2013</td><td>3.572</td><td>7.725</td><td>0.484</td><td>52.56</td><td>0.578</td><td>0.061</td><td>0.823</td><td>0.621</td><td>0.273</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & Country & year & Score & GDP & Family & Health & Freedom & Generosity & Corruption & Positive.affect & Negative.affect\\\\\n",
       "  & <chr> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Afghanistan & 2008 & 3.724 & 7.370 & 0.451 & 50.80 & 0.718 & 0.168 & 0.882 & 0.518 & 0.258\\\\\n",
       "\t2 & Afghanistan & 2009 & 4.402 & 7.540 & 0.552 & 51.20 & 0.679 & 0.190 & 0.850 & 0.584 & 0.237\\\\\n",
       "\t3 & Afghanistan & 2010 & 4.758 & 7.647 & 0.539 & 51.60 & 0.600 & 0.121 & 0.707 & 0.618 & 0.275\\\\\n",
       "\t4 & Afghanistan & 2011 & 3.832 & 7.620 & 0.521 & 51.92 & 0.496 & 0.162 & 0.731 & 0.611 & 0.267\\\\\n",
       "\t5 & Afghanistan & 2012 & 3.783 & 7.705 & 0.521 & 52.24 & 0.531 & 0.236 & 0.776 & 0.710 & 0.268\\\\\n",
       "\t6 & Afghanistan & 2013 & 3.572 & 7.725 & 0.484 & 52.56 & 0.578 & 0.061 & 0.823 & 0.621 & 0.273\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 11\n",
       "\n",
       "| <!--/--> | Country &lt;chr&gt; | year &lt;int&gt; | Score &lt;dbl&gt; | GDP &lt;dbl&gt; | Family &lt;dbl&gt; | Health &lt;dbl&gt; | Freedom &lt;dbl&gt; | Generosity &lt;dbl&gt; | Corruption &lt;dbl&gt; | Positive.affect &lt;dbl&gt; | Negative.affect &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | Afghanistan | 2008 | 3.724 | 7.370 | 0.451 | 50.80 | 0.718 | 0.168 | 0.882 | 0.518 | 0.258 |\n",
       "| 2 | Afghanistan | 2009 | 4.402 | 7.540 | 0.552 | 51.20 | 0.679 | 0.190 | 0.850 | 0.584 | 0.237 |\n",
       "| 3 | Afghanistan | 2010 | 4.758 | 7.647 | 0.539 | 51.60 | 0.600 | 0.121 | 0.707 | 0.618 | 0.275 |\n",
       "| 4 | Afghanistan | 2011 | 3.832 | 7.620 | 0.521 | 51.92 | 0.496 | 0.162 | 0.731 | 0.611 | 0.267 |\n",
       "| 5 | Afghanistan | 2012 | 3.783 | 7.705 | 0.521 | 52.24 | 0.531 | 0.236 | 0.776 | 0.710 | 0.268 |\n",
       "| 6 | Afghanistan | 2013 | 3.572 | 7.725 | 0.484 | 52.56 | 0.578 | 0.061 | 0.823 | 0.621 | 0.273 |\n",
       "\n"
      ],
      "text/plain": [
       "  Country     year Score GDP   Family Health Freedom Generosity Corruption\n",
       "1 Afghanistan 2008 3.724 7.370 0.451  50.80  0.718   0.168      0.882     \n",
       "2 Afghanistan 2009 4.402 7.540 0.552  51.20  0.679   0.190      0.850     \n",
       "3 Afghanistan 2010 4.758 7.647 0.539  51.60  0.600   0.121      0.707     \n",
       "4 Afghanistan 2011 3.832 7.620 0.521  51.92  0.496   0.162      0.731     \n",
       "5 Afghanistan 2012 3.783 7.705 0.521  52.24  0.531   0.236      0.776     \n",
       "6 Afghanistan 2013 3.572 7.725 0.484  52.56  0.578   0.061      0.823     \n",
       "  Positive.affect Negative.affect\n",
       "1 0.518           0.258          \n",
       "2 0.584           0.237          \n",
       "3 0.618           0.275          \n",
       "4 0.611           0.267          \n",
       "5 0.710           0.268          \n",
       "6 0.621           0.273          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(happ_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 95 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Country</th><th scope=col>year</th><th scope=col>Score</th><th scope=col>GDP</th><th scope=col>Family</th><th scope=col>Health</th><th scope=col>Freedom</th><th scope=col>Generosity</th><th scope=col>Corruption</th><th scope=col>Positive.affect</th><th scope=col>Negative.affect</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>25</th><td>Albania               </td><td>2020</td><td>5.365</td><td> 9.497</td><td>0.710</td><td>69.3</td><td>0.754</td><td> 0.007</td><td>0.891</td><td>0.679</td><td>0.265</td></tr>\n",
       "\t<tr><th scope=row>52</th><td>Argentina             </td><td>2020</td><td>5.901</td><td> 9.850</td><td>0.897</td><td>69.2</td><td>0.823</td><td>-0.122</td><td>0.816</td><td>0.764</td><td>0.342</td></tr>\n",
       "\t<tr><th scope=row>80</th><td>Australia             </td><td>2020</td><td>7.137</td><td>10.760</td><td>0.937</td><td>74.2</td><td>0.905</td><td> 0.210</td><td>0.491</td><td>0.769</td><td>0.205</td></tr>\n",
       "\t<tr><th scope=row>93</th><td>Austria               </td><td>2020</td><td>7.213</td><td>10.851</td><td>0.925</td><td>73.6</td><td>0.912</td><td> 0.011</td><td>0.464</td><td>0.769</td><td>0.206</td></tr>\n",
       "\t<tr><th scope=row>118</th><td>Bahrain               </td><td>2020</td><td>6.173</td><td>10.620</td><td>0.848</td><td>69.7</td><td>0.945</td><td> 0.132</td><td>   NA</td><td>0.790</td><td>0.297</td></tr>\n",
       "\t<tr><th scope=row>133</th><td>Bangladesh            </td><td>2020</td><td>5.280</td><td> 8.472</td><td>0.739</td><td>65.3</td><td>0.777</td><td>-0.009</td><td>0.742</td><td>0.582</td><td>0.332</td></tr>\n",
       "\t<tr><th scope=row>161</th><td>Belgium               </td><td>2020</td><td>6.839</td><td>10.771</td><td>0.904</td><td>72.4</td><td>0.767</td><td>-0.164</td><td>0.634</td><td>0.647</td><td>0.260</td></tr>\n",
       "\t<tr><th scope=row>175</th><td>Benin                 </td><td>2020</td><td>4.408</td><td> 8.102</td><td>0.507</td><td>55.1</td><td>0.783</td><td>-0.083</td><td>0.532</td><td>0.609</td><td>0.305</td></tr>\n",
       "\t<tr><th scope=row>193</th><td>Bolivia               </td><td>2020</td><td>5.559</td><td> 8.998</td><td>0.805</td><td>64.2</td><td>0.877</td><td>-0.054</td><td>0.868</td><td>0.790</td><td>0.382</td></tr>\n",
       "\t<tr><th scope=row>206</th><td>Bosnia and Herzegovina</td><td>2020</td><td>5.516</td><td> 9.583</td><td>0.899</td><td>68.4</td><td>0.740</td><td> 0.138</td><td>0.916</td><td>0.644</td><td>0.325</td></tr>\n",
       "\t<tr><th scope=row>233</th><td>Brazil                </td><td>2020</td><td>6.110</td><td> 9.522</td><td>0.831</td><td>66.8</td><td>0.786</td><td>-0.053</td><td>0.729</td><td>0.692</td><td>0.389</td></tr>\n",
       "\t<tr><th scope=row>245</th><td>Bulgaria              </td><td>2020</td><td>5.598</td><td> 9.991</td><td>0.916</td><td>67.2</td><td>0.818</td><td>-0.004</td><td>0.901</td><td>0.706</td><td>0.221</td></tr>\n",
       "\t<tr><th scope=row>278</th><td>Cambodia              </td><td>2020</td><td>4.377</td><td> 8.362</td><td>0.724</td><td>62.4</td><td>0.963</td><td> 0.052</td><td>0.863</td><td>0.878</td><td>0.390</td></tr>\n",
       "\t<tr><th scope=row>293</th><td>Cameroon              </td><td>2020</td><td>5.241</td><td> 8.175</td><td>0.720</td><td>54.3</td><td>0.675</td><td> 0.049</td><td>0.837</td><td>0.630</td><td>0.386</td></tr>\n",
       "\t<tr><th scope=row>308</th><td>Canada                </td><td>2020</td><td>7.025</td><td>10.730</td><td>0.931</td><td>74.0</td><td>0.887</td><td> 0.050</td><td>0.434</td><td>0.796</td><td>0.307</td></tr>\n",
       "\t<tr><th scope=row>342</th><td>Chile                 </td><td>2020</td><td>6.151</td><td>10.020</td><td>0.888</td><td>70.1</td><td>0.781</td><td> 0.033</td><td>0.812</td><td>0.815</td><td>0.336</td></tr>\n",
       "\t<tr><th scope=row>357</th><td>China                 </td><td>2020</td><td>5.771</td><td> 9.702</td><td>0.808</td><td>69.9</td><td>0.891</td><td>-0.103</td><td>   NA</td><td>0.789</td><td>0.245</td></tr>\n",
       "\t<tr><th scope=row>372</th><td>Colombia              </td><td>2020</td><td>5.709</td><td> 9.495</td><td>0.797</td><td>68.3</td><td>0.840</td><td>-0.085</td><td>0.808</td><td>0.795</td><td>0.340</td></tr>\n",
       "\t<tr><th scope=row>423</th><td>Croatia               </td><td>2020</td><td>6.508</td><td>10.166</td><td>0.923</td><td>71.4</td><td>0.837</td><td>-0.063</td><td>0.961</td><td>0.743</td><td>0.286</td></tr>\n",
       "\t<tr><th scope=row>437</th><td>Cyprus                </td><td>2020</td><td>6.260</td><td>    NA</td><td>0.806</td><td>74.1</td><td>0.763</td><td>    NA</td><td>0.816</td><td>0.759</td><td>0.284</td></tr>\n",
       "\t<tr><th scope=row>449</th><td>Czech Republic        </td><td>2020</td><td>6.897</td><td>10.530</td><td>0.964</td><td>71.3</td><td>0.906</td><td>-0.127</td><td>0.884</td><td>0.832</td><td>0.290</td></tr>\n",
       "\t<tr><th scope=row>464</th><td>Denmark               </td><td>2020</td><td>7.515</td><td>10.910</td><td>0.947</td><td>73.0</td><td>0.938</td><td> 0.052</td><td>0.214</td><td>0.818</td><td>0.227</td></tr>\n",
       "\t<tr><th scope=row>483</th><td>Dominican Republic    </td><td>2020</td><td>5.168</td><td> 9.802</td><td>0.806</td><td>66.4</td><td>0.835</td><td>-0.128</td><td>0.636</td><td>0.734</td><td>0.314</td></tr>\n",
       "\t<tr><th scope=row>498</th><td>Ecuador               </td><td>2020</td><td>5.354</td><td> 9.244</td><td>0.804</td><td>69.1</td><td>0.829</td><td>-0.157</td><td>0.855</td><td>0.790</td><td>0.416</td></tr>\n",
       "\t<tr><th scope=row>513</th><td>Egypt                 </td><td>2020</td><td>4.472</td><td> 9.383</td><td>0.673</td><td>62.3</td><td>0.770</td><td>-0.112</td><td>   NA</td><td>0.599</td><td>0.442</td></tr>\n",
       "\t<tr><th scope=row>528</th><td>El Salvador           </td><td>2020</td><td>5.462</td><td> 9.019</td><td>0.696</td><td>66.7</td><td>0.924</td><td>-0.126</td><td>0.583</td><td>0.839</td><td>0.329</td></tr>\n",
       "\t<tr><th scope=row>542</th><td>Estonia               </td><td>2020</td><td>6.453</td><td>10.459</td><td>0.958</td><td>69.0</td><td>0.954</td><td>-0.082</td><td>0.398</td><td>0.807</td><td>0.188</td></tr>\n",
       "\t<tr><th scope=row>551</th><td>Ethiopia              </td><td>2020</td><td>4.549</td><td> 7.711</td><td>0.823</td><td>59.5</td><td>0.769</td><td> 0.188</td><td>0.784</td><td>0.669</td><td>0.252</td></tr>\n",
       "\t<tr><th scope=row>564</th><td>Finland               </td><td>2020</td><td>7.889</td><td>10.750</td><td>0.962</td><td>72.1</td><td>0.962</td><td>-0.116</td><td>0.164</td><td>0.744</td><td>0.193</td></tr>\n",
       "\t<tr><th scope=row>579</th><td>France                </td><td>2020</td><td>6.714</td><td>10.643</td><td>0.947</td><td>74.2</td><td>0.823</td><td>-0.169</td><td>0.565</td><td>0.732</td><td>0.231</td></tr>\n",
       "\t<tr><th scope=row>...</th><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "\t<tr><th scope=row>1300</th><td>North Macedonia         </td><td>2020</td><td>5.054</td><td> 9.690</td><td>0.750</td><td>65.56</td><td>0.787</td><td> 0.131</td><td>0.877</td><td>0.605</td><td>0.365</td></tr>\n",
       "\t<tr><th scope=row>1310</th><td>Norway                  </td><td>2020</td><td>7.290</td><td>11.042</td><td>0.956</td><td>73.40</td><td>0.965</td><td> 0.075</td><td>0.271</td><td>0.823</td><td>0.216</td></tr>\n",
       "\t<tr><th scope=row>1395</th><td>Philippines             </td><td>2020</td><td>5.080</td><td> 9.061</td><td>0.781</td><td>62.10</td><td>0.932</td><td>-0.116</td><td>0.744</td><td>0.804</td><td>0.327</td></tr>\n",
       "\t<tr><th scope=row>1409</th><td>Poland                  </td><td>2020</td><td>6.139</td><td>10.371</td><td>0.953</td><td>70.10</td><td>0.767</td><td>-0.007</td><td>0.787</td><td>0.760</td><td>0.329</td></tr>\n",
       "\t<tr><th scope=row>1422</th><td>Portugal                </td><td>2020</td><td>5.768</td><td>10.371</td><td>0.875</td><td>72.80</td><td>0.913</td><td>-0.238</td><td>0.867</td><td>0.648</td><td>0.383</td></tr>\n",
       "\t<tr><th scope=row>1455</th><td>Russia                  </td><td>2020</td><td>5.495</td><td>10.162</td><td>0.887</td><td>65.10</td><td>0.714</td><td>-0.071</td><td>0.823</td><td>0.645</td><td>0.190</td></tr>\n",
       "\t<tr><th scope=row>1482</th><td>Saudi Arabia            </td><td>2020</td><td>6.560</td><td>10.701</td><td>0.890</td><td>66.90</td><td>0.884</td><td>-0.111</td><td>   NA</td><td>0.754</td><td>0.251</td></tr>\n",
       "\t<tr><th scope=row>1509</th><td>Serbia                  </td><td>2020</td><td>6.042</td><td> 9.788</td><td>0.852</td><td>69.00</td><td>0.843</td><td> 0.149</td><td>0.824</td><td>0.603</td><td>0.358</td></tr>\n",
       "\t<tr><th scope=row>1546</th><td>Slovakia                </td><td>2020</td><td>6.519</td><td>10.332</td><td>0.954</td><td>69.50</td><td>0.762</td><td>-0.075</td><td>0.901</td><td>0.764</td><td>0.274</td></tr>\n",
       "\t<tr><th scope=row>1559</th><td>Slovenia                </td><td>2020</td><td>6.462</td><td>10.478</td><td>0.953</td><td>71.70</td><td>0.958</td><td>-0.081</td><td>0.797</td><td>0.610</td><td>0.314</td></tr>\n",
       "\t<tr><th scope=row>1581</th><td>South Africa            </td><td>2020</td><td>4.947</td><td> 9.332</td><td>0.891</td><td>57.30</td><td>0.757</td><td>-0.015</td><td>0.912</td><td>0.820</td><td>0.294</td></tr>\n",
       "\t<tr><th scope=row>1596</th><td>South Korea             </td><td>2020</td><td>5.793</td><td>10.648</td><td>0.808</td><td>74.20</td><td>0.711</td><td>-0.106</td><td>0.665</td><td>0.640</td><td>0.247</td></tr>\n",
       "\t<tr><th scope=row>1615</th><td>Spain                   </td><td>2020</td><td>6.502</td><td>10.488</td><td>0.935</td><td>75.00</td><td>0.783</td><td>-0.121</td><td>0.730</td><td>0.686</td><td>0.317</td></tr>\n",
       "\t<tr><th scope=row>1652</th><td>Sweden                  </td><td>2020</td><td>7.314</td><td>10.838</td><td>0.936</td><td>72.80</td><td>0.951</td><td> 0.091</td><td>0.203</td><td>0.766</td><td>0.222</td></tr>\n",
       "\t<tr><th scope=row>1662</th><td>Switzerland             </td><td>2020</td><td>7.508</td><td>11.081</td><td>0.946</td><td>74.70</td><td>0.917</td><td>-0.064</td><td>0.280</td><td>0.769</td><td>0.193</td></tr>\n",
       "\t<tr><th scope=row>1682</th><td>Taiwan Province of China</td><td>2020</td><td>6.751</td><td>    NA</td><td>0.901</td><td>   NA</td><td>0.799</td><td>    NA</td><td>0.711</td><td>0.845</td><td>0.083</td></tr>\n",
       "\t<tr><th scope=row>1697</th><td>Tajikistan              </td><td>2020</td><td>5.373</td><td> 8.080</td><td>0.790</td><td>64.70</td><td>   NA</td><td>-0.040</td><td>0.550</td><td>0.749</td><td>0.344</td></tr>\n",
       "\t<tr><th scope=row>1712</th><td>Tanzania                </td><td>2020</td><td>3.786</td><td> 7.881</td><td>0.740</td><td>58.50</td><td>0.830</td><td> 0.295</td><td>0.521</td><td>0.686</td><td>0.271</td></tr>\n",
       "\t<tr><th scope=row>1727</th><td>Thailand                </td><td>2020</td><td>5.885</td><td> 9.769</td><td>0.867</td><td>67.60</td><td>0.840</td><td> 0.273</td><td>0.918</td><td>0.783</td><td>0.326</td></tr>\n",
       "\t<tr><th scope=row>1753</th><td>Tunisia                 </td><td>2020</td><td>4.731</td><td> 9.231</td><td>0.719</td><td>67.50</td><td>0.668</td><td>-0.202</td><td>0.877</td><td>0.585</td><td>0.439</td></tr>\n",
       "\t<tr><th scope=row>1768</th><td>Turkey                  </td><td>2020</td><td>4.862</td><td>10.219</td><td>0.857</td><td>67.60</td><td>0.510</td><td>-0.111</td><td>0.774</td><td>0.384</td><td>0.440</td></tr>\n",
       "\t<tr><th scope=row>1793</th><td>Uganda                  </td><td>2020</td><td>4.641</td><td> 7.684</td><td>0.800</td><td>56.50</td><td>0.687</td><td> 0.147</td><td>0.878</td><td>0.699</td><td>0.425</td></tr>\n",
       "\t<tr><th scope=row>1808</th><td>Ukraine                 </td><td>2020</td><td>5.270</td><td> 9.428</td><td>0.885</td><td>65.20</td><td>0.784</td><td> 0.126</td><td>0.946</td><td>0.688</td><td>0.285</td></tr>\n",
       "\t<tr><th scope=row>1821</th><td>United Arab Emirates    </td><td>2020</td><td>6.458</td><td>11.053</td><td>0.827</td><td>67.50</td><td>0.942</td><td> 0.060</td><td>   NA</td><td>0.752</td><td>0.298</td></tr>\n",
       "\t<tr><th scope=row>1836</th><td>United Kingdom          </td><td>2020</td><td>6.798</td><td>10.626</td><td>0.929</td><td>72.70</td><td>0.885</td><td> 0.203</td><td>0.490</td><td>0.758</td><td>0.225</td></tr>\n",
       "\t<tr><th scope=row>1851</th><td>United States           </td><td>2020</td><td>7.028</td><td>11.001</td><td>0.937</td><td>68.10</td><td>0.850</td><td> 0.034</td><td>0.678</td><td>0.787</td><td>0.295</td></tr>\n",
       "\t<tr><th scope=row>1866</th><td>Uruguay                 </td><td>2020</td><td>6.310</td><td> 9.937</td><td>0.921</td><td>69.20</td><td>0.908</td><td>-0.084</td><td>0.491</td><td>0.807</td><td>0.265</td></tr>\n",
       "\t<tr><th scope=row>1894</th><td>Venezuela               </td><td>2020</td><td>4.574</td><td>    NA</td><td>0.805</td><td>66.90</td><td>0.612</td><td>    NA</td><td>0.811</td><td>0.722</td><td>0.396</td></tr>\n",
       "\t<tr><th scope=row>1934</th><td>Zambia                  </td><td>2020</td><td>4.838</td><td> 8.117</td><td>0.767</td><td>56.30</td><td>0.750</td><td> 0.056</td><td>0.810</td><td>0.691</td><td>0.345</td></tr>\n",
       "\t<tr><th scope=row>1949</th><td>Zimbabwe                </td><td>2020</td><td>3.160</td><td> 7.829</td><td>0.717</td><td>56.80</td><td>0.643</td><td>-0.009</td><td>0.789</td><td>0.703</td><td>0.346</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 95 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & Country & year & Score & GDP & Family & Health & Freedom & Generosity & Corruption & Positive.affect & Negative.affect\\\\\n",
       "  & <chr> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t25 & Albania                & 2020 & 5.365 &  9.497 & 0.710 & 69.3 & 0.754 &  0.007 & 0.891 & 0.679 & 0.265\\\\\n",
       "\t52 & Argentina              & 2020 & 5.901 &  9.850 & 0.897 & 69.2 & 0.823 & -0.122 & 0.816 & 0.764 & 0.342\\\\\n",
       "\t80 & Australia              & 2020 & 7.137 & 10.760 & 0.937 & 74.2 & 0.905 &  0.210 & 0.491 & 0.769 & 0.205\\\\\n",
       "\t93 & Austria                & 2020 & 7.213 & 10.851 & 0.925 & 73.6 & 0.912 &  0.011 & 0.464 & 0.769 & 0.206\\\\\n",
       "\t118 & Bahrain                & 2020 & 6.173 & 10.620 & 0.848 & 69.7 & 0.945 &  0.132 &    NA & 0.790 & 0.297\\\\\n",
       "\t133 & Bangladesh             & 2020 & 5.280 &  8.472 & 0.739 & 65.3 & 0.777 & -0.009 & 0.742 & 0.582 & 0.332\\\\\n",
       "\t161 & Belgium                & 2020 & 6.839 & 10.771 & 0.904 & 72.4 & 0.767 & -0.164 & 0.634 & 0.647 & 0.260\\\\\n",
       "\t175 & Benin                  & 2020 & 4.408 &  8.102 & 0.507 & 55.1 & 0.783 & -0.083 & 0.532 & 0.609 & 0.305\\\\\n",
       "\t193 & Bolivia                & 2020 & 5.559 &  8.998 & 0.805 & 64.2 & 0.877 & -0.054 & 0.868 & 0.790 & 0.382\\\\\n",
       "\t206 & Bosnia and Herzegovina & 2020 & 5.516 &  9.583 & 0.899 & 68.4 & 0.740 &  0.138 & 0.916 & 0.644 & 0.325\\\\\n",
       "\t233 & Brazil                 & 2020 & 6.110 &  9.522 & 0.831 & 66.8 & 0.786 & -0.053 & 0.729 & 0.692 & 0.389\\\\\n",
       "\t245 & Bulgaria               & 2020 & 5.598 &  9.991 & 0.916 & 67.2 & 0.818 & -0.004 & 0.901 & 0.706 & 0.221\\\\\n",
       "\t278 & Cambodia               & 2020 & 4.377 &  8.362 & 0.724 & 62.4 & 0.963 &  0.052 & 0.863 & 0.878 & 0.390\\\\\n",
       "\t293 & Cameroon               & 2020 & 5.241 &  8.175 & 0.720 & 54.3 & 0.675 &  0.049 & 0.837 & 0.630 & 0.386\\\\\n",
       "\t308 & Canada                 & 2020 & 7.025 & 10.730 & 0.931 & 74.0 & 0.887 &  0.050 & 0.434 & 0.796 & 0.307\\\\\n",
       "\t342 & Chile                  & 2020 & 6.151 & 10.020 & 0.888 & 70.1 & 0.781 &  0.033 & 0.812 & 0.815 & 0.336\\\\\n",
       "\t357 & China                  & 2020 & 5.771 &  9.702 & 0.808 & 69.9 & 0.891 & -0.103 &    NA & 0.789 & 0.245\\\\\n",
       "\t372 & Colombia               & 2020 & 5.709 &  9.495 & 0.797 & 68.3 & 0.840 & -0.085 & 0.808 & 0.795 & 0.340\\\\\n",
       "\t423 & Croatia                & 2020 & 6.508 & 10.166 & 0.923 & 71.4 & 0.837 & -0.063 & 0.961 & 0.743 & 0.286\\\\\n",
       "\t437 & Cyprus                 & 2020 & 6.260 &     NA & 0.806 & 74.1 & 0.763 &     NA & 0.816 & 0.759 & 0.284\\\\\n",
       "\t449 & Czech Republic         & 2020 & 6.897 & 10.530 & 0.964 & 71.3 & 0.906 & -0.127 & 0.884 & 0.832 & 0.290\\\\\n",
       "\t464 & Denmark                & 2020 & 7.515 & 10.910 & 0.947 & 73.0 & 0.938 &  0.052 & 0.214 & 0.818 & 0.227\\\\\n",
       "\t483 & Dominican Republic     & 2020 & 5.168 &  9.802 & 0.806 & 66.4 & 0.835 & -0.128 & 0.636 & 0.734 & 0.314\\\\\n",
       "\t498 & Ecuador                & 2020 & 5.354 &  9.244 & 0.804 & 69.1 & 0.829 & -0.157 & 0.855 & 0.790 & 0.416\\\\\n",
       "\t513 & Egypt                  & 2020 & 4.472 &  9.383 & 0.673 & 62.3 & 0.770 & -0.112 &    NA & 0.599 & 0.442\\\\\n",
       "\t528 & El Salvador            & 2020 & 5.462 &  9.019 & 0.696 & 66.7 & 0.924 & -0.126 & 0.583 & 0.839 & 0.329\\\\\n",
       "\t542 & Estonia                & 2020 & 6.453 & 10.459 & 0.958 & 69.0 & 0.954 & -0.082 & 0.398 & 0.807 & 0.188\\\\\n",
       "\t551 & Ethiopia               & 2020 & 4.549 &  7.711 & 0.823 & 59.5 & 0.769 &  0.188 & 0.784 & 0.669 & 0.252\\\\\n",
       "\t564 & Finland                & 2020 & 7.889 & 10.750 & 0.962 & 72.1 & 0.962 & -0.116 & 0.164 & 0.744 & 0.193\\\\\n",
       "\t579 & France                 & 2020 & 6.714 & 10.643 & 0.947 & 74.2 & 0.823 & -0.169 & 0.565 & 0.732 & 0.231\\\\\n",
       "\t... & ... & ... & ... & ... & ... & ... & ... & ... & ... & ... & ...\\\\\n",
       "\t1300 & North Macedonia          & 2020 & 5.054 &  9.690 & 0.750 & 65.56 & 0.787 &  0.131 & 0.877 & 0.605 & 0.365\\\\\n",
       "\t1310 & Norway                   & 2020 & 7.290 & 11.042 & 0.956 & 73.40 & 0.965 &  0.075 & 0.271 & 0.823 & 0.216\\\\\n",
       "\t1395 & Philippines              & 2020 & 5.080 &  9.061 & 0.781 & 62.10 & 0.932 & -0.116 & 0.744 & 0.804 & 0.327\\\\\n",
       "\t1409 & Poland                   & 2020 & 6.139 & 10.371 & 0.953 & 70.10 & 0.767 & -0.007 & 0.787 & 0.760 & 0.329\\\\\n",
       "\t1422 & Portugal                 & 2020 & 5.768 & 10.371 & 0.875 & 72.80 & 0.913 & -0.238 & 0.867 & 0.648 & 0.383\\\\\n",
       "\t1455 & Russia                   & 2020 & 5.495 & 10.162 & 0.887 & 65.10 & 0.714 & -0.071 & 0.823 & 0.645 & 0.190\\\\\n",
       "\t1482 & Saudi Arabia             & 2020 & 6.560 & 10.701 & 0.890 & 66.90 & 0.884 & -0.111 &    NA & 0.754 & 0.251\\\\\n",
       "\t1509 & Serbia                   & 2020 & 6.042 &  9.788 & 0.852 & 69.00 & 0.843 &  0.149 & 0.824 & 0.603 & 0.358\\\\\n",
       "\t1546 & Slovakia                 & 2020 & 6.519 & 10.332 & 0.954 & 69.50 & 0.762 & -0.075 & 0.901 & 0.764 & 0.274\\\\\n",
       "\t1559 & Slovenia                 & 2020 & 6.462 & 10.478 & 0.953 & 71.70 & 0.958 & -0.081 & 0.797 & 0.610 & 0.314\\\\\n",
       "\t1581 & South Africa             & 2020 & 4.947 &  9.332 & 0.891 & 57.30 & 0.757 & -0.015 & 0.912 & 0.820 & 0.294\\\\\n",
       "\t1596 & South Korea              & 2020 & 5.793 & 10.648 & 0.808 & 74.20 & 0.711 & -0.106 & 0.665 & 0.640 & 0.247\\\\\n",
       "\t1615 & Spain                    & 2020 & 6.502 & 10.488 & 0.935 & 75.00 & 0.783 & -0.121 & 0.730 & 0.686 & 0.317\\\\\n",
       "\t1652 & Sweden                   & 2020 & 7.314 & 10.838 & 0.936 & 72.80 & 0.951 &  0.091 & 0.203 & 0.766 & 0.222\\\\\n",
       "\t1662 & Switzerland              & 2020 & 7.508 & 11.081 & 0.946 & 74.70 & 0.917 & -0.064 & 0.280 & 0.769 & 0.193\\\\\n",
       "\t1682 & Taiwan Province of China & 2020 & 6.751 &     NA & 0.901 &    NA & 0.799 &     NA & 0.711 & 0.845 & 0.083\\\\\n",
       "\t1697 & Tajikistan               & 2020 & 5.373 &  8.080 & 0.790 & 64.70 &    NA & -0.040 & 0.550 & 0.749 & 0.344\\\\\n",
       "\t1712 & Tanzania                 & 2020 & 3.786 &  7.881 & 0.740 & 58.50 & 0.830 &  0.295 & 0.521 & 0.686 & 0.271\\\\\n",
       "\t1727 & Thailand                 & 2020 & 5.885 &  9.769 & 0.867 & 67.60 & 0.840 &  0.273 & 0.918 & 0.783 & 0.326\\\\\n",
       "\t1753 & Tunisia                  & 2020 & 4.731 &  9.231 & 0.719 & 67.50 & 0.668 & -0.202 & 0.877 & 0.585 & 0.439\\\\\n",
       "\t1768 & Turkey                   & 2020 & 4.862 & 10.219 & 0.857 & 67.60 & 0.510 & -0.111 & 0.774 & 0.384 & 0.440\\\\\n",
       "\t1793 & Uganda                   & 2020 & 4.641 &  7.684 & 0.800 & 56.50 & 0.687 &  0.147 & 0.878 & 0.699 & 0.425\\\\\n",
       "\t1808 & Ukraine                  & 2020 & 5.270 &  9.428 & 0.885 & 65.20 & 0.784 &  0.126 & 0.946 & 0.688 & 0.285\\\\\n",
       "\t1821 & United Arab Emirates     & 2020 & 6.458 & 11.053 & 0.827 & 67.50 & 0.942 &  0.060 &    NA & 0.752 & 0.298\\\\\n",
       "\t1836 & United Kingdom           & 2020 & 6.798 & 10.626 & 0.929 & 72.70 & 0.885 &  0.203 & 0.490 & 0.758 & 0.225\\\\\n",
       "\t1851 & United States            & 2020 & 7.028 & 11.001 & 0.937 & 68.10 & 0.850 &  0.034 & 0.678 & 0.787 & 0.295\\\\\n",
       "\t1866 & Uruguay                  & 2020 & 6.310 &  9.937 & 0.921 & 69.20 & 0.908 & -0.084 & 0.491 & 0.807 & 0.265\\\\\n",
       "\t1894 & Venezuela                & 2020 & 4.574 &     NA & 0.805 & 66.90 & 0.612 &     NA & 0.811 & 0.722 & 0.396\\\\\n",
       "\t1934 & Zambia                   & 2020 & 4.838 &  8.117 & 0.767 & 56.30 & 0.750 &  0.056 & 0.810 & 0.691 & 0.345\\\\\n",
       "\t1949 & Zimbabwe                 & 2020 & 3.160 &  7.829 & 0.717 & 56.80 & 0.643 & -0.009 & 0.789 & 0.703 & 0.346\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 95 × 11\n",
       "\n",
       "| <!--/--> | Country &lt;chr&gt; | year &lt;int&gt; | Score &lt;dbl&gt; | GDP &lt;dbl&gt; | Family &lt;dbl&gt; | Health &lt;dbl&gt; | Freedom &lt;dbl&gt; | Generosity &lt;dbl&gt; | Corruption &lt;dbl&gt; | Positive.affect &lt;dbl&gt; | Negative.affect &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 25 | Albania                | 2020 | 5.365 |  9.497 | 0.710 | 69.3 | 0.754 |  0.007 | 0.891 | 0.679 | 0.265 |\n",
       "| 52 | Argentina              | 2020 | 5.901 |  9.850 | 0.897 | 69.2 | 0.823 | -0.122 | 0.816 | 0.764 | 0.342 |\n",
       "| 80 | Australia              | 2020 | 7.137 | 10.760 | 0.937 | 74.2 | 0.905 |  0.210 | 0.491 | 0.769 | 0.205 |\n",
       "| 93 | Austria                | 2020 | 7.213 | 10.851 | 0.925 | 73.6 | 0.912 |  0.011 | 0.464 | 0.769 | 0.206 |\n",
       "| 118 | Bahrain                | 2020 | 6.173 | 10.620 | 0.848 | 69.7 | 0.945 |  0.132 |    NA | 0.790 | 0.297 |\n",
       "| 133 | Bangladesh             | 2020 | 5.280 |  8.472 | 0.739 | 65.3 | 0.777 | -0.009 | 0.742 | 0.582 | 0.332 |\n",
       "| 161 | Belgium                | 2020 | 6.839 | 10.771 | 0.904 | 72.4 | 0.767 | -0.164 | 0.634 | 0.647 | 0.260 |\n",
       "| 175 | Benin                  | 2020 | 4.408 |  8.102 | 0.507 | 55.1 | 0.783 | -0.083 | 0.532 | 0.609 | 0.305 |\n",
       "| 193 | Bolivia                | 2020 | 5.559 |  8.998 | 0.805 | 64.2 | 0.877 | -0.054 | 0.868 | 0.790 | 0.382 |\n",
       "| 206 | Bosnia and Herzegovina | 2020 | 5.516 |  9.583 | 0.899 | 68.4 | 0.740 |  0.138 | 0.916 | 0.644 | 0.325 |\n",
       "| 233 | Brazil                 | 2020 | 6.110 |  9.522 | 0.831 | 66.8 | 0.786 | -0.053 | 0.729 | 0.692 | 0.389 |\n",
       "| 245 | Bulgaria               | 2020 | 5.598 |  9.991 | 0.916 | 67.2 | 0.818 | -0.004 | 0.901 | 0.706 | 0.221 |\n",
       "| 278 | Cambodia               | 2020 | 4.377 |  8.362 | 0.724 | 62.4 | 0.963 |  0.052 | 0.863 | 0.878 | 0.390 |\n",
       "| 293 | Cameroon               | 2020 | 5.241 |  8.175 | 0.720 | 54.3 | 0.675 |  0.049 | 0.837 | 0.630 | 0.386 |\n",
       "| 308 | Canada                 | 2020 | 7.025 | 10.730 | 0.931 | 74.0 | 0.887 |  0.050 | 0.434 | 0.796 | 0.307 |\n",
       "| 342 | Chile                  | 2020 | 6.151 | 10.020 | 0.888 | 70.1 | 0.781 |  0.033 | 0.812 | 0.815 | 0.336 |\n",
       "| 357 | China                  | 2020 | 5.771 |  9.702 | 0.808 | 69.9 | 0.891 | -0.103 |    NA | 0.789 | 0.245 |\n",
       "| 372 | Colombia               | 2020 | 5.709 |  9.495 | 0.797 | 68.3 | 0.840 | -0.085 | 0.808 | 0.795 | 0.340 |\n",
       "| 423 | Croatia                | 2020 | 6.508 | 10.166 | 0.923 | 71.4 | 0.837 | -0.063 | 0.961 | 0.743 | 0.286 |\n",
       "| 437 | Cyprus                 | 2020 | 6.260 |     NA | 0.806 | 74.1 | 0.763 |     NA | 0.816 | 0.759 | 0.284 |\n",
       "| 449 | Czech Republic         | 2020 | 6.897 | 10.530 | 0.964 | 71.3 | 0.906 | -0.127 | 0.884 | 0.832 | 0.290 |\n",
       "| 464 | Denmark                | 2020 | 7.515 | 10.910 | 0.947 | 73.0 | 0.938 |  0.052 | 0.214 | 0.818 | 0.227 |\n",
       "| 483 | Dominican Republic     | 2020 | 5.168 |  9.802 | 0.806 | 66.4 | 0.835 | -0.128 | 0.636 | 0.734 | 0.314 |\n",
       "| 498 | Ecuador                | 2020 | 5.354 |  9.244 | 0.804 | 69.1 | 0.829 | -0.157 | 0.855 | 0.790 | 0.416 |\n",
       "| 513 | Egypt                  | 2020 | 4.472 |  9.383 | 0.673 | 62.3 | 0.770 | -0.112 |    NA | 0.599 | 0.442 |\n",
       "| 528 | El Salvador            | 2020 | 5.462 |  9.019 | 0.696 | 66.7 | 0.924 | -0.126 | 0.583 | 0.839 | 0.329 |\n",
       "| 542 | Estonia                | 2020 | 6.453 | 10.459 | 0.958 | 69.0 | 0.954 | -0.082 | 0.398 | 0.807 | 0.188 |\n",
       "| 551 | Ethiopia               | 2020 | 4.549 |  7.711 | 0.823 | 59.5 | 0.769 |  0.188 | 0.784 | 0.669 | 0.252 |\n",
       "| 564 | Finland                | 2020 | 7.889 | 10.750 | 0.962 | 72.1 | 0.962 | -0.116 | 0.164 | 0.744 | 0.193 |\n",
       "| 579 | France                 | 2020 | 6.714 | 10.643 | 0.947 | 74.2 | 0.823 | -0.169 | 0.565 | 0.732 | 0.231 |\n",
       "| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n",
       "| 1300 | North Macedonia          | 2020 | 5.054 |  9.690 | 0.750 | 65.56 | 0.787 |  0.131 | 0.877 | 0.605 | 0.365 |\n",
       "| 1310 | Norway                   | 2020 | 7.290 | 11.042 | 0.956 | 73.40 | 0.965 |  0.075 | 0.271 | 0.823 | 0.216 |\n",
       "| 1395 | Philippines              | 2020 | 5.080 |  9.061 | 0.781 | 62.10 | 0.932 | -0.116 | 0.744 | 0.804 | 0.327 |\n",
       "| 1409 | Poland                   | 2020 | 6.139 | 10.371 | 0.953 | 70.10 | 0.767 | -0.007 | 0.787 | 0.760 | 0.329 |\n",
       "| 1422 | Portugal                 | 2020 | 5.768 | 10.371 | 0.875 | 72.80 | 0.913 | -0.238 | 0.867 | 0.648 | 0.383 |\n",
       "| 1455 | Russia                   | 2020 | 5.495 | 10.162 | 0.887 | 65.10 | 0.714 | -0.071 | 0.823 | 0.645 | 0.190 |\n",
       "| 1482 | Saudi Arabia             | 2020 | 6.560 | 10.701 | 0.890 | 66.90 | 0.884 | -0.111 |    NA | 0.754 | 0.251 |\n",
       "| 1509 | Serbia                   | 2020 | 6.042 |  9.788 | 0.852 | 69.00 | 0.843 |  0.149 | 0.824 | 0.603 | 0.358 |\n",
       "| 1546 | Slovakia                 | 2020 | 6.519 | 10.332 | 0.954 | 69.50 | 0.762 | -0.075 | 0.901 | 0.764 | 0.274 |\n",
       "| 1559 | Slovenia                 | 2020 | 6.462 | 10.478 | 0.953 | 71.70 | 0.958 | -0.081 | 0.797 | 0.610 | 0.314 |\n",
       "| 1581 | South Africa             | 2020 | 4.947 |  9.332 | 0.891 | 57.30 | 0.757 | -0.015 | 0.912 | 0.820 | 0.294 |\n",
       "| 1596 | South Korea              | 2020 | 5.793 | 10.648 | 0.808 | 74.20 | 0.711 | -0.106 | 0.665 | 0.640 | 0.247 |\n",
       "| 1615 | Spain                    | 2020 | 6.502 | 10.488 | 0.935 | 75.00 | 0.783 | -0.121 | 0.730 | 0.686 | 0.317 |\n",
       "| 1652 | Sweden                   | 2020 | 7.314 | 10.838 | 0.936 | 72.80 | 0.951 |  0.091 | 0.203 | 0.766 | 0.222 |\n",
       "| 1662 | Switzerland              | 2020 | 7.508 | 11.081 | 0.946 | 74.70 | 0.917 | -0.064 | 0.280 | 0.769 | 0.193 |\n",
       "| 1682 | Taiwan Province of China | 2020 | 6.751 |     NA | 0.901 |    NA | 0.799 |     NA | 0.711 | 0.845 | 0.083 |\n",
       "| 1697 | Tajikistan               | 2020 | 5.373 |  8.080 | 0.790 | 64.70 |    NA | -0.040 | 0.550 | 0.749 | 0.344 |\n",
       "| 1712 | Tanzania                 | 2020 | 3.786 |  7.881 | 0.740 | 58.50 | 0.830 |  0.295 | 0.521 | 0.686 | 0.271 |\n",
       "| 1727 | Thailand                 | 2020 | 5.885 |  9.769 | 0.867 | 67.60 | 0.840 |  0.273 | 0.918 | 0.783 | 0.326 |\n",
       "| 1753 | Tunisia                  | 2020 | 4.731 |  9.231 | 0.719 | 67.50 | 0.668 | -0.202 | 0.877 | 0.585 | 0.439 |\n",
       "| 1768 | Turkey                   | 2020 | 4.862 | 10.219 | 0.857 | 67.60 | 0.510 | -0.111 | 0.774 | 0.384 | 0.440 |\n",
       "| 1793 | Uganda                   | 2020 | 4.641 |  7.684 | 0.800 | 56.50 | 0.687 |  0.147 | 0.878 | 0.699 | 0.425 |\n",
       "| 1808 | Ukraine                  | 2020 | 5.270 |  9.428 | 0.885 | 65.20 | 0.784 |  0.126 | 0.946 | 0.688 | 0.285 |\n",
       "| 1821 | United Arab Emirates     | 2020 | 6.458 | 11.053 | 0.827 | 67.50 | 0.942 |  0.060 |    NA | 0.752 | 0.298 |\n",
       "| 1836 | United Kingdom           | 2020 | 6.798 | 10.626 | 0.929 | 72.70 | 0.885 |  0.203 | 0.490 | 0.758 | 0.225 |\n",
       "| 1851 | United States            | 2020 | 7.028 | 11.001 | 0.937 | 68.10 | 0.850 |  0.034 | 0.678 | 0.787 | 0.295 |\n",
       "| 1866 | Uruguay                  | 2020 | 6.310 |  9.937 | 0.921 | 69.20 | 0.908 | -0.084 | 0.491 | 0.807 | 0.265 |\n",
       "| 1894 | Venezuela                | 2020 | 4.574 |     NA | 0.805 | 66.90 | 0.612 |     NA | 0.811 | 0.722 | 0.396 |\n",
       "| 1934 | Zambia                   | 2020 | 4.838 |  8.117 | 0.767 | 56.30 | 0.750 |  0.056 | 0.810 | 0.691 | 0.345 |\n",
       "| 1949 | Zimbabwe                 | 2020 | 3.160 |  7.829 | 0.717 | 56.80 | 0.643 | -0.009 | 0.789 | 0.703 | 0.346 |\n",
       "\n"
      ],
      "text/plain": [
       "     Country                  year Score GDP    Family Health Freedom\n",
       "25   Albania                  2020 5.365  9.497 0.710  69.3   0.754  \n",
       "52   Argentina                2020 5.901  9.850 0.897  69.2   0.823  \n",
       "80   Australia                2020 7.137 10.760 0.937  74.2   0.905  \n",
       "93   Austria                  2020 7.213 10.851 0.925  73.6   0.912  \n",
       "118  Bahrain                  2020 6.173 10.620 0.848  69.7   0.945  \n",
       "133  Bangladesh               2020 5.280  8.472 0.739  65.3   0.777  \n",
       "161  Belgium                  2020 6.839 10.771 0.904  72.4   0.767  \n",
       "175  Benin                    2020 4.408  8.102 0.507  55.1   0.783  \n",
       "193  Bolivia                  2020 5.559  8.998 0.805  64.2   0.877  \n",
       "206  Bosnia and Herzegovina   2020 5.516  9.583 0.899  68.4   0.740  \n",
       "233  Brazil                   2020 6.110  9.522 0.831  66.8   0.786  \n",
       "245  Bulgaria                 2020 5.598  9.991 0.916  67.2   0.818  \n",
       "278  Cambodia                 2020 4.377  8.362 0.724  62.4   0.963  \n",
       "293  Cameroon                 2020 5.241  8.175 0.720  54.3   0.675  \n",
       "308  Canada                   2020 7.025 10.730 0.931  74.0   0.887  \n",
       "342  Chile                    2020 6.151 10.020 0.888  70.1   0.781  \n",
       "357  China                    2020 5.771  9.702 0.808  69.9   0.891  \n",
       "372  Colombia                 2020 5.709  9.495 0.797  68.3   0.840  \n",
       "423  Croatia                  2020 6.508 10.166 0.923  71.4   0.837  \n",
       "437  Cyprus                   2020 6.260     NA 0.806  74.1   0.763  \n",
       "449  Czech Republic           2020 6.897 10.530 0.964  71.3   0.906  \n",
       "464  Denmark                  2020 7.515 10.910 0.947  73.0   0.938  \n",
       "483  Dominican Republic       2020 5.168  9.802 0.806  66.4   0.835  \n",
       "498  Ecuador                  2020 5.354  9.244 0.804  69.1   0.829  \n",
       "513  Egypt                    2020 4.472  9.383 0.673  62.3   0.770  \n",
       "528  El Salvador              2020 5.462  9.019 0.696  66.7   0.924  \n",
       "542  Estonia                  2020 6.453 10.459 0.958  69.0   0.954  \n",
       "551  Ethiopia                 2020 4.549  7.711 0.823  59.5   0.769  \n",
       "564  Finland                  2020 7.889 10.750 0.962  72.1   0.962  \n",
       "579  France                   2020 6.714 10.643 0.947  74.2   0.823  \n",
       "...  ...                      ...  ...   ...    ...    ...    ...    \n",
       "1300 North Macedonia          2020 5.054  9.690 0.750  65.56  0.787  \n",
       "1310 Norway                   2020 7.290 11.042 0.956  73.40  0.965  \n",
       "1395 Philippines              2020 5.080  9.061 0.781  62.10  0.932  \n",
       "1409 Poland                   2020 6.139 10.371 0.953  70.10  0.767  \n",
       "1422 Portugal                 2020 5.768 10.371 0.875  72.80  0.913  \n",
       "1455 Russia                   2020 5.495 10.162 0.887  65.10  0.714  \n",
       "1482 Saudi Arabia             2020 6.560 10.701 0.890  66.90  0.884  \n",
       "1509 Serbia                   2020 6.042  9.788 0.852  69.00  0.843  \n",
       "1546 Slovakia                 2020 6.519 10.332 0.954  69.50  0.762  \n",
       "1559 Slovenia                 2020 6.462 10.478 0.953  71.70  0.958  \n",
       "1581 South Africa             2020 4.947  9.332 0.891  57.30  0.757  \n",
       "1596 South Korea              2020 5.793 10.648 0.808  74.20  0.711  \n",
       "1615 Spain                    2020 6.502 10.488 0.935  75.00  0.783  \n",
       "1652 Sweden                   2020 7.314 10.838 0.936  72.80  0.951  \n",
       "1662 Switzerland              2020 7.508 11.081 0.946  74.70  0.917  \n",
       "1682 Taiwan Province of China 2020 6.751     NA 0.901     NA  0.799  \n",
       "1697 Tajikistan               2020 5.373  8.080 0.790  64.70     NA  \n",
       "1712 Tanzania                 2020 3.786  7.881 0.740  58.50  0.830  \n",
       "1727 Thailand                 2020 5.885  9.769 0.867  67.60  0.840  \n",
       "1753 Tunisia                  2020 4.731  9.231 0.719  67.50  0.668  \n",
       "1768 Turkey                   2020 4.862 10.219 0.857  67.60  0.510  \n",
       "1793 Uganda                   2020 4.641  7.684 0.800  56.50  0.687  \n",
       "1808 Ukraine                  2020 5.270  9.428 0.885  65.20  0.784  \n",
       "1821 United Arab Emirates     2020 6.458 11.053 0.827  67.50  0.942  \n",
       "1836 United Kingdom           2020 6.798 10.626 0.929  72.70  0.885  \n",
       "1851 United States            2020 7.028 11.001 0.937  68.10  0.850  \n",
       "1866 Uruguay                  2020 6.310  9.937 0.921  69.20  0.908  \n",
       "1894 Venezuela                2020 4.574     NA 0.805  66.90  0.612  \n",
       "1934 Zambia                   2020 4.838  8.117 0.767  56.30  0.750  \n",
       "1949 Zimbabwe                 2020 3.160  7.829 0.717  56.80  0.643  \n",
       "     Generosity Corruption Positive.affect Negative.affect\n",
       "25    0.007     0.891      0.679           0.265          \n",
       "52   -0.122     0.816      0.764           0.342          \n",
       "80    0.210     0.491      0.769           0.205          \n",
       "93    0.011     0.464      0.769           0.206          \n",
       "118   0.132        NA      0.790           0.297          \n",
       "133  -0.009     0.742      0.582           0.332          \n",
       "161  -0.164     0.634      0.647           0.260          \n",
       "175  -0.083     0.532      0.609           0.305          \n",
       "193  -0.054     0.868      0.790           0.382          \n",
       "206   0.138     0.916      0.644           0.325          \n",
       "233  -0.053     0.729      0.692           0.389          \n",
       "245  -0.004     0.901      0.706           0.221          \n",
       "278   0.052     0.863      0.878           0.390          \n",
       "293   0.049     0.837      0.630           0.386          \n",
       "308   0.050     0.434      0.796           0.307          \n",
       "342   0.033     0.812      0.815           0.336          \n",
       "357  -0.103        NA      0.789           0.245          \n",
       "372  -0.085     0.808      0.795           0.340          \n",
       "423  -0.063     0.961      0.743           0.286          \n",
       "437      NA     0.816      0.759           0.284          \n",
       "449  -0.127     0.884      0.832           0.290          \n",
       "464   0.052     0.214      0.818           0.227          \n",
       "483  -0.128     0.636      0.734           0.314          \n",
       "498  -0.157     0.855      0.790           0.416          \n",
       "513  -0.112        NA      0.599           0.442          \n",
       "528  -0.126     0.583      0.839           0.329          \n",
       "542  -0.082     0.398      0.807           0.188          \n",
       "551   0.188     0.784      0.669           0.252          \n",
       "564  -0.116     0.164      0.744           0.193          \n",
       "579  -0.169     0.565      0.732           0.231          \n",
       "...  ...        ...        ...             ...            \n",
       "1300  0.131     0.877      0.605           0.365          \n",
       "1310  0.075     0.271      0.823           0.216          \n",
       "1395 -0.116     0.744      0.804           0.327          \n",
       "1409 -0.007     0.787      0.760           0.329          \n",
       "1422 -0.238     0.867      0.648           0.383          \n",
       "1455 -0.071     0.823      0.645           0.190          \n",
       "1482 -0.111        NA      0.754           0.251          \n",
       "1509  0.149     0.824      0.603           0.358          \n",
       "1546 -0.075     0.901      0.764           0.274          \n",
       "1559 -0.081     0.797      0.610           0.314          \n",
       "1581 -0.015     0.912      0.820           0.294          \n",
       "1596 -0.106     0.665      0.640           0.247          \n",
       "1615 -0.121     0.730      0.686           0.317          \n",
       "1652  0.091     0.203      0.766           0.222          \n",
       "1662 -0.064     0.280      0.769           0.193          \n",
       "1682     NA     0.711      0.845           0.083          \n",
       "1697 -0.040     0.550      0.749           0.344          \n",
       "1712  0.295     0.521      0.686           0.271          \n",
       "1727  0.273     0.918      0.783           0.326          \n",
       "1753 -0.202     0.877      0.585           0.439          \n",
       "1768 -0.111     0.774      0.384           0.440          \n",
       "1793  0.147     0.878      0.699           0.425          \n",
       "1808  0.126     0.946      0.688           0.285          \n",
       "1821  0.060        NA      0.752           0.298          \n",
       "1836  0.203     0.490      0.758           0.225          \n",
       "1851  0.034     0.678      0.787           0.295          \n",
       "1866 -0.084     0.491      0.807           0.265          \n",
       "1894     NA     0.811      0.722           0.396          \n",
       "1934  0.056     0.810      0.691           0.345          \n",
       "1949 -0.009     0.789      0.703           0.346          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "happ_2021=happ_all[happ_all$year == 2020, ]  \n",
    "happ_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim(happ_2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 95 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Country</th><th scope=col>year</th><th scope=col>Score</th><th scope=col>GDP</th><th scope=col>Family</th><th scope=col>Health</th><th scope=col>Freedom</th><th scope=col>Generosity</th><th scope=col>Corruption</th><th scope=col>Positive.affect</th><th scope=col>Negative.affect</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1949</th><td>Zimbabwe                 </td><td>2020</td><td>3.160</td><td> 7.829</td><td>0.717</td><td>56.80</td><td>0.643</td><td>-0.009</td><td>0.789</td><td>0.703</td><td>0.346</td></tr>\n",
       "\t<tr><th scope=row>1712</th><td>Tanzania                 </td><td>2020</td><td>3.786</td><td> 7.881</td><td>0.740</td><td>58.50</td><td>0.830</td><td> 0.295</td><td>0.521</td><td>0.686</td><td>0.271</td></tr>\n",
       "\t<tr><th scope=row>875</th><td>Jordan                   </td><td>2020</td><td>4.094</td><td> 9.150</td><td>0.709</td><td>67.20</td><td>0.779</td><td>-0.150</td><td>   NA</td><td>   NA</td><td>   NA</td></tr>\n",
       "\t<tr><th scope=row>747</th><td>India                    </td><td>2020</td><td>4.225</td><td> 8.703</td><td>0.617</td><td>60.90</td><td>0.906</td><td> 0.075</td><td>0.780</td><td>0.752</td><td>0.383</td></tr>\n",
       "\t<tr><th scope=row>278</th><td>Cambodia                 </td><td>2020</td><td>4.377</td><td> 8.362</td><td>0.724</td><td>62.40</td><td>0.963</td><td> 0.052</td><td>0.863</td><td>0.878</td><td>0.390</td></tr>\n",
       "\t<tr><th scope=row>175</th><td>Benin                    </td><td>2020</td><td>4.408</td><td> 8.102</td><td>0.507</td><td>55.10</td><td>0.783</td><td>-0.083</td><td>0.532</td><td>0.609</td><td>0.305</td></tr>\n",
       "\t<tr><th scope=row>1191</th><td>Myanmar                  </td><td>2020</td><td>4.431</td><td> 8.554</td><td>0.796</td><td>59.60</td><td>0.825</td><td> 0.470</td><td>0.647</td><td>0.800</td><td>0.289</td></tr>\n",
       "\t<tr><th scope=row>1197</th><td>Namibia                  </td><td>2020</td><td>4.451</td><td> 9.104</td><td>0.741</td><td>57.10</td><td>0.666</td><td>-0.104</td><td>0.810</td><td>0.648</td><td>0.248</td></tr>\n",
       "\t<tr><th scope=row>513</th><td>Egypt                    </td><td>2020</td><td>4.472</td><td> 9.383</td><td>0.673</td><td>62.30</td><td>0.770</td><td>-0.112</td><td>   NA</td><td>0.599</td><td>0.442</td></tr>\n",
       "\t<tr><th scope=row>905</th><td>Kenya                    </td><td>2020</td><td>4.547</td><td> 8.365</td><td>0.674</td><td>61.30</td><td>0.702</td><td> 0.260</td><td>0.837</td><td>0.733</td><td>0.297</td></tr>\n",
       "\t<tr><th scope=row>551</th><td>Ethiopia                 </td><td>2020</td><td>4.549</td><td> 7.711</td><td>0.823</td><td>59.50</td><td>0.769</td><td> 0.188</td><td>0.784</td><td>0.669</td><td>0.252</td></tr>\n",
       "\t<tr><th scope=row>1894</th><td>Venezuela                </td><td>2020</td><td>4.574</td><td>    NA</td><td>0.805</td><td>66.90</td><td>0.612</td><td>    NA</td><td>0.811</td><td>0.722</td><td>0.396</td></tr>\n",
       "\t<tr><th scope=row>1793</th><td>Uganda                   </td><td>2020</td><td>4.641</td><td> 7.684</td><td>0.800</td><td>56.50</td><td>0.687</td><td> 0.147</td><td>0.878</td><td>0.699</td><td>0.425</td></tr>\n",
       "\t<tr><th scope=row>1753</th><td>Tunisia                  </td><td>2020</td><td>4.731</td><td> 9.231</td><td>0.719</td><td>67.50</td><td>0.668</td><td>-0.202</td><td>0.877</td><td>0.585</td><td>0.439</td></tr>\n",
       "\t<tr><th scope=row>786</th><td>Iraq                     </td><td>2020</td><td>4.785</td><td> 9.167</td><td>0.708</td><td>61.40</td><td>0.700</td><td>-0.021</td><td>0.849</td><td>0.644</td><td>0.532</td></tr>\n",
       "\t<tr><th scope=row>1174</th><td>Morocco                  </td><td>2020</td><td>4.803</td><td> 8.871</td><td>0.553</td><td>66.50</td><td>0.819</td><td>-0.229</td><td>0.803</td><td>0.587</td><td>0.256</td></tr>\n",
       "\t<tr><th scope=row>1934</th><td>Zambia                   </td><td>2020</td><td>4.838</td><td> 8.117</td><td>0.767</td><td>56.30</td><td>0.750</td><td> 0.056</td><td>0.810</td><td>0.691</td><td>0.345</td></tr>\n",
       "\t<tr><th scope=row>1768</th><td>Turkey                   </td><td>2020</td><td>4.862</td><td>10.219</td><td>0.857</td><td>67.60</td><td>0.510</td><td>-0.111</td><td>0.774</td><td>0.384</td><td>0.440</td></tr>\n",
       "\t<tr><th scope=row>774</th><td>Iran                     </td><td>2020</td><td>4.865</td><td>    NA</td><td>0.757</td><td>66.60</td><td>0.600</td><td>    NA</td><td>0.710</td><td>0.582</td><td>0.470</td></tr>\n",
       "\t<tr><th scope=row>1581</th><td>South Africa             </td><td>2020</td><td>4.947</td><td> 9.332</td><td>0.891</td><td>57.30</td><td>0.757</td><td>-0.015</td><td>0.912</td><td>0.820</td><td>0.294</td></tr>\n",
       "\t<tr><th scope=row>1300</th><td>North Macedonia          </td><td>2020</td><td>5.054</td><td> 9.690</td><td>0.750</td><td>65.56</td><td>0.787</td><td> 0.131</td><td>0.877</td><td>0.605</td><td>0.365</td></tr>\n",
       "\t<tr><th scope=row>1395</th><td>Philippines              </td><td>2020</td><td>5.080</td><td> 9.061</td><td>0.781</td><td>62.10</td><td>0.932</td><td>-0.116</td><td>0.744</td><td>0.804</td><td>0.327</td></tr>\n",
       "\t<tr><th scope=row>606</th><td>Georgia                  </td><td>2020</td><td>5.123</td><td> 9.569</td><td>0.718</td><td>64.10</td><td>0.764</td><td>-0.221</td><td>0.583</td><td>0.611</td><td>0.295</td></tr>\n",
       "\t<tr><th scope=row>483</th><td>Dominican Republic       </td><td>2020</td><td>5.168</td><td> 9.802</td><td>0.806</td><td>66.40</td><td>0.835</td><td>-0.128</td><td>0.636</td><td>0.734</td><td>0.314</td></tr>\n",
       "\t<tr><th scope=row>293</th><td>Cameroon                 </td><td>2020</td><td>5.241</td><td> 8.175</td><td>0.720</td><td>54.30</td><td>0.675</td><td> 0.049</td><td>0.837</td><td>0.630</td><td>0.386</td></tr>\n",
       "\t<tr><th scope=row>839</th><td>Ivory Coast              </td><td>2020</td><td>5.257</td><td> 8.565</td><td>0.613</td><td>50.70</td><td>0.770</td><td> 0.016</td><td>0.777</td><td>0.693</td><td>0.340</td></tr>\n",
       "\t<tr><th scope=row>1808</th><td>Ukraine                  </td><td>2020</td><td>5.270</td><td> 9.428</td><td>0.885</td><td>65.20</td><td>0.784</td><td> 0.126</td><td>0.946</td><td>0.688</td><td>0.285</td></tr>\n",
       "\t<tr><th scope=row>133</th><td>Bangladesh               </td><td>2020</td><td>5.280</td><td> 8.472</td><td>0.739</td><td>65.30</td><td>0.777</td><td>-0.009</td><td>0.742</td><td>0.582</td><td>0.332</td></tr>\n",
       "\t<tr><th scope=row>954</th><td>Laos                     </td><td>2020</td><td>5.284</td><td> 8.960</td><td>0.660</td><td>59.50</td><td>0.915</td><td> 0.141</td><td>0.748</td><td>0.822</td><td>0.358</td></tr>\n",
       "\t<tr><th scope=row>710</th><td>Hong Kong S.A.R. of China</td><td>2020</td><td>5.295</td><td>    NA</td><td>0.813</td><td>   NA</td><td>0.705</td><td>    NA</td><td>0.380</td><td>0.609</td><td>0.210</td></tr>\n",
       "\t<tr><th scope=row>...</th><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "\t<tr><th scope=row>1866</th><td>Uruguay                 </td><td>2020</td><td>6.310</td><td> 9.937</td><td>0.921</td><td>69.2</td><td>0.908</td><td>-0.084</td><td>0.491</td><td>0.807</td><td>0.265</td></tr>\n",
       "\t<tr><th scope=row>1016</th><td>Lithuania               </td><td>2020</td><td>6.391</td><td>10.504</td><td>0.953</td><td>68.5</td><td>0.824</td><td>-0.122</td><td>0.829</td><td>0.660</td><td>0.202</td></tr>\n",
       "\t<tr><th scope=row>542</th><td>Estonia                 </td><td>2020</td><td>6.453</td><td>10.459</td><td>0.958</td><td>69.0</td><td>0.954</td><td>-0.082</td><td>0.398</td><td>0.807</td><td>0.188</td></tr>\n",
       "\t<tr><th scope=row>1821</th><td>United Arab Emirates    </td><td>2020</td><td>6.458</td><td>11.053</td><td>0.827</td><td>67.5</td><td>0.942</td><td> 0.060</td><td>   NA</td><td>0.752</td><td>0.298</td></tr>\n",
       "\t<tr><th scope=row>1559</th><td>Slovenia                </td><td>2020</td><td>6.462</td><td>10.478</td><td>0.953</td><td>71.7</td><td>0.958</td><td>-0.081</td><td>0.797</td><td>0.610</td><td>0.314</td></tr>\n",
       "\t<tr><th scope=row>830</th><td>Italy                   </td><td>2020</td><td>6.488</td><td>10.563</td><td>0.890</td><td>74.0</td><td>0.718</td><td>-0.150</td><td>0.844</td><td>0.670</td><td>0.311</td></tr>\n",
       "\t<tr><th scope=row>1615</th><td>Spain                   </td><td>2020</td><td>6.502</td><td>10.488</td><td>0.935</td><td>75.0</td><td>0.783</td><td>-0.121</td><td>0.730</td><td>0.686</td><td>0.317</td></tr>\n",
       "\t<tr><th scope=row>423</th><td>Croatia                 </td><td>2020</td><td>6.508</td><td>10.166</td><td>0.923</td><td>71.4</td><td>0.837</td><td>-0.063</td><td>0.961</td><td>0.743</td><td>0.286</td></tr>\n",
       "\t<tr><th scope=row>1546</th><td>Slovakia                </td><td>2020</td><td>6.519</td><td>10.332</td><td>0.954</td><td>69.5</td><td>0.762</td><td>-0.075</td><td>0.901</td><td>0.764</td><td>0.274</td></tr>\n",
       "\t<tr><th scope=row>1482</th><td>Saudi Arabia            </td><td>2020</td><td>6.560</td><td>10.701</td><td>0.890</td><td>66.9</td><td>0.884</td><td>-0.111</td><td>   NA</td><td>0.754</td><td>0.251</td></tr>\n",
       "\t<tr><th scope=row>579</th><td>France                  </td><td>2020</td><td>6.714</td><td>10.643</td><td>0.947</td><td>74.2</td><td>0.823</td><td>-0.169</td><td>0.565</td><td>0.732</td><td>0.231</td></tr>\n",
       "\t<tr><th scope=row>1682</th><td>Taiwan Province of China</td><td>2020</td><td>6.751</td><td>    NA</td><td>0.901</td><td>  NA</td><td>0.799</td><td>    NA</td><td>0.711</td><td>0.845</td><td>0.083</td></tr>\n",
       "\t<tr><th scope=row>1836</th><td>United Kingdom          </td><td>2020</td><td>6.798</td><td>10.626</td><td>0.929</td><td>72.7</td><td>0.885</td><td> 0.203</td><td>0.490</td><td>0.758</td><td>0.225</td></tr>\n",
       "\t<tr><th scope=row>161</th><td>Belgium                 </td><td>2020</td><td>6.839</td><td>10.771</td><td>0.904</td><td>72.4</td><td>0.767</td><td>-0.164</td><td>0.634</td><td>0.647</td><td>0.260</td></tr>\n",
       "\t<tr><th scope=row>449</th><td>Czech Republic          </td><td>2020</td><td>6.897</td><td>10.530</td><td>0.964</td><td>71.3</td><td>0.906</td><td>-0.127</td><td>0.884</td><td>0.832</td><td>0.290</td></tr>\n",
       "\t<tr><th scope=row>308</th><td>Canada                  </td><td>2020</td><td>7.025</td><td>10.730</td><td>0.931</td><td>74.0</td><td>0.887</td><td> 0.050</td><td>0.434</td><td>0.796</td><td>0.307</td></tr>\n",
       "\t<tr><th scope=row>1851</th><td>United States           </td><td>2020</td><td>7.028</td><td>11.001</td><td>0.937</td><td>68.1</td><td>0.850</td><td> 0.034</td><td>0.678</td><td>0.787</td><td>0.295</td></tr>\n",
       "\t<tr><th scope=row>800</th><td>Ireland                 </td><td>2020</td><td>7.035</td><td>11.323</td><td>0.960</td><td>72.5</td><td>0.882</td><td> 0.014</td><td>0.356</td><td>0.797</td><td>0.246</td></tr>\n",
       "\t<tr><th scope=row>80</th><td>Australia               </td><td>2020</td><td>7.137</td><td>10.760</td><td>0.937</td><td>74.2</td><td>0.905</td><td> 0.210</td><td>0.491</td><td>0.769</td><td>0.205</td></tr>\n",
       "\t<tr><th scope=row>815</th><td>Israel                  </td><td>2020</td><td>7.195</td><td>10.538</td><td>0.959</td><td>73.7</td><td>0.831</td><td>-0.049</td><td>0.748</td><td>0.621</td><td>0.243</td></tr>\n",
       "\t<tr><th scope=row>93</th><td>Austria                 </td><td>2020</td><td>7.213</td><td>10.851</td><td>0.925</td><td>73.6</td><td>0.912</td><td> 0.011</td><td>0.464</td><td>0.769</td><td>0.206</td></tr>\n",
       "\t<tr><th scope=row>1239</th><td>New Zealand             </td><td>2020</td><td>7.257</td><td>10.600</td><td>0.952</td><td>73.6</td><td>0.918</td><td> 0.125</td><td>0.283</td><td>0.849</td><td>0.209</td></tr>\n",
       "\t<tr><th scope=row>1310</th><td>Norway                  </td><td>2020</td><td>7.290</td><td>11.042</td><td>0.956</td><td>73.4</td><td>0.965</td><td> 0.075</td><td>0.271</td><td>0.823</td><td>0.216</td></tr>\n",
       "\t<tr><th scope=row>621</th><td>Germany                 </td><td>2020</td><td>7.312</td><td>10.833</td><td>0.905</td><td>72.8</td><td>0.864</td><td>-0.060</td><td>0.424</td><td>0.760</td><td>0.206</td></tr>\n",
       "\t<tr><th scope=row>1652</th><td>Sweden                  </td><td>2020</td><td>7.314</td><td>10.838</td><td>0.936</td><td>72.8</td><td>0.951</td><td> 0.091</td><td>0.203</td><td>0.766</td><td>0.222</td></tr>\n",
       "\t<tr><th scope=row>1225</th><td>Netherlands             </td><td>2020</td><td>7.504</td><td>10.901</td><td>0.944</td><td>72.5</td><td>0.935</td><td> 0.151</td><td>0.281</td><td>0.784</td><td>0.247</td></tr>\n",
       "\t<tr><th scope=row>1662</th><td>Switzerland             </td><td>2020</td><td>7.508</td><td>11.081</td><td>0.946</td><td>74.7</td><td>0.917</td><td>-0.064</td><td>0.280</td><td>0.769</td><td>0.193</td></tr>\n",
       "\t<tr><th scope=row>464</th><td>Denmark                 </td><td>2020</td><td>7.515</td><td>10.910</td><td>0.947</td><td>73.0</td><td>0.938</td><td> 0.052</td><td>0.214</td><td>0.818</td><td>0.227</td></tr>\n",
       "\t<tr><th scope=row>732</th><td>Iceland                 </td><td>2020</td><td>7.575</td><td>10.824</td><td>0.983</td><td>73.0</td><td>0.949</td><td> 0.160</td><td>0.644</td><td>0.863</td><td>0.172</td></tr>\n",
       "\t<tr><th scope=row>564</th><td>Finland                 </td><td>2020</td><td>7.889</td><td>10.750</td><td>0.962</td><td>72.1</td><td>0.962</td><td>-0.116</td><td>0.164</td><td>0.744</td><td>0.193</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 95 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & Country & year & Score & GDP & Family & Health & Freedom & Generosity & Corruption & Positive.affect & Negative.affect\\\\\n",
       "  & <chr> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1949 & Zimbabwe                  & 2020 & 3.160 &  7.829 & 0.717 & 56.80 & 0.643 & -0.009 & 0.789 & 0.703 & 0.346\\\\\n",
       "\t1712 & Tanzania                  & 2020 & 3.786 &  7.881 & 0.740 & 58.50 & 0.830 &  0.295 & 0.521 & 0.686 & 0.271\\\\\n",
       "\t875 & Jordan                    & 2020 & 4.094 &  9.150 & 0.709 & 67.20 & 0.779 & -0.150 &    NA &    NA &    NA\\\\\n",
       "\t747 & India                     & 2020 & 4.225 &  8.703 & 0.617 & 60.90 & 0.906 &  0.075 & 0.780 & 0.752 & 0.383\\\\\n",
       "\t278 & Cambodia                  & 2020 & 4.377 &  8.362 & 0.724 & 62.40 & 0.963 &  0.052 & 0.863 & 0.878 & 0.390\\\\\n",
       "\t175 & Benin                     & 2020 & 4.408 &  8.102 & 0.507 & 55.10 & 0.783 & -0.083 & 0.532 & 0.609 & 0.305\\\\\n",
       "\t1191 & Myanmar                   & 2020 & 4.431 &  8.554 & 0.796 & 59.60 & 0.825 &  0.470 & 0.647 & 0.800 & 0.289\\\\\n",
       "\t1197 & Namibia                   & 2020 & 4.451 &  9.104 & 0.741 & 57.10 & 0.666 & -0.104 & 0.810 & 0.648 & 0.248\\\\\n",
       "\t513 & Egypt                     & 2020 & 4.472 &  9.383 & 0.673 & 62.30 & 0.770 & -0.112 &    NA & 0.599 & 0.442\\\\\n",
       "\t905 & Kenya                     & 2020 & 4.547 &  8.365 & 0.674 & 61.30 & 0.702 &  0.260 & 0.837 & 0.733 & 0.297\\\\\n",
       "\t551 & Ethiopia                  & 2020 & 4.549 &  7.711 & 0.823 & 59.50 & 0.769 &  0.188 & 0.784 & 0.669 & 0.252\\\\\n",
       "\t1894 & Venezuela                 & 2020 & 4.574 &     NA & 0.805 & 66.90 & 0.612 &     NA & 0.811 & 0.722 & 0.396\\\\\n",
       "\t1793 & Uganda                    & 2020 & 4.641 &  7.684 & 0.800 & 56.50 & 0.687 &  0.147 & 0.878 & 0.699 & 0.425\\\\\n",
       "\t1753 & Tunisia                   & 2020 & 4.731 &  9.231 & 0.719 & 67.50 & 0.668 & -0.202 & 0.877 & 0.585 & 0.439\\\\\n",
       "\t786 & Iraq                      & 2020 & 4.785 &  9.167 & 0.708 & 61.40 & 0.700 & -0.021 & 0.849 & 0.644 & 0.532\\\\\n",
       "\t1174 & Morocco                   & 2020 & 4.803 &  8.871 & 0.553 & 66.50 & 0.819 & -0.229 & 0.803 & 0.587 & 0.256\\\\\n",
       "\t1934 & Zambia                    & 2020 & 4.838 &  8.117 & 0.767 & 56.30 & 0.750 &  0.056 & 0.810 & 0.691 & 0.345\\\\\n",
       "\t1768 & Turkey                    & 2020 & 4.862 & 10.219 & 0.857 & 67.60 & 0.510 & -0.111 & 0.774 & 0.384 & 0.440\\\\\n",
       "\t774 & Iran                      & 2020 & 4.865 &     NA & 0.757 & 66.60 & 0.600 &     NA & 0.710 & 0.582 & 0.470\\\\\n",
       "\t1581 & South Africa              & 2020 & 4.947 &  9.332 & 0.891 & 57.30 & 0.757 & -0.015 & 0.912 & 0.820 & 0.294\\\\\n",
       "\t1300 & North Macedonia           & 2020 & 5.054 &  9.690 & 0.750 & 65.56 & 0.787 &  0.131 & 0.877 & 0.605 & 0.365\\\\\n",
       "\t1395 & Philippines               & 2020 & 5.080 &  9.061 & 0.781 & 62.10 & 0.932 & -0.116 & 0.744 & 0.804 & 0.327\\\\\n",
       "\t606 & Georgia                   & 2020 & 5.123 &  9.569 & 0.718 & 64.10 & 0.764 & -0.221 & 0.583 & 0.611 & 0.295\\\\\n",
       "\t483 & Dominican Republic        & 2020 & 5.168 &  9.802 & 0.806 & 66.40 & 0.835 & -0.128 & 0.636 & 0.734 & 0.314\\\\\n",
       "\t293 & Cameroon                  & 2020 & 5.241 &  8.175 & 0.720 & 54.30 & 0.675 &  0.049 & 0.837 & 0.630 & 0.386\\\\\n",
       "\t839 & Ivory Coast               & 2020 & 5.257 &  8.565 & 0.613 & 50.70 & 0.770 &  0.016 & 0.777 & 0.693 & 0.340\\\\\n",
       "\t1808 & Ukraine                   & 2020 & 5.270 &  9.428 & 0.885 & 65.20 & 0.784 &  0.126 & 0.946 & 0.688 & 0.285\\\\\n",
       "\t133 & Bangladesh                & 2020 & 5.280 &  8.472 & 0.739 & 65.30 & 0.777 & -0.009 & 0.742 & 0.582 & 0.332\\\\\n",
       "\t954 & Laos                      & 2020 & 5.284 &  8.960 & 0.660 & 59.50 & 0.915 &  0.141 & 0.748 & 0.822 & 0.358\\\\\n",
       "\t710 & Hong Kong S.A.R. of China & 2020 & 5.295 &     NA & 0.813 &    NA & 0.705 &     NA & 0.380 & 0.609 & 0.210\\\\\n",
       "\t... & ... & ... & ... & ... & ... & ... & ... & ... & ... & ... & ...\\\\\n",
       "\t1866 & Uruguay                  & 2020 & 6.310 &  9.937 & 0.921 & 69.2 & 0.908 & -0.084 & 0.491 & 0.807 & 0.265\\\\\n",
       "\t1016 & Lithuania                & 2020 & 6.391 & 10.504 & 0.953 & 68.5 & 0.824 & -0.122 & 0.829 & 0.660 & 0.202\\\\\n",
       "\t542 & Estonia                  & 2020 & 6.453 & 10.459 & 0.958 & 69.0 & 0.954 & -0.082 & 0.398 & 0.807 & 0.188\\\\\n",
       "\t1821 & United Arab Emirates     & 2020 & 6.458 & 11.053 & 0.827 & 67.5 & 0.942 &  0.060 &    NA & 0.752 & 0.298\\\\\n",
       "\t1559 & Slovenia                 & 2020 & 6.462 & 10.478 & 0.953 & 71.7 & 0.958 & -0.081 & 0.797 & 0.610 & 0.314\\\\\n",
       "\t830 & Italy                    & 2020 & 6.488 & 10.563 & 0.890 & 74.0 & 0.718 & -0.150 & 0.844 & 0.670 & 0.311\\\\\n",
       "\t1615 & Spain                    & 2020 & 6.502 & 10.488 & 0.935 & 75.0 & 0.783 & -0.121 & 0.730 & 0.686 & 0.317\\\\\n",
       "\t423 & Croatia                  & 2020 & 6.508 & 10.166 & 0.923 & 71.4 & 0.837 & -0.063 & 0.961 & 0.743 & 0.286\\\\\n",
       "\t1546 & Slovakia                 & 2020 & 6.519 & 10.332 & 0.954 & 69.5 & 0.762 & -0.075 & 0.901 & 0.764 & 0.274\\\\\n",
       "\t1482 & Saudi Arabia             & 2020 & 6.560 & 10.701 & 0.890 & 66.9 & 0.884 & -0.111 &    NA & 0.754 & 0.251\\\\\n",
       "\t579 & France                   & 2020 & 6.714 & 10.643 & 0.947 & 74.2 & 0.823 & -0.169 & 0.565 & 0.732 & 0.231\\\\\n",
       "\t1682 & Taiwan Province of China & 2020 & 6.751 &     NA & 0.901 &   NA & 0.799 &     NA & 0.711 & 0.845 & 0.083\\\\\n",
       "\t1836 & United Kingdom           & 2020 & 6.798 & 10.626 & 0.929 & 72.7 & 0.885 &  0.203 & 0.490 & 0.758 & 0.225\\\\\n",
       "\t161 & Belgium                  & 2020 & 6.839 & 10.771 & 0.904 & 72.4 & 0.767 & -0.164 & 0.634 & 0.647 & 0.260\\\\\n",
       "\t449 & Czech Republic           & 2020 & 6.897 & 10.530 & 0.964 & 71.3 & 0.906 & -0.127 & 0.884 & 0.832 & 0.290\\\\\n",
       "\t308 & Canada                   & 2020 & 7.025 & 10.730 & 0.931 & 74.0 & 0.887 &  0.050 & 0.434 & 0.796 & 0.307\\\\\n",
       "\t1851 & United States            & 2020 & 7.028 & 11.001 & 0.937 & 68.1 & 0.850 &  0.034 & 0.678 & 0.787 & 0.295\\\\\n",
       "\t800 & Ireland                  & 2020 & 7.035 & 11.323 & 0.960 & 72.5 & 0.882 &  0.014 & 0.356 & 0.797 & 0.246\\\\\n",
       "\t80 & Australia                & 2020 & 7.137 & 10.760 & 0.937 & 74.2 & 0.905 &  0.210 & 0.491 & 0.769 & 0.205\\\\\n",
       "\t815 & Israel                   & 2020 & 7.195 & 10.538 & 0.959 & 73.7 & 0.831 & -0.049 & 0.748 & 0.621 & 0.243\\\\\n",
       "\t93 & Austria                  & 2020 & 7.213 & 10.851 & 0.925 & 73.6 & 0.912 &  0.011 & 0.464 & 0.769 & 0.206\\\\\n",
       "\t1239 & New Zealand              & 2020 & 7.257 & 10.600 & 0.952 & 73.6 & 0.918 &  0.125 & 0.283 & 0.849 & 0.209\\\\\n",
       "\t1310 & Norway                   & 2020 & 7.290 & 11.042 & 0.956 & 73.4 & 0.965 &  0.075 & 0.271 & 0.823 & 0.216\\\\\n",
       "\t621 & Germany                  & 2020 & 7.312 & 10.833 & 0.905 & 72.8 & 0.864 & -0.060 & 0.424 & 0.760 & 0.206\\\\\n",
       "\t1652 & Sweden                   & 2020 & 7.314 & 10.838 & 0.936 & 72.8 & 0.951 &  0.091 & 0.203 & 0.766 & 0.222\\\\\n",
       "\t1225 & Netherlands              & 2020 & 7.504 & 10.901 & 0.944 & 72.5 & 0.935 &  0.151 & 0.281 & 0.784 & 0.247\\\\\n",
       "\t1662 & Switzerland              & 2020 & 7.508 & 11.081 & 0.946 & 74.7 & 0.917 & -0.064 & 0.280 & 0.769 & 0.193\\\\\n",
       "\t464 & Denmark                  & 2020 & 7.515 & 10.910 & 0.947 & 73.0 & 0.938 &  0.052 & 0.214 & 0.818 & 0.227\\\\\n",
       "\t732 & Iceland                  & 2020 & 7.575 & 10.824 & 0.983 & 73.0 & 0.949 &  0.160 & 0.644 & 0.863 & 0.172\\\\\n",
       "\t564 & Finland                  & 2020 & 7.889 & 10.750 & 0.962 & 72.1 & 0.962 & -0.116 & 0.164 & 0.744 & 0.193\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 95 × 11\n",
       "\n",
       "| <!--/--> | Country &lt;chr&gt; | year &lt;int&gt; | Score &lt;dbl&gt; | GDP &lt;dbl&gt; | Family &lt;dbl&gt; | Health &lt;dbl&gt; | Freedom &lt;dbl&gt; | Generosity &lt;dbl&gt; | Corruption &lt;dbl&gt; | Positive.affect &lt;dbl&gt; | Negative.affect &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1949 | Zimbabwe                  | 2020 | 3.160 |  7.829 | 0.717 | 56.80 | 0.643 | -0.009 | 0.789 | 0.703 | 0.346 |\n",
       "| 1712 | Tanzania                  | 2020 | 3.786 |  7.881 | 0.740 | 58.50 | 0.830 |  0.295 | 0.521 | 0.686 | 0.271 |\n",
       "| 875 | Jordan                    | 2020 | 4.094 |  9.150 | 0.709 | 67.20 | 0.779 | -0.150 |    NA |    NA |    NA |\n",
       "| 747 | India                     | 2020 | 4.225 |  8.703 | 0.617 | 60.90 | 0.906 |  0.075 | 0.780 | 0.752 | 0.383 |\n",
       "| 278 | Cambodia                  | 2020 | 4.377 |  8.362 | 0.724 | 62.40 | 0.963 |  0.052 | 0.863 | 0.878 | 0.390 |\n",
       "| 175 | Benin                     | 2020 | 4.408 |  8.102 | 0.507 | 55.10 | 0.783 | -0.083 | 0.532 | 0.609 | 0.305 |\n",
       "| 1191 | Myanmar                   | 2020 | 4.431 |  8.554 | 0.796 | 59.60 | 0.825 |  0.470 | 0.647 | 0.800 | 0.289 |\n",
       "| 1197 | Namibia                   | 2020 | 4.451 |  9.104 | 0.741 | 57.10 | 0.666 | -0.104 | 0.810 | 0.648 | 0.248 |\n",
       "| 513 | Egypt                     | 2020 | 4.472 |  9.383 | 0.673 | 62.30 | 0.770 | -0.112 |    NA | 0.599 | 0.442 |\n",
       "| 905 | Kenya                     | 2020 | 4.547 |  8.365 | 0.674 | 61.30 | 0.702 |  0.260 | 0.837 | 0.733 | 0.297 |\n",
       "| 551 | Ethiopia                  | 2020 | 4.549 |  7.711 | 0.823 | 59.50 | 0.769 |  0.188 | 0.784 | 0.669 | 0.252 |\n",
       "| 1894 | Venezuela                 | 2020 | 4.574 |     NA | 0.805 | 66.90 | 0.612 |     NA | 0.811 | 0.722 | 0.396 |\n",
       "| 1793 | Uganda                    | 2020 | 4.641 |  7.684 | 0.800 | 56.50 | 0.687 |  0.147 | 0.878 | 0.699 | 0.425 |\n",
       "| 1753 | Tunisia                   | 2020 | 4.731 |  9.231 | 0.719 | 67.50 | 0.668 | -0.202 | 0.877 | 0.585 | 0.439 |\n",
       "| 786 | Iraq                      | 2020 | 4.785 |  9.167 | 0.708 | 61.40 | 0.700 | -0.021 | 0.849 | 0.644 | 0.532 |\n",
       "| 1174 | Morocco                   | 2020 | 4.803 |  8.871 | 0.553 | 66.50 | 0.819 | -0.229 | 0.803 | 0.587 | 0.256 |\n",
       "| 1934 | Zambia                    | 2020 | 4.838 |  8.117 | 0.767 | 56.30 | 0.750 |  0.056 | 0.810 | 0.691 | 0.345 |\n",
       "| 1768 | Turkey                    | 2020 | 4.862 | 10.219 | 0.857 | 67.60 | 0.510 | -0.111 | 0.774 | 0.384 | 0.440 |\n",
       "| 774 | Iran                      | 2020 | 4.865 |     NA | 0.757 | 66.60 | 0.600 |     NA | 0.710 | 0.582 | 0.470 |\n",
       "| 1581 | South Africa              | 2020 | 4.947 |  9.332 | 0.891 | 57.30 | 0.757 | -0.015 | 0.912 | 0.820 | 0.294 |\n",
       "| 1300 | North Macedonia           | 2020 | 5.054 |  9.690 | 0.750 | 65.56 | 0.787 |  0.131 | 0.877 | 0.605 | 0.365 |\n",
       "| 1395 | Philippines               | 2020 | 5.080 |  9.061 | 0.781 | 62.10 | 0.932 | -0.116 | 0.744 | 0.804 | 0.327 |\n",
       "| 606 | Georgia                   | 2020 | 5.123 |  9.569 | 0.718 | 64.10 | 0.764 | -0.221 | 0.583 | 0.611 | 0.295 |\n",
       "| 483 | Dominican Republic        | 2020 | 5.168 |  9.802 | 0.806 | 66.40 | 0.835 | -0.128 | 0.636 | 0.734 | 0.314 |\n",
       "| 293 | Cameroon                  | 2020 | 5.241 |  8.175 | 0.720 | 54.30 | 0.675 |  0.049 | 0.837 | 0.630 | 0.386 |\n",
       "| 839 | Ivory Coast               | 2020 | 5.257 |  8.565 | 0.613 | 50.70 | 0.770 |  0.016 | 0.777 | 0.693 | 0.340 |\n",
       "| 1808 | Ukraine                   | 2020 | 5.270 |  9.428 | 0.885 | 65.20 | 0.784 |  0.126 | 0.946 | 0.688 | 0.285 |\n",
       "| 133 | Bangladesh                | 2020 | 5.280 |  8.472 | 0.739 | 65.30 | 0.777 | -0.009 | 0.742 | 0.582 | 0.332 |\n",
       "| 954 | Laos                      | 2020 | 5.284 |  8.960 | 0.660 | 59.50 | 0.915 |  0.141 | 0.748 | 0.822 | 0.358 |\n",
       "| 710 | Hong Kong S.A.R. of China | 2020 | 5.295 |     NA | 0.813 |    NA | 0.705 |     NA | 0.380 | 0.609 | 0.210 |\n",
       "| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n",
       "| 1866 | Uruguay                  | 2020 | 6.310 |  9.937 | 0.921 | 69.2 | 0.908 | -0.084 | 0.491 | 0.807 | 0.265 |\n",
       "| 1016 | Lithuania                | 2020 | 6.391 | 10.504 | 0.953 | 68.5 | 0.824 | -0.122 | 0.829 | 0.660 | 0.202 |\n",
       "| 542 | Estonia                  | 2020 | 6.453 | 10.459 | 0.958 | 69.0 | 0.954 | -0.082 | 0.398 | 0.807 | 0.188 |\n",
       "| 1821 | United Arab Emirates     | 2020 | 6.458 | 11.053 | 0.827 | 67.5 | 0.942 |  0.060 |    NA | 0.752 | 0.298 |\n",
       "| 1559 | Slovenia                 | 2020 | 6.462 | 10.478 | 0.953 | 71.7 | 0.958 | -0.081 | 0.797 | 0.610 | 0.314 |\n",
       "| 830 | Italy                    | 2020 | 6.488 | 10.563 | 0.890 | 74.0 | 0.718 | -0.150 | 0.844 | 0.670 | 0.311 |\n",
       "| 1615 | Spain                    | 2020 | 6.502 | 10.488 | 0.935 | 75.0 | 0.783 | -0.121 | 0.730 | 0.686 | 0.317 |\n",
       "| 423 | Croatia                  | 2020 | 6.508 | 10.166 | 0.923 | 71.4 | 0.837 | -0.063 | 0.961 | 0.743 | 0.286 |\n",
       "| 1546 | Slovakia                 | 2020 | 6.519 | 10.332 | 0.954 | 69.5 | 0.762 | -0.075 | 0.901 | 0.764 | 0.274 |\n",
       "| 1482 | Saudi Arabia             | 2020 | 6.560 | 10.701 | 0.890 | 66.9 | 0.884 | -0.111 |    NA | 0.754 | 0.251 |\n",
       "| 579 | France                   | 2020 | 6.714 | 10.643 | 0.947 | 74.2 | 0.823 | -0.169 | 0.565 | 0.732 | 0.231 |\n",
       "| 1682 | Taiwan Province of China | 2020 | 6.751 |     NA | 0.901 |   NA | 0.799 |     NA | 0.711 | 0.845 | 0.083 |\n",
       "| 1836 | United Kingdom           | 2020 | 6.798 | 10.626 | 0.929 | 72.7 | 0.885 |  0.203 | 0.490 | 0.758 | 0.225 |\n",
       "| 161 | Belgium                  | 2020 | 6.839 | 10.771 | 0.904 | 72.4 | 0.767 | -0.164 | 0.634 | 0.647 | 0.260 |\n",
       "| 449 | Czech Republic           | 2020 | 6.897 | 10.530 | 0.964 | 71.3 | 0.906 | -0.127 | 0.884 | 0.832 | 0.290 |\n",
       "| 308 | Canada                   | 2020 | 7.025 | 10.730 | 0.931 | 74.0 | 0.887 |  0.050 | 0.434 | 0.796 | 0.307 |\n",
       "| 1851 | United States            | 2020 | 7.028 | 11.001 | 0.937 | 68.1 | 0.850 |  0.034 | 0.678 | 0.787 | 0.295 |\n",
       "| 800 | Ireland                  | 2020 | 7.035 | 11.323 | 0.960 | 72.5 | 0.882 |  0.014 | 0.356 | 0.797 | 0.246 |\n",
       "| 80 | Australia                | 2020 | 7.137 | 10.760 | 0.937 | 74.2 | 0.905 |  0.210 | 0.491 | 0.769 | 0.205 |\n",
       "| 815 | Israel                   | 2020 | 7.195 | 10.538 | 0.959 | 73.7 | 0.831 | -0.049 | 0.748 | 0.621 | 0.243 |\n",
       "| 93 | Austria                  | 2020 | 7.213 | 10.851 | 0.925 | 73.6 | 0.912 |  0.011 | 0.464 | 0.769 | 0.206 |\n",
       "| 1239 | New Zealand              | 2020 | 7.257 | 10.600 | 0.952 | 73.6 | 0.918 |  0.125 | 0.283 | 0.849 | 0.209 |\n",
       "| 1310 | Norway                   | 2020 | 7.290 | 11.042 | 0.956 | 73.4 | 0.965 |  0.075 | 0.271 | 0.823 | 0.216 |\n",
       "| 621 | Germany                  | 2020 | 7.312 | 10.833 | 0.905 | 72.8 | 0.864 | -0.060 | 0.424 | 0.760 | 0.206 |\n",
       "| 1652 | Sweden                   | 2020 | 7.314 | 10.838 | 0.936 | 72.8 | 0.951 |  0.091 | 0.203 | 0.766 | 0.222 |\n",
       "| 1225 | Netherlands              | 2020 | 7.504 | 10.901 | 0.944 | 72.5 | 0.935 |  0.151 | 0.281 | 0.784 | 0.247 |\n",
       "| 1662 | Switzerland              | 2020 | 7.508 | 11.081 | 0.946 | 74.7 | 0.917 | -0.064 | 0.280 | 0.769 | 0.193 |\n",
       "| 464 | Denmark                  | 2020 | 7.515 | 10.910 | 0.947 | 73.0 | 0.938 |  0.052 | 0.214 | 0.818 | 0.227 |\n",
       "| 732 | Iceland                  | 2020 | 7.575 | 10.824 | 0.983 | 73.0 | 0.949 |  0.160 | 0.644 | 0.863 | 0.172 |\n",
       "| 564 | Finland                  | 2020 | 7.889 | 10.750 | 0.962 | 72.1 | 0.962 | -0.116 | 0.164 | 0.744 | 0.193 |\n",
       "\n"
      ],
      "text/plain": [
       "     Country                   year Score GDP    Family Health Freedom\n",
       "1949 Zimbabwe                  2020 3.160  7.829 0.717  56.80  0.643  \n",
       "1712 Tanzania                  2020 3.786  7.881 0.740  58.50  0.830  \n",
       "875  Jordan                    2020 4.094  9.150 0.709  67.20  0.779  \n",
       "747  India                     2020 4.225  8.703 0.617  60.90  0.906  \n",
       "278  Cambodia                  2020 4.377  8.362 0.724  62.40  0.963  \n",
       "175  Benin                     2020 4.408  8.102 0.507  55.10  0.783  \n",
       "1191 Myanmar                   2020 4.431  8.554 0.796  59.60  0.825  \n",
       "1197 Namibia                   2020 4.451  9.104 0.741  57.10  0.666  \n",
       "513  Egypt                     2020 4.472  9.383 0.673  62.30  0.770  \n",
       "905  Kenya                     2020 4.547  8.365 0.674  61.30  0.702  \n",
       "551  Ethiopia                  2020 4.549  7.711 0.823  59.50  0.769  \n",
       "1894 Venezuela                 2020 4.574     NA 0.805  66.90  0.612  \n",
       "1793 Uganda                    2020 4.641  7.684 0.800  56.50  0.687  \n",
       "1753 Tunisia                   2020 4.731  9.231 0.719  67.50  0.668  \n",
       "786  Iraq                      2020 4.785  9.167 0.708  61.40  0.700  \n",
       "1174 Morocco                   2020 4.803  8.871 0.553  66.50  0.819  \n",
       "1934 Zambia                    2020 4.838  8.117 0.767  56.30  0.750  \n",
       "1768 Turkey                    2020 4.862 10.219 0.857  67.60  0.510  \n",
       "774  Iran                      2020 4.865     NA 0.757  66.60  0.600  \n",
       "1581 South Africa              2020 4.947  9.332 0.891  57.30  0.757  \n",
       "1300 North Macedonia           2020 5.054  9.690 0.750  65.56  0.787  \n",
       "1395 Philippines               2020 5.080  9.061 0.781  62.10  0.932  \n",
       "606  Georgia                   2020 5.123  9.569 0.718  64.10  0.764  \n",
       "483  Dominican Republic        2020 5.168  9.802 0.806  66.40  0.835  \n",
       "293  Cameroon                  2020 5.241  8.175 0.720  54.30  0.675  \n",
       "839  Ivory Coast               2020 5.257  8.565 0.613  50.70  0.770  \n",
       "1808 Ukraine                   2020 5.270  9.428 0.885  65.20  0.784  \n",
       "133  Bangladesh                2020 5.280  8.472 0.739  65.30  0.777  \n",
       "954  Laos                      2020 5.284  8.960 0.660  59.50  0.915  \n",
       "710  Hong Kong S.A.R. of China 2020 5.295     NA 0.813     NA  0.705  \n",
       "...  ...                       ...  ...   ...    ...    ...    ...    \n",
       "1866 Uruguay                   2020 6.310  9.937 0.921  69.2   0.908  \n",
       "1016 Lithuania                 2020 6.391 10.504 0.953  68.5   0.824  \n",
       "542  Estonia                   2020 6.453 10.459 0.958  69.0   0.954  \n",
       "1821 United Arab Emirates      2020 6.458 11.053 0.827  67.5   0.942  \n",
       "1559 Slovenia                  2020 6.462 10.478 0.953  71.7   0.958  \n",
       "830  Italy                     2020 6.488 10.563 0.890  74.0   0.718  \n",
       "1615 Spain                     2020 6.502 10.488 0.935  75.0   0.783  \n",
       "423  Croatia                   2020 6.508 10.166 0.923  71.4   0.837  \n",
       "1546 Slovakia                  2020 6.519 10.332 0.954  69.5   0.762  \n",
       "1482 Saudi Arabia              2020 6.560 10.701 0.890  66.9   0.884  \n",
       "579  France                    2020 6.714 10.643 0.947  74.2   0.823  \n",
       "1682 Taiwan Province of China  2020 6.751     NA 0.901    NA   0.799  \n",
       "1836 United Kingdom            2020 6.798 10.626 0.929  72.7   0.885  \n",
       "161  Belgium                   2020 6.839 10.771 0.904  72.4   0.767  \n",
       "449  Czech Republic            2020 6.897 10.530 0.964  71.3   0.906  \n",
       "308  Canada                    2020 7.025 10.730 0.931  74.0   0.887  \n",
       "1851 United States             2020 7.028 11.001 0.937  68.1   0.850  \n",
       "800  Ireland                   2020 7.035 11.323 0.960  72.5   0.882  \n",
       "80   Australia                 2020 7.137 10.760 0.937  74.2   0.905  \n",
       "815  Israel                    2020 7.195 10.538 0.959  73.7   0.831  \n",
       "93   Austria                   2020 7.213 10.851 0.925  73.6   0.912  \n",
       "1239 New Zealand               2020 7.257 10.600 0.952  73.6   0.918  \n",
       "1310 Norway                    2020 7.290 11.042 0.956  73.4   0.965  \n",
       "621  Germany                   2020 7.312 10.833 0.905  72.8   0.864  \n",
       "1652 Sweden                    2020 7.314 10.838 0.936  72.8   0.951  \n",
       "1225 Netherlands               2020 7.504 10.901 0.944  72.5   0.935  \n",
       "1662 Switzerland               2020 7.508 11.081 0.946  74.7   0.917  \n",
       "464  Denmark                   2020 7.515 10.910 0.947  73.0   0.938  \n",
       "732  Iceland                   2020 7.575 10.824 0.983  73.0   0.949  \n",
       "564  Finland                   2020 7.889 10.750 0.962  72.1   0.962  \n",
       "     Generosity Corruption Positive.affect Negative.affect\n",
       "1949 -0.009     0.789      0.703           0.346          \n",
       "1712  0.295     0.521      0.686           0.271          \n",
       "875  -0.150        NA         NA              NA          \n",
       "747   0.075     0.780      0.752           0.383          \n",
       "278   0.052     0.863      0.878           0.390          \n",
       "175  -0.083     0.532      0.609           0.305          \n",
       "1191  0.470     0.647      0.800           0.289          \n",
       "1197 -0.104     0.810      0.648           0.248          \n",
       "513  -0.112        NA      0.599           0.442          \n",
       "905   0.260     0.837      0.733           0.297          \n",
       "551   0.188     0.784      0.669           0.252          \n",
       "1894     NA     0.811      0.722           0.396          \n",
       "1793  0.147     0.878      0.699           0.425          \n",
       "1753 -0.202     0.877      0.585           0.439          \n",
       "786  -0.021     0.849      0.644           0.532          \n",
       "1174 -0.229     0.803      0.587           0.256          \n",
       "1934  0.056     0.810      0.691           0.345          \n",
       "1768 -0.111     0.774      0.384           0.440          \n",
       "774      NA     0.710      0.582           0.470          \n",
       "1581 -0.015     0.912      0.820           0.294          \n",
       "1300  0.131     0.877      0.605           0.365          \n",
       "1395 -0.116     0.744      0.804           0.327          \n",
       "606  -0.221     0.583      0.611           0.295          \n",
       "483  -0.128     0.636      0.734           0.314          \n",
       "293   0.049     0.837      0.630           0.386          \n",
       "839   0.016     0.777      0.693           0.340          \n",
       "1808  0.126     0.946      0.688           0.285          \n",
       "133  -0.009     0.742      0.582           0.332          \n",
       "954   0.141     0.748      0.822           0.358          \n",
       "710      NA     0.380      0.609           0.210          \n",
       "...  ...        ...        ...             ...            \n",
       "1866 -0.084     0.491      0.807           0.265          \n",
       "1016 -0.122     0.829      0.660           0.202          \n",
       "542  -0.082     0.398      0.807           0.188          \n",
       "1821  0.060        NA      0.752           0.298          \n",
       "1559 -0.081     0.797      0.610           0.314          \n",
       "830  -0.150     0.844      0.670           0.311          \n",
       "1615 -0.121     0.730      0.686           0.317          \n",
       "423  -0.063     0.961      0.743           0.286          \n",
       "1546 -0.075     0.901      0.764           0.274          \n",
       "1482 -0.111        NA      0.754           0.251          \n",
       "579  -0.169     0.565      0.732           0.231          \n",
       "1682     NA     0.711      0.845           0.083          \n",
       "1836  0.203     0.490      0.758           0.225          \n",
       "161  -0.164     0.634      0.647           0.260          \n",
       "449  -0.127     0.884      0.832           0.290          \n",
       "308   0.050     0.434      0.796           0.307          \n",
       "1851  0.034     0.678      0.787           0.295          \n",
       "800   0.014     0.356      0.797           0.246          \n",
       "80    0.210     0.491      0.769           0.205          \n",
       "815  -0.049     0.748      0.621           0.243          \n",
       "93    0.011     0.464      0.769           0.206          \n",
       "1239  0.125     0.283      0.849           0.209          \n",
       "1310  0.075     0.271      0.823           0.216          \n",
       "621  -0.060     0.424      0.760           0.206          \n",
       "1652  0.091     0.203      0.766           0.222          \n",
       "1225  0.151     0.281      0.784           0.247          \n",
       "1662 -0.064     0.280      0.769           0.193          \n",
       "464   0.052     0.214      0.818           0.227          \n",
       "732   0.160     0.644      0.863           0.172          \n",
       "564  -0.116     0.164      0.744           0.193          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "happ_2021[order(happ_2021$Score),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 20</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ï..Country.name</th><th scope=col>Regional.indicator</th><th scope=col>Ladder.score</th><th scope=col>Standard.error.of.ladder.score</th><th scope=col>upperwhisker</th><th scope=col>lowerwhisker</th><th scope=col>Logged.GDP.per.capita</th><th scope=col>Social.support</th><th scope=col>Healthy.life.expectancy</th><th scope=col>Freedom.to.make.life.choices</th><th scope=col>Generosity</th><th scope=col>Perceptions.of.corruption</th><th scope=col>Ladder.score.in.Dystopia</th><th scope=col>Explained.by..Log.GDP.per.capita</th><th scope=col>Explained.by..Social.support</th><th scope=col>Explained.by..Healthy.life.expectancy</th><th scope=col>Explained.by..Freedom.to.make.life.choices</th><th scope=col>Explained.by..Generosity</th><th scope=col>Explained.by..Perceptions.of.corruption</th><th scope=col>Dystopia...residual</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Finland    </td><td>Western Europe</td><td>7.842</td><td>0.032</td><td>7.904</td><td>7.780</td><td>10.775</td><td>0.954</td><td>72.0</td><td>0.949</td><td>-0.098</td><td>0.186</td><td>2.43</td><td>1.446</td><td>1.106</td><td>0.741</td><td>0.691</td><td>0.124</td><td>0.481</td><td>3.253</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Denmark    </td><td>Western Europe</td><td>7.620</td><td>0.035</td><td>7.687</td><td>7.552</td><td>10.933</td><td>0.954</td><td>72.7</td><td>0.946</td><td> 0.030</td><td>0.179</td><td>2.43</td><td>1.502</td><td>1.108</td><td>0.763</td><td>0.686</td><td>0.208</td><td>0.485</td><td>2.868</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Switzerland</td><td>Western Europe</td><td>7.571</td><td>0.036</td><td>7.643</td><td>7.500</td><td>11.117</td><td>0.942</td><td>74.4</td><td>0.919</td><td> 0.025</td><td>0.292</td><td>2.43</td><td>1.566</td><td>1.079</td><td>0.816</td><td>0.653</td><td>0.204</td><td>0.413</td><td>2.839</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Iceland    </td><td>Western Europe</td><td>7.554</td><td>0.059</td><td>7.670</td><td>7.438</td><td>10.878</td><td>0.983</td><td>73.0</td><td>0.955</td><td> 0.160</td><td>0.673</td><td>2.43</td><td>1.482</td><td>1.172</td><td>0.772</td><td>0.698</td><td>0.293</td><td>0.170</td><td>2.967</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Netherlands</td><td>Western Europe</td><td>7.464</td><td>0.027</td><td>7.518</td><td>7.410</td><td>10.932</td><td>0.942</td><td>72.4</td><td>0.913</td><td> 0.175</td><td>0.338</td><td>2.43</td><td>1.501</td><td>1.079</td><td>0.753</td><td>0.647</td><td>0.302</td><td>0.384</td><td>2.798</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Norway     </td><td>Western Europe</td><td>7.392</td><td>0.035</td><td>7.462</td><td>7.323</td><td>11.053</td><td>0.954</td><td>73.3</td><td>0.960</td><td> 0.093</td><td>0.270</td><td>2.43</td><td>1.543</td><td>1.108</td><td>0.782</td><td>0.703</td><td>0.249</td><td>0.427</td><td>2.580</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 20\n",
       "\\begin{tabular}{r|llllllllllllllllllll}\n",
       "  & ï..Country.name & Regional.indicator & Ladder.score & Standard.error.of.ladder.score & upperwhisker & lowerwhisker & Logged.GDP.per.capita & Social.support & Healthy.life.expectancy & Freedom.to.make.life.choices & Generosity & Perceptions.of.corruption & Ladder.score.in.Dystopia & Explained.by..Log.GDP.per.capita & Explained.by..Social.support & Explained.by..Healthy.life.expectancy & Explained.by..Freedom.to.make.life.choices & Explained.by..Generosity & Explained.by..Perceptions.of.corruption & Dystopia...residual\\\\\n",
       "  & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Finland     & Western Europe & 7.842 & 0.032 & 7.904 & 7.780 & 10.775 & 0.954 & 72.0 & 0.949 & -0.098 & 0.186 & 2.43 & 1.446 & 1.106 & 0.741 & 0.691 & 0.124 & 0.481 & 3.253\\\\\n",
       "\t2 & Denmark     & Western Europe & 7.620 & 0.035 & 7.687 & 7.552 & 10.933 & 0.954 & 72.7 & 0.946 &  0.030 & 0.179 & 2.43 & 1.502 & 1.108 & 0.763 & 0.686 & 0.208 & 0.485 & 2.868\\\\\n",
       "\t3 & Switzerland & Western Europe & 7.571 & 0.036 & 7.643 & 7.500 & 11.117 & 0.942 & 74.4 & 0.919 &  0.025 & 0.292 & 2.43 & 1.566 & 1.079 & 0.816 & 0.653 & 0.204 & 0.413 & 2.839\\\\\n",
       "\t4 & Iceland     & Western Europe & 7.554 & 0.059 & 7.670 & 7.438 & 10.878 & 0.983 & 73.0 & 0.955 &  0.160 & 0.673 & 2.43 & 1.482 & 1.172 & 0.772 & 0.698 & 0.293 & 0.170 & 2.967\\\\\n",
       "\t5 & Netherlands & Western Europe & 7.464 & 0.027 & 7.518 & 7.410 & 10.932 & 0.942 & 72.4 & 0.913 &  0.175 & 0.338 & 2.43 & 1.501 & 1.079 & 0.753 & 0.647 & 0.302 & 0.384 & 2.798\\\\\n",
       "\t6 & Norway      & Western Europe & 7.392 & 0.035 & 7.462 & 7.323 & 11.053 & 0.954 & 73.3 & 0.960 &  0.093 & 0.270 & 2.43 & 1.543 & 1.108 & 0.782 & 0.703 & 0.249 & 0.427 & 2.580\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 20\n",
       "\n",
       "| <!--/--> | ï..Country.name &lt;chr&gt; | Regional.indicator &lt;chr&gt; | Ladder.score &lt;dbl&gt; | Standard.error.of.ladder.score &lt;dbl&gt; | upperwhisker &lt;dbl&gt; | lowerwhisker &lt;dbl&gt; | Logged.GDP.per.capita &lt;dbl&gt; | Social.support &lt;dbl&gt; | Healthy.life.expectancy &lt;dbl&gt; | Freedom.to.make.life.choices &lt;dbl&gt; | Generosity &lt;dbl&gt; | Perceptions.of.corruption &lt;dbl&gt; | Ladder.score.in.Dystopia &lt;dbl&gt; | Explained.by..Log.GDP.per.capita &lt;dbl&gt; | Explained.by..Social.support &lt;dbl&gt; | Explained.by..Healthy.life.expectancy &lt;dbl&gt; | Explained.by..Freedom.to.make.life.choices &lt;dbl&gt; | Explained.by..Generosity &lt;dbl&gt; | Explained.by..Perceptions.of.corruption &lt;dbl&gt; | Dystopia...residual &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | Finland     | Western Europe | 7.842 | 0.032 | 7.904 | 7.780 | 10.775 | 0.954 | 72.0 | 0.949 | -0.098 | 0.186 | 2.43 | 1.446 | 1.106 | 0.741 | 0.691 | 0.124 | 0.481 | 3.253 |\n",
       "| 2 | Denmark     | Western Europe | 7.620 | 0.035 | 7.687 | 7.552 | 10.933 | 0.954 | 72.7 | 0.946 |  0.030 | 0.179 | 2.43 | 1.502 | 1.108 | 0.763 | 0.686 | 0.208 | 0.485 | 2.868 |\n",
       "| 3 | Switzerland | Western Europe | 7.571 | 0.036 | 7.643 | 7.500 | 11.117 | 0.942 | 74.4 | 0.919 |  0.025 | 0.292 | 2.43 | 1.566 | 1.079 | 0.816 | 0.653 | 0.204 | 0.413 | 2.839 |\n",
       "| 4 | Iceland     | Western Europe | 7.554 | 0.059 | 7.670 | 7.438 | 10.878 | 0.983 | 73.0 | 0.955 |  0.160 | 0.673 | 2.43 | 1.482 | 1.172 | 0.772 | 0.698 | 0.293 | 0.170 | 2.967 |\n",
       "| 5 | Netherlands | Western Europe | 7.464 | 0.027 | 7.518 | 7.410 | 10.932 | 0.942 | 72.4 | 0.913 |  0.175 | 0.338 | 2.43 | 1.501 | 1.079 | 0.753 | 0.647 | 0.302 | 0.384 | 2.798 |\n",
       "| 6 | Norway      | Western Europe | 7.392 | 0.035 | 7.462 | 7.323 | 11.053 | 0.954 | 73.3 | 0.960 |  0.093 | 0.270 | 2.43 | 1.543 | 1.108 | 0.782 | 0.703 | 0.249 | 0.427 | 2.580 |\n",
       "\n"
      ],
      "text/plain": [
       "  ï..Country.name Regional.indicator Ladder.score\n",
       "1 Finland         Western Europe     7.842       \n",
       "2 Denmark         Western Europe     7.620       \n",
       "3 Switzerland     Western Europe     7.571       \n",
       "4 Iceland         Western Europe     7.554       \n",
       "5 Netherlands     Western Europe     7.464       \n",
       "6 Norway          Western Europe     7.392       \n",
       "  Standard.error.of.ladder.score upperwhisker lowerwhisker\n",
       "1 0.032                          7.904        7.780       \n",
       "2 0.035                          7.687        7.552       \n",
       "3 0.036                          7.643        7.500       \n",
       "4 0.059                          7.670        7.438       \n",
       "5 0.027                          7.518        7.410       \n",
       "6 0.035                          7.462        7.323       \n",
       "  Logged.GDP.per.capita Social.support Healthy.life.expectancy\n",
       "1 10.775                0.954          72.0                   \n",
       "2 10.933                0.954          72.7                   \n",
       "3 11.117                0.942          74.4                   \n",
       "4 10.878                0.983          73.0                   \n",
       "5 10.932                0.942          72.4                   \n",
       "6 11.053                0.954          73.3                   \n",
       "  Freedom.to.make.life.choices Generosity Perceptions.of.corruption\n",
       "1 0.949                        -0.098     0.186                    \n",
       "2 0.946                         0.030     0.179                    \n",
       "3 0.919                         0.025     0.292                    \n",
       "4 0.955                         0.160     0.673                    \n",
       "5 0.913                         0.175     0.338                    \n",
       "6 0.960                         0.093     0.270                    \n",
       "  Ladder.score.in.Dystopia Explained.by..Log.GDP.per.capita\n",
       "1 2.43                     1.446                           \n",
       "2 2.43                     1.502                           \n",
       "3 2.43                     1.566                           \n",
       "4 2.43                     1.482                           \n",
       "5 2.43                     1.501                           \n",
       "6 2.43                     1.543                           \n",
       "  Explained.by..Social.support Explained.by..Healthy.life.expectancy\n",
       "1 1.106                        0.741                                \n",
       "2 1.108                        0.763                                \n",
       "3 1.079                        0.816                                \n",
       "4 1.172                        0.772                                \n",
       "5 1.079                        0.753                                \n",
       "6 1.108                        0.782                                \n",
       "  Explained.by..Freedom.to.make.life.choices Explained.by..Generosity\n",
       "1 0.691                                      0.124                   \n",
       "2 0.686                                      0.208                   \n",
       "3 0.653                                      0.204                   \n",
       "4 0.698                                      0.293                   \n",
       "5 0.647                                      0.302                   \n",
       "6 0.703                                      0.249                   \n",
       "  Explained.by..Perceptions.of.corruption Dystopia...residual\n",
       "1 0.481                                   3.253              \n",
       "2 0.485                                   2.868              \n",
       "3 0.413                                   2.839              \n",
       "4 0.170                                   2.967              \n",
       "5 0.384                                   2.798              \n",
       "6 0.427                                   2.580              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "happ_2021 <- read.csv(\"world-happiness-report-2021.csv\")\n",
    "head(happ_2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "happ_2021 <- happ_2021[, -c(2,4,5,6,13,14,15,16,17,18,19)] #dropping unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Renaming columns for convenience\n",
    "happ_2021 <- happ_2021 %>% rename(c(\"Country\" = \"ï..Country.name\" ,\n",
    "                      \"Score\" = \"Ladder.score\",\n",
    "                      \"GDP\" = \"Logged.GDP.per.capita\", \n",
    "                      \"Family\" = \"Social.support\",\n",
    "                      \"Health\" = \"Healthy.life.expectancy\", \n",
    "                      \"Freedom\" = \"Freedom.to.make.life.choices\",\n",
    "                      \"Corruption\" = \"Perceptions.of.corruption\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Country</th><th scope=col>Score</th><th scope=col>GDP</th><th scope=col>Family</th><th scope=col>Health</th><th scope=col>Freedom</th><th scope=col>Generosity</th><th scope=col>Corruption</th><th scope=col>Dystopia...residual</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Finland    </td><td>7.842</td><td>10.775</td><td>0.954</td><td>72.0</td><td>0.949</td><td>-0.098</td><td>0.186</td><td>3.253</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Denmark    </td><td>7.620</td><td>10.933</td><td>0.954</td><td>72.7</td><td>0.946</td><td> 0.030</td><td>0.179</td><td>2.868</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Switzerland</td><td>7.571</td><td>11.117</td><td>0.942</td><td>74.4</td><td>0.919</td><td> 0.025</td><td>0.292</td><td>2.839</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Iceland    </td><td>7.554</td><td>10.878</td><td>0.983</td><td>73.0</td><td>0.955</td><td> 0.160</td><td>0.673</td><td>2.967</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Netherlands</td><td>7.464</td><td>10.932</td><td>0.942</td><td>72.4</td><td>0.913</td><td> 0.175</td><td>0.338</td><td>2.798</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Norway     </td><td>7.392</td><td>11.053</td><td>0.954</td><td>73.3</td><td>0.960</td><td> 0.093</td><td>0.270</td><td>2.580</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & Country & Score & GDP & Family & Health & Freedom & Generosity & Corruption & Dystopia...residual\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Finland     & 7.842 & 10.775 & 0.954 & 72.0 & 0.949 & -0.098 & 0.186 & 3.253\\\\\n",
       "\t2 & Denmark     & 7.620 & 10.933 & 0.954 & 72.7 & 0.946 &  0.030 & 0.179 & 2.868\\\\\n",
       "\t3 & Switzerland & 7.571 & 11.117 & 0.942 & 74.4 & 0.919 &  0.025 & 0.292 & 2.839\\\\\n",
       "\t4 & Iceland     & 7.554 & 10.878 & 0.983 & 73.0 & 0.955 &  0.160 & 0.673 & 2.967\\\\\n",
       "\t5 & Netherlands & 7.464 & 10.932 & 0.942 & 72.4 & 0.913 &  0.175 & 0.338 & 2.798\\\\\n",
       "\t6 & Norway      & 7.392 & 11.053 & 0.954 & 73.3 & 0.960 &  0.093 & 0.270 & 2.580\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| <!--/--> | Country &lt;chr&gt; | Score &lt;dbl&gt; | GDP &lt;dbl&gt; | Family &lt;dbl&gt; | Health &lt;dbl&gt; | Freedom &lt;dbl&gt; | Generosity &lt;dbl&gt; | Corruption &lt;dbl&gt; | Dystopia...residual &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | Finland     | 7.842 | 10.775 | 0.954 | 72.0 | 0.949 | -0.098 | 0.186 | 3.253 |\n",
       "| 2 | Denmark     | 7.620 | 10.933 | 0.954 | 72.7 | 0.946 |  0.030 | 0.179 | 2.868 |\n",
       "| 3 | Switzerland | 7.571 | 11.117 | 0.942 | 74.4 | 0.919 |  0.025 | 0.292 | 2.839 |\n",
       "| 4 | Iceland     | 7.554 | 10.878 | 0.983 | 73.0 | 0.955 |  0.160 | 0.673 | 2.967 |\n",
       "| 5 | Netherlands | 7.464 | 10.932 | 0.942 | 72.4 | 0.913 |  0.175 | 0.338 | 2.798 |\n",
       "| 6 | Norway      | 7.392 | 11.053 | 0.954 | 73.3 | 0.960 |  0.093 | 0.270 | 2.580 |\n",
       "\n"
      ],
      "text/plain": [
       "  Country     Score GDP    Family Health Freedom Generosity Corruption\n",
       "1 Finland     7.842 10.775 0.954  72.0   0.949   -0.098     0.186     \n",
       "2 Denmark     7.620 10.933 0.954  72.7   0.946    0.030     0.179     \n",
       "3 Switzerland 7.571 11.117 0.942  74.4   0.919    0.025     0.292     \n",
       "4 Iceland     7.554 10.878 0.983  73.0   0.955    0.160     0.673     \n",
       "5 Netherlands 7.464 10.932 0.942  72.4   0.913    0.175     0.338     \n",
       "6 Norway      7.392 11.053 0.954  73.3   0.960    0.093     0.270     \n",
       "  Dystopia...residual\n",
       "1 3.253              \n",
       "2 2.868              \n",
       "3 2.839              \n",
       "4 2.967              \n",
       "5 2.798              \n",
       "6 2.580              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(happ_2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Rank</th><th scope=col>Country</th><th scope=col>Score</th><th scope=col>GDP</th><th scope=col>Family</th><th scope=col>Health</th><th scope=col>Freedom</th><th scope=col>Generosity</th><th scope=col>Corruption</th><th scope=col>Dystopia...residual</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>Finland    </td><td>7.842</td><td>10.775</td><td>0.954</td><td>72.0</td><td>0.949</td><td>-0.098</td><td>0.186</td><td>3.253</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>2</td><td>Denmark    </td><td>7.620</td><td>10.933</td><td>0.954</td><td>72.7</td><td>0.946</td><td> 0.030</td><td>0.179</td><td>2.868</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>3</td><td>Switzerland</td><td>7.571</td><td>11.117</td><td>0.942</td><td>74.4</td><td>0.919</td><td> 0.025</td><td>0.292</td><td>2.839</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>4</td><td>Iceland    </td><td>7.554</td><td>10.878</td><td>0.983</td><td>73.0</td><td>0.955</td><td> 0.160</td><td>0.673</td><td>2.967</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>5</td><td>Netherlands</td><td>7.464</td><td>10.932</td><td>0.942</td><td>72.4</td><td>0.913</td><td> 0.175</td><td>0.338</td><td>2.798</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>6</td><td>Norway     </td><td>7.392</td><td>11.053</td><td>0.954</td><td>73.3</td><td>0.960</td><td> 0.093</td><td>0.270</td><td>2.580</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 10\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & Rank & Country & Score & GDP & Family & Health & Freedom & Generosity & Corruption & Dystopia...residual\\\\\n",
       "  & <int> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 1 & Finland     & 7.842 & 10.775 & 0.954 & 72.0 & 0.949 & -0.098 & 0.186 & 3.253\\\\\n",
       "\t2 & 2 & Denmark     & 7.620 & 10.933 & 0.954 & 72.7 & 0.946 &  0.030 & 0.179 & 2.868\\\\\n",
       "\t3 & 3 & Switzerland & 7.571 & 11.117 & 0.942 & 74.4 & 0.919 &  0.025 & 0.292 & 2.839\\\\\n",
       "\t4 & 4 & Iceland     & 7.554 & 10.878 & 0.983 & 73.0 & 0.955 &  0.160 & 0.673 & 2.967\\\\\n",
       "\t5 & 5 & Netherlands & 7.464 & 10.932 & 0.942 & 72.4 & 0.913 &  0.175 & 0.338 & 2.798\\\\\n",
       "\t6 & 6 & Norway      & 7.392 & 11.053 & 0.954 & 73.3 & 0.960 &  0.093 & 0.270 & 2.580\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 10\n",
       "\n",
       "| <!--/--> | Rank &lt;int&gt; | Country &lt;chr&gt; | Score &lt;dbl&gt; | GDP &lt;dbl&gt; | Family &lt;dbl&gt; | Health &lt;dbl&gt; | Freedom &lt;dbl&gt; | Generosity &lt;dbl&gt; | Corruption &lt;dbl&gt; | Dystopia...residual &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 1 | Finland     | 7.842 | 10.775 | 0.954 | 72.0 | 0.949 | -0.098 | 0.186 | 3.253 |\n",
       "| 2 | 2 | Denmark     | 7.620 | 10.933 | 0.954 | 72.7 | 0.946 |  0.030 | 0.179 | 2.868 |\n",
       "| 3 | 3 | Switzerland | 7.571 | 11.117 | 0.942 | 74.4 | 0.919 |  0.025 | 0.292 | 2.839 |\n",
       "| 4 | 4 | Iceland     | 7.554 | 10.878 | 0.983 | 73.0 | 0.955 |  0.160 | 0.673 | 2.967 |\n",
       "| 5 | 5 | Netherlands | 7.464 | 10.932 | 0.942 | 72.4 | 0.913 |  0.175 | 0.338 | 2.798 |\n",
       "| 6 | 6 | Norway      | 7.392 | 11.053 | 0.954 | 73.3 | 0.960 |  0.093 | 0.270 | 2.580 |\n",
       "\n"
      ],
      "text/plain": [
       "  Rank Country     Score GDP    Family Health Freedom Generosity Corruption\n",
       "1 1    Finland     7.842 10.775 0.954  72.0   0.949   -0.098     0.186     \n",
       "2 2    Denmark     7.620 10.933 0.954  72.7   0.946    0.030     0.179     \n",
       "3 3    Switzerland 7.571 11.117 0.942  74.4   0.919    0.025     0.292     \n",
       "4 4    Iceland     7.554 10.878 0.983  73.0   0.955    0.160     0.673     \n",
       "5 5    Netherlands 7.464 10.932 0.942  72.4   0.913    0.175     0.338     \n",
       "6 6    Norway      7.392 11.053 0.954  73.3   0.960    0.093     0.270     \n",
       "  Dystopia...residual\n",
       "1 3.253              \n",
       "2 2.868              \n",
       "3 2.839              \n",
       "4 2.967              \n",
       "5 2.798              \n",
       "6 2.580              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "happ_2021 <- happ_2021 %>% mutate(Rank = row_number())\n",
    "happ_2021 <- happ_2021 %>% select(Rank, Country, everything())\n",
    "head(happ_2021)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Correlation matrix </b> <br>\n",
    "To find out which factors correlate the most with the happiness quotient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Rank       Score         GDP      Family     Health\n",
      "Rank                 1.00000000 -0.98426526 -0.79698747 -0.75102401 -0.7759416\n",
      "Score               -0.98426526  1.00000000  0.78975970  0.75688765  0.7680995\n",
      "GDP                 -0.79698747  0.78975970  1.00000000  0.78529868  0.8594606\n",
      "Family              -0.75102401  0.75688765  0.78529868  1.00000000  0.7232561\n",
      "Health              -0.77594156  0.76809946  0.85946062  0.72325613  1.0000000\n",
      "Freedom             -0.60641135  0.60775307  0.43232349  0.48292977  0.4614939\n",
      "Generosity           0.03966234 -0.01779928 -0.19928640 -0.11494585 -0.1617503\n",
      "Corruption           0.40065425 -0.42114000 -0.34233742 -0.20320698 -0.3643735\n",
      "Dystopia...residual -0.46354759  0.49200990 -0.04111918  0.00668051 -0.0234329\n",
      "                       Freedom  Generosity  Corruption Dystopia...residual\n",
      "Rank                -0.6064113  0.03966234  0.40065425         -0.46354759\n",
      "Score                0.6077531 -0.01779928 -0.42114000          0.49200990\n",
      "GDP                  0.4323235 -0.19928640 -0.34233742         -0.04111918\n",
      "Family               0.4829298 -0.11494585 -0.20320698          0.00668051\n",
      "Health               0.4614939 -0.16175028 -0.36437353         -0.02343290\n",
      "Freedom              1.0000000  0.16943737 -0.40136297          0.10024227\n",
      "Generosity           0.1694374  1.00000000 -0.16396173         -0.02721188\n",
      "Corruption          -0.4013630 -0.16396173  1.00000000          0.00447841\n",
      "Dystopia...residual  0.1002423 -0.02721188  0.00447841          1.00000000\n"
     ]
    }
   ],
   "source": [
    "library(corrgram) \n",
    "library(corrplot)\n",
    "\n",
    "#library(rpart) \n",
    "\n",
    "#library(ggplot2) \n",
    "#library(ggthemes) \n",
    "num.cols <- sapply(happ_2021,is.numeric) \n",
    "cor.data <- cor(happ_2021[,num.cols]) \n",
    "print(cor.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Rank       Score         GDP      Family     Health\n",
      "Rank                 1.00000000 -0.98426526 -0.79698747 -0.75102401 -0.7759416\n",
      "Score               -0.98426526  1.00000000  0.78975970  0.75688765  0.7680995\n",
      "GDP                 -0.79698747  0.78975970  1.00000000  0.78529868  0.8594606\n",
      "Family              -0.75102401  0.75688765  0.78529868  1.00000000  0.7232561\n",
      "Health              -0.77594156  0.76809946  0.85946062  0.72325613  1.0000000\n",
      "Freedom             -0.60641135  0.60775307  0.43232349  0.48292977  0.4614939\n",
      "Generosity           0.03966234 -0.01779928 -0.19928640 -0.11494585 -0.1617503\n",
      "Corruption           0.40065425 -0.42114000 -0.34233742 -0.20320698 -0.3643735\n",
      "Dystopia...residual -0.46354759  0.49200990 -0.04111918  0.00668051 -0.0234329\n",
      "                       Freedom  Generosity  Corruption Dystopia...residual\n",
      "Rank                -0.6064113  0.03966234  0.40065425         -0.46354759\n",
      "Score                0.6077531 -0.01779928 -0.42114000          0.49200990\n",
      "GDP                  0.4323235 -0.19928640 -0.34233742         -0.04111918\n",
      "Family               0.4829298 -0.11494585 -0.20320698          0.00668051\n",
      "Health               0.4614939 -0.16175028 -0.36437353         -0.02343290\n",
      "Freedom              1.0000000  0.16943737 -0.40136297          0.10024227\n",
      "Generosity           0.1694374  1.00000000 -0.16396173         -0.02721188\n",
      "Corruption          -0.4013630 -0.16396173  1.00000000          0.00447841\n",
      "Dystopia...residual  0.1002423 -0.02721188  0.00447841          1.00000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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zm3byE1oxqKsIMNaXP6xz3E7tbFxU99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\nYII=",
      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(corrplot(cor.data, method = 'color'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence, happiness most strongly correlates with the attributes of GDP, Health, Social Support(Family) and Freedom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Rank</th><th scope=col>Country</th><th scope=col>Score</th><th scope=col>GDP</th><th scope=col>Family</th><th scope=col>Health</th><th scope=col>Freedom</th><th scope=col>Generosity</th><th scope=col>Corruption</th><th scope=col>Dystopia...residual</th><th scope=col>Continent</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>Finland    </td><td>7.842</td><td>10.775</td><td>0.954</td><td>72.0</td><td>0.949</td><td>-0.098</td><td>0.186</td><td>3.253</td><td>Europe</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>2</td><td>Denmark    </td><td>7.620</td><td>10.933</td><td>0.954</td><td>72.7</td><td>0.946</td><td> 0.030</td><td>0.179</td><td>2.868</td><td>Europe</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>3</td><td>Switzerland</td><td>7.571</td><td>11.117</td><td>0.942</td><td>74.4</td><td>0.919</td><td> 0.025</td><td>0.292</td><td>2.839</td><td>Europe</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>4</td><td>Iceland    </td><td>7.554</td><td>10.878</td><td>0.983</td><td>73.0</td><td>0.955</td><td> 0.160</td><td>0.673</td><td>2.967</td><td>Europe</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>5</td><td>Netherlands</td><td>7.464</td><td>10.932</td><td>0.942</td><td>72.4</td><td>0.913</td><td> 0.175</td><td>0.338</td><td>2.798</td><td>Europe</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>6</td><td>Norway     </td><td>7.392</td><td>11.053</td><td>0.954</td><td>73.3</td><td>0.960</td><td> 0.093</td><td>0.270</td><td>2.580</td><td>Europe</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & Rank & Country & Score & GDP & Family & Health & Freedom & Generosity & Corruption & Dystopia...residual & Continent\\\\\n",
       "  & <int> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
       "\\hline\n",
       "\t1 & 1 & Finland     & 7.842 & 10.775 & 0.954 & 72.0 & 0.949 & -0.098 & 0.186 & 3.253 & Europe\\\\\n",
       "\t2 & 2 & Denmark     & 7.620 & 10.933 & 0.954 & 72.7 & 0.946 &  0.030 & 0.179 & 2.868 & Europe\\\\\n",
       "\t3 & 3 & Switzerland & 7.571 & 11.117 & 0.942 & 74.4 & 0.919 &  0.025 & 0.292 & 2.839 & Europe\\\\\n",
       "\t4 & 4 & Iceland     & 7.554 & 10.878 & 0.983 & 73.0 & 0.955 &  0.160 & 0.673 & 2.967 & Europe\\\\\n",
       "\t5 & 5 & Netherlands & 7.464 & 10.932 & 0.942 & 72.4 & 0.913 &  0.175 & 0.338 & 2.798 & Europe\\\\\n",
       "\t6 & 6 & Norway      & 7.392 & 11.053 & 0.954 & 73.3 & 0.960 &  0.093 & 0.270 & 2.580 & Europe\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 11\n",
       "\n",
       "| <!--/--> | Rank &lt;int&gt; | Country &lt;chr&gt; | Score &lt;dbl&gt; | GDP &lt;dbl&gt; | Family &lt;dbl&gt; | Health &lt;dbl&gt; | Freedom &lt;dbl&gt; | Generosity &lt;dbl&gt; | Corruption &lt;dbl&gt; | Dystopia...residual &lt;dbl&gt; | Continent &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 1 | Finland     | 7.842 | 10.775 | 0.954 | 72.0 | 0.949 | -0.098 | 0.186 | 3.253 | Europe |\n",
       "| 2 | 2 | Denmark     | 7.620 | 10.933 | 0.954 | 72.7 | 0.946 |  0.030 | 0.179 | 2.868 | Europe |\n",
       "| 3 | 3 | Switzerland | 7.571 | 11.117 | 0.942 | 74.4 | 0.919 |  0.025 | 0.292 | 2.839 | Europe |\n",
       "| 4 | 4 | Iceland     | 7.554 | 10.878 | 0.983 | 73.0 | 0.955 |  0.160 | 0.673 | 2.967 | Europe |\n",
       "| 5 | 5 | Netherlands | 7.464 | 10.932 | 0.942 | 72.4 | 0.913 |  0.175 | 0.338 | 2.798 | Europe |\n",
       "| 6 | 6 | Norway      | 7.392 | 11.053 | 0.954 | 73.3 | 0.960 |  0.093 | 0.270 | 2.580 | Europe |\n",
       "\n"
      ],
      "text/plain": [
       "  Rank Country     Score GDP    Family Health Freedom Generosity Corruption\n",
       "1 1    Finland     7.842 10.775 0.954  72.0   0.949   -0.098     0.186     \n",
       "2 2    Denmark     7.620 10.933 0.954  72.7   0.946    0.030     0.179     \n",
       "3 3    Switzerland 7.571 11.117 0.942  74.4   0.919    0.025     0.292     \n",
       "4 4    Iceland     7.554 10.878 0.983  73.0   0.955    0.160     0.673     \n",
       "5 5    Netherlands 7.464 10.932 0.942  72.4   0.913    0.175     0.338     \n",
       "6 6    Norway      7.392 11.053 0.954  73.3   0.960    0.093     0.270     \n",
       "  Dystopia...residual Continent\n",
       "1 3.253               Europe   \n",
       "2 2.868               Europe   \n",
       "3 2.839               Europe   \n",
       "4 2.967               Europe   \n",
       "5 2.798               Europe   \n",
       "6 2.580               Europe   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t149 obs. of  11 variables:\n",
      " $ Rank               : int  1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ Country            : chr  \"Finland\" \"Denmark\" \"Switzerland\" \"Iceland\" ...\n",
      " $ Score              : num  7.84 7.62 7.57 7.55 7.46 ...\n",
      " $ GDP                : num  10.8 10.9 11.1 10.9 10.9 ...\n",
      " $ Family             : num  0.954 0.954 0.942 0.983 0.942 0.954 0.934 0.908 0.948 0.934 ...\n",
      " $ Health             : num  72 72.7 74.4 73 72.4 73.3 72.7 72.6 73.4 73.3 ...\n",
      " $ Freedom            : num  0.949 0.946 0.919 0.955 0.913 0.96 0.945 0.907 0.929 0.908 ...\n",
      " $ Generosity         : num  -0.098 0.03 0.025 0.16 0.175 0.093 0.086 -0.034 0.134 0.042 ...\n",
      " $ Corruption         : num  0.186 0.179 0.292 0.673 0.338 0.27 0.237 0.386 0.242 0.481 ...\n",
      " $ Dystopia...residual: num  3.25 2.87 2.84 2.97 2.8 ...\n",
      " $ Continent          : Factor w/ 6 levels \"Africa\",\"Asia\",..: 4 4 4 4 4 4 4 4 3 4 ...\n"
     ]
    }
   ],
   "source": [
    "# Creating a new column for continents\n",
    "\n",
    "happ_2021$Continent <- NA\n",
    "\n",
    "happ_2021$Continent[which(happ_2021$Country %in% c(\"Israel\", \"United Arab Emirates\", \"Singapore\", \"Thailand\", \"Taiwan Province of China\",\n",
    "                                   \"Qatar\", \"Saudi Arabia\", \"Kuwait\", \"Bahrain\", \"Malaysia\", \"Uzbekistan\", \"Japan\",\n",
    "                                   \"South Korea\", \"Turkmenistan\", \"Kazakhstan\", \"Turkey\", \"Hong Kong S.A.R., China\", \"Philippines\",\n",
    "                                   \"Jordan\", \"China\", \"Pakistan\", \"Indonesia\", \"Azerbaijan\", \"Lebanon\", \"Vietnam\",\n",
    "                                   \"Tajikistan\", \"Bhutan\", \"Kyrgyzstan\", \"Nepal\", \"Mongolia\", \"Palestinian Territories\",\n",
    "                                   \"Iran\", \"Bangladesh\", \"Myanmar\", \"Iraq\", \"Sri Lanka\", \"Armenia\", \"India\", \"Georgia\",\n",
    "                                   \"Cambodia\", \"Afghanistan\", \"Yemen\", \"Syria\"))] <- \"Asia\"\n",
    "happ_2021$Continent[which(happ_2021$Country %in% c(\"Norway\", \"Denmark\", \"Iceland\", \"Switzerland\", \"Finland\",\n",
    "                                   \"Netherlands\", \"Sweden\", \"Austria\", \"Ireland\", \"Germany\",\n",
    "                                   \"Belgium\", \"Luxembourg\", \"United Kingdom\", \"Czech Republic\",\n",
    "                                   \"Malta\", \"France\", \"Spain\", \"Slovakia\", \"Poland\", \"Italy\",\n",
    "                                   \"Russia\", \"Lithuania\", \"Latvia\", \"Moldova\", \"Romania\",\n",
    "                                   \"Slovenia\", \"North Cyprus\", \"Cyprus\", \"Estonia\", \"Belarus\",\n",
    "                                   \"Serbia\", \"Hungary\", \"Croatia\", \"Kosovo\", \"Montenegro\",\n",
    "                                   \"Greece\", \"Portugal\", \"Bosnia and Herzegovina\", \"Macedonia\",\n",
    "                                   \"Bulgaria\", \"Albania\", \"Ukraine\"))] <- \"Europe\"\n",
    "happ_2021$Continent[which(happ_2021$Country %in% c(\"Canada\", \"Costa Rica\", \"United States\", \"Mexico\",  \n",
    "                                   \"Panama\",\"Trinidad and Tobago\", \"El Salvador\", \"Belize\", \"Guatemala\",\n",
    "                                   \"Jamaica\", \"Nicaragua\", \"Dominican Republic\", \"Honduras\",\n",
    "                                   \"Haiti\"))] <- \"North America\"\n",
    "happ_2021$Continent[which(happ_2021$Country %in% c(\"Chile\", \"Brazil\", \"Argentina\", \"Uruguay\",\n",
    "                                   \"Colombia\", \"Ecuador\", \"Bolivia\", \"Peru\",\n",
    "                                   \"Paraguay\", \"Venezuela\"))] <- \"South America\"\n",
    "happ_2021$Continent[which(happ_2021$Country %in% c(\"New Zealand\", \"Australia\"))] <- \"Australia\"\n",
    "happ_2021$Continent[which(is.na(happ_2021$Continent))] <- \"Africa\"\n",
    "\n",
    "\n",
    "# Changing Continent column to factor\n",
    "\n",
    "happ_2021$Continent <- as.factor(happ_2021$Continent)\n",
    "\n",
    "head(happ_2021)\n",
    "str(happ_2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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0VeN3SvyAtnsLYxWIvoVoPcK/LCeeLo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\nwWCJyMVgsETkYvh/qonNVfcjWD0AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=8)\n",
    "gg1 <- ggplot(happ_2021,\n",
    "              aes(x=Continent,\n",
    "                  y=Score,\n",
    "                  color=Continent))+\n",
    "  geom_point() + theme_bw() \n",
    "gg1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Prediction </b> <br>\n",
    "Splitting dataset to training and testing datasets and using Linear Regression to predict the dependent variable for a country (Happiness Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(caTools)\n",
    "set.seed(123)\n",
    "dataset <- happ_2021[3:10] #relevant columns into training\n",
    "split = sample.split(dataset$Score, SplitRatio = 0.7) #splitting\n",
    "training_set = subset(dataset, split == TRUE) #training set\n",
    "test_set = subset(dataset, split == FALSE) #test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = Score ~ ., data = training_set)\n",
       "\n",
       "Residuals:\n",
       "       Min         1Q     Median         3Q        Max \n",
       "-0.0019526 -0.0005332 -0.0001061  0.0005323  0.0020082 \n",
       "\n",
       "Coefficients:\n",
       "                      Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)         -4.563e+00  1.203e-03   -3792   <2e-16 ***\n",
       "GDP                  3.494e-01  1.557e-04    2244   <2e-16 ***\n",
       "Family               2.252e+00  1.232e-03    1828   <2e-16 ***\n",
       "Health               3.147e-02  2.359e-05    1334   <2e-16 ***\n",
       "Freedom              1.219e+00  9.658e-04    1262   <2e-16 ***\n",
       "Generosity           6.518e-01  5.931e-04    1099   <2e-16 ***\n",
       "Corruption          -6.386e-01  5.500e-04   -1161   <2e-16 ***\n",
       "Dystopia...residual  9.999e-01  1.530e-04    6535   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 0.0008327 on 96 degrees of freedom\n",
       "Multiple R-squared:      1,\tAdjusted R-squared:      1 \n",
       "F-statistic: 2.448e+07 on 7 and 96 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regressor_lm = lm(formula = Score ~ .,\n",
    "               data = training_set)\n",
    "\n",
    "summary(regressor_lm) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An adjusted R-squared of 1 is obtained. The Multiple Linear Regression predicts happiness scores with an accuracy of 100%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MJNzo89SW/y4CrZbgiTN4XCUtA7DE9hBbCXffHzZnyXobD86B2GN+srj+Gj2EsW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\nCgsA3PB/ksbYG1BAWr8AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_lm = predict(regressor_lm, newdata = test_set)\n",
    "\n",
    "Pred_Actual_lm <- as.data.frame(cbind(Prediction = y_pred_lm, Actual = test_set$Score))\n",
    "\n",
    "gg.lm <- ggplot(Pred_Actual_lm, aes(Actual, Prediction )) +\n",
    "  geom_point() + theme_bw() + geom_abline() +\n",
    "  labs(title = \"Multiple Linear Regression\", x = \"Actual happiness score\",\n",
    "       y = \"Predicted happiness score\")\n",
    "\n",
    "gg.lm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Mean Squared Error (Multiple Linear Regression): 0\"\n"
     ]
    }
   ],
   "source": [
    "MSE.lm <- sum((test_set$Happiness.Score - y_pred_lm)^2)/nrow(test_set)\n",
    "print(paste(\"Mean Squared Error (Multiple Linear Regression):\", MSE.lm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Happiness Score:  6.45\"\n"
     ]
    }
   ],
   "source": [
    "test1 <- data.frame(GDP = 10.555,\n",
    "                  Family = 0.867,\n",
    "                  Health = 71.02,\n",
    "                  Freedom = 0.923,\n",
    "                  Generosity = 0.021,\n",
    "                  Corruption = 0.723,\n",
    "                  Dystopia...residual = 2.463)\n",
    "print(paste(\"Happiness Score: \", round(predict(regressor_lm, test1), 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On providing the above test inputs, the model predicts this set's Happiness Score to be 6.45 which would put this sample country somewhere in the top 30 happiest countries in the current scenario. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RK7U+Du/pOq/2A/3Wz+qDM1LtOM33LjSgQuKJjYxLIKtlJijwk8eyTDcEdoVVmP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\nIGMQGCBjEBggYxAYIGMQGCBjEBggY/4XaXrVo0ZYlY4AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 480
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=8, repr.plot.height=6)\n",
    "\n",
    "ggplot(happ_2021[1:10,], aes(x = reorder(Country, Score), y=Score, fill = Country)) +  ggtitle(\"Top 10 happiest countries in 2021\", subtitle = \"9 of the happiest countries are in Europe\") + geom_bar(stat=\"identity\", width=0.7) + theme(plot.title = element_text(size=20)) + coord_flip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 480
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(happ_2021[140:149,], aes(x = reorder(Country, -Score), y=Score, fill = Country)) + ggtitle(\"Top 10 saddest countries in 2021\") + geom_bar(stat=\"identity\", width=0.7) + theme(plot.title = element_text(size=20)) + coord_flip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cor <- happ_2021 %>% \n",
    "    select(corruption = Corruption,\n",
    "           generosity = Generosity,\n",
    "           freedom = Freedom, \n",
    "           life_expectancy = Health, \n",
    "           social_support = Family,\n",
    "           GDP_per_capita = GDP, \n",
    "           happiness = Score\n",
    "           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'rwa' was built under R version 4.0.5\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Variables</th><th scope=col>Raw.RelWeight</th><th scope=col>Rescaled.RelWeight</th><th scope=col>Sign</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>corruption     </td><td>0.056954334</td><td> 7.5351657</td><td>-</td></tr>\n",
       "\t<tr><td>generosity     </td><td>0.005739968</td><td> 0.7594086</td><td>+</td></tr>\n",
       "\t<tr><td>freedom        </td><td>0.128909062</td><td>17.0549117</td><td>+</td></tr>\n",
       "\t<tr><td>life_expectancy</td><td>0.177156666</td><td>23.4381606</td><td>+</td></tr>\n",
       "\t<tr><td>social_support </td><td>0.191970060</td><td>25.3980005</td><td>+</td></tr>\n",
       "\t<tr><td>GDP_per_capita </td><td>0.195117048</td><td>25.8143529</td><td>+</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " Variables & Raw.RelWeight & Rescaled.RelWeight & Sign\\\\\n",
       " <chr> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t corruption      & 0.056954334 &  7.5351657 & -\\\\\n",
       "\t generosity      & 0.005739968 &  0.7594086 & +\\\\\n",
       "\t freedom         & 0.128909062 & 17.0549117 & +\\\\\n",
       "\t life\\_expectancy & 0.177156666 & 23.4381606 & +\\\\\n",
       "\t social\\_support  & 0.191970060 & 25.3980005 & +\\\\\n",
       "\t GDP\\_per\\_capita  & 0.195117048 & 25.8143529 & +\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| Variables &lt;chr&gt; | Raw.RelWeight &lt;dbl&gt; | Rescaled.RelWeight &lt;dbl&gt; | Sign &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| corruption      | 0.056954334 |  7.5351657 | - |\n",
       "| generosity      | 0.005739968 |  0.7594086 | + |\n",
       "| freedom         | 0.128909062 | 17.0549117 | + |\n",
       "| life_expectancy | 0.177156666 | 23.4381606 | + |\n",
       "| social_support  | 0.191970060 | 25.3980005 | + |\n",
       "| GDP_per_capita  | 0.195117048 | 25.8143529 | + |\n",
       "\n"
      ],
      "text/plain": [
       "  Variables       Raw.RelWeight Rescaled.RelWeight Sign\n",
       "1 corruption      0.056954334    7.5351657         -   \n",
       "2 generosity      0.005739968    0.7594086         +   \n",
       "3 freedom         0.128909062   17.0549117         +   \n",
       "4 life_expectancy 0.177156666   23.4381606         +   \n",
       "5 social_support  0.191970060   25.3980005         +   \n",
       "6 GDP_per_capita  0.195117048   25.8143529         +   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#library(rwa)\n",
    "predictors = colnames(df_cor)[1:6]\n",
    "outcome = 'happiness'\n",
    "rwa <- rwa(df_cor,\n",
    "    outcome = outcome,\n",
    "    predictors = predictors,\n",
    "    applysigns = FALSE) \n",
    "\n",
    "rsquare <- rwa$rsquare\n",
    "(relative_weight <- rwa$result)\n",
    "n <- rwa$n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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34I5LCNfXdeaqInnZ7RXBnTbGYxv7GV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\nmQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 480
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(relative_weight, aes(x = reorder(Variables, Rescaled.RelWeight), y=Rescaled.RelWeight, fill = Variables)) +  ggtitle(\"Top important factors affecting the \\n happiness index in 2021\", subtitle = \"1. GDP 2. Social Support 3. Health\") + geom_bar(stat=\"identity\", width=0.7) + theme(plot.title = element_text(size=20), plot.subtitle = element_text(size=15)) + coord_flip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Performing some visualizations on data from India </b> <br>\n",
    "This year India stood 139th out of 149 countries in the Happiness Report. Last year, India's rank was 140th."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 15 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Country</th><th scope=col>year</th><th scope=col>Score</th><th scope=col>GDP</th><th scope=col>Family</th><th scope=col>Health</th><th scope=col>Freedom</th><th scope=col>Generosity</th><th scope=col>Corruption</th><th scope=col>Positive.affect</th><th scope=col>Negative.affect</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>733</th><td>India</td><td>2006</td><td>5.348</td><td>8.145</td><td>0.707</td><td>55.72</td><td>0.774</td><td>    NA</td><td>0.855</td><td>0.687</td><td>0.199</td></tr>\n",
       "\t<tr><th scope=row>734</th><td>India</td><td>2007</td><td>5.027</td><td>8.204</td><td>0.569</td><td>56.14</td><td>0.729</td><td>-0.051</td><td>0.862</td><td>0.668</td><td>0.253</td></tr>\n",
       "\t<tr><th scope=row>735</th><td>India</td><td>2008</td><td>5.146</td><td>8.220</td><td>0.684</td><td>56.56</td><td>0.756</td><td>-0.072</td><td>0.891</td><td>0.674</td><td>0.259</td></tr>\n",
       "\t<tr><th scope=row>736</th><td>India</td><td>2009</td><td>4.522</td><td>8.281</td><td>0.653</td><td>56.98</td><td>0.679</td><td>-0.026</td><td>0.895</td><td>0.771</td><td>0.301</td></tr>\n",
       "\t<tr><th scope=row>737</th><td>India</td><td>2010</td><td>4.989</td><td>8.349</td><td>0.605</td><td>57.40</td><td>0.783</td><td> 0.058</td><td>0.863</td><td>0.697</td><td>0.267</td></tr>\n",
       "\t<tr><th scope=row>738</th><td>India</td><td>2011</td><td>4.635</td><td>8.387</td><td>0.553</td><td>57.70</td><td>0.838</td><td>-0.038</td><td>0.908</td><td>0.648</td><td>0.232</td></tr>\n",
       "\t<tr><th scope=row>739</th><td>India</td><td>2012</td><td>4.720</td><td>8.428</td><td>0.511</td><td>58.00</td><td>0.609</td><td> 0.067</td><td>0.830</td><td>0.629</td><td>0.295</td></tr>\n",
       "\t<tr><th scope=row>740</th><td>India</td><td>2013</td><td>4.428</td><td>8.478</td><td>0.553</td><td>58.30</td><td>0.740</td><td> 0.084</td><td>0.832</td><td>0.680</td><td>0.330</td></tr>\n",
       "\t<tr><th scope=row>741</th><td>India</td><td>2014</td><td>4.424</td><td>8.538</td><td>0.621</td><td>58.60</td><td>0.809</td><td>-0.026</td><td>0.832</td><td>0.711</td><td>0.285</td></tr>\n",
       "\t<tr><th scope=row>742</th><td>India</td><td>2015</td><td>4.342</td><td>8.604</td><td>0.610</td><td>58.90</td><td>0.777</td><td>-0.005</td><td>0.776</td><td>0.701</td><td>0.322</td></tr>\n",
       "\t<tr><th scope=row>743</th><td>India</td><td>2016</td><td>4.179</td><td>8.673</td><td>0.614</td><td>59.30</td><td>0.820</td><td> 0.046</td><td>0.765</td><td>0.695</td><td>0.346</td></tr>\n",
       "\t<tr><th scope=row>744</th><td>India</td><td>2017</td><td>4.046</td><td>8.730</td><td>0.607</td><td>59.70</td><td>0.886</td><td>-0.042</td><td>0.781</td><td>0.682</td><td>0.318</td></tr>\n",
       "\t<tr><th scope=row>745</th><td>India</td><td>2018</td><td>3.818</td><td>8.779</td><td>0.638</td><td>60.10</td><td>0.890</td><td> 0.085</td><td>0.805</td><td>0.657</td><td>0.357</td></tr>\n",
       "\t<tr><th scope=row>746</th><td>India</td><td>2019</td><td>3.249</td><td>8.818</td><td>0.561</td><td>60.50</td><td>0.876</td><td> 0.112</td><td>0.752</td><td>0.648</td><td>0.466</td></tr>\n",
       "\t<tr><th scope=row>747</th><td>India</td><td>2020</td><td>4.225</td><td>8.703</td><td>0.617</td><td>60.90</td><td>0.906</td><td> 0.075</td><td>0.780</td><td>0.752</td><td>0.383</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 15 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & Country & year & Score & GDP & Family & Health & Freedom & Generosity & Corruption & Positive.affect & Negative.affect\\\\\n",
       "  & <chr> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t733 & India & 2006 & 5.348 & 8.145 & 0.707 & 55.72 & 0.774 &     NA & 0.855 & 0.687 & 0.199\\\\\n",
       "\t734 & India & 2007 & 5.027 & 8.204 & 0.569 & 56.14 & 0.729 & -0.051 & 0.862 & 0.668 & 0.253\\\\\n",
       "\t735 & India & 2008 & 5.146 & 8.220 & 0.684 & 56.56 & 0.756 & -0.072 & 0.891 & 0.674 & 0.259\\\\\n",
       "\t736 & India & 2009 & 4.522 & 8.281 & 0.653 & 56.98 & 0.679 & -0.026 & 0.895 & 0.771 & 0.301\\\\\n",
       "\t737 & India & 2010 & 4.989 & 8.349 & 0.605 & 57.40 & 0.783 &  0.058 & 0.863 & 0.697 & 0.267\\\\\n",
       "\t738 & India & 2011 & 4.635 & 8.387 & 0.553 & 57.70 & 0.838 & -0.038 & 0.908 & 0.648 & 0.232\\\\\n",
       "\t739 & India & 2012 & 4.720 & 8.428 & 0.511 & 58.00 & 0.609 &  0.067 & 0.830 & 0.629 & 0.295\\\\\n",
       "\t740 & India & 2013 & 4.428 & 8.478 & 0.553 & 58.30 & 0.740 &  0.084 & 0.832 & 0.680 & 0.330\\\\\n",
       "\t741 & India & 2014 & 4.424 & 8.538 & 0.621 & 58.60 & 0.809 & -0.026 & 0.832 & 0.711 & 0.285\\\\\n",
       "\t742 & India & 2015 & 4.342 & 8.604 & 0.610 & 58.90 & 0.777 & -0.005 & 0.776 & 0.701 & 0.322\\\\\n",
       "\t743 & India & 2016 & 4.179 & 8.673 & 0.614 & 59.30 & 0.820 &  0.046 & 0.765 & 0.695 & 0.346\\\\\n",
       "\t744 & India & 2017 & 4.046 & 8.730 & 0.607 & 59.70 & 0.886 & -0.042 & 0.781 & 0.682 & 0.318\\\\\n",
       "\t745 & India & 2018 & 3.818 & 8.779 & 0.638 & 60.10 & 0.890 &  0.085 & 0.805 & 0.657 & 0.357\\\\\n",
       "\t746 & India & 2019 & 3.249 & 8.818 & 0.561 & 60.50 & 0.876 &  0.112 & 0.752 & 0.648 & 0.466\\\\\n",
       "\t747 & India & 2020 & 4.225 & 8.703 & 0.617 & 60.90 & 0.906 &  0.075 & 0.780 & 0.752 & 0.383\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 15 × 11\n",
       "\n",
       "| <!--/--> | Country &lt;chr&gt; | year &lt;int&gt; | Score &lt;dbl&gt; | GDP &lt;dbl&gt; | Family &lt;dbl&gt; | Health &lt;dbl&gt; | Freedom &lt;dbl&gt; | Generosity &lt;dbl&gt; | Corruption &lt;dbl&gt; | Positive.affect &lt;dbl&gt; | Negative.affect &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 733 | India | 2006 | 5.348 | 8.145 | 0.707 | 55.72 | 0.774 |     NA | 0.855 | 0.687 | 0.199 |\n",
       "| 734 | India | 2007 | 5.027 | 8.204 | 0.569 | 56.14 | 0.729 | -0.051 | 0.862 | 0.668 | 0.253 |\n",
       "| 735 | India | 2008 | 5.146 | 8.220 | 0.684 | 56.56 | 0.756 | -0.072 | 0.891 | 0.674 | 0.259 |\n",
       "| 736 | India | 2009 | 4.522 | 8.281 | 0.653 | 56.98 | 0.679 | -0.026 | 0.895 | 0.771 | 0.301 |\n",
       "| 737 | India | 2010 | 4.989 | 8.349 | 0.605 | 57.40 | 0.783 |  0.058 | 0.863 | 0.697 | 0.267 |\n",
       "| 738 | India | 2011 | 4.635 | 8.387 | 0.553 | 57.70 | 0.838 | -0.038 | 0.908 | 0.648 | 0.232 |\n",
       "| 739 | India | 2012 | 4.720 | 8.428 | 0.511 | 58.00 | 0.609 |  0.067 | 0.830 | 0.629 | 0.295 |\n",
       "| 740 | India | 2013 | 4.428 | 8.478 | 0.553 | 58.30 | 0.740 |  0.084 | 0.832 | 0.680 | 0.330 |\n",
       "| 741 | India | 2014 | 4.424 | 8.538 | 0.621 | 58.60 | 0.809 | -0.026 | 0.832 | 0.711 | 0.285 |\n",
       "| 742 | India | 2015 | 4.342 | 8.604 | 0.610 | 58.90 | 0.777 | -0.005 | 0.776 | 0.701 | 0.322 |\n",
       "| 743 | India | 2016 | 4.179 | 8.673 | 0.614 | 59.30 | 0.820 |  0.046 | 0.765 | 0.695 | 0.346 |\n",
       "| 744 | India | 2017 | 4.046 | 8.730 | 0.607 | 59.70 | 0.886 | -0.042 | 0.781 | 0.682 | 0.318 |\n",
       "| 745 | India | 2018 | 3.818 | 8.779 | 0.638 | 60.10 | 0.890 |  0.085 | 0.805 | 0.657 | 0.357 |\n",
       "| 746 | India | 2019 | 3.249 | 8.818 | 0.561 | 60.50 | 0.876 |  0.112 | 0.752 | 0.648 | 0.466 |\n",
       "| 747 | India | 2020 | 4.225 | 8.703 | 0.617 | 60.90 | 0.906 |  0.075 | 0.780 | 0.752 | 0.383 |\n",
       "\n"
      ],
      "text/plain": [
       "    Country year Score GDP   Family Health Freedom Generosity Corruption\n",
       "733 India   2006 5.348 8.145 0.707  55.72  0.774       NA     0.855     \n",
       "734 India   2007 5.027 8.204 0.569  56.14  0.729   -0.051     0.862     \n",
       "735 India   2008 5.146 8.220 0.684  56.56  0.756   -0.072     0.891     \n",
       "736 India   2009 4.522 8.281 0.653  56.98  0.679   -0.026     0.895     \n",
       "737 India   2010 4.989 8.349 0.605  57.40  0.783    0.058     0.863     \n",
       "738 India   2011 4.635 8.387 0.553  57.70  0.838   -0.038     0.908     \n",
       "739 India   2012 4.720 8.428 0.511  58.00  0.609    0.067     0.830     \n",
       "740 India   2013 4.428 8.478 0.553  58.30  0.740    0.084     0.832     \n",
       "741 India   2014 4.424 8.538 0.621  58.60  0.809   -0.026     0.832     \n",
       "742 India   2015 4.342 8.604 0.610  58.90  0.777   -0.005     0.776     \n",
       "743 India   2016 4.179 8.673 0.614  59.30  0.820    0.046     0.765     \n",
       "744 India   2017 4.046 8.730 0.607  59.70  0.886   -0.042     0.781     \n",
       "745 India   2018 3.818 8.779 0.638  60.10  0.890    0.085     0.805     \n",
       "746 India   2019 3.249 8.818 0.561  60.50  0.876    0.112     0.752     \n",
       "747 India   2020 4.225 8.703 0.617  60.90  0.906    0.075     0.780     \n",
       "    Positive.affect Negative.affect\n",
       "733 0.687           0.199          \n",
       "734 0.668           0.253          \n",
       "735 0.674           0.259          \n",
       "736 0.771           0.301          \n",
       "737 0.697           0.267          \n",
       "738 0.648           0.232          \n",
       "739 0.629           0.295          \n",
       "740 0.680           0.330          \n",
       "741 0.711           0.285          \n",
       "742 0.701           0.322          \n",
       "743 0.695           0.346          \n",
       "744 0.682           0.318          \n",
       "745 0.657           0.357          \n",
       "746 0.648           0.466          \n",
       "747 0.752           0.383          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "happ_ind=happ_all[happ_all$Country == \"India\", ]  \n",
    "happ_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r8ErCtAwPkKhIL6PyESB3x7CfxFyeCNgShfaQvAGP53iMGPFg14c2wcgbmIlY2A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\nwxAwgGEIGMAwBAxgGAIGMAwBAxjm/wMP9amnK4NjogAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 480
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data = happ_ind, mapping=aes(x = year, y = Score, color=Score)) + geom_line() + ggtitle(\"Happiness Score of India over the last 15 years\") + theme(plot.title = element_text(size=20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hNIKeDK5j+48I6QAWMZaAmE9jufCSQVcGXz7114R8iAsQyUBIL7nc0E0gq4svl3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\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 480
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data = happ_ind, mapping=aes(x = year, y = GDP, color=GDP)) + geom_line() + ggtitle(\"GDP Score of India over the last 15 years\") + theme(plot.title = element_text(size=20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D9teAMDCvh+DQwCA44AP214A7jChw9og8/4QZiooBpqCAbsygCt8AFuUONEQTXU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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 480
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data = happ_ind, mapping=aes(x = year, y = Health, color=Health)) + geom_line() + ggtitle(\"Health infrastructure in India over the last 15 years\") + theme(plot.title = element_text(size=20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "4.4732"
      ],
      "text/latex": [
       "4.4732"
      ],
      "text/markdown": [
       "4.4732"
      ],
      "text/plain": [
       "[1] 4.4732"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "5.46670548999487"
      ],
      "text/latex": [
       "5.46670548999487"
      ],
      "text/markdown": [
       "5.46670548999487"
      ],
      "text/plain": [
       "[1] 5.466705"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean(happ_ind$Score)\n",
    "mean(happ_all$Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "66.603"
      ],
      "text/latex": [
       "66.603"
      ],
      "text/markdown": [
       "66.603"
      ],
      "text/plain": [
       "[1] 66.603"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "median(happ_2021$Health)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1.15860144766408"
      ],
      "text/latex": [
       "1.15860144766408"
      ],
      "text/markdown": [
       "1.15860144766408"
      ],
      "text/plain": [
       "[1] 1.158601"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd(happ_2021$GDP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 1 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Country</th><th scope=col>year</th><th scope=col>Score</th><th scope=col>GDP</th><th scope=col>Family</th><th scope=col>Health</th><th scope=col>Freedom</th><th scope=col>Generosity</th><th scope=col>Corruption</th><th scope=col>Positive.affect</th><th scope=col>Negative.affect</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>450</th><td>Denmark</td><td>2005</td><td>8.019</td><td>10.851</td><td>0.972</td><td>69.6</td><td>0.971</td><td>NA</td><td>0.237</td><td>0.86</td><td>0.154</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 1 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & Country & year & Score & GDP & Family & Health & Freedom & Generosity & Corruption & Positive.affect & Negative.affect\\\\\n",
       "  & <chr> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t450 & Denmark & 2005 & 8.019 & 10.851 & 0.972 & 69.6 & 0.971 & NA & 0.237 & 0.86 & 0.154\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 1 × 11\n",
       "\n",
       "| <!--/--> | Country &lt;chr&gt; | year &lt;int&gt; | Score &lt;dbl&gt; | GDP &lt;dbl&gt; | Family &lt;dbl&gt; | Health &lt;dbl&gt; | Freedom &lt;dbl&gt; | Generosity &lt;dbl&gt; | Corruption &lt;dbl&gt; | Positive.affect &lt;dbl&gt; | Negative.affect &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 450 | Denmark | 2005 | 8.019 | 10.851 | 0.972 | 69.6 | 0.971 | NA | 0.237 | 0.86 | 0.154 |\n",
       "\n"
      ],
      "text/plain": [
       "    Country year Score GDP    Family Health Freedom Generosity Corruption\n",
       "450 Denmark 2005 8.019 10.851 0.972  69.6   0.971   NA         0.237     \n",
       "    Positive.affect Negative.affect\n",
       "450 0.86            0.154          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_country=happ_all[happ_all$Score == max(happ_all$Score), ]  \n",
    "max_country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Country with the highest ever happiness score in 15 years is Denmark in 2005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 1 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Country</th><th scope=col>year</th><th scope=col>Score</th><th scope=col>GDP</th><th scope=col>Family</th><th scope=col>Health</th><th scope=col>Freedom</th><th scope=col>Generosity</th><th scope=col>Corruption</th><th scope=col>Positive.affect</th><th scope=col>Negative.affect</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>12</th><td>Afghanistan</td><td>2019</td><td>2.375</td><td>7.697</td><td>0.42</td><td>52.4</td><td>0.394</td><td>-0.108</td><td>0.924</td><td>0.351</td><td>0.502</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 1 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & Country & year & Score & GDP & Family & Health & Freedom & Generosity & Corruption & Positive.affect & Negative.affect\\\\\n",
       "  & <chr> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t12 & Afghanistan & 2019 & 2.375 & 7.697 & 0.42 & 52.4 & 0.394 & -0.108 & 0.924 & 0.351 & 0.502\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 1 × 11\n",
       "\n",
       "| <!--/--> | Country &lt;chr&gt; | year &lt;int&gt; | Score &lt;dbl&gt; | GDP &lt;dbl&gt; | Family &lt;dbl&gt; | Health &lt;dbl&gt; | Freedom &lt;dbl&gt; | Generosity &lt;dbl&gt; | Corruption &lt;dbl&gt; | Positive.affect &lt;dbl&gt; | Negative.affect &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 12 | Afghanistan | 2019 | 2.375 | 7.697 | 0.42 | 52.4 | 0.394 | -0.108 | 0.924 | 0.351 | 0.502 |\n",
       "\n"
      ],
      "text/plain": [
       "   Country     year Score GDP   Family Health Freedom Generosity Corruption\n",
       "12 Afghanistan 2019 2.375 7.697 0.42   52.4   0.394   -0.108     0.924     \n",
       "   Positive.affect Negative.affect\n",
       "12 0.351           0.502          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_country=happ_all[happ_all$Score == min(happ_all$Score), ]  \n",
    "min_country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Country with the lowest ever happiness score in 15 years is Afghanistan in 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Rank</th><th scope=col>Country</th><th scope=col>Score</th><th scope=col>GDP</th><th scope=col>Family</th><th scope=col>Health</th><th scope=col>Freedom</th><th scope=col>Generosity</th><th scope=col>Corruption</th><th scope=col>Dystopia...residual</th><th scope=col>Continent</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>Finland    </td><td>7.842</td><td>10.775</td><td>0.954</td><td>72.0</td><td>0.949</td><td>-0.098</td><td>0.186</td><td>3.253</td><td>Europe</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>2</td><td>Denmark    </td><td>7.620</td><td>10.933</td><td>0.954</td><td>72.7</td><td>0.946</td><td> 0.030</td><td>0.179</td><td>2.868</td><td>Europe</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>3</td><td>Switzerland</td><td>7.571</td><td>11.117</td><td>0.942</td><td>74.4</td><td>0.919</td><td> 0.025</td><td>0.292</td><td>2.839</td><td>Europe</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>4</td><td>Iceland    </td><td>7.554</td><td>10.878</td><td>0.983</td><td>73.0</td><td>0.955</td><td> 0.160</td><td>0.673</td><td>2.967</td><td>Europe</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>5</td><td>Netherlands</td><td>7.464</td><td>10.932</td><td>0.942</td><td>72.4</td><td>0.913</td><td> 0.175</td><td>0.338</td><td>2.798</td><td>Europe</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>6</td><td>Norway     </td><td>7.392</td><td>11.053</td><td>0.954</td><td>73.3</td><td>0.960</td><td> 0.093</td><td>0.270</td><td>2.580</td><td>Europe</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & Rank & Country & Score & GDP & Family & Health & Freedom & Generosity & Corruption & Dystopia...residual & Continent\\\\\n",
       "  & <int> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
       "\\hline\n",
       "\t1 & 1 & Finland     & 7.842 & 10.775 & 0.954 & 72.0 & 0.949 & -0.098 & 0.186 & 3.253 & Europe\\\\\n",
       "\t2 & 2 & Denmark     & 7.620 & 10.933 & 0.954 & 72.7 & 0.946 &  0.030 & 0.179 & 2.868 & Europe\\\\\n",
       "\t3 & 3 & Switzerland & 7.571 & 11.117 & 0.942 & 74.4 & 0.919 &  0.025 & 0.292 & 2.839 & Europe\\\\\n",
       "\t4 & 4 & Iceland     & 7.554 & 10.878 & 0.983 & 73.0 & 0.955 &  0.160 & 0.673 & 2.967 & Europe\\\\\n",
       "\t5 & 5 & Netherlands & 7.464 & 10.932 & 0.942 & 72.4 & 0.913 &  0.175 & 0.338 & 2.798 & Europe\\\\\n",
       "\t6 & 6 & Norway      & 7.392 & 11.053 & 0.954 & 73.3 & 0.960 &  0.093 & 0.270 & 2.580 & Europe\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 11\n",
       "\n",
       "| <!--/--> | Rank &lt;int&gt; | Country &lt;chr&gt; | Score &lt;dbl&gt; | GDP &lt;dbl&gt; | Family &lt;dbl&gt; | Health &lt;dbl&gt; | Freedom &lt;dbl&gt; | Generosity &lt;dbl&gt; | Corruption &lt;dbl&gt; | Dystopia...residual &lt;dbl&gt; | Continent &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 1 | Finland     | 7.842 | 10.775 | 0.954 | 72.0 | 0.949 | -0.098 | 0.186 | 3.253 | Europe |\n",
       "| 2 | 2 | Denmark     | 7.620 | 10.933 | 0.954 | 72.7 | 0.946 |  0.030 | 0.179 | 2.868 | Europe |\n",
       "| 3 | 3 | Switzerland | 7.571 | 11.117 | 0.942 | 74.4 | 0.919 |  0.025 | 0.292 | 2.839 | Europe |\n",
       "| 4 | 4 | Iceland     | 7.554 | 10.878 | 0.983 | 73.0 | 0.955 |  0.160 | 0.673 | 2.967 | Europe |\n",
       "| 5 | 5 | Netherlands | 7.464 | 10.932 | 0.942 | 72.4 | 0.913 |  0.175 | 0.338 | 2.798 | Europe |\n",
       "| 6 | 6 | Norway      | 7.392 | 11.053 | 0.954 | 73.3 | 0.960 |  0.093 | 0.270 | 2.580 | Europe |\n",
       "\n"
      ],
      "text/plain": [
       "  Rank Country     Score GDP    Family Health Freedom Generosity Corruption\n",
       "1 1    Finland     7.842 10.775 0.954  72.0   0.949   -0.098     0.186     \n",
       "2 2    Denmark     7.620 10.933 0.954  72.7   0.946    0.030     0.179     \n",
       "3 3    Switzerland 7.571 11.117 0.942  74.4   0.919    0.025     0.292     \n",
       "4 4    Iceland     7.554 10.878 0.983  73.0   0.955    0.160     0.673     \n",
       "5 5    Netherlands 7.464 10.932 0.942  72.4   0.913    0.175     0.338     \n",
       "6 6    Norway      7.392 11.053 0.954  73.3   0.960    0.093     0.270     \n",
       "  Dystopia...residual Continent\n",
       "1 3.253               Europe   \n",
       "2 2.868               Europe   \n",
       "3 2.839               Europe   \n",
       "4 2.967               Europe   \n",
       "5 2.798               Europe   \n",
       "6 2.580               Europe   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(happ_2021)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Comparing the increase in Happiness coefficients world-wide Pre-Pandemic and Amidst-Pandemic </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'tidyr' was built under R version 4.0.5\"\n",
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      "-- \u001b[1mAttaching packages\u001b[22m ------------------------------------------------------------------------------- tidyverse 1.3.1 --\n",
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      "\n"
     ]
    }
   ],
   "source": [
    "country_region_dict = happ_2021 %>% select(country = Country, region = Continent) %>% unique()\n",
    "\n",
    "df_happy_increase <- happ_all %>% \n",
    "    filter(year >= 2018) %>%\n",
    "    left_join(country_region_dict, by = c('Country' = 'country')) %>%\n",
    "    select(country = Country, year, ladder = Score)  %>%\n",
    "    pivot_wider(names_from = 'year', names_prefix = 'year', values_from = 'ladder') %>%\n",
    "    mutate(increase_in_2019 = ifelse(year2019>year2018, 1, 0),\n",
    "          increase_in_2020 = ifelse(year2020>year2019,1,0))\n",
    "\n",
    "df_increase_in_2019 <- df_happy_increase %>% summarize(pct = mean(increase_in_2019, na.rm = TRUE))\n",
    "df_increase_in_2020 <- df_happy_increase %>% summarize(pct = mean(increase_in_2020, na.rm = TRUE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'ggpubr' was built under R version 4.0.5\"\n",
      "\n",
      "Attaching package: 'ggpubr'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:plyr':\n",
      "\n",
      "    mutate\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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xEbxkxaOVYTADLoDnD6mEnVrM1JrO+pk0mbx0xaOVYTADLoDnD6mEneVdzJX6IM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\nwAAYBgEGwDD/Bwzw0TEBIwArAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 480
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#library(ggpubr)\n",
    "donut_plot <- function(df, title = '', subtitle = '', caption = '') {\n",
    "ggplot(df) +\n",
    "    geom_rect(aes(ymax = 1, ymin = 0, xmax = 2, xmin=1.2, fill = \"base\"))  +\n",
    "    geom_rect(aes(ymax = pct, ymin = 0, xmax = 2.2, xmin = 1.2, fill = 'main')) +\n",
    "    geom_text(x = 0, y = 0, label = paste0(round(df$pct*100,0),'%'), size = 16) + \n",
    "    coord_polar(theta = 'y') +\n",
    "    xlim(c(0,2.2)) +\n",
    "    labs(title = title, subtitle = subtitle, caption = caption) +\n",
    "    theme_void() + \n",
    "    theme(plot.title = element_text(size=22, face=\"bold\"),\n",
    "          plot.subtitle = element_text(size = 20, hjust = 0.5),\n",
    "          plot.caption = element_text(size = 15),\n",
    "          legend.position = 'None')}\n",
    "\n",
    "p1 <- donut_plot(df_increase_in_2019, title = 'Percent of countries with increased happiness\\n Pre-Covid(2018 - 2019) and Amidst-Covid(2019-2020)\\n', subtitle = '2018 -> 2019')\n",
    "p2 <- donut_plot(df_increase_in_2020, title = '\\n', subtitle = '2019 -> 2020')\n",
    "figure <- ggarrange(p1, p2, ncol = 2, nrow = 1)\n",
    "figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "4.0.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}