Kaggle-World_Happiness.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# World Happiness Report\n",
    "Happiness scored according to economic production, social support, etc.\n",
    "\n",
    "Dataset source: **Kaggle**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Context**\n",
    "\n",
    "The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015, and the fourth in the 2016 Update. The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness. \n",
    "\n",
    "**Content**\n",
    "\n",
    "The happiness scores and rankings use data from the Gallup World Poll. The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for the years 2013-2016 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.\n",
    "\n",
    "**Inspiration**\n",
    "\n",
    "What countries or regions rank the highest in overall happiness and each of the six factors contributing to happiness? How did country ranks or scores change between the 2015 and 2016 as well as the 2016 and 2017 reports? Did any country experience a significant increase or decrease in happiness?\n",
    "\n",
    "**What is Dystopia?**\n",
    "\n",
    "Dystopia is an imaginary country that has the world’s least-happy people. The purpose in establishing Dystopia is to have a benchmark against which all countries can be favorably compared (no country performs more poorly than Dystopia) in terms of each of the six key variables, thus allowing each sub-bar to be of positive width. The lowest scores observed for the six key variables, therefore, characterize Dystopia. Since life would be very unpleasant in a country with the world’s lowest incomes, lowest life expectancy, lowest generosity, most corruption, least freedom and least social support, it is referred to as “Dystopia,” in contrast to Utopia.\n",
    "\n",
    "**What are the residuals?**\n",
    "\n",
    "The residuals, or unexplained components, differ for each country, reflecting the extent to which the six variables either over- or under-explain average 2014-2016 life evaluations. These residuals have an average value of approximately zero over the whole set of countries. Figure 2.2 shows the average residual for each country when the equation in Table 2.1 is applied to average 2014- 2016 data for the six variables in that country. We combine these residuals with the estimate for life evaluations in Dystopia so that the combined bar will always have positive values. As can be seen in Figure 2.2, although some life evaluation residuals are quite large, occasionally exceeding one point on the scale from 0 to 10, they are always much smaller than the calculated value in Dystopia, where the average life is rated at 1.85 on the 0 to 10 scale.\n",
    "\n",
    "**What do the columns succeeding the Happiness Score(like Family, Generosity, etc.) describe?**\n",
    "\n",
    "The following columns: GDP per Capita, Family, Life Expectancy, Freedom, Generosity, Trust Government Corruption describe the extent to which these factors contribute in evaluating the happiness in each country. The Dystopia Residual metric actually is the Dystopia Happiness Score(1.85) + the Residual value or the unexplained value for each country as stated in the previous answer.\n",
    "\n",
    "If you add all these factors up, you get the happiness score so it might be un-reliable to model them to predict Happiness Scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Installing package into ‘/home/nbuser/R’\n",
      "(as ‘lib’ is unspecified)\n",
      "also installing the dependencies ‘stringr’, ‘reshape2’\n",
      "\n",
      "Warning message in install.packages(\"ggplot2\"):\n",
      "“installation of package ‘ggplot2’ had non-zero exit status”Installing package into ‘/home/nbuser/R’\n",
      "(as ‘lib’ is unspecified)\n",
      "also installing the dependencies ‘pillar’, ‘tibble’, ‘tidyselect’\n",
      "\n",
      "Warning message in install.packages(\"dplyr\"):\n",
      "“installation of package ‘pillar’ had non-zero exit status”Warning message in install.packages(\"dplyr\"):\n",
      "“installation of package ‘tidyselect’ had non-zero exit status”Warning message in install.packages(\"dplyr\"):\n",
      "“installation of package ‘tibble’ had non-zero exit status”Warning message in install.packages(\"dplyr\"):\n",
      "“installation of package ‘dplyr’ had non-zero exit status”Installing package into ‘/home/nbuser/R’\n",
      "(as ‘lib’ is unspecified)\n",
      "Installing package into ‘/home/nbuser/R’\n",
      "(as ‘lib’ is unspecified)\n",
      "Warning message:\n",
      "“package ‘ggcorplot’ is not available (for R version 3.4.1)”Installing package into ‘/home/nbuser/R’\n",
      "(as ‘lib’ is unspecified)\n",
      "Installing package into ‘/home/nbuser/R’\n",
      "(as ‘lib’ is unspecified)\n"
     ]
    }
   ],
   "source": [
    "# Install necessary packages\n",
    "\n",
    "install.packages(\"ggplot2\")\n",
    "install.packages(\"dplyr\")\n",
    "install.packages(\"reshape2\")\n",
    "install.packages(\"ggcorplot\")\n",
    "install.packages('stringr')\n",
    "install.packages('directlabels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Removing packages from ‘/home/nbuser/R’\n",
      "(as ‘lib’ is unspecified)\n"
     ]
    }
   ],
   "source": [
    "pkgs <- c('stringr','reshape2')\n",
    "remove.packages(pkgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load datasets\n",
    "data_2015 = read.csv(\"https://www.dropbox.com/s/2nttlu32fkv9zbb/2015.csv?raw=1\", header=TRUE)\n",
    "data_2016 = read.csv(\"https://www.dropbox.com/s/pto714xl0c3xqsu/2016.csv?raw=1\", header=TRUE)\n",
    "data_2017 = read.csv(\"https://www.dropbox.com/s/ggr4eb76hhfqlpc/2017.csv?raw=1\", header=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t158 obs. of  12 variables:\n",
      " $ Country                      : Factor w/ 158 levels \"Afghanistan\",..: 136 59 38 106 25 46 100 135 101 7 ...\n",
      " $ Region                       : Factor w/ 10 levels \"Australia and New Zealand\",..: 10 10 10 10 6 10 10 10 1 1 ...\n",
      " $ Happiness.Rank               : int  1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ Happiness.Score              : num  7.59 7.56 7.53 7.52 7.43 ...\n",
      " $ Standard.Error               : num  0.0341 0.0488 0.0333 0.0388 0.0355 ...\n",
      " $ Economy..GDP.per.Capita.     : num  1.4 1.3 1.33 1.46 1.33 ...\n",
      " $ Family                       : num  1.35 1.4 1.36 1.33 1.32 ...\n",
      " $ Health..Life.Expectancy.     : num  0.941 0.948 0.875 0.885 0.906 ...\n",
      " $ Freedom                      : num  0.666 0.629 0.649 0.67 0.633 ...\n",
      " $ Trust..Government.Corruption.: num  0.42 0.141 0.484 0.365 0.33 ...\n",
      " $ Generosity                   : num  0.297 0.436 0.341 0.347 0.458 ...\n",
      " $ Dystopia.Residual            : num  2.52 2.7 2.49 2.47 2.45 ...\n"
     ]
    }
   ],
   "source": [
    "# Examine the structure of 2015 dataset\n",
    "str(data_2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change column names of data_2015\n",
    "colnames(data_2015) <- c(\"Country\", \"Region_2015\", \"HappRank_2015\",\"HappScore_2015\",\"StdError_2015\",\n",
    "                         \"GDP_2015\",\"Family_2015\",\"LifeExp_2015\",\"Freedom_2015\",\"TrustGovt_2015\",\"Generosity_2015\",\"Dystopia_Residual_2015\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t157 obs. of  13 variables:\n",
      " $ Country                      : Factor w/ 157 levels \"Afghanistan\",..: 38 135 58 104 45 26 98 99 7 134 ...\n",
      " $ Region                       : Factor w/ 10 levels \"Australia and New Zealand\",..: 10 10 10 10 10 6 10 1 1 10 ...\n",
      " $ Happiness.Rank               : int  1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ Happiness.Score              : num  7.53 7.51 7.5 7.5 7.41 ...\n",
      " $ Lower.Confidence.Interval    : num  7.46 7.43 7.33 7.42 7.35 ...\n",
      " $ Upper.Confidence.Interval    : num  7.59 7.59 7.67 7.58 7.47 ...\n",
      " $ Economy..GDP.per.Capita.     : num  1.44 1.53 1.43 1.58 1.41 ...\n",
      " $ Family                       : num  1.16 1.15 1.18 1.13 1.13 ...\n",
      " $ Health..Life.Expectancy.     : num  0.795 0.863 0.867 0.796 0.811 ...\n",
      " $ Freedom                      : num  0.579 0.586 0.566 0.596 0.571 ...\n",
      " $ Trust..Government.Corruption.: num  0.445 0.412 0.15 0.358 0.41 ...\n",
      " $ Generosity                   : num  0.362 0.281 0.477 0.379 0.255 ...\n",
      " $ Dystopia.Residual            : num  2.74 2.69 2.83 2.66 2.83 ...\n"
     ]
    }
   ],
   "source": [
    "# Examine structure of 2016 dataset\n",
    "str(data_2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change column names of data_2016 dataset\n",
    "colnames(data_2016) <- c(\"Country\", \"Region_2016\", \"HappRank_2016\",\"HappScore_2016\",\"Lower_Conf_interval_2016\",\"Upper_Conf_interval_2016\",\n",
    "                         \"GDP_2016\",\"Family_2016\",\"LifeExp_2016\",\"Freedom_2016\",\"TrustGovt_2016\",\"Generosity_2016\",\"Dystopia_Residual_2016\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t155 obs. of  12 variables:\n",
      " $ Country                      : Factor w/ 155 levels \"Afghanistan\",..: 105 38 58 133 45 99 26 100 132 7 ...\n",
      " $ Happiness.Rank               : int  1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ Happiness.Score              : num  7.54 7.52 7.5 7.49 7.47 ...\n",
      " $ Whisker.high                 : num  7.59 7.58 7.62 7.56 7.53 ...\n",
      " $ Whisker.low                  : num  7.48 7.46 7.39 7.43 7.41 ...\n",
      " $ Economy..GDP.per.Capita.     : num  1.62 1.48 1.48 1.56 1.44 ...\n",
      " $ Family                       : num  1.53 1.55 1.61 1.52 1.54 ...\n",
      " $ Health..Life.Expectancy.     : num  0.797 0.793 0.834 0.858 0.809 ...\n",
      " $ Freedom                      : num  0.635 0.626 0.627 0.62 0.618 ...\n",
      " $ Generosity                   : num  0.362 0.355 0.476 0.291 0.245 ...\n",
      " $ Trust..Government.Corruption.: num  0.316 0.401 0.154 0.367 0.383 ...\n",
      " $ Dystopia.Residual            : num  2.28 2.31 2.32 2.28 2.43 ...\n"
     ]
    }
   ],
   "source": [
    "# Examine structure of data_2017 dataset\n",
    "str(data_2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add region column from data_2015 to data_2017\n",
    "\n",
    "new_data_2017 <- merge(data_2017, data_2015[,c(\"Country\",\"Region_2015\")],by=\"Country\",all.x=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t155 obs. of  13 variables:\n",
      " $ Country                      : Factor w/ 155 levels \"Afghanistan\",..: 1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ Happiness.Rank               : int  141 109 53 140 24 121 10 13 85 41 ...\n",
      " $ Happiness.Score              : num  3.79 4.64 5.87 3.8 6.6 ...\n",
      " $ Whisker.high                 : num  3.87 4.75 5.98 3.95 6.69 ...\n",
      " $ Whisker.low                  : num  3.71 4.54 5.77 3.64 6.51 ...\n",
      " $ Economy..GDP.per.Capita.     : num  0.401 0.996 1.092 0.858 1.185 ...\n",
      " $ Family                       : num  0.582 0.804 1.146 1.104 1.44 ...\n",
      " $ Health..Life.Expectancy.     : num  0.1807 0.7312 0.6176 0.0499 0.6951 ...\n",
      " $ Freedom                      : num  0.106 0.381 0.233 0 0.495 ...\n",
      " $ Generosity                   : num  0.3119 0.2013 0.0694 0.0979 0.1095 ...\n",
      " $ Trust..Government.Corruption.: num  0.0612 0.0399 0.1461 0.0697 0.0597 ...\n",
      " $ Dystopia.Residual            : num  2.15 1.49 2.57 1.61 2.61 ...\n",
      " $ Region_2015                  : Factor w/ 10 levels \"Australia and New Zealand\",..: 8 2 5 9 4 2 1 10 2 5 ...\n"
     ]
    }
   ],
   "source": [
    "# Examine structure of new_data_2017\n",
    "\n",
    "str(new_data_2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change column name of 'Region_2015' to 'Region'\n",
    "colnames(new_data_2017)[13] <- \"Region\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t155 obs. of  13 variables:\n",
      " $ Country                      : Factor w/ 155 levels \"Afghanistan\",..: 1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ Happiness.Rank               : int  141 109 53 140 24 121 10 13 85 41 ...\n",
      " $ Happiness.Score              : num  3.79 4.64 5.87 3.8 6.6 ...\n",
      " $ Whisker.high                 : num  3.87 4.75 5.98 3.95 6.69 ...\n",
      " $ Whisker.low                  : num  3.71 4.54 5.77 3.64 6.51 ...\n",
      " $ Economy..GDP.per.Capita.     : num  0.401 0.996 1.092 0.858 1.185 ...\n",
      " $ Family                       : num  0.582 0.804 1.146 1.104 1.44 ...\n",
      " $ Health..Life.Expectancy.     : num  0.1807 0.7312 0.6176 0.0499 0.6951 ...\n",
      " $ Freedom                      : num  0.106 0.381 0.233 0 0.495 ...\n",
      " $ Generosity                   : num  0.3119 0.2013 0.0694 0.0979 0.1095 ...\n",
      " $ Trust..Government.Corruption.: num  0.0612 0.0399 0.1461 0.0697 0.0597 ...\n",
      " $ Dystopia.Residual            : num  2.15 1.49 2.57 1.61 2.61 ...\n",
      " $ Region                       : Factor w/ 10 levels \"Australia and New Zealand\",..: 8 2 5 9 4 2 1 10 2 5 ...\n"
     ]
    }
   ],
   "source": [
    "str(new_data_2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>na_count</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Country</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Happiness.Rank</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Happiness.Score</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Whisker.high</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Whisker.low</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Economy..GDP.per.Capita.</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Family</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Health..Life.Expectancy.</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Freedom</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Generosity</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Trust..Government.Corruption.</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Dystopia.Residual</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Region</th><td>6</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "  & na\\_count\\\\\n",
       "\\hline\n",
       "\tCountry & 0\\\\\n",
       "\tHappiness.Rank & 0\\\\\n",
       "\tHappiness.Score & 0\\\\\n",
       "\tWhisker.high & 0\\\\\n",
       "\tWhisker.low & 0\\\\\n",
       "\tEconomy..GDP.per.Capita. & 0\\\\\n",
       "\tFamily & 0\\\\\n",
       "\tHealth..Life.Expectancy. & 0\\\\\n",
       "\tFreedom & 0\\\\\n",
       "\tGenerosity & 0\\\\\n",
       "\tTrust..Government.Corruption. & 0\\\\\n",
       "\tDystopia.Residual & 0\\\\\n",
       "\tRegion & 6\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | na_count |\n",
       "|---|---|\n",
       "| Country | 0 |\n",
       "| Happiness.Rank | 0 |\n",
       "| Happiness.Score | 0 |\n",
       "| Whisker.high | 0 |\n",
       "| Whisker.low | 0 |\n",
       "| Economy..GDP.per.Capita. | 0 |\n",
       "| Family | 0 |\n",
       "| Health..Life.Expectancy. | 0 |\n",
       "| Freedom | 0 |\n",
       "| Generosity | 0 |\n",
       "| Trust..Government.Corruption. | 0 |\n",
       "| Dystopia.Residual | 0 |\n",
       "| Region | 6 |\n",
       "\n"
      ],
      "text/plain": [
       "                              na_count\n",
       "Country                       0       \n",
       "Happiness.Rank                0       \n",
       "Happiness.Score               0       \n",
       "Whisker.high                  0       \n",
       "Whisker.low                   0       \n",
       "Economy..GDP.per.Capita.      0       \n",
       "Family                        0       \n",
       "Health..Life.Expectancy.      0       \n",
       "Freedom                       0       \n",
       "Generosity                    0       \n",
       "Trust..Government.Corruption. 0       \n",
       "Dystopia.Residual             0       \n",
       "Region                        6       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find NA count in new_data_2017\n",
    "na_count <-sapply(new_data_2017, function(y) sum(length(which(is.na(y)))))\n",
    "na_count <- data.frame(na_count)\n",
    "na_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Country</th><th scope=col>Happiness.Rank</th><th scope=col>Happiness.Score</th><th scope=col>Whisker.high</th><th scope=col>Whisker.low</th><th scope=col>Economy..GDP.per.Capita.</th><th scope=col>Family</th><th scope=col>Health..Life.Expectancy.</th><th scope=col>Freedom</th><th scope=col>Generosity</th><th scope=col>Trust..Government.Corruption.</th><th scope=col>Dystopia.Residual</th><th scope=col>Region</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>14</th><td>Belize                  </td><td> 50                     </td><td>5.956                   </td><td>6.197242                </td><td>5.714757                </td><td>0.90797532              </td><td>1.0814178               </td><td>0.4501918               </td><td>0.5475094               </td><td>0.24001564              </td><td>0.09658107              </td><td>2.6319556               </td><td>NA                      </td></tr>\n",
       "\t<tr><th scope=row>56</th><td>Hong Kong S.A.R., China </td><td> 71                     </td><td>5.472                   </td><td>5.549594                </td><td>5.394406                </td><td>1.55167484              </td><td>1.2627909               </td><td>0.9430624               </td><td>0.4909686               </td><td>0.37446579              </td><td>0.29393375              </td><td>0.5546331               </td><td>NA                      </td></tr>\n",
       "\t<tr><th scope=row>97</th><td>Namibia                 </td><td>111                     </td><td>4.574                   </td><td>4.770355                </td><td>4.377645                </td><td>0.96443433              </td><td>1.0984708               </td><td>0.3386118               </td><td>0.5203035               </td><td>0.07713374              </td><td>0.09314697              </td><td>1.4818902               </td><td>NA                      </td></tr>\n",
       "\t<tr><th scope=row>125</th><td>Somalia                 </td><td> 93                     </td><td>5.151                   </td><td>5.242484                </td><td>5.059516                </td><td>0.02264318              </td><td>0.7211514               </td><td>0.1139891               </td><td>0.6021270               </td><td>0.29163131              </td><td>0.28241032              </td><td>3.1174846               </td><td>NA                      </td></tr>\n",
       "\t<tr><th scope=row>128</th><td>South Sudan             </td><td>147                     </td><td>3.591                   </td><td>3.725539                </td><td>3.456462                </td><td>0.39724863              </td><td>0.6013231               </td><td>0.1634860               </td><td>0.1470624               </td><td>0.28567082              </td><td>0.11679351              </td><td>1.8795674               </td><td>NA                      </td></tr>\n",
       "\t<tr><th scope=row>135</th><td>Taiwan Province of China</td><td> 33                     </td><td>6.422                   </td><td>6.494596                </td><td>6.349404                </td><td>1.43362653              </td><td>1.3845654               </td><td>0.7939842               </td><td>0.3614666               </td><td>0.25836048              </td><td>0.06382924              </td><td>2.1266074               </td><td>NA                      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & Country & Happiness.Rank & Happiness.Score & Whisker.high & Whisker.low & Economy..GDP.per.Capita. & Family & Health..Life.Expectancy. & Freedom & Generosity & Trust..Government.Corruption. & Dystopia.Residual & Region\\\\\n",
       "\\hline\n",
       "\t14 & Belize                   &  50                      & 5.956                    & 6.197242                 & 5.714757                 & 0.90797532               & 1.0814178                & 0.4501918                & 0.5475094                & 0.24001564               & 0.09658107               & 2.6319556                & NA                      \\\\\n",
       "\t56 & Hong Kong S.A.R., China  &  71                      & 5.472                    & 5.549594                 & 5.394406                 & 1.55167484               & 1.2627909                & 0.9430624                & 0.4909686                & 0.37446579               & 0.29393375               & 0.5546331                & NA                      \\\\\n",
       "\t97 & Namibia                  & 111                      & 4.574                    & 4.770355                 & 4.377645                 & 0.96443433               & 1.0984708                & 0.3386118                & 0.5203035                & 0.07713374               & 0.09314697               & 1.4818902                & NA                      \\\\\n",
       "\t125 & Somalia                  &  93                      & 5.151                    & 5.242484                 & 5.059516                 & 0.02264318               & 0.7211514                & 0.1139891                & 0.6021270                & 0.29163131               & 0.28241032               & 3.1174846                & NA                      \\\\\n",
       "\t128 & South Sudan              & 147                      & 3.591                    & 3.725539                 & 3.456462                 & 0.39724863               & 0.6013231                & 0.1634860                & 0.1470624                & 0.28567082               & 0.11679351               & 1.8795674                & NA                      \\\\\n",
       "\t135 & Taiwan Province of China &  33                      & 6.422                    & 6.494596                 & 6.349404                 & 1.43362653               & 1.3845654                & 0.7939842                & 0.3614666                & 0.25836048               & 0.06382924               & 2.1266074                & NA                      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Country | Happiness.Rank | Happiness.Score | Whisker.high | Whisker.low | Economy..GDP.per.Capita. | Family | Health..Life.Expectancy. | Freedom | Generosity | Trust..Government.Corruption. | Dystopia.Residual | Region |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 14 | Belize                   |  50                      | 5.956                    | 6.197242                 | 5.714757                 | 0.90797532               | 1.0814178                | 0.4501918                | 0.5475094                | 0.24001564               | 0.09658107               | 2.6319556                | NA                       |\n",
       "| 56 | Hong Kong S.A.R., China  |  71                      | 5.472                    | 5.549594                 | 5.394406                 | 1.55167484               | 1.2627909                | 0.9430624                | 0.4909686                | 0.37446579               | 0.29393375               | 0.5546331                | NA                       |\n",
       "| 97 | Namibia                  | 111                      | 4.574                    | 4.770355                 | 4.377645                 | 0.96443433               | 1.0984708                | 0.3386118                | 0.5203035                | 0.07713374               | 0.09314697               | 1.4818902                | NA                       |\n",
       "| 125 | Somalia                  |  93                      | 5.151                    | 5.242484                 | 5.059516                 | 0.02264318               | 0.7211514                | 0.1139891                | 0.6021270                | 0.29163131               | 0.28241032               | 3.1174846                | NA                       |\n",
       "| 128 | South Sudan              | 147                      | 3.591                    | 3.725539                 | 3.456462                 | 0.39724863               | 0.6013231                | 0.1634860                | 0.1470624                | 0.28567082               | 0.11679351               | 1.8795674                | NA                       |\n",
       "| 135 | Taiwan Province of China |  33                      | 6.422                    | 6.494596                 | 6.349404                 | 1.43362653               | 1.3845654                | 0.7939842                | 0.3614666                | 0.25836048               | 0.06382924               | 2.1266074                | NA                       |\n",
       "\n"
      ],
      "text/plain": [
       "    Country                  Happiness.Rank Happiness.Score Whisker.high\n",
       "14  Belize                    50            5.956           6.197242    \n",
       "56  Hong Kong S.A.R., China   71            5.472           5.549594    \n",
       "97  Namibia                  111            4.574           4.770355    \n",
       "125 Somalia                   93            5.151           5.242484    \n",
       "128 South Sudan              147            3.591           3.725539    \n",
       "135 Taiwan Province of China  33            6.422           6.494596    \n",
       "    Whisker.low Economy..GDP.per.Capita. Family    Health..Life.Expectancy.\n",
       "14  5.714757    0.90797532               1.0814178 0.4501918               \n",
       "56  5.394406    1.55167484               1.2627909 0.9430624               \n",
       "97  4.377645    0.96443433               1.0984708 0.3386118               \n",
       "125 5.059516    0.02264318               0.7211514 0.1139891               \n",
       "128 3.456462    0.39724863               0.6013231 0.1634860               \n",
       "135 6.349404    1.43362653               1.3845654 0.7939842               \n",
       "    Freedom   Generosity Trust..Government.Corruption. Dystopia.Residual Region\n",
       "14  0.5475094 0.24001564 0.09658107                    2.6319556         NA    \n",
       "56  0.4909686 0.37446579 0.29393375                    0.5546331         NA    \n",
       "97  0.5203035 0.07713374 0.09314697                    1.4818902         NA    \n",
       "125 0.6021270 0.29163131 0.28241032                    3.1174846         NA    \n",
       "128 0.1470624 0.28567082 0.11679351                    1.8795674         NA    \n",
       "135 0.3614666 0.25836048 0.06382924                    2.1266074         NA    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Display NA rows in Region column\n",
    "new_data_2017[which(is.na(new_data_2017$Region)),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill NA values in new_data_2017 Region column\n",
    "new_data_2017[14,13] <- data_2016[52,2]\n",
    "new_data_2017[56,13] <- data_2016[75,2]\n",
    "new_data_2017[97,13] <- data_2016[113,2]\n",
    "new_data_2017[125,13] <- data_2016[76,2]\n",
    "new_data_2017[128,13] <- data_2016[143,2]\n",
    "new_data_2017[135,13] <- data_2016[35,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t155 obs. of  13 variables:\n",
      " $ Country                      : Factor w/ 155 levels \"Afghanistan\",..: 1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ Happiness.Rank               : int  141 109 53 140 24 121 10 13 85 41 ...\n",
      " $ Happiness.Score              : num  3.79 4.64 5.87 3.8 6.6 ...\n",
      " $ Whisker.high                 : num  3.87 4.75 5.98 3.95 6.69 ...\n",
      " $ Whisker.low                  : num  3.71 4.54 5.77 3.64 6.51 ...\n",
      " $ Economy..GDP.per.Capita.     : num  0.401 0.996 1.092 0.858 1.185 ...\n",
      " $ Family                       : num  0.582 0.804 1.146 1.104 1.44 ...\n",
      " $ Health..Life.Expectancy.     : num  0.1807 0.7312 0.6176 0.0499 0.6951 ...\n",
      " $ Freedom                      : num  0.106 0.381 0.233 0 0.495 ...\n",
      " $ Generosity                   : num  0.3119 0.2013 0.0694 0.0979 0.1095 ...\n",
      " $ Trust..Government.Corruption.: num  0.0612 0.0399 0.1461 0.0697 0.0597 ...\n",
      " $ Dystopia.Residual            : num  2.15 1.49 2.57 1.61 2.61 ...\n",
      " $ Region                       : Factor w/ 10 levels \"Australia and New Zealand\",..: 8 2 5 9 4 2 1 10 2 5 ...\n"
     ]
    }
   ],
   "source": [
    "str(new_data_2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change column names of new_data_2017 dataset\n",
    "colnames(new_data_2017) <- c(\"Country\",\"HappRank_2017\",\"HappScore_2017\",\"Whisker_high_2017\",\"Whisker_low_2017\",\n",
    "                         \"GDP_2017\",\"Family_2017\",\"LifeExp_2017\",\"Freedom_2017\",\"Generosity_2017\",\"TrustGovt_2017\",\"Dystopia_Residual_2017\",\"Region_2017\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t155 obs. of  13 variables:\n",
      " $ Country               : Factor w/ 155 levels \"Afghanistan\",..: 1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ HappRank_2017         : int  141 109 53 140 24 121 10 13 85 41 ...\n",
      " $ HappScore_2017        : num  3.79 4.64 5.87 3.8 6.6 ...\n",
      " $ Whisker_high_2017     : num  3.87 4.75 5.98 3.95 6.69 ...\n",
      " $ Whisker_low_2017      : num  3.71 4.54 5.77 3.64 6.51 ...\n",
      " $ GDP_2017              : num  0.401 0.996 1.092 0.858 1.185 ...\n",
      " $ Family_2017           : num  0.582 0.804 1.146 1.104 1.44 ...\n",
      " $ LifeExp_2017          : num  0.1807 0.7312 0.6176 0.0499 0.6951 ...\n",
      " $ Freedom_2017          : num  0.106 0.381 0.233 0 0.495 ...\n",
      " $ Generosity_2017       : num  0.3119 0.2013 0.0694 0.0979 0.1095 ...\n",
      " $ TrustGovt_2017        : num  0.0612 0.0399 0.1461 0.0697 0.0597 ...\n",
      " $ Dystopia_Residual_2017: num  2.15 1.49 2.57 1.61 2.61 ...\n",
      " $ Region_2017           : Factor w/ 10 levels \"Australia and New Zealand\",..: 8 2 5 9 4 2 1 10 2 5 ...\n"
     ]
    }
   ],
   "source": [
    "str(new_data_2017)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining all three data sets (2015, 2016, 2017) into one single data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge 2017 and 2016 data sets\n",
    "Merge_2017_16 <- merge(new_data_2017, data_2016,all.x=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge 2015 data set with Merge_2017_16\n",
    "Total_data <- merge(Merge_2017_16, data_2015, all.x=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t155 obs. of  36 variables:\n",
      " $ Country                 : Factor w/ 155 levels \"Afghanistan\",..: 1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ HappRank_2017           : int  141 109 53 140 24 121 10 13 85 41 ...\n",
      " $ HappScore_2017          : num  3.79 4.64 5.87 3.8 6.6 ...\n",
      " $ Whisker_high_2017       : num  3.87 4.75 5.98 3.95 6.69 ...\n",
      " $ Whisker_low_2017        : num  3.71 4.54 5.77 3.64 6.51 ...\n",
      " $ GDP_2017                : num  0.401 0.996 1.092 0.858 1.185 ...\n",
      " $ Family_2017             : num  0.582 0.804 1.146 1.104 1.44 ...\n",
      " $ LifeExp_2017            : num  0.1807 0.7312 0.6176 0.0499 0.6951 ...\n",
      " $ Freedom_2017            : num  0.106 0.381 0.233 0 0.495 ...\n",
      " $ Generosity_2017         : num  0.3119 0.2013 0.0694 0.0979 0.1095 ...\n",
      " $ TrustGovt_2017          : num  0.0612 0.0399 0.1461 0.0697 0.0597 ...\n",
      " $ Dystopia_Residual_2017  : num  2.15 1.49 2.57 1.61 2.61 ...\n",
      " $ Region_2017             : Factor w/ 10 levels \"Australia and New Zealand\",..: 8 2 5 9 4 2 1 10 2 5 ...\n",
      " $ Region_2016             : Factor w/ 10 levels \"Australia and New Zealand\",..: 8 2 5 9 4 2 1 10 2 5 ...\n",
      " $ HappRank_2016           : int  154 109 38 141 26 121 9 12 81 42 ...\n",
      " $ HappScore_2016          : num  3.36 4.66 6.36 3.87 6.65 ...\n",
      " $ Lower_Conf_interval_2016: num  3.29 4.55 6.23 3.75 6.56 ...\n",
      " $ Upper_Conf_interval_2016: num  3.43 4.76 6.48 3.98 6.74 ...\n",
      " $ GDP_2016                : num  0.382 0.955 1.053 0.847 1.151 ...\n",
      " $ Family_2016             : num  0.11 0.502 0.833 0.664 1.066 ...\n",
      " $ LifeExp_2016            : num  0.1734 0.7301 0.618 0.0499 0.6971 ...\n",
      " $ Freedom_2016            : num  0.1643 0.31866 0.21006 0.00589 0.42284 ...\n",
      " $ TrustGovt_2016          : num  0.0711 0.053 0.1616 0.0843 0.073 ...\n",
      " $ Generosity_2016         : num  0.3127 0.1684 0.0704 0.1207 0.1099 ...\n",
      " $ Dystopia_Residual_2016  : num  2.15 1.93 3.41 2.09 3.13 ...\n",
      " $ Region_2015             : Factor w/ 10 levels \"Australia and New Zealand\",..: 8 2 5 9 4 2 1 10 2 5 ...\n",
      " $ HappRank_2015           : int  153 95 68 137 30 127 10 13 80 49 ...\n",
      " $ HappScore_2015          : num  3.58 4.96 5.61 4.03 6.57 ...\n",
      " $ StdError_2015           : num  0.0308 0.0501 0.051 0.0476 0.0461 ...\n",
      " $ GDP_2015                : num  0.32 0.879 0.939 0.758 1.054 ...\n",
      " $ Family_2015             : num  0.303 0.804 1.078 0.86 1.248 ...\n",
      " $ LifeExp_2015            : num  0.303 0.813 0.618 0.167 0.787 ...\n",
      " $ Freedom_2015            : num  0.234 0.357 0.286 0.104 0.45 ...\n",
      " $ TrustGovt_2015          : num  0.0972 0.0641 0.1738 0.0712 0.0848 ...\n",
      " $ Generosity_2015         : num  0.3651 0.1427 0.0782 0.1234 0.1145 ...\n",
      " $ Dystopia_Residual_2015  : num  1.95 1.9 2.43 1.95 2.84 ...\n"
     ]
    }
   ],
   "source": [
    "# Total_data has all three data sets combined (2017, 2016 and 2015)\n",
    "str(Total_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>na_count</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Country</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>HappRank_2017</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>HappScore_2017</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Whisker_high_2017</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Whisker_low_2017</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>GDP_2017</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Family_2017</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>LifeExp_2017</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Freedom_2017</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Generosity_2017</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>TrustGovt_2017</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Dystopia_Residual_2017</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Region_2017</th><td>0</td></tr>\n",
       "\t<tr><th scope=row>Region_2016</th><td>5</td></tr>\n",
       "\t<tr><th scope=row>HappRank_2016</th><td>5</td></tr>\n",
       "\t<tr><th scope=row>HappScore_2016</th><td>5</td></tr>\n",
       "\t<tr><th scope=row>Lower_Conf_interval_2016</th><td>5</td></tr>\n",
       "\t<tr><th scope=row>Upper_Conf_interval_2016</th><td>5</td></tr>\n",
       "\t<tr><th scope=row>GDP_2016</th><td>5</td></tr>\n",
       "\t<tr><th scope=row>Family_2016</th><td>5</td></tr>\n",
       "\t<tr><th scope=row>LifeExp_2016</th><td>5</td></tr>\n",
       "\t<tr><th scope=row>Freedom_2016</th><td>5</td></tr>\n",
       "\t<tr><th scope=row>TrustGovt_2016</th><td>5</td></tr>\n",
       "\t<tr><th scope=row>Generosity_2016</th><td>5</td></tr>\n",
       "\t<tr><th scope=row>Dystopia_Residual_2016</th><td>5</td></tr>\n",
       "\t<tr><th scope=row>Region_2015</th><td>6</td></tr>\n",
       "\t<tr><th scope=row>HappRank_2015</th><td>6</td></tr>\n",
       "\t<tr><th scope=row>HappScore_2015</th><td>6</td></tr>\n",
       "\t<tr><th scope=row>StdError_2015</th><td>6</td></tr>\n",
       "\t<tr><th scope=row>GDP_2015</th><td>6</td></tr>\n",
       "\t<tr><th scope=row>Family_2015</th><td>6</td></tr>\n",
       "\t<tr><th scope=row>LifeExp_2015</th><td>6</td></tr>\n",
       "\t<tr><th scope=row>Freedom_2015</th><td>6</td></tr>\n",
       "\t<tr><th scope=row>TrustGovt_2015</th><td>6</td></tr>\n",
       "\t<tr><th scope=row>Generosity_2015</th><td>6</td></tr>\n",
       "\t<tr><th scope=row>Dystopia_Residual_2015</th><td>6</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "  & na\\_count\\\\\n",
       "\\hline\n",
       "\tCountry & 0\\\\\n",
       "\tHappRank\\_2017 & 0\\\\\n",
       "\tHappScore\\_2017 & 0\\\\\n",
       "\tWhisker\\_high\\_2017 & 0\\\\\n",
       "\tWhisker\\_low\\_2017 & 0\\\\\n",
       "\tGDP\\_2017 & 0\\\\\n",
       "\tFamily\\_2017 & 0\\\\\n",
       "\tLifeExp\\_2017 & 0\\\\\n",
       "\tFreedom\\_2017 & 0\\\\\n",
       "\tGenerosity\\_2017 & 0\\\\\n",
       "\tTrustGovt\\_2017 & 0\\\\\n",
       "\tDystopia\\_Residual\\_2017 & 0\\\\\n",
       "\tRegion\\_2017 & 0\\\\\n",
       "\tRegion\\_2016 & 5\\\\\n",
       "\tHappRank\\_2016 & 5\\\\\n",
       "\tHappScore\\_2016 & 5\\\\\n",
       "\tLower\\_Conf\\_interval\\_2016 & 5\\\\\n",
       "\tUpper\\_Conf\\_interval\\_2016 & 5\\\\\n",
       "\tGDP\\_2016 & 5\\\\\n",
       "\tFamily\\_2016 & 5\\\\\n",
       "\tLifeExp\\_2016 & 5\\\\\n",
       "\tFreedom\\_2016 & 5\\\\\n",
       "\tTrustGovt\\_2016 & 5\\\\\n",
       "\tGenerosity\\_2016 & 5\\\\\n",
       "\tDystopia\\_Residual\\_2016 & 5\\\\\n",
       "\tRegion\\_2015 & 6\\\\\n",
       "\tHappRank\\_2015 & 6\\\\\n",
       "\tHappScore\\_2015 & 6\\\\\n",
       "\tStdError\\_2015 & 6\\\\\n",
       "\tGDP\\_2015 & 6\\\\\n",
       "\tFamily\\_2015 & 6\\\\\n",
       "\tLifeExp\\_2015 & 6\\\\\n",
       "\tFreedom\\_2015 & 6\\\\\n",
       "\tTrustGovt\\_2015 & 6\\\\\n",
       "\tGenerosity\\_2015 & 6\\\\\n",
       "\tDystopia\\_Residual\\_2015 & 6\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | na_count |\n",
       "|---|---|\n",
       "| Country | 0 |\n",
       "| HappRank_2017 | 0 |\n",
       "| HappScore_2017 | 0 |\n",
       "| Whisker_high_2017 | 0 |\n",
       "| Whisker_low_2017 | 0 |\n",
       "| GDP_2017 | 0 |\n",
       "| Family_2017 | 0 |\n",
       "| LifeExp_2017 | 0 |\n",
       "| Freedom_2017 | 0 |\n",
       "| Generosity_2017 | 0 |\n",
       "| TrustGovt_2017 | 0 |\n",
       "| Dystopia_Residual_2017 | 0 |\n",
       "| Region_2017 | 0 |\n",
       "| Region_2016 | 5 |\n",
       "| HappRank_2016 | 5 |\n",
       "| HappScore_2016 | 5 |\n",
       "| Lower_Conf_interval_2016 | 5 |\n",
       "| Upper_Conf_interval_2016 | 5 |\n",
       "| GDP_2016 | 5 |\n",
       "| Family_2016 | 5 |\n",
       "| LifeExp_2016 | 5 |\n",
       "| Freedom_2016 | 5 |\n",
       "| TrustGovt_2016 | 5 |\n",
       "| Generosity_2016 | 5 |\n",
       "| Dystopia_Residual_2016 | 5 |\n",
       "| Region_2015 | 6 |\n",
       "| HappRank_2015 | 6 |\n",
       "| HappScore_2015 | 6 |\n",
       "| StdError_2015 | 6 |\n",
       "| GDP_2015 | 6 |\n",
       "| Family_2015 | 6 |\n",
       "| LifeExp_2015 | 6 |\n",
       "| Freedom_2015 | 6 |\n",
       "| TrustGovt_2015 | 6 |\n",
       "| Generosity_2015 | 6 |\n",
       "| Dystopia_Residual_2015 | 6 |\n",
       "\n"
      ],
      "text/plain": [
       "                         na_count\n",
       "Country                  0       \n",
       "HappRank_2017            0       \n",
       "HappScore_2017           0       \n",
       "Whisker_high_2017        0       \n",
       "Whisker_low_2017         0       \n",
       "GDP_2017                 0       \n",
       "Family_2017              0       \n",
       "LifeExp_2017             0       \n",
       "Freedom_2017             0       \n",
       "Generosity_2017          0       \n",
       "TrustGovt_2017           0       \n",
       "Dystopia_Residual_2017   0       \n",
       "Region_2017              0       \n",
       "Region_2016              5       \n",
       "HappRank_2016            5       \n",
       "HappScore_2016           5       \n",
       "Lower_Conf_interval_2016 5       \n",
       "Upper_Conf_interval_2016 5       \n",
       "GDP_2016                 5       \n",
       "Family_2016              5       \n",
       "LifeExp_2016             5       \n",
       "Freedom_2016             5       \n",
       "TrustGovt_2016           5       \n",
       "Generosity_2016          5       \n",
       "Dystopia_Residual_2016   5       \n",
       "Region_2015              6       \n",
       "HappRank_2015            6       \n",
       "HappScore_2015           6       \n",
       "StdError_2015            6       \n",
       "GDP_2015                 6       \n",
       "Family_2015              6       \n",
       "LifeExp_2015             6       \n",
       "Freedom_2015             6       \n",
       "TrustGovt_2015           6       \n",
       "Generosity_2015          6       \n",
       "Dystopia_Residual_2015   6       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find NA values in Total_data\n",
    "na_count <-sapply(Total_data, function(y) sum(length(which(is.na(y)))))\n",
    "na_count <- data.frame(na_count)\n",
    "na_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Country</th><th scope=col>Region_2016</th><th scope=col>HappRank_2016</th><th scope=col>HappScore_2016</th><th scope=col>Lower_Conf_interval_2016</th><th scope=col>Upper_Conf_interval_2016</th><th scope=col>GDP_2016</th><th scope=col>Family_2016</th><th scope=col>LifeExp_2016</th><th scope=col>Freedom_2016</th><th scope=col>TrustGovt_2016</th><th scope=col>Generosity_2016</th><th scope=col>Dystopia_Residual_2016</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>27</th><td>Central African Republic</td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td></tr>\n",
       "\t<tr><th scope=row>56</th><td>Hong Kong S.A.R., China </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td></tr>\n",
       "\t<tr><th scope=row>77</th><td>Lesotho                 </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td></tr>\n",
       "\t<tr><th scope=row>95</th><td>Mozambique              </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td></tr>\n",
       "\t<tr><th scope=row>135</th><td>Taiwan Province of China</td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & Country & Region\\_2016 & HappRank\\_2016 & HappScore\\_2016 & Lower\\_Conf\\_interval\\_2016 & Upper\\_Conf\\_interval\\_2016 & GDP\\_2016 & Family\\_2016 & LifeExp\\_2016 & Freedom\\_2016 & TrustGovt\\_2016 & Generosity\\_2016 & Dystopia\\_Residual\\_2016\\\\\n",
       "\\hline\n",
       "\t27 & Central African Republic & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                      \\\\\n",
       "\t56 & Hong Kong S.A.R., China  & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                      \\\\\n",
       "\t77 & Lesotho                  & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                      \\\\\n",
       "\t95 & Mozambique               & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                      \\\\\n",
       "\t135 & Taiwan Province of China & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Country | Region_2016 | HappRank_2016 | HappScore_2016 | Lower_Conf_interval_2016 | Upper_Conf_interval_2016 | GDP_2016 | Family_2016 | LifeExp_2016 | Freedom_2016 | TrustGovt_2016 | Generosity_2016 | Dystopia_Residual_2016 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 27 | Central African Republic | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       |\n",
       "| 56 | Hong Kong S.A.R., China  | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       |\n",
       "| 77 | Lesotho                  | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       |\n",
       "| 95 | Mozambique               | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       |\n",
       "| 135 | Taiwan Province of China | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       |\n",
       "\n"
      ],
      "text/plain": [
       "    Country                  Region_2016 HappRank_2016 HappScore_2016\n",
       "27  Central African Republic NA          NA            NA            \n",
       "56  Hong Kong S.A.R., China  NA          NA            NA            \n",
       "77  Lesotho                  NA          NA            NA            \n",
       "95  Mozambique               NA          NA            NA            \n",
       "135 Taiwan Province of China NA          NA            NA            \n",
       "    Lower_Conf_interval_2016 Upper_Conf_interval_2016 GDP_2016 Family_2016\n",
       "27  NA                       NA                       NA       NA         \n",
       "56  NA                       NA                       NA       NA         \n",
       "77  NA                       NA                       NA       NA         \n",
       "95  NA                       NA                       NA       NA         \n",
       "135 NA                       NA                       NA       NA         \n",
       "    LifeExp_2016 Freedom_2016 TrustGovt_2016 Generosity_2016\n",
       "27  NA           NA           NA             NA             \n",
       "56  NA           NA           NA             NA             \n",
       "77  NA           NA           NA             NA             \n",
       "95  NA           NA           NA             NA             \n",
       "135 NA           NA           NA             NA             \n",
       "    Dystopia_Residual_2016\n",
       "27  NA                    \n",
       "56  NA                    \n",
       "77  NA                    \n",
       "95  NA                    \n",
       "135 NA                    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find which rows has NA values in Total_data for year 2016\n",
    "\n",
    "Total_data[which(is.na(Total_data$HappRank_2016)),c(1,14:25)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Country</th><th scope=col>Region_2016</th><th scope=col>HappRank_2016</th><th scope=col>HappScore_2016</th><th scope=col>Lower_Conf_interval_2016</th><th scope=col>Upper_Conf_interval_2016</th><th scope=col>GDP_2016</th><th scope=col>Family_2016</th><th scope=col>LifeExp_2016</th><th scope=col>Freedom_2016</th><th scope=col>TrustGovt_2016</th><th scope=col>Generosity_2016</th><th scope=col>Dystopia_Residual_2016</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>35</th><td>Taiwan      </td><td>Eastern Asia</td><td>34          </td><td>6.379       </td><td>6.305       </td><td>6.453       </td><td>1.39729     </td><td>0.92624     </td><td>0.79565     </td><td>0.32377     </td><td>0.0663      </td><td>0.25495     </td><td>2.61523     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & Country & Region\\_2016 & HappRank\\_2016 & HappScore\\_2016 & Lower\\_Conf\\_interval\\_2016 & Upper\\_Conf\\_interval\\_2016 & GDP\\_2016 & Family\\_2016 & LifeExp\\_2016 & Freedom\\_2016 & TrustGovt\\_2016 & Generosity\\_2016 & Dystopia\\_Residual\\_2016\\\\\n",
       "\\hline\n",
       "\t35 & Taiwan       & Eastern Asia & 34           & 6.379        & 6.305        & 6.453        & 1.39729      & 0.92624      & 0.79565      & 0.32377      & 0.0663       & 0.25495      & 2.61523     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Country | Region_2016 | HappRank_2016 | HappScore_2016 | Lower_Conf_interval_2016 | Upper_Conf_interval_2016 | GDP_2016 | Family_2016 | LifeExp_2016 | Freedom_2016 | TrustGovt_2016 | Generosity_2016 | Dystopia_Residual_2016 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 35 | Taiwan       | Eastern Asia | 34           | 6.379        | 6.305        | 6.453        | 1.39729      | 0.92624      | 0.79565      | 0.32377      | 0.0663       | 0.25495      | 2.61523      |\n",
       "\n"
      ],
      "text/plain": [
       "   Country Region_2016  HappRank_2016 HappScore_2016 Lower_Conf_interval_2016\n",
       "35 Taiwan  Eastern Asia 34            6.379          6.305                   \n",
       "   Upper_Conf_interval_2016 GDP_2016 Family_2016 LifeExp_2016 Freedom_2016\n",
       "35 6.453                    1.39729  0.92624     0.79565      0.32377     \n",
       "   TrustGovt_2016 Generosity_2016 Dystopia_Residual_2016\n",
       "35 0.0663         0.25495         2.61523               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Taiwan name in 2017 dataset is 'Taiwan Province of China' whereas in 2016 it is 'Taiwan'\n",
    "data_2016[data_2016$Country=='Taiwan',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values from 2016 dataset\n",
    "Total_data[135,14:25] <- data_2016[35,2:13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Country</th><th scope=col>Region_2015</th><th scope=col>HappRank_2015</th><th scope=col>HappScore_2015</th><th scope=col>StdError_2015</th><th scope=col>GDP_2015</th><th scope=col>Family_2015</th><th scope=col>LifeExp_2015</th><th scope=col>Freedom_2015</th><th scope=col>TrustGovt_2015</th><th scope=col>Generosity_2015</th><th scope=col>Dystopia_Residual_2015</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>14</th><td>Belize                  </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td></tr>\n",
       "\t<tr><th scope=row>56</th><td>Hong Kong S.A.R., China </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td></tr>\n",
       "\t<tr><th scope=row>97</th><td>Namibia                 </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td></tr>\n",
       "\t<tr><th scope=row>125</th><td>Somalia                 </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td></tr>\n",
       "\t<tr><th scope=row>128</th><td>South Sudan             </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td></tr>\n",
       "\t<tr><th scope=row>135</th><td>Taiwan Province of China</td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & Country & Region\\_2015 & HappRank\\_2015 & HappScore\\_2015 & StdError\\_2015 & GDP\\_2015 & Family\\_2015 & LifeExp\\_2015 & Freedom\\_2015 & TrustGovt\\_2015 & Generosity\\_2015 & Dystopia\\_Residual\\_2015\\\\\n",
       "\\hline\n",
       "\t14 & Belize                   & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                      \\\\\n",
       "\t56 & Hong Kong S.A.R., China  & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                      \\\\\n",
       "\t97 & Namibia                  & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                      \\\\\n",
       "\t125 & Somalia                  & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                      \\\\\n",
       "\t128 & South Sudan              & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                      \\\\\n",
       "\t135 & Taiwan Province of China & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & NA                      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Country | Region_2015 | HappRank_2015 | HappScore_2015 | StdError_2015 | GDP_2015 | Family_2015 | LifeExp_2015 | Freedom_2015 | TrustGovt_2015 | Generosity_2015 | Dystopia_Residual_2015 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 14 | Belize                   | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       |\n",
       "| 56 | Hong Kong S.A.R., China  | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       |\n",
       "| 97 | Namibia                  | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       |\n",
       "| 125 | Somalia                  | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       |\n",
       "| 128 | South Sudan              | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       |\n",
       "| 135 | Taiwan Province of China | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       |\n",
       "\n"
      ],
      "text/plain": [
       "    Country                  Region_2015 HappRank_2015 HappScore_2015\n",
       "14  Belize                   NA          NA            NA            \n",
       "56  Hong Kong S.A.R., China  NA          NA            NA            \n",
       "97  Namibia                  NA          NA            NA            \n",
       "125 Somalia                  NA          NA            NA            \n",
       "128 South Sudan              NA          NA            NA            \n",
       "135 Taiwan Province of China NA          NA            NA            \n",
       "    StdError_2015 GDP_2015 Family_2015 LifeExp_2015 Freedom_2015 TrustGovt_2015\n",
       "14  NA            NA       NA          NA           NA           NA            \n",
       "56  NA            NA       NA          NA           NA           NA            \n",
       "97  NA            NA       NA          NA           NA           NA            \n",
       "125 NA            NA       NA          NA           NA           NA            \n",
       "128 NA            NA       NA          NA           NA           NA            \n",
       "135 NA            NA       NA          NA           NA           NA            \n",
       "    Generosity_2015 Dystopia_Residual_2015\n",
       "14  NA              NA                    \n",
       "56  NA              NA                    \n",
       "97  NA              NA                    \n",
       "125 NA              NA                    \n",
       "128 NA              NA                    \n",
       "135 NA              NA                    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find which rows has NA values in Total_data for year 2015\n",
    "\n",
    "Total_data[which(is.na(Total_data$HappRank_2015)),c(1,26:36)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Country</th><th scope=col>Region_2015</th><th scope=col>HappRank_2015</th><th scope=col>HappScore_2015</th><th scope=col>StdError_2015</th><th scope=col>GDP_2015</th><th scope=col>Family_2015</th><th scope=col>LifeExp_2015</th><th scope=col>Freedom_2015</th><th scope=col>TrustGovt_2015</th><th scope=col>Generosity_2015</th><th scope=col>Dystopia_Residual_2015</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>38</th><td>Taiwan      </td><td>Eastern Asia</td><td>38          </td><td>6.298       </td><td>0.03868     </td><td>1.29098     </td><td>1.07617     </td><td>0.8753      </td><td>0.3974      </td><td>0.08129     </td><td>0.25376     </td><td>2.32323     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & Country & Region\\_2015 & HappRank\\_2015 & HappScore\\_2015 & StdError\\_2015 & GDP\\_2015 & Family\\_2015 & LifeExp\\_2015 & Freedom\\_2015 & TrustGovt\\_2015 & Generosity\\_2015 & Dystopia\\_Residual\\_2015\\\\\n",
       "\\hline\n",
       "\t38 & Taiwan       & Eastern Asia & 38           & 6.298        & 0.03868      & 1.29098      & 1.07617      & 0.8753       & 0.3974       & 0.08129      & 0.25376      & 2.32323     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Country | Region_2015 | HappRank_2015 | HappScore_2015 | StdError_2015 | GDP_2015 | Family_2015 | LifeExp_2015 | Freedom_2015 | TrustGovt_2015 | Generosity_2015 | Dystopia_Residual_2015 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 38 | Taiwan       | Eastern Asia | 38           | 6.298        | 0.03868      | 1.29098      | 1.07617      | 0.8753       | 0.3974       | 0.08129      | 0.25376      | 2.32323      |\n",
       "\n"
      ],
      "text/plain": [
       "   Country Region_2015  HappRank_2015 HappScore_2015 StdError_2015 GDP_2015\n",
       "38 Taiwan  Eastern Asia 38            6.298          0.03868       1.29098 \n",
       "   Family_2015 LifeExp_2015 Freedom_2015 TrustGovt_2015 Generosity_2015\n",
       "38 1.07617     0.8753       0.3974       0.08129        0.25376        \n",
       "   Dystopia_Residual_2015\n",
       "38 2.32323               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Taiwan name in 2017 dataset is 'Taiwan Province of China' whereas in 2015 it is 'Taiwan'\n",
    "data_2015[data_2015$Country=='Taiwan',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values\n",
    "Total_data[135,26:36] <- data_2015[38,2:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Country</th><th scope=col>HappRank_2017</th><th scope=col>HappScore_2017</th><th scope=col>Whisker_high_2017</th><th scope=col>Whisker_low_2017</th><th scope=col>GDP_2017</th><th scope=col>Family_2017</th><th scope=col>LifeExp_2017</th><th scope=col>Freedom_2017</th><th scope=col>Generosity_2017</th><th scope=col>...</th><th scope=col>HappRank_2015</th><th scope=col>HappScore_2015</th><th scope=col>StdError_2015</th><th scope=col>GDP_2015</th><th scope=col>Family_2015</th><th scope=col>LifeExp_2015</th><th scope=col>Freedom_2015</th><th scope=col>TrustGovt_2015</th><th scope=col>Generosity_2015</th><th scope=col>Dystopia_Residual_2015</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Afghanistan</td><td>141        </td><td>3.794      </td><td>3.873661   </td><td>3.714338   </td><td>0.4014772  </td><td>0.5815433  </td><td>0.18074678 </td><td>0.1061795  </td><td>0.31187093 </td><td>...        </td><td>153        </td><td>3.575      </td><td>0.03084    </td><td>0.31982    </td><td>0.30285    </td><td>0.30335    </td><td>0.23414    </td><td>0.09719    </td><td>0.36510    </td><td>1.95210    </td></tr>\n",
       "\t<tr><td>Albania    </td><td>109        </td><td>4.644      </td><td>4.752464   </td><td>4.535536   </td><td>0.9961928  </td><td>0.8036852  </td><td>0.73115975 </td><td>0.3814986  </td><td>0.20131294 </td><td>...        </td><td> 95        </td><td>4.959      </td><td>0.05013    </td><td>0.87867    </td><td>0.80434    </td><td>0.81325    </td><td>0.35733    </td><td>0.06413    </td><td>0.14272    </td><td>1.89894    </td></tr>\n",
       "\t<tr><td>Algeria    </td><td> 53        </td><td>5.872      </td><td>5.978286   </td><td>5.765714   </td><td>1.0918645  </td><td>1.1462175  </td><td>0.61758465 </td><td>0.2333358  </td><td>0.06943665 </td><td>...        </td><td> 68        </td><td>5.605      </td><td>0.05099    </td><td>0.93929    </td><td>1.07772    </td><td>0.61766    </td><td>0.28579    </td><td>0.17383    </td><td>0.07822    </td><td>2.43209    </td></tr>\n",
       "\t<tr><td>Angola     </td><td>140        </td><td>3.795      </td><td>3.951642   </td><td>3.638358   </td><td>0.8584282  </td><td>1.1044120  </td><td>0.04986867 </td><td>0.0000000  </td><td>0.09792649 </td><td>...        </td><td>137        </td><td>4.033      </td><td>0.04758    </td><td>0.75778    </td><td>0.86040    </td><td>0.16683    </td><td>0.10384    </td><td>0.07122    </td><td>0.12344    </td><td>1.94939    </td></tr>\n",
       "\t<tr><td>Argentina  </td><td> 24        </td><td>6.599      </td><td>6.690085   </td><td>6.507915   </td><td>1.1852955  </td><td>1.4404511  </td><td>0.69513708 </td><td>0.4945192  </td><td>0.10945706 </td><td>...        </td><td> 30        </td><td>6.574      </td><td>0.04612    </td><td>1.05351    </td><td>1.24823    </td><td>0.78723    </td><td>0.44974    </td><td>0.08484    </td><td>0.11451    </td><td>2.83600    </td></tr>\n",
       "\t<tr><td>Armenia    </td><td>121        </td><td>4.376      </td><td>4.466735   </td><td>4.285265   </td><td>0.9005967  </td><td>1.0074837  </td><td>0.63752443 </td><td>0.1983033  </td><td>0.08348809 </td><td>...        </td><td>127        </td><td>4.350      </td><td>0.04763    </td><td>0.76821    </td><td>0.77711    </td><td>0.72990    </td><td>0.19847    </td><td>0.03900    </td><td>0.07855    </td><td>1.75873    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllllllllllll}\n",
       " Country & HappRank\\_2017 & HappScore\\_2017 & Whisker\\_high\\_2017 & Whisker\\_low\\_2017 & GDP\\_2017 & Family\\_2017 & LifeExp\\_2017 & Freedom\\_2017 & Generosity\\_2017 & TrustGovt\\_2017 & Dystopia\\_Residual\\_2017 & Region\\_2017 & Region\\_2016 & HappRank\\_2016 & HappScore\\_2016 & Lower\\_Conf\\_interval\\_2016 & Upper\\_Conf\\_interval\\_2016 & GDP\\_2016 & Family\\_2016 & LifeExp\\_2016 & Freedom\\_2016 & TrustGovt\\_2016 & Generosity\\_2016 & Dystopia\\_Residual\\_2016 & Region\\_2015 & HappRank\\_2015 & HappScore\\_2015 & StdError\\_2015 & GDP\\_2015 & Family\\_2015 & LifeExp\\_2015 & Freedom\\_2015 & TrustGovt\\_2015 & Generosity\\_2015 & Dystopia\\_Residual\\_2015\\\\\n",
       "\\hline\n",
       "\t Afghanistan                     & 141                             & 3.794                           & 3.873661                        & 3.714338                        & 0.4014772                       & 0.5815433                       & 0.18074678                      & 0.1061795                       & 0.31187093                      & 0.06115783                      & 2.150801                        & Southern Asia                   & Southern Asia                   & 154                             & 3.360                           & 3.288                           & 3.432                           & 0.38227                         & 0.11037                         & 0.17344                         & 0.16430                         & 0.07112                         & 0.31268                         & 2.14558                         & Southern Asia                   & 153                             & 3.575                           & 0.03084                         & 0.31982                         & 0.30285                         & 0.30335                         & 0.23414                         & 0.09719                         & 0.36510                         & 1.95210                        \\\\\n",
       "\t Albania                         & 109                             & 4.644                           & 4.752464                        & 4.535536                        & 0.9961928                       & 0.8036852                       & 0.73115975                      & 0.3814986                       & 0.20131294                      & 0.03986422                      & 1.490442                        & Central and Eastern Europe      & Central and Eastern Europe      & 109                             & 4.655                           & 4.546                           & 4.764                           & 0.95530                         & 0.50163                         & 0.73007                         & 0.31866                         & 0.05301                         & 0.16840                         & 1.92816                         & Central and Eastern Europe      &  95                             & 4.959                           & 0.05013                         & 0.87867                         & 0.80434                         & 0.81325                         & 0.35733                         & 0.06413                         & 0.14272                         & 1.89894                        \\\\\n",
       "\t Algeria                         &  53                             & 5.872                           & 5.978286                        & 5.765714                        & 1.0918645                       & 1.1462175                       & 0.61758465                      & 0.2333358                       & 0.06943665                      & 0.14609611                      & 2.567604                        & Middle East and Northern Africa & Middle East and Northern Africa &  38                             & 6.355                           & 6.227                           & 6.483                           & 1.05266                         & 0.83309                         & 0.61804                         & 0.21006                         & 0.16157                         & 0.07044                         & 3.40904                         & Middle East and Northern Africa &  68                             & 5.605                           & 0.05099                         & 0.93929                         & 1.07772                         & 0.61766                         & 0.28579                         & 0.17383                         & 0.07822                         & 2.43209                        \\\\\n",
       "\t Angola                          & 140                             & 3.795                           & 3.951642                        & 3.638358                        & 0.8584282                       & 1.1044120                       & 0.04986867                      & 0.0000000                       & 0.09792649                      & 0.06972034                      & 1.614482                        & Sub-Saharan Africa              & Sub-Saharan Africa              & 141                             & 3.866                           & 3.753                           & 3.979                           & 0.84731                         & 0.66366                         & 0.04991                         & 0.00589                         & 0.08434                         & 0.12071                         & 2.09459                         & Sub-Saharan Africa              & 137                             & 4.033                           & 0.04758                         & 0.75778                         & 0.86040                         & 0.16683                         & 0.10384                         & 0.07122                         & 0.12344                         & 1.94939                        \\\\\n",
       "\t Argentina                       &  24                             & 6.599                           & 6.690085                        & 6.507915                        & 1.1852955                       & 1.4404511                       & 0.69513708                      & 0.4945192                       & 0.10945706                      & 0.05973989                      & 2.614005                        & Latin America and Caribbean     & Latin America and Caribbean     &  26                             & 6.650                           & 6.560                           & 6.740                           & 1.15137                         & 1.06612                         & 0.69711                         & 0.42284                         & 0.07296                         & 0.10989                         & 3.12985                         & Latin America and Caribbean     &  30                             & 6.574                           & 0.04612                         & 1.05351                         & 1.24823                         & 0.78723                         & 0.44974                         & 0.08484                         & 0.11451                         & 2.83600                        \\\\\n",
       "\t Armenia                         & 121                             & 4.376                           & 4.466735                        & 4.285265                        & 0.9005967                       & 1.0074837                       & 0.63752443                      & 0.1983033                       & 0.08348809                      & 0.02667442                      & 1.521499                        & Central and Eastern Europe      & Central and Eastern Europe      & 121                             & 4.360                           & 4.266                           & 4.454                           & 0.86086                         & 0.62477                         & 0.64083                         & 0.14037                         & 0.03616                         & 0.07793                         & 1.97864                         & Central and Eastern Europe      & 127                             & 4.350                           & 0.04763                         & 0.76821                         & 0.77711                         & 0.72990                         & 0.19847                         & 0.03900                         & 0.07855                         & 1.75873                        \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Country | HappRank_2017 | HappScore_2017 | Whisker_high_2017 | Whisker_low_2017 | GDP_2017 | Family_2017 | LifeExp_2017 | Freedom_2017 | Generosity_2017 | ... | HappRank_2015 | HappScore_2015 | StdError_2015 | GDP_2015 | Family_2015 | LifeExp_2015 | Freedom_2015 | TrustGovt_2015 | Generosity_2015 | Dystopia_Residual_2015 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Afghanistan | 141         | 3.794       | 3.873661    | 3.714338    | 0.4014772   | 0.5815433   | 0.18074678  | 0.1061795   | 0.31187093  | ...         | 153         | 3.575       | 0.03084     | 0.31982     | 0.30285     | 0.30335     | 0.23414     | 0.09719     | 0.36510     | 1.95210     |\n",
       "| Albania     | 109         | 4.644       | 4.752464    | 4.535536    | 0.9961928   | 0.8036852   | 0.73115975  | 0.3814986   | 0.20131294  | ...         |  95         | 4.959       | 0.05013     | 0.87867     | 0.80434     | 0.81325     | 0.35733     | 0.06413     | 0.14272     | 1.89894     |\n",
       "| Algeria     |  53         | 5.872       | 5.978286    | 5.765714    | 1.0918645   | 1.1462175   | 0.61758465  | 0.2333358   | 0.06943665  | ...         |  68         | 5.605       | 0.05099     | 0.93929     | 1.07772     | 0.61766     | 0.28579     | 0.17383     | 0.07822     | 2.43209     |\n",
       "| Angola      | 140         | 3.795       | 3.951642    | 3.638358    | 0.8584282   | 1.1044120   | 0.04986867  | 0.0000000   | 0.09792649  | ...         | 137         | 4.033       | 0.04758     | 0.75778     | 0.86040     | 0.16683     | 0.10384     | 0.07122     | 0.12344     | 1.94939     |\n",
       "| Argentina   |  24         | 6.599       | 6.690085    | 6.507915    | 1.1852955   | 1.4404511   | 0.69513708  | 0.4945192   | 0.10945706  | ...         |  30         | 6.574       | 0.04612     | 1.05351     | 1.24823     | 0.78723     | 0.44974     | 0.08484     | 0.11451     | 2.83600     |\n",
       "| Armenia     | 121         | 4.376       | 4.466735    | 4.285265    | 0.9005967   | 1.0074837   | 0.63752443  | 0.1983033   | 0.08348809  | ...         | 127         | 4.350       | 0.04763     | 0.76821     | 0.77711     | 0.72990     | 0.19847     | 0.03900     | 0.07855     | 1.75873     |\n",
       "\n"
      ],
      "text/plain": [
       "  Country     HappRank_2017 HappScore_2017 Whisker_high_2017 Whisker_low_2017\n",
       "1 Afghanistan 141           3.794          3.873661          3.714338        \n",
       "2 Albania     109           4.644          4.752464          4.535536        \n",
       "3 Algeria      53           5.872          5.978286          5.765714        \n",
       "4 Angola      140           3.795          3.951642          3.638358        \n",
       "5 Argentina    24           6.599          6.690085          6.507915        \n",
       "6 Armenia     121           4.376          4.466735          4.285265        \n",
       "  GDP_2017  Family_2017 LifeExp_2017 Freedom_2017 Generosity_2017 ...\n",
       "1 0.4014772 0.5815433   0.18074678   0.1061795    0.31187093      ...\n",
       "2 0.9961928 0.8036852   0.73115975   0.3814986    0.20131294      ...\n",
       "3 1.0918645 1.1462175   0.61758465   0.2333358    0.06943665      ...\n",
       "4 0.8584282 1.1044120   0.04986867   0.0000000    0.09792649      ...\n",
       "5 1.1852955 1.4404511   0.69513708   0.4945192    0.10945706      ...\n",
       "6 0.9005967 1.0074837   0.63752443   0.1983033    0.08348809      ...\n",
       "  HappRank_2015 HappScore_2015 StdError_2015 GDP_2015 Family_2015 LifeExp_2015\n",
       "1 153           3.575          0.03084       0.31982  0.30285     0.30335     \n",
       "2  95           4.959          0.05013       0.87867  0.80434     0.81325     \n",
       "3  68           5.605          0.05099       0.93929  1.07772     0.61766     \n",
       "4 137           4.033          0.04758       0.75778  0.86040     0.16683     \n",
       "5  30           6.574          0.04612       1.05351  1.24823     0.78723     \n",
       "6 127           4.350          0.04763       0.76821  0.77711     0.72990     \n",
       "  Freedom_2015 TrustGovt_2015 Generosity_2015 Dystopia_Residual_2015\n",
       "1 0.23414      0.09719        0.36510         1.95210               \n",
       "2 0.35733      0.06413        0.14272         1.89894               \n",
       "3 0.28579      0.17383        0.07822         2.43209               \n",
       "4 0.10384      0.07122        0.12344         1.94939               \n",
       "5 0.44974      0.08484        0.11451         2.83600               \n",
       "6 0.19847      0.03900        0.07855         1.75873               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examine first 6 rows of 2015 dataset\n",
    "head(Total_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Country</th><th scope=col>HappRank_2017</th><th scope=col>HappScore_2017</th><th scope=col>Whisker_high_2017</th><th scope=col>Whisker_low_2017</th><th scope=col>GDP_2017</th><th scope=col>Family_2017</th><th scope=col>LifeExp_2017</th><th scope=col>Freedom_2017</th><th scope=col>Generosity_2017</th><th scope=col>...</th><th scope=col>HappRank_2015</th><th scope=col>HappScore_2015</th><th scope=col>StdError_2015</th><th scope=col>GDP_2015</th><th scope=col>Family_2015</th><th scope=col>LifeExp_2015</th><th scope=col>Freedom_2015</th><th scope=col>TrustGovt_2015</th><th scope=col>Generosity_2015</th><th scope=col>Dystopia_Residual_2015</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>150</th><td>Uzbekistan</td><td> 47       </td><td>5.971     </td><td>6.065538  </td><td>5.876463  </td><td>0.7864411 </td><td>1.5489691 </td><td>0.4982726 </td><td>0.6582487 </td><td>0.41598365</td><td>...       </td><td> 44       </td><td>6.003     </td><td>0.04361   </td><td>0.63244   </td><td>1.34043   </td><td>0.59772   </td><td>0.65821   </td><td>0.30826   </td><td>0.22837   </td><td>2.23741   </td></tr>\n",
       "\t<tr><th scope=row>151</th><td>Venezuela </td><td> 82       </td><td>5.250     </td><td>5.370032  </td><td>5.129968  </td><td>1.1284312 </td><td>1.4313376 </td><td>0.6171442 </td><td>0.1539971 </td><td>0.06501963</td><td>...       </td><td> 23       </td><td>6.810     </td><td>0.06476   </td><td>1.04424   </td><td>1.25596   </td><td>0.72052   </td><td>0.42908   </td><td>0.11069   </td><td>0.05841   </td><td>3.19131   </td></tr>\n",
       "\t<tr><th scope=row>152</th><td>Vietnam   </td><td> 94       </td><td>5.074     </td><td>5.147281  </td><td>5.000719  </td><td>0.7885476 </td><td>1.2774913 </td><td>0.6521690 </td><td>0.5710556 </td><td>0.23496805</td><td>...       </td><td> 75       </td><td>5.360     </td><td>0.03107   </td><td>0.63216   </td><td>0.91226   </td><td>0.74676   </td><td>0.59444   </td><td>0.10441   </td><td>0.16860   </td><td>2.20173   </td></tr>\n",
       "\t<tr><th scope=row>153</th><td>Yemen     </td><td>146       </td><td>3.593     </td><td>3.692750  </td><td>3.493250  </td><td>0.5916834 </td><td>0.9353822 </td><td>0.3100809 </td><td>0.2494637 </td><td>0.10412521</td><td>...       </td><td>136       </td><td>4.077     </td><td>0.04367   </td><td>0.54649   </td><td>0.68093   </td><td>0.40064   </td><td>0.35571   </td><td>0.07854   </td><td>0.09131   </td><td>1.92313   </td></tr>\n",
       "\t<tr><th scope=row>154</th><td>Zambia    </td><td>116       </td><td>4.514     </td><td>4.644106  </td><td>4.383894  </td><td>0.6364068 </td><td>1.0031873 </td><td>0.2578359 </td><td>0.4616035 </td><td>0.24958014</td><td>...       </td><td> 85       </td><td>5.129     </td><td>0.06988   </td><td>0.47038   </td><td>0.91612   </td><td>0.29924   </td><td>0.48827   </td><td>0.12468   </td><td>0.19591   </td><td>2.63430   </td></tr>\n",
       "\t<tr><th scope=row>155</th><td>Zimbabwe  </td><td>138       </td><td>3.875     </td><td>3.978700  </td><td>3.771300  </td><td>0.3758465 </td><td>1.0830959 </td><td>0.1967638 </td><td>0.3363842 </td><td>0.18914349</td><td>...       </td><td>115       </td><td>4.610     </td><td>0.04290   </td><td>0.27100   </td><td>1.03276   </td><td>0.33475   </td><td>0.25861   </td><td>0.08079   </td><td>0.18987   </td><td>2.44191   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllllllllllll}\n",
       "  & Country & HappRank\\_2017 & HappScore\\_2017 & Whisker\\_high\\_2017 & Whisker\\_low\\_2017 & GDP\\_2017 & Family\\_2017 & LifeExp\\_2017 & Freedom\\_2017 & Generosity\\_2017 & TrustGovt\\_2017 & Dystopia\\_Residual\\_2017 & Region\\_2017 & Region\\_2016 & HappRank\\_2016 & HappScore\\_2016 & Lower\\_Conf\\_interval\\_2016 & Upper\\_Conf\\_interval\\_2016 & GDP\\_2016 & Family\\_2016 & LifeExp\\_2016 & Freedom\\_2016 & TrustGovt\\_2016 & Generosity\\_2016 & Dystopia\\_Residual\\_2016 & Region\\_2015 & HappRank\\_2015 & HappScore\\_2015 & StdError\\_2015 & GDP\\_2015 & Family\\_2015 & LifeExp\\_2015 & Freedom\\_2015 & TrustGovt\\_2015 & Generosity\\_2015 & Dystopia\\_Residual\\_2015\\\\\n",
       "\\hline\n",
       "\t150 & Uzbekistan                      &  47                             & 5.971                           & 6.065538                        & 5.876463                        & 0.7864411                       & 1.5489691                       & 0.4982726                       & 0.6582487                       & 0.41598365                      & 0.24652822                      & 1.816914                        & Central and Eastern Europe      & Central and Eastern Europe      &  49                             & 5.987                           & 5.896                           & 6.078                           & 0.73591                         & 1.16810                         & 0.50163                         & 0.60848                         & 0.28333                         & 0.34326                         & 2.34638                         & Central and Eastern Europe      &  44                             & 6.003                           & 0.04361                         & 0.63244                         & 1.34043                         & 0.59772                         & 0.65821                         & 0.30826                         & 0.22837                         & 2.23741                        \\\\\n",
       "\t151 & Venezuela                       &  82                             & 5.250                           & 5.370032                        & 5.129968                        & 1.1284312                       & 1.4313376                       & 0.6171442                       & 0.1539971                       & 0.06501963                      & 0.06449112                      & 1.789464                        & Latin America and Caribbean     & Latin America and Caribbean     &  44                             & 6.084                           & 5.973                           & 6.195                           & 1.13367                         & 1.03302                         & 0.61904                         & 0.19847                         & 0.08304                         & 0.04250                         & 2.97468                         & Latin America and Caribbean     &  23                             & 6.810                           & 0.06476                         & 1.04424                         & 1.25596                         & 0.72052                         & 0.42908                         & 0.11069                         & 0.05841                         & 3.19131                        \\\\\n",
       "\t152 & Vietnam                         &  94                             & 5.074                           & 5.147281                        & 5.000719                        & 0.7885476                       & 1.2774913                       & 0.6521690                       & 0.5710556                       & 0.23496805                      & 0.08763324                      & 1.462319                        & Southeastern Asia               & Southeastern Asia               &  96                             & 5.061                           & 4.991                           & 5.131                           & 0.74037                         & 0.79117                         & 0.66157                         & 0.55954                         & 0.11556                         & 0.25075                         & 1.94180                         & Southeastern Asia               &  75                             & 5.360                           & 0.03107                         & 0.63216                         & 0.91226                         & 0.74676                         & 0.59444                         & 0.10441                         & 0.16860                         & 2.20173                        \\\\\n",
       "\t153 & Yemen                           & 146                             & 3.593                           & 3.692750                        & 3.493250                        & 0.5916834                       & 0.9353822                       & 0.3100809                       & 0.2494637                       & 0.10412521                      & 0.05676742                      & 1.345601                        & Middle East and Northern Africa & Middle East and Northern Africa & 147                             & 3.724                           & 3.621                           & 3.827                           & 0.57939                         & 0.47493                         & 0.31048                         & 0.22870                         & 0.05892                         & 0.09821                         & 1.97295                         & Middle East and Northern Africa & 136                             & 4.077                           & 0.04367                         & 0.54649                         & 0.68093                         & 0.40064                         & 0.35571                         & 0.07854                         & 0.09131                         & 1.92313                        \\\\\n",
       "\t154 & Zambia                          & 116                             & 4.514                           & 4.644106                        & 4.383894                        & 0.6364068                       & 1.0031873                       & 0.2578359                       & 0.4616035                       & 0.24958014                      & 0.07821355                      & 1.826705                        & Sub-Saharan Africa              & Sub-Saharan Africa              & 106                             & 4.795                           & 4.645                           & 4.945                           & 0.61202                         & 0.63760                         & 0.23573                         & 0.42662                         & 0.11479                         & 0.17866                         & 2.58991                         & Sub-Saharan Africa              &  85                             & 5.129                           & 0.06988                         & 0.47038                         & 0.91612                         & 0.29924                         & 0.48827                         & 0.12468                         & 0.19591                         & 2.63430                        \\\\\n",
       "\t155 & Zimbabwe                        & 138                             & 3.875                           & 3.978700                        & 3.771300                        & 0.3758465                       & 1.0830959                       & 0.1967638                       & 0.3363842                       & 0.18914349                      & 0.09537538                      & 1.597970                        & Sub-Saharan Africa              & Sub-Saharan Africa              & 131                             & 4.193                           & 4.101                           & 4.285                           & 0.35041                         & 0.71478                         & 0.15950                         & 0.25429                         & 0.08582                         & 0.18503                         & 2.44270                         & Sub-Saharan Africa              & 115                             & 4.610                           & 0.04290                         & 0.27100                         & 1.03276                         & 0.33475                         & 0.25861                         & 0.08079                         & 0.18987                         & 2.44191                        \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Country | HappRank_2017 | HappScore_2017 | Whisker_high_2017 | Whisker_low_2017 | GDP_2017 | Family_2017 | LifeExp_2017 | Freedom_2017 | Generosity_2017 | ... | HappRank_2015 | HappScore_2015 | StdError_2015 | GDP_2015 | Family_2015 | LifeExp_2015 | Freedom_2015 | TrustGovt_2015 | Generosity_2015 | Dystopia_Residual_2015 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 150 | Uzbekistan |  47        | 5.971      | 6.065538   | 5.876463   | 0.7864411  | 1.5489691  | 0.4982726  | 0.6582487  | 0.41598365 | ...        |  44        | 6.003      | 0.04361    | 0.63244    | 1.34043    | 0.59772    | 0.65821    | 0.30826    | 0.22837    | 2.23741    |\n",
       "| 151 | Venezuela  |  82        | 5.250      | 5.370032   | 5.129968   | 1.1284312  | 1.4313376  | 0.6171442  | 0.1539971  | 0.06501963 | ...        |  23        | 6.810      | 0.06476    | 1.04424    | 1.25596    | 0.72052    | 0.42908    | 0.11069    | 0.05841    | 3.19131    |\n",
       "| 152 | Vietnam    |  94        | 5.074      | 5.147281   | 5.000719   | 0.7885476  | 1.2774913  | 0.6521690  | 0.5710556  | 0.23496805 | ...        |  75        | 5.360      | 0.03107    | 0.63216    | 0.91226    | 0.74676    | 0.59444    | 0.10441    | 0.16860    | 2.20173    |\n",
       "| 153 | Yemen      | 146        | 3.593      | 3.692750   | 3.493250   | 0.5916834  | 0.9353822  | 0.3100809  | 0.2494637  | 0.10412521 | ...        | 136        | 4.077      | 0.04367    | 0.54649    | 0.68093    | 0.40064    | 0.35571    | 0.07854    | 0.09131    | 1.92313    |\n",
       "| 154 | Zambia     | 116        | 4.514      | 4.644106   | 4.383894   | 0.6364068  | 1.0031873  | 0.2578359  | 0.4616035  | 0.24958014 | ...        |  85        | 5.129      | 0.06988    | 0.47038    | 0.91612    | 0.29924    | 0.48827    | 0.12468    | 0.19591    | 2.63430    |\n",
       "| 155 | Zimbabwe   | 138        | 3.875      | 3.978700   | 3.771300   | 0.3758465  | 1.0830959  | 0.1967638  | 0.3363842  | 0.18914349 | ...        | 115        | 4.610      | 0.04290    | 0.27100    | 1.03276    | 0.33475    | 0.25861    | 0.08079    | 0.18987    | 2.44191    |\n",
       "\n"
      ],
      "text/plain": [
       "    Country    HappRank_2017 HappScore_2017 Whisker_high_2017 Whisker_low_2017\n",
       "150 Uzbekistan  47           5.971          6.065538          5.876463        \n",
       "151 Venezuela   82           5.250          5.370032          5.129968        \n",
       "152 Vietnam     94           5.074          5.147281          5.000719        \n",
       "153 Yemen      146           3.593          3.692750          3.493250        \n",
       "154 Zambia     116           4.514          4.644106          4.383894        \n",
       "155 Zimbabwe   138           3.875          3.978700          3.771300        \n",
       "    GDP_2017  Family_2017 LifeExp_2017 Freedom_2017 Generosity_2017 ...\n",
       "150 0.7864411 1.5489691   0.4982726    0.6582487    0.41598365      ...\n",
       "151 1.1284312 1.4313376   0.6171442    0.1539971    0.06501963      ...\n",
       "152 0.7885476 1.2774913   0.6521690    0.5710556    0.23496805      ...\n",
       "153 0.5916834 0.9353822   0.3100809    0.2494637    0.10412521      ...\n",
       "154 0.6364068 1.0031873   0.2578359    0.4616035    0.24958014      ...\n",
       "155 0.3758465 1.0830959   0.1967638    0.3363842    0.18914349      ...\n",
       "    HappRank_2015 HappScore_2015 StdError_2015 GDP_2015 Family_2015\n",
       "150  44           6.003          0.04361       0.63244  1.34043    \n",
       "151  23           6.810          0.06476       1.04424  1.25596    \n",
       "152  75           5.360          0.03107       0.63216  0.91226    \n",
       "153 136           4.077          0.04367       0.54649  0.68093    \n",
       "154  85           5.129          0.06988       0.47038  0.91612    \n",
       "155 115           4.610          0.04290       0.27100  1.03276    \n",
       "    LifeExp_2015 Freedom_2015 TrustGovt_2015 Generosity_2015\n",
       "150 0.59772      0.65821      0.30826        0.22837        \n",
       "151 0.72052      0.42908      0.11069        0.05841        \n",
       "152 0.74676      0.59444      0.10441        0.16860        \n",
       "153 0.40064      0.35571      0.07854        0.09131        \n",
       "154 0.29924      0.48827      0.12468        0.19591        \n",
       "155 0.33475      0.25861      0.08079        0.18987        \n",
       "    Dystopia_Residual_2015\n",
       "150 2.23741               \n",
       "151 3.19131               \n",
       "152 2.20173               \n",
       "153 1.92313               \n",
       "154 2.63430               \n",
       "155 2.44191               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examine last 6 rows of 2015 dataset\n",
    "tail(Total_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Number of rows in 2015 data: 158'"
      ],
      "text/latex": [
       "'Number of rows in 2015 data: 158'"
      ],
      "text/markdown": [
       "'Number of rows in 2015 data: 158'"
      ],
      "text/plain": [
       "[1] \"Number of rows in 2015 data: 158\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Number of rows in 2016 data: 157'"
      ],
      "text/latex": [
       "'Number of rows in 2016 data: 157'"
      ],
      "text/markdown": [
       "'Number of rows in 2016 data: 157'"
      ],
      "text/plain": [
       "[1] \"Number of rows in 2016 data: 157\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Number of rows in 2017 data: 155'"
      ],
      "text/latex": [
       "'Number of rows in 2017 data: 155'"
      ],
      "text/markdown": [
       "'Number of rows in 2017 data: 155'"
      ],
      "text/plain": [
       "[1] \"Number of rows in 2017 data: 155\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Number of rows in Total Data: 155'"
      ],
      "text/latex": [
       "'Number of rows in Total Data: 155'"
      ],
      "text/markdown": [
       "'Number of rows in Total Data: 155'"
      ],
      "text/plain": [
       "[1] \"Number of rows in Total Data: 155\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find number of rows in datasets\n",
    "\n",
    "sprintf(\"Number of rows in 2015 data: %s\",nrow(data_2015))\n",
    "sprintf(\"Number of rows in 2016 data: %s\",nrow(data_2016))\n",
    "sprintf(\"Number of rows in 2017 data: %s\",nrow(new_data_2017))\n",
    "sprintf(\"Number of rows in Total Data: %s\",nrow(Total_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Country</th><th scope=col>HappRank_2015</th><th scope=col>HappRank_2016</th><th scope=col>HappRank_2017</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>India</td><td>117  </td><td>118  </td><td>122  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " Country & HappRank\\_2015 & HappRank\\_2016 & HappRank\\_2017\\\\\n",
       "\\hline\n",
       "\t India & 117   & 118   & 122  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Country | HappRank_2015 | HappRank_2016 | HappRank_2017 |\n",
       "|---|---|---|---|\n",
       "| India | 117   | 118   | 122   |\n",
       "\n"
      ],
      "text/plain": [
       "  Country HappRank_2015 HappRank_2016 HappRank_2017\n",
       "1 India   117           118           122          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Curious to know where India stand\n",
    "library(dplyr)\n",
    "\n",
    "Total_data %>%\n",
    "    select(Country, HappRank_2015, HappRank_2016, HappRank_2017) %>%\n",
    "    dplyr::filter(Country=='India')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**India's happiness rank slipped every year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Country</th><th scope=col>HappRank_2015</th><th scope=col>HappRank_2016</th><th scope=col>HappRank_2017</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>China</td><td>84   </td><td>83   </td><td>79   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " Country & HappRank\\_2015 & HappRank\\_2016 & HappRank\\_2017\\\\\n",
       "\\hline\n",
       "\t China & 84    & 83    & 79   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Country | HappRank_2015 | HappRank_2016 | HappRank_2017 |\n",
       "|---|---|---|---|\n",
       "| China | 84    | 83    | 79    |\n",
       "\n"
      ],
      "text/plain": [
       "  Country HappRank_2015 HappRank_2016 HappRank_2017\n",
       "1 China   84            83            79           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Curious to know where China stand\n",
    "library(dplyr)\n",
    "\n",
    "Total_data %>%\n",
    "    select(Country, HappRank_2015, HappRank_2016, HappRank_2017) %>%\n",
    "    dplyr::filter(Country=='China')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**China's happiness rank improved every year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Country</th><th scope=col>HappRank_2015</th><th scope=col>HappRank_2016</th><th scope=col>HappRank_2017</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Bhutan</td><td>79    </td><td>84    </td><td>97    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " Country & HappRank\\_2015 & HappRank\\_2016 & HappRank\\_2017\\\\\n",
       "\\hline\n",
       "\t Bhutan & 79     & 84     & 97    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Country | HappRank_2015 | HappRank_2016 | HappRank_2017 |\n",
       "|---|---|---|---|\n",
       "| Bhutan | 79     | 84     | 97     |\n",
       "\n"
      ],
      "text/plain": [
       "  Country HappRank_2015 HappRank_2016 HappRank_2017\n",
       "1 Bhutan  79            84            97           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Curious to know where Bhutan stand\n",
    "library(dplyr)\n",
    "\n",
    "Total_data %>%\n",
    "    select(Country, HappRank_2015, HappRank_2016, HappRank_2017) %>%\n",
    "    dplyr::filter(Country=='Bhutan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is interesting. **Bhutan which is known as 'Happiest place on earth' whose happiness rank decreased every year. 2017 being worse compared to 2016 and 2015.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Country</th><th scope=col>HappRank_2015</th><th scope=col>HappRank_2016</th><th scope=col>HappRank_2017</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>United States</td><td>15           </td><td>13           </td><td>14           </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " Country & HappRank\\_2015 & HappRank\\_2016 & HappRank\\_2017\\\\\n",
       "\\hline\n",
       "\t United States & 15            & 13            & 14           \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Country | HappRank_2015 | HappRank_2016 | HappRank_2017 |\n",
       "|---|---|---|---|\n",
       "| United States | 15            | 13            | 14            |\n",
       "\n"
      ],
      "text/plain": [
       "  Country       HappRank_2015 HappRank_2016 HappRank_2017\n",
       "1 United States 15            13            14           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# where does US stand?\n",
    "\n",
    "library(dplyr)\n",
    "\n",
    "Total_data %>%\n",
    "    select(Country, HappRank_2015, HappRank_2016, HappRank_2017) %>%\n",
    "    dplyr::filter(Country=='United States')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**United States happiness rank is interesting. Rank improved in 2016 but decreased by 1 rank in 2017.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Country</th><th scope=col>HappRank_2017</th><th scope=col>HappScore_2017</th><th scope=col>Whisker_high_2017</th><th scope=col>Whisker_low_2017</th><th scope=col>GDP_2017</th><th scope=col>Family_2017</th><th scope=col>LifeExp_2017</th><th scope=col>Freedom_2017</th><th scope=col>Generosity_2017</th><th scope=col>...</th><th scope=col>HappRank_2015</th><th scope=col>HappScore_2015</th><th scope=col>StdError_2015</th><th scope=col>GDP_2015</th><th scope=col>Family_2015</th><th scope=col>LifeExp_2015</th><th scope=col>Freedom_2015</th><th scope=col>TrustGovt_2015</th><th scope=col>Generosity_2015</th><th scope=col>Dystopia_Residual_2015</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Norway     </td><td>1          </td><td>7.537      </td><td>7.594445   </td><td>7.479556   </td><td>1.616463   </td><td>1.533524   </td><td>0.7966665  </td><td>0.6354226  </td><td>0.3620122  </td><td>...        </td><td>4          </td><td>7.522      </td><td>0.03880    </td><td>1.45900    </td><td>1.33095    </td><td>0.88521    </td><td>0.66973    </td><td>0.36503    </td><td>0.34699    </td><td>2.46531    </td></tr>\n",
       "\t<tr><td>Denmark    </td><td>2          </td><td>7.522      </td><td>7.581728   </td><td>7.462272   </td><td>1.482383   </td><td>1.551122   </td><td>0.7925655  </td><td>0.6260067  </td><td>0.3552805  </td><td>...        </td><td>3          </td><td>7.527      </td><td>0.03328    </td><td>1.32548    </td><td>1.36058    </td><td>0.87464    </td><td>0.64938    </td><td>0.48357    </td><td>0.34139    </td><td>2.49204    </td></tr>\n",
       "\t<tr><td>Iceland    </td><td>3          </td><td>7.504      </td><td>7.622030   </td><td>7.385970   </td><td>1.480633   </td><td>1.610574   </td><td>0.8335521  </td><td>0.6271626  </td><td>0.4755402  </td><td>...        </td><td>2          </td><td>7.561      </td><td>0.04884    </td><td>1.30232    </td><td>1.40223    </td><td>0.94784    </td><td>0.62877    </td><td>0.14145    </td><td>0.43630    </td><td>2.70201    </td></tr>\n",
       "\t<tr><td>Switzerland</td><td>4          </td><td>7.494      </td><td>7.561772   </td><td>7.426227   </td><td>1.564980   </td><td>1.516912   </td><td>0.8581313  </td><td>0.6200706  </td><td>0.2905493  </td><td>...        </td><td>1          </td><td>7.587      </td><td>0.03411    </td><td>1.39651    </td><td>1.34951    </td><td>0.94143    </td><td>0.66557    </td><td>0.41978    </td><td>0.29678    </td><td>2.51738    </td></tr>\n",
       "\t<tr><td>Finland    </td><td>5          </td><td>7.469      </td><td>7.527542   </td><td>7.410458   </td><td>1.443572   </td><td>1.540247   </td><td>0.8091577  </td><td>0.6179509  </td><td>0.2454828  </td><td>...        </td><td>6          </td><td>7.406      </td><td>0.03140    </td><td>1.29025    </td><td>1.31826    </td><td>0.88911    </td><td>0.64169    </td><td>0.41372    </td><td>0.23351    </td><td>2.61955    </td></tr>\n",
       "\t<tr><td>Netherlands</td><td>6          </td><td>7.377      </td><td>7.427426   </td><td>7.326574   </td><td>1.503945   </td><td>1.428939   </td><td>0.8106961  </td><td>0.5853845  </td><td>0.4704898  </td><td>...        </td><td>7          </td><td>7.378      </td><td>0.02799    </td><td>1.32944    </td><td>1.28017    </td><td>0.89284    </td><td>0.61576    </td><td>0.31814    </td><td>0.47610    </td><td>2.46570    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllllllllllll}\n",
       " Country & HappRank\\_2017 & HappScore\\_2017 & Whisker\\_high\\_2017 & Whisker\\_low\\_2017 & GDP\\_2017 & Family\\_2017 & LifeExp\\_2017 & Freedom\\_2017 & Generosity\\_2017 & TrustGovt\\_2017 & Dystopia\\_Residual\\_2017 & Region\\_2017 & Region\\_2016 & HappRank\\_2016 & HappScore\\_2016 & Lower\\_Conf\\_interval\\_2016 & Upper\\_Conf\\_interval\\_2016 & GDP\\_2016 & Family\\_2016 & LifeExp\\_2016 & Freedom\\_2016 & TrustGovt\\_2016 & Generosity\\_2016 & Dystopia\\_Residual\\_2016 & Region\\_2015 & HappRank\\_2015 & HappScore\\_2015 & StdError\\_2015 & GDP\\_2015 & Family\\_2015 & LifeExp\\_2015 & Freedom\\_2015 & TrustGovt\\_2015 & Generosity\\_2015 & Dystopia\\_Residual\\_2015\\\\\n",
       "\\hline\n",
       "\t Norway         & 1              & 7.537          & 7.594445       & 7.479556       & 1.616463       & 1.533524       & 0.7966665      & 0.6354226      & 0.3620122      & 0.3159638      & 2.277027       & Western Europe & Western Europe & 4              & 7.498          & 7.421          & 7.575          & 1.57744        & 1.12690        & 0.79579        & 0.59609        & 0.35776        & 0.37895        & 2.66465        & Western Europe & 4              & 7.522          & 0.03880        & 1.45900        & 1.33095        & 0.88521        & 0.66973        & 0.36503        & 0.34699        & 2.46531       \\\\\n",
       "\t Denmark        & 2              & 7.522          & 7.581728       & 7.462272       & 1.482383       & 1.551122       & 0.7925655      & 0.6260067      & 0.3552805      & 0.4007701      & 2.313707       & Western Europe & Western Europe & 1              & 7.526          & 7.460          & 7.592          & 1.44178        & 1.16374        & 0.79504        & 0.57941        & 0.44453        & 0.36171        & 2.73939        & Western Europe & 3              & 7.527          & 0.03328        & 1.32548        & 1.36058        & 0.87464        & 0.64938        & 0.48357        & 0.34139        & 2.49204       \\\\\n",
       "\t Iceland        & 3              & 7.504          & 7.622030       & 7.385970       & 1.480633       & 1.610574       & 0.8335521      & 0.6271626      & 0.4755402      & 0.1535266      & 2.322715       & Western Europe & Western Europe & 3              & 7.501          & 7.333          & 7.669          & 1.42666        & 1.18326        & 0.86733        & 0.56624        & 0.14975        & 0.47678        & 2.83137        & Western Europe & 2              & 7.561          & 0.04884        & 1.30232        & 1.40223        & 0.94784        & 0.62877        & 0.14145        & 0.43630        & 2.70201       \\\\\n",
       "\t Switzerland    & 4              & 7.494          & 7.561772       & 7.426227       & 1.564980       & 1.516912       & 0.8581313      & 0.6200706      & 0.2905493      & 0.3670073      & 2.276716       & Western Europe & Western Europe & 2              & 7.509          & 7.428          & 7.590          & 1.52733        & 1.14524        & 0.86303        & 0.58557        & 0.41203        & 0.28083        & 2.69463        & Western Europe & 1              & 7.587          & 0.03411        & 1.39651        & 1.34951        & 0.94143        & 0.66557        & 0.41978        & 0.29678        & 2.51738       \\\\\n",
       "\t Finland        & 5              & 7.469          & 7.527542       & 7.410458       & 1.443572       & 1.540247       & 0.8091577      & 0.6179509      & 0.2454828      & 0.3826115      & 2.430182       & Western Europe & Western Europe & 5              & 7.413          & 7.351          & 7.475          & 1.40598        & 1.13464        & 0.81091        & 0.57104        & 0.41004        & 0.25492        & 2.82596        & Western Europe & 6              & 7.406          & 0.03140        & 1.29025        & 1.31826        & 0.88911        & 0.64169        & 0.41372        & 0.23351        & 2.61955       \\\\\n",
       "\t Netherlands    & 6              & 7.377          & 7.427426       & 7.326574       & 1.503945       & 1.428939       & 0.8106961      & 0.5853845      & 0.4704898      & 0.2826618      & 2.294804       & Western Europe & Western Europe & 7              & 7.339          & 7.284          & 7.394          & 1.46468        & 1.02912        & 0.81231        & 0.55211        & 0.29927        & 0.47416        & 2.70749        & Western Europe & 7              & 7.378          & 0.02799        & 1.32944        & 1.28017        & 0.89284        & 0.61576        & 0.31814        & 0.47610        & 2.46570       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Country | HappRank_2017 | HappScore_2017 | Whisker_high_2017 | Whisker_low_2017 | GDP_2017 | Family_2017 | LifeExp_2017 | Freedom_2017 | Generosity_2017 | ... | HappRank_2015 | HappScore_2015 | StdError_2015 | GDP_2015 | Family_2015 | LifeExp_2015 | Freedom_2015 | TrustGovt_2015 | Generosity_2015 | Dystopia_Residual_2015 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Norway      | 1           | 7.537       | 7.594445    | 7.479556    | 1.616463    | 1.533524    | 0.7966665   | 0.6354226   | 0.3620122   | ...         | 4           | 7.522       | 0.03880     | 1.45900     | 1.33095     | 0.88521     | 0.66973     | 0.36503     | 0.34699     | 2.46531     |\n",
       "| Denmark     | 2           | 7.522       | 7.581728    | 7.462272    | 1.482383    | 1.551122    | 0.7925655   | 0.6260067   | 0.3552805   | ...         | 3           | 7.527       | 0.03328     | 1.32548     | 1.36058     | 0.87464     | 0.64938     | 0.48357     | 0.34139     | 2.49204     |\n",
       "| Iceland     | 3           | 7.504       | 7.622030    | 7.385970    | 1.480633    | 1.610574    | 0.8335521   | 0.6271626   | 0.4755402   | ...         | 2           | 7.561       | 0.04884     | 1.30232     | 1.40223     | 0.94784     | 0.62877     | 0.14145     | 0.43630     | 2.70201     |\n",
       "| Switzerland | 4           | 7.494       | 7.561772    | 7.426227    | 1.564980    | 1.516912    | 0.8581313   | 0.6200706   | 0.2905493   | ...         | 1           | 7.587       | 0.03411     | 1.39651     | 1.34951     | 0.94143     | 0.66557     | 0.41978     | 0.29678     | 2.51738     |\n",
       "| Finland     | 5           | 7.469       | 7.527542    | 7.410458    | 1.443572    | 1.540247    | 0.8091577   | 0.6179509   | 0.2454828   | ...         | 6           | 7.406       | 0.03140     | 1.29025     | 1.31826     | 0.88911     | 0.64169     | 0.41372     | 0.23351     | 2.61955     |\n",
       "| Netherlands | 6           | 7.377       | 7.427426    | 7.326574    | 1.503945    | 1.428939    | 0.8106961   | 0.5853845   | 0.4704898   | ...         | 7           | 7.378       | 0.02799     | 1.32944     | 1.28017     | 0.89284     | 0.61576     | 0.31814     | 0.47610     | 2.46570     |\n",
       "\n"
      ],
      "text/plain": [
       "  Country     HappRank_2017 HappScore_2017 Whisker_high_2017 Whisker_low_2017\n",
       "1 Norway      1             7.537          7.594445          7.479556        \n",
       "2 Denmark     2             7.522          7.581728          7.462272        \n",
       "3 Iceland     3             7.504          7.622030          7.385970        \n",
       "4 Switzerland 4             7.494          7.561772          7.426227        \n",
       "5 Finland     5             7.469          7.527542          7.410458        \n",
       "6 Netherlands 6             7.377          7.427426          7.326574        \n",
       "  GDP_2017 Family_2017 LifeExp_2017 Freedom_2017 Generosity_2017 ...\n",
       "1 1.616463 1.533524    0.7966665    0.6354226    0.3620122       ...\n",
       "2 1.482383 1.551122    0.7925655    0.6260067    0.3552805       ...\n",
       "3 1.480633 1.610574    0.8335521    0.6271626    0.4755402       ...\n",
       "4 1.564980 1.516912    0.8581313    0.6200706    0.2905493       ...\n",
       "5 1.443572 1.540247    0.8091577    0.6179509    0.2454828       ...\n",
       "6 1.503945 1.428939    0.8106961    0.5853845    0.4704898       ...\n",
       "  HappRank_2015 HappScore_2015 StdError_2015 GDP_2015 Family_2015 LifeExp_2015\n",
       "1 4             7.522          0.03880       1.45900  1.33095     0.88521     \n",
       "2 3             7.527          0.03328       1.32548  1.36058     0.87464     \n",
       "3 2             7.561          0.04884       1.30232  1.40223     0.94784     \n",
       "4 1             7.587          0.03411       1.39651  1.34951     0.94143     \n",
       "5 6             7.406          0.03140       1.29025  1.31826     0.88911     \n",
       "6 7             7.378          0.02799       1.32944  1.28017     0.89284     \n",
       "  Freedom_2015 TrustGovt_2015 Generosity_2015 Dystopia_Residual_2015\n",
       "1 0.66973      0.36503        0.34699         2.46531               \n",
       "2 0.64938      0.48357        0.34139         2.49204               \n",
       "3 0.62877      0.14145        0.43630         2.70201               \n",
       "4 0.66557      0.41978        0.29678         2.51738               \n",
       "5 0.64169      0.41372        0.23351         2.61955               \n",
       "6 0.61576      0.31814        0.47610         2.46570               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter out top 50 countries\n",
    "\n",
    "library(dplyr)\n",
    "\n",
    "top50 <- Total_data %>%\n",
    "                 dplyr::filter(HappRank_2017>=1 & HappRank_2017<=50) %>%\n",
    "                 arrange(desc(HappScore_2017))\n",
    "\n",
    "head(top50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Country</th><th scope=col>HappRank_2017</th><th scope=col>HappScore_2017</th><th scope=col>Whisker_high_2017</th><th scope=col>Whisker_low_2017</th><th scope=col>GDP_2017</th><th scope=col>Family_2017</th><th scope=col>LifeExp_2017</th><th scope=col>Freedom_2017</th><th scope=col>Generosity_2017</th><th scope=col>...</th><th scope=col>HappRank_2015</th><th scope=col>HappScore_2015</th><th scope=col>StdError_2015</th><th scope=col>GDP_2015</th><th scope=col>Family_2015</th><th scope=col>LifeExp_2015</th><th scope=col>Freedom_2015</th><th scope=col>TrustGovt_2015</th><th scope=col>Generosity_2015</th><th scope=col>Dystopia_Residual_2015</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Afghanistan</td><td>141        </td><td>3.794      </td><td>3.873661   </td><td>3.714338   </td><td>0.4014772  </td><td>0.5815433  </td><td>0.18074678 </td><td>0.1061795  </td><td>0.31187093 </td><td>...        </td><td>153        </td><td>3.575      </td><td>0.03084    </td><td>0.31982    </td><td>0.30285    </td><td>0.30335    </td><td>0.23414    </td><td>0.09719    </td><td>0.36510    </td><td>1.95210    </td></tr>\n",
       "\t<tr><td>Albania    </td><td>109        </td><td>4.644      </td><td>4.752464   </td><td>4.535536   </td><td>0.9961928  </td><td>0.8036852  </td><td>0.73115975 </td><td>0.3814986  </td><td>0.20131294 </td><td>...        </td><td> 95        </td><td>4.959      </td><td>0.05013    </td><td>0.87867    </td><td>0.80434    </td><td>0.81325    </td><td>0.35733    </td><td>0.06413    </td><td>0.14272    </td><td>1.89894    </td></tr>\n",
       "\t<tr><td>Angola     </td><td>140        </td><td>3.795      </td><td>3.951642   </td><td>3.638358   </td><td>0.8584282  </td><td>1.1044120  </td><td>0.04986867 </td><td>0.0000000  </td><td>0.09792649 </td><td>...        </td><td>137        </td><td>4.033      </td><td>0.04758    </td><td>0.75778    </td><td>0.86040    </td><td>0.16683    </td><td>0.10384    </td><td>0.07122    </td><td>0.12344    </td><td>1.94939    </td></tr>\n",
       "\t<tr><td>Armenia    </td><td>121        </td><td>4.376      </td><td>4.466735   </td><td>4.285265   </td><td>0.9005967  </td><td>1.0074837  </td><td>0.63752443 </td><td>0.1983033  </td><td>0.08348809 </td><td>...        </td><td>127        </td><td>4.350      </td><td>0.04763    </td><td>0.76821    </td><td>0.77711    </td><td>0.72990    </td><td>0.19847    </td><td>0.03900    </td><td>0.07855    </td><td>1.75873    </td></tr>\n",
       "\t<tr><td>Bangladesh </td><td>110        </td><td>4.608      </td><td>4.689822   </td><td>4.526178   </td><td>0.5866830  </td><td>0.7351317  </td><td>0.53324103 </td><td>0.4783567  </td><td>0.17225535 </td><td>...        </td><td>109        </td><td>4.694      </td><td>0.03077    </td><td>0.39753    </td><td>0.43106    </td><td>0.60164    </td><td>0.40820    </td><td>0.12569    </td><td>0.21222    </td><td>2.51767    </td></tr>\n",
       "\t<tr><td>Benin      </td><td>143        </td><td>3.657      </td><td>3.745784   </td><td>3.568217   </td><td>0.4310854  </td><td>0.4352998  </td><td>0.20993021 </td><td>0.4259628  </td><td>0.20794846 </td><td>...        </td><td>155        </td><td>3.340      </td><td>0.03656    </td><td>0.28665    </td><td>0.35386    </td><td>0.31910    </td><td>0.48450    </td><td>0.08010    </td><td>0.18260    </td><td>1.63328    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllllllllllll}\n",
       " Country & HappRank\\_2017 & HappScore\\_2017 & Whisker\\_high\\_2017 & Whisker\\_low\\_2017 & GDP\\_2017 & Family\\_2017 & LifeExp\\_2017 & Freedom\\_2017 & Generosity\\_2017 & TrustGovt\\_2017 & Dystopia\\_Residual\\_2017 & Region\\_2017 & Region\\_2016 & HappRank\\_2016 & HappScore\\_2016 & Lower\\_Conf\\_interval\\_2016 & Upper\\_Conf\\_interval\\_2016 & GDP\\_2016 & Family\\_2016 & LifeExp\\_2016 & Freedom\\_2016 & TrustGovt\\_2016 & Generosity\\_2016 & Dystopia\\_Residual\\_2016 & Region\\_2015 & HappRank\\_2015 & HappScore\\_2015 & StdError\\_2015 & GDP\\_2015 & Family\\_2015 & LifeExp\\_2015 & Freedom\\_2015 & TrustGovt\\_2015 & Generosity\\_2015 & Dystopia\\_Residual\\_2015\\\\\n",
       "\\hline\n",
       "\t Afghanistan                & 141                        & 3.794                      & 3.873661                   & 3.714338                   & 0.4014772                  & 0.5815433                  & 0.18074678                 & 0.1061795                  & 0.31187093                 & 0.06115783                 & 2.150801                   & Southern Asia              & Southern Asia              & 154                        & 3.360                      & 3.288                      & 3.432                      & 0.38227                    & 0.11037                    & 0.17344                    & 0.16430                    & 0.07112                    & 0.31268                    & 2.14558                    & Southern Asia              & 153                        & 3.575                      & 0.03084                    & 0.31982                    & 0.30285                    & 0.30335                    & 0.23414                    & 0.09719                    & 0.36510                    & 1.95210                   \\\\\n",
       "\t Albania                    & 109                        & 4.644                      & 4.752464                   & 4.535536                   & 0.9961928                  & 0.8036852                  & 0.73115975                 & 0.3814986                  & 0.20131294                 & 0.03986422                 & 1.490442                   & Central and Eastern Europe & Central and Eastern Europe & 109                        & 4.655                      & 4.546                      & 4.764                      & 0.95530                    & 0.50163                    & 0.73007                    & 0.31866                    & 0.05301                    & 0.16840                    & 1.92816                    & Central and Eastern Europe &  95                        & 4.959                      & 0.05013                    & 0.87867                    & 0.80434                    & 0.81325                    & 0.35733                    & 0.06413                    & 0.14272                    & 1.89894                   \\\\\n",
       "\t Angola                     & 140                        & 3.795                      & 3.951642                   & 3.638358                   & 0.8584282                  & 1.1044120                  & 0.04986867                 & 0.0000000                  & 0.09792649                 & 0.06972034                 & 1.614482                   & Sub-Saharan Africa         & Sub-Saharan Africa         & 141                        & 3.866                      & 3.753                      & 3.979                      & 0.84731                    & 0.66366                    & 0.04991                    & 0.00589                    & 0.08434                    & 0.12071                    & 2.09459                    & Sub-Saharan Africa         & 137                        & 4.033                      & 0.04758                    & 0.75778                    & 0.86040                    & 0.16683                    & 0.10384                    & 0.07122                    & 0.12344                    & 1.94939                   \\\\\n",
       "\t Armenia                    & 121                        & 4.376                      & 4.466735                   & 4.285265                   & 0.9005967                  & 1.0074837                  & 0.63752443                 & 0.1983033                  & 0.08348809                 & 0.02667442                 & 1.521499                   & Central and Eastern Europe & Central and Eastern Europe & 121                        & 4.360                      & 4.266                      & 4.454                      & 0.86086                    & 0.62477                    & 0.64083                    & 0.14037                    & 0.03616                    & 0.07793                    & 1.97864                    & Central and Eastern Europe & 127                        & 4.350                      & 0.04763                    & 0.76821                    & 0.77711                    & 0.72990                    & 0.19847                    & 0.03900                    & 0.07855                    & 1.75873                   \\\\\n",
       "\t Bangladesh                 & 110                        & 4.608                      & 4.689822                   & 4.526178                   & 0.5866830                  & 0.7351317                  & 0.53324103                 & 0.4783567                  & 0.17225535                 & 0.12371786                 & 1.978736                   & Southern Asia              & Southern Asia              & 110                        & 4.643                      & 4.560                      & 4.726                      & 0.54177                    & 0.24749                    & 0.52989                    & 0.39778                    & 0.12583                    & 0.19132                    & 2.60904                    & Southern Asia              & 109                        & 4.694                      & 0.03077                    & 0.39753                    & 0.43106                    & 0.60164                    & 0.40820                    & 0.12569                    & 0.21222                    & 2.51767                   \\\\\n",
       "\t Benin                      & 143                        & 3.657                      & 3.745784                   & 3.568217                   & 0.4310854                  & 0.4352998                  & 0.20993021                 & 0.4259628                  & 0.20794846                 & 0.06092902                 & 1.885631                   & Sub-Saharan Africa         & Sub-Saharan Africa         & 153                        & 3.484                      & 3.404                      & 3.564                      & 0.39499                    & 0.10419                    & 0.21028                    & 0.39747                    & 0.06681                    & 0.20180                    & 2.10812                    & Sub-Saharan Africa         & 155                        & 3.340                      & 0.03656                    & 0.28665                    & 0.35386                    & 0.31910                    & 0.48450                    & 0.08010                    & 0.18260                    & 1.63328                   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Country | HappRank_2017 | HappScore_2017 | Whisker_high_2017 | Whisker_low_2017 | GDP_2017 | Family_2017 | LifeExp_2017 | Freedom_2017 | Generosity_2017 | ... | HappRank_2015 | HappScore_2015 | StdError_2015 | GDP_2015 | Family_2015 | LifeExp_2015 | Freedom_2015 | TrustGovt_2015 | Generosity_2015 | Dystopia_Residual_2015 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Afghanistan | 141         | 3.794       | 3.873661    | 3.714338    | 0.4014772   | 0.5815433   | 0.18074678  | 0.1061795   | 0.31187093  | ...         | 153         | 3.575       | 0.03084     | 0.31982     | 0.30285     | 0.30335     | 0.23414     | 0.09719     | 0.36510     | 1.95210     |\n",
       "| Albania     | 109         | 4.644       | 4.752464    | 4.535536    | 0.9961928   | 0.8036852   | 0.73115975  | 0.3814986   | 0.20131294  | ...         |  95         | 4.959       | 0.05013     | 0.87867     | 0.80434     | 0.81325     | 0.35733     | 0.06413     | 0.14272     | 1.89894     |\n",
       "| Angola      | 140         | 3.795       | 3.951642    | 3.638358    | 0.8584282   | 1.1044120   | 0.04986867  | 0.0000000   | 0.09792649  | ...         | 137         | 4.033       | 0.04758     | 0.75778     | 0.86040     | 0.16683     | 0.10384     | 0.07122     | 0.12344     | 1.94939     |\n",
       "| Armenia     | 121         | 4.376       | 4.466735    | 4.285265    | 0.9005967   | 1.0074837   | 0.63752443  | 0.1983033   | 0.08348809  | ...         | 127         | 4.350       | 0.04763     | 0.76821     | 0.77711     | 0.72990     | 0.19847     | 0.03900     | 0.07855     | 1.75873     |\n",
       "| Bangladesh  | 110         | 4.608       | 4.689822    | 4.526178    | 0.5866830   | 0.7351317   | 0.53324103  | 0.4783567   | 0.17225535  | ...         | 109         | 4.694       | 0.03077     | 0.39753     | 0.43106     | 0.60164     | 0.40820     | 0.12569     | 0.21222     | 2.51767     |\n",
       "| Benin       | 143         | 3.657       | 3.745784    | 3.568217    | 0.4310854   | 0.4352998   | 0.20993021  | 0.4259628   | 0.20794846  | ...         | 155         | 3.340       | 0.03656     | 0.28665     | 0.35386     | 0.31910     | 0.48450     | 0.08010     | 0.18260     | 1.63328     |\n",
       "\n"
      ],
      "text/plain": [
       "  Country     HappRank_2017 HappScore_2017 Whisker_high_2017 Whisker_low_2017\n",
       "1 Afghanistan 141           3.794          3.873661          3.714338        \n",
       "2 Albania     109           4.644          4.752464          4.535536        \n",
       "3 Angola      140           3.795          3.951642          3.638358        \n",
       "4 Armenia     121           4.376          4.466735          4.285265        \n",
       "5 Bangladesh  110           4.608          4.689822          4.526178        \n",
       "6 Benin       143           3.657          3.745784          3.568217        \n",
       "  GDP_2017  Family_2017 LifeExp_2017 Freedom_2017 Generosity_2017 ...\n",
       "1 0.4014772 0.5815433   0.18074678   0.1061795    0.31187093      ...\n",
       "2 0.9961928 0.8036852   0.73115975   0.3814986    0.20131294      ...\n",
       "3 0.8584282 1.1044120   0.04986867   0.0000000    0.09792649      ...\n",
       "4 0.9005967 1.0074837   0.63752443   0.1983033    0.08348809      ...\n",
       "5 0.5866830 0.7351317   0.53324103   0.4783567    0.17225535      ...\n",
       "6 0.4310854 0.4352998   0.20993021   0.4259628    0.20794846      ...\n",
       "  HappRank_2015 HappScore_2015 StdError_2015 GDP_2015 Family_2015 LifeExp_2015\n",
       "1 153           3.575          0.03084       0.31982  0.30285     0.30335     \n",
       "2  95           4.959          0.05013       0.87867  0.80434     0.81325     \n",
       "3 137           4.033          0.04758       0.75778  0.86040     0.16683     \n",
       "4 127           4.350          0.04763       0.76821  0.77711     0.72990     \n",
       "5 109           4.694          0.03077       0.39753  0.43106     0.60164     \n",
       "6 155           3.340          0.03656       0.28665  0.35386     0.31910     \n",
       "  Freedom_2015 TrustGovt_2015 Generosity_2015 Dystopia_Residual_2015\n",
       "1 0.23414      0.09719        0.36510         1.95210               \n",
       "2 0.35733      0.06413        0.14272         1.89894               \n",
       "3 0.10384      0.07122        0.12344         1.94939               \n",
       "4 0.19847      0.03900        0.07855         1.75873               \n",
       "5 0.40820      0.12569        0.21222         2.51767               \n",
       "6 0.48450      0.08010        0.18260         1.63328               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter out last 50 countries\n",
    "\n",
    "library(dplyr)\n",
    "\n",
    "last50 <- Total_data %>%\n",
    "            dplyr::filter(HappRank_2017>=max(Total_data$HappRank_2017)-50 & HappRank_2017<=max(Total_data$HappRank_2017))\n",
    "\n",
    "head(last50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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8RIqyk5y5JznOeIppbT20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\n5tv++/8A61H9kN/z823/AH3/APWoA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6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\n/wDfdZ9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8S6Bc67b3Xhe+1uzj13R3l1K50yXVZEEOkRxm5ihBQXR8\n0FC2CFLMSuRxS8OeAda1HxBYWOo+EdbbTZfiDY65uvtGS3ha2/s6VPNeOEeXGBIg3KcEbk3/\nADNW6inNQXlr/wCA/wCb+77uP2kow5mu/wCbt+SXzPrSHx74YuPD8uvReI9Jl0OJismppfRG\n2QggEGUNtByQOvesLwH8V7L4geM/GGi6dHBNZaAbLytTtbtZ470XEPm5XaMKF+7wzZ68dK+f\n/FfgbUrL46eI9ck8J32u+CdM8R2d/daHZ2Pmi536SIluooTgTeVNncFycknBKmu//Z40CW3+\nIXxY1eHwjf8Ag7QtauLCXT7a8sxamQLAyyuEHCkvliOo38gE4qoxVpX6Jfjy/wCb+77t73Sf\nr+bX6fj9/sniP/UQ/wC8f5Vg1xPwt/Z40j4BX99f6V4k8T+IZNQiWB4vEmoLdxxhTuygCLg+\n/PFejf2zJ/zwt/8Avj/69YlGfRWh/bMn/PC3/wC+P/r0f2zJ/wA8Lf8A74/+vQBn0Vof2zJ/\nzwt/++P/AK9H9syf88Lf/vj/AOvQBSh/10f+8Ktaz/yEpvw/kKlj1iRpFHkQDJA4T/69T6jq\nb297JGIoWAxyy5PQe9AGNRWh/bMn/PC3/wC+P/r0f2zJ/wA8Lf8A74/+vQBn0Vof2zJ/zwt/\n++P/AK9H9syf88Lf/vj/AOvQBn0Vof2zJ/zwt/8Avj/69H9syf8APC3/AO+P/r0AZ9FaH9sy\nf88Lf/vj/wCvR/bMn/PC3/74/wDr0AZ9FaH9syf88Lf/AL4/+vR/bMn/ADwt/wDvj/69AGfR\nWh/bMn/PC3/74/8Ar0f2zJ/zwt/++P8A69AFef8A49bb6N/Oq9a0urSLBC3kwncDwU4HP1qH\n+2ZP+eFv/wB8f/XoAz6K0P7Zk/54W/8A3x/9ej+2ZP8Anhb/APfH/wBegDPorQ/tmT/nhb/9\n8f8A16P7Zk/54W//AHx/9egDPorQ/tmT/nhb/wDfH/16P7Zk/wCeFv8A98f/AF6AM+itD+2Z\nP+eFv/3x/wDXo/tmT/nhb/8AfH/16AM+itD+2ZP+eFv/AN8f/Xo/tmT/AJ4W/wD3x/8AXoAz\n60LL/kGX3/AP50f2zJ/zwt/++P8A69W7bUnksbqQxRAptwAvBye9AGJRWh/bMn/PC3/74/8A\nr0f2zJ/zwt/++P8A69AGfRWh/bMn/PC3/wC+P/r0f2zJ/wA8Lf8A74/+vQBn0Vof2zJ/zwt/\n++P/AK9H9syf88Lf/vj/AOvQBn0Vof2zJ/zwt/8Avj/69H9syf8APC3/AO+P/r0AZ9FaH9sy\nf88Lf/vj/wCvR/bMn/PC3/74/wDr0AZ9FaH9syf88Lf/AL4/+vR/bMn/ADwt/wDvj/69AFfT\n/wDj8i+tV61rTVnluY0MMIBPUJz/ADqH+2ZP+eFv/wB8f/XoAz6K0P7Zk/54W/8A3x/9ej+2\nZP8Anhb/APfH/wBegDPorQ/tmT/nhb/98f8A16P7Zk/54W//AHx/9egDPorQ/tmT/nhb/wDf\nH/16P7Zk/wCeFv8A98f/AF6AM+itD+2ZP+eFv/3x/wDXo/tmT/nhb/8AfH/16AM+itD+2ZP+\neFv/AN8f/Xo/tmT/AJ4W/wD3x/8AXoA8O/aG/aw+Hn7MVjp8vjTUZ1vNQ3G003T4PPuZlXG5\nwuQFUZAyzAE8DODWz8Cv2hPBH7RvhKTxB4J1Nr22gl8i6t54jFcWsmMhZEPTI5BBKnnBODj5\nH/4KdfsefEH9oTxj4b8c+BLGHW57HTBpN3o6XEcEiqs0kqSp5jBWz5zBhkH5VwDk47v/AIJk\n/sveMv2YPCPi+/8AGsNtZ6z4mltcaSsyzm1itxLtLshKb2MzcKTgKvOSQAD7V1z/AI/z/uis\n+tzVNSe2uigiicbQcuuTVP8AtmT/AJ4W/wD3x/8AXoAz6K0P7Zk/54W//fH/ANej+2ZP+eFv\n/wB8f/XoAz6K0P7Zk/54W/8A3x/9ej+2ZP8Anhb/APfH/wBegDPorQ/tmT/nhb/98f8A16P7\nZk/54W//AHx/9egDPorQ/tmT/nhb/wDfH/16P7Zk/wCeFv8A98f/AF6AM+itD+2ZP+eFv/3x\n/wDXo/tmT/nhb/8AfH/16AM+rEn/AB4Q/wDXR/5LVj+2ZP8Anhb/APfH/wBepn1aQWsb+TDl\nnYY2ccAe/vQBk0Vof2zJ/wA8Lf8A74/+vR/bMn/PC3/74/8Ar0AZ9FaH9syf88Lf/vj/AOvR\n/bMn/PC3/wC+P/r0AZ9FaH9syf8APC3/AO+P/r0f2zJ/zwt/++P/AK9AGfRWh/bMn/PC3/74\n/wDr0f2zJ/zwt/8Avj/69AGfRWh/bMn/ADwt/wDvj/69H9syf88Lf/vj/wCvQBn0Vof2zJ/z\nwt/++P8A69H9syf88Lf/AL4/+vQAaZ/x7X3/AFyrPrcstSeaG6YxRKUTcAq4z9ap/wBsyf8A\nPC3/AO+P/r0AZ9FaH9syf88Lf/vj/wCvR/bMn/PC3/74/wDr0AZ9FaH9syf88Lf/AL4/+vR/\nbMn/ADwt/wDvj/69AFax/wCP23/66L/OuvrnrbVpJbmFDDCAzqMhORz9a6GgAooooAKKKKAC\niiigAooooAKKKKACiiigAooooAKKKKACiiuf+IPjG3+HvgbXvEt3E08GlWUt40KHBk2KSEB7\nEkAZ96Taim2NJt2R0FFfOGveIviL4Q8O2HifxB8VPCuh6zfw/a7bwjq1vbWljNgKzWy3EjiU\nsA6gybiASMjBr0XW/jppekvptraaNrHijU7vTotUktPDUCX32e3k+5I0gdUKsQ23axLbSQCK\npq25Kaeq2PSqK8nsf2kfDeveJdD0Tw/p+seIp9XsbfU4rjT7ZTBDayTPC8srMylPKaMh1IyC\nQACcgZ+i/tUeH9es/D11a+GfFTW/iFG/slzp8ZF5Koy8K4lO1lAYln2phGIcgZoaa3BNM9oo\nrzJP2gNAl8GDXo9M1ua4/tR9EOhw2Qk1Bb5CQ0BRWKAgKW3b9uP4qoSftL+HbfSra5uNG8QW\n19NraeHn0iWxUXkF48BmjV0D4w6gYZSwy65wMkFne39dP8194m0tX/X9WZ65RXl+lftCaDqG\nv2Oi3Wl6xo+p3GqnRprfUYYkNncm3NxEspWRhiVAdhQsCRg4rqvB3j/TfHD+IP7OjuVg0XUp\ndKmuJ0VY5ZogvmGPBJKqW2kkD5lbGcZpf1+X+a+8d1/Xz/yZe8R/6iH/AHj/ACrBrH8L/Gvw\nP8Y557PwX4ks/EN1ZqJriK0LExoTgE5A710f9l3f/PBqBlSirf8AZd3/AM8Go/su7/54NQBU\noq3/AGXd/wDPBqP7Lu/+eDUAV4f9dH/vCrWs/wDISm/D+Qoi0y6WVCYWABFWNU0+4mvpXSJm\nU4wR9BQBlUVb/su7/wCeDUf2Xd/88GoAqUVb/su7/wCeDUf2Xd/88GoAqUVb/su7/wCeDUf2\nXd/88GoAqUVb/su7/wCeDUf2Xd/88GoAqUVb/su7/wCeDUf2Xd/88GoAqUVb/su7/wCeDUf2\nXd/88GoAZP8A8ett9G/nVetGXTrlreBRCxKg5HpzUP8AZd3/AM8GoAqUVb/su7/54NR/Zd3/\nAM8GoAqUVb/su7/54NR/Zd3/AM8GoAqUVb/su7/54NR/Zd3/AM8GoAqUVb/su7/54NR/Zd3/\nAM8GoAqUVb/su7/54NR/Zd3/AM8GoAqVoWX/ACDL7/gH86i/su7/AOeDVctbG4TT7tGiYO+3\naPXmgDIoq3/Zd3/zwaj+y7v/AJ4NQBUoq3/Zd3/zwaj+y7v/AJ4NQBUoq3/Zd3/zwaj+y7v/\nAJ4NQBUoq3/Zd3/zwaj+y7v/AJ4NQBUoq3/Zd3/zwaj+y7v/AJ4NQBUoq3/Zd3/zwaj+y7v/\nAJ4NQAzT/wDj8i+tV60bLTrmO6jZoWCg8mof7Lu/+eDUAVKKt/2Xd/8APBqP7Lu/+eDUAVKK\nt/2Xd/8APBqP7Lu/+eDUAVKKt/2Xd/8APBqP7Lu/+eDUAVKKt/2Xd/8APBqP7Lu/+eDUAVKK\ntHS7oD/UNX5ZeKf+CyOpWfxHuI9D8CaffeCILlole5uZEvrmINjzAw+SMsOQpVsZAzQB+odF\nUPBWsxePvBug+J9Jjlk0rWrCDUrR3GGMM0ayISM8Haw4ra/su7/54NQBLrn/AB/n/dFZ9bGr\n2M894WjiZl2gZFUv7Lu/+eDUAVKKt/2Xd/8APBqP7Lu/+eDUAVKKt/2Xd/8APBqP7Lu/+eDU\nAVKKt/2Xd/8APBqP7Lu/+eDUAVKKt/2Xd/8APBqP7Lu/+eDUAVKKt/2Xd/8APBqP7Lu/+eDU\nAVKsSf8AHhD/ANdH/ktP/su7/wCeDVM+nXJs4kELbg7Ej8F/woAzqKt/2Xd/88Go/su7/wCe\nDUAVKKt/2Xd/88Go/su7/wCeDUAVKKt/2Xd/88Go/su7/wCeDUAVKKt/2Xd/88Go/su7/wCe\nDUAVKKt/2Xd/88Go/su7/wCeDUAVKKt/2Xd/88Go/su7/wCeDUAS6Z/x7X3/AFyrPrYsLGeK\nC7V4mUvHhQe5ql/Zd3/zwagCpRVv+y7v/ng1H9l3f/PBqAKlFW/7Lu/+eDUf2Xd/88GoAjsf\n+P23/wCui/zrr65m0065juoWaFgodST+NdNQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU\nUAFFFFABWR4v8L2Pjfwrq/h/UlZrDVLWSzn8s4YI6lSVPYjOQfUVr0Umk1ZjTs7o+f8AU/hH\n8TvEHhX/AIQ7V9V8HahpMVo9hB4juNPll1NYSu3cIm/drKVCguHxkZ2kisTWf2VL4P4dv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+tV6saf/wAfkX1qvQAUUUUAFFFFACModSrAMp6gjIpaKKACiiigAooooAKKKKANDXP+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\n4o4boLdI4ZMEgfd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EfEPhrxfqMM9lo0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1PQND/aE0DWNQ0qxl0vWtIvL3Vn0SWDU7ZIWsrsW/npHN85/1ifcKbgTxxXVeD/iBpvjd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\nH5F9ar1rWllbJcxst6rsDwuwjP61D9gtP+f9f+/Z/wAaA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++hQBQoq//Yl5/wA8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7Du/wDnmP8AvoVRzRmgDUl0e6aCFQgyoOfmHrUP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\nBX0//j8i+tV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/8A3yaPs8v/ADyf/vk0AR0VJ9nl/wCeT/8AfJo+zy/88n/75NAE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\nhy3s9SQyNCyySNFbmTO8wxFjHEWyQSi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   ],
   "source": [
    "# Plot Happiness Score for top 50 Countries\n",
    "\n",
    "require(repr)\n",
    "options(repr.plot.width = 8, repr.plot.height = 10)\n",
    "\n",
    "library(ggplot2)\n",
    "\n",
    "ggplot(aes(HappScore_2017, reorder(Country,HappScore_2017)), data=top50) +\n",
    "    geom_point() +\n",
    "    xlab(\"Happiness Score\") +\n",
    "    ylab(\"Country\") +\n",
    "    ggtitle(\"Happiness Score for Top 50 countries for 2017\")+\n",
    "    theme(plot.title = element_text(hjust = 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 6 places were occupied by European countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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EHW8XPXa/4Xj+l9DCoqjj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\nm/76/wDr0AZ9FaHnab/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y3Wy+wXOxpvK+XeTjPXjFAGHRWht0z+/cfp/hRt0z+/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\nVv8A981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OlocEWkGAf3koPLdFQnqTgc/wDFXxt8NfFGneB9K0R9KvPBNit1bxz3\nlzcx6NFNGkIWB4oULTTBZAyKzKMFjliePrmilbS39f0v63G3rp/Xf7z4j+E2m6d478TfDDw5\nrsb6jaafJ4ptptOuo54kEQlt3gieKUl/LCNGyo5OAEzyKv8Ahbw3Y+HfCPwn8R2KSxa6/wAQ\nZNGOoPO7zfYRc3kAttzE/uvLiQbOnGcZJr7NpksMc4USRrIFYOAwzgg5B+oNaRlyzhLpHfz1\nvd/l8zKEOWMk9btv00en9dj4Rkt/DS+HPE6aUmqR/Gk+Kr5vD/li4E3OottMP/LP7Pjf5mfl\nz5m7nFXNYv8AR3EumeIba2OlSeMPEs/2jW7m4j0oyi7CqjxQKWmmwxKKWUAB8ZJ4+5aKypr2\ncFDskvut/kU43afm399/w1Pn39nQ6l4n/Z/1zw/p2tTaNrdrdanp1revay79NLSSG3YQznft\nRHjZUdshcAmul+Fvgfxd8OdPvrbxz45l+JF3cyrJbXU9itkbZAMFAFZt2TzmvXawfEf+vh/3\nT/Oq7BFcv4/iyv8Ab7P/AJ8F/wC/h/wo+32f/Pgv/fw/4Vn0UFGh9vs/+fBf+/h/wo+32f8A\nz4L/AN/D/hWfRQBofb7P/nwX/v4f8KPt9n/z4L/38P8AhWfRQBrXd5apdSq1kHYMQW34z+lQ\n/b7P/nwX/v4f8Kr6h/x/XH++f51XoA0Pt9n/AM+C/wDfw/4Ufb7P/nwX/v4f8Kz6KAND7fZ/\n8+C/9/D/AIUfb7P/AJ8F/wC/h/wrPooA0Pt9n/z4L/38P+FH2+z/AOfBf+/h/wAKz6KAND7f\nZ/8APgv/AH8P+FH2+z/58F/7+H/Cs+igDbhurc6dO4tAqBhlN/X8aqfb7P8A58F/7+H/AAot\n/wDkD3f++v8AMVn0AaH2+z/58F/7+H/Cj7fZ/wDPgv8A38P+FZ9FAGh9vs/+fBf+/h/wo+32\nf/Pgv/fw/wCFZ9FAGh9vs/8AnwX/AL+H/Cj7fZ/8+C/9/D/hWfRQBofb7P8A58F/7+H/AAo+\n32f/AD4L/wB/D/hWfRQBofb7P/nwX/v4f8Kmu7y1S5lVrIOwY5bfjP6Vk1Y1D/j9n/3jQBY+\n32f/AD4L/wB/D/hR9vs/+fBf+/h/wrPooA0Pt9n/AM+C/wDfw/4Ufb7P/nwX/v4f8Kz6KAND\n7fZ/8+C/9/D/AIUfb7P/AJ8F/wC/h/wrPooA0Pt9n/z4L/38P+FH2+z/AOfBf+/h/wAKz6KA\nND7fZ/8APgv/AH8P+FW4bq3bT7hxaBUUruTf97n1rErQtv8AkEXn+8v86AD7fZ/8+C/9/D/h\nR9vs/wDnwX/v4f8ACs+igDQ+32f/AD4L/wB/D/hR9vs/+fBf+/h/wrPooA0Pt9n/AM+C/wDf\nw/4Ufb7P/nwX/v4f8Kz6KAND7fZ/8+C/9/D/AIUfb7P/AJ8F/wC/h/wrPooA0Pt9n/z4L/38\nP+FH2+z/AOfBf+/h/wAKz6KANa7vLVLmRWsg7A8tvxn9Kh+32f8Az4L/AN/D/hVfUP8Aj9m/\n3qr0AaH2+z/58F/7+H/Cj7fZ/wDPgv8A38P+FZ9FAGh9vs/+fBf+/h/wo+32f/Pgv/fw/wCF\nZ9FAGh9vs/8AnwX/AL+H/Cj7fZ/8+C/9/D/hWfRQBofb7P8A58F/7+H/AAo+32f/AD4L/wB/\nD/hWfRQBuQXVu2n3Li1CopXcm/73NU/t9n/z4L/38P8AhRa/8gi9+q/zrPoA0Pt9n/z4L/38\nP+FH2+z/AOfBf+/h/wAKz6KAND7fZ/8APgv/AH8P+FH2+z/58F/7+H/Cs+igDQ+32f8Az4L/\nAN/D/hR9vs/+fBf+/h/wrPooA0Pt9n/z4L/38P8AhR9vs/8AnwX/AL+H/Cs+igDQ+32f/Pgv\n/fw/4VNd3lqlw4ayDsDy2/r+lZNWNQ/4/Jfr/SgCx9vs/wDnwX/v4f8ACj7fZ/8APgv/AH8P\n+FZ9FAGh9vs/+fBf+/h/wo+32f8Az4L/AN/D/hWfRQBofb7P/nwX/v4f8KPt9n/z4L/38P8A\nhWfRQBofb7P/AJ8F/wC/h/wo+32f/Pgv/fw/4Vn0UAaH2+z/AOfBf+/h/wAK+Y/+CkHwu8V/\nGf8AZU1fSfAthNdajZ6la6hc6ZasWlvrdN4aNV/iIZkkC9T5fGTgH6MrQs/+QVe/8B/nQB+N\nH/BM39nz4l6Z+0/ofi3UPC2s+H/DeiR3X9o3GqWslok++CSJYFDgb23upIGdu3JwcZ/Zf7fZ\n/wDPgv8A38P+FZ9FAGh9vs/+fBf+/h/wo+32f/Pgv/fw/wCFZ9FAGh9vs/8AnwX/AL+H/Cj7\nfZ/8+C/9/D/hWfRQBofb7P8A58F/7+H/AAo+32f/AD4L/wB/D/hWfRQBofb7P/nwX/v4f8KP\nt9n/AM+C/wDfw/4Vn0UAa13eWqXDhrIOwx82/GeKh+32f/Pgv/fw/wCFV7//AI+5Pw/kKr0A\natte2r3MKrZBGLqA2/OOfpXQ1yFj/wAftv8A9dF/nXX0AFFFFABRRRQAUUUUAFFFFABRRRQA\nUUUUAFFFFABRRRQAUUUUAFFFFABXmPin4ut4Q+LT6HqRtrbwxbeFLrxFeXpikeeIwzohxtJy\noRmO0KWJAwe1enV4L8avhj4l8W+N/EeoaTpv2u0u/h5qmhQyefEm+9mkVo4sMwIyAfmPyjuR\nWcm1a39aM0jbllft+q/S517/ALR3w8j0OLWG18/2fPdvZW0iWVw73ciIruYI1jLyoA65dFK8\n9at6h8fPAOmeG9I16bxHA2m6s7pYtBFLNLcMhIkCxIpk+QjDfL8p64rhvHngzxtBa/D6PQ9K\nubnT9L0prS8tdFvbWyvoZtkSqBcSg4hwjhhEwJIU/MABXEfDT4X/ABK+FviGy8SHwmuvT241\nPTpdNGtxtJ5VzdrdJdRTS43HOY337XO0Ng5rR6SaW2uvp/mYRk3FNrV2/L9D6E8JfFXwn458\nNX3iHRdctrrQ7GSSK51B90MMRjUM5LSBRtAYEt09+Kgj8V6J42Bn8O6xp+vwwfJLJpd0lysb\nHkBihIBx2NeYfs06GninwD8T9J8S6VbpHqXi7WINR0uOQyQqJNokhDgDcvzFcgDPXiu68KfC\n7wr8Gbeey8FaLb+HrW9cTXEVqWIkcDAJ3E9q0qR5Wl5J/ekyl1+ZsfZJ/wDnjJ/3waPsk/8A\nzxk/74NS/wBq3f8Az3aj+1bv/nu1ZjIvsk//ADxk/wC+DR9kn/54yf8AfBqX+1bv/nu1H9q3\nf/PdqAIvsk//ADxk/wC+DR9kn/54yf8AfBqX+1bv/nu1H9q3f/PdqAHX9tM17ORE5Bc4IU1B\n9kn/AOeMn/fBq3e6lcx3cyrMwUOQBUP9q3f/AD3agCL7JP8A88ZP++DR9kn/AOeMn/fBqX+1\nbv8A57tR/at3/wA92oAi+yT/APPGT/vg0fZJ/wDnjJ/3wal/tW7/AOe7Uf2rd/8APdqAIvsk\n/wDzxk/74NH2Sf8A54yf98Gpf7Vu/wDnu1H9q3f/AD3agCL7JP8A88ZP++DR9kn/AOeMn/fB\nqX+1bv8A57tR/at3/wA92oAswW8o0m5UxuGLLgbTk81R+yT/APPGT/vg1pQ39w2mXEhlJdWU\nBvSqX9q3f/PdqAIvsk//ADxk/wC+DR9kn/54yf8AfBqX+1bv/nu1H9q3f/PdqAIvsk//ADxk\n/wC+DR9kn/54yf8AfBqX+1bv/nu1H9q3f/PdqAIvsk//ADxk/wC+DR9kn/54yf8AfBqX+1bv\n/nu1H9q3f/PdqAIvsk//ADxk/wC+DR9kn/54yf8AfBqX+1bv/nu1H9q3f/PdqAIvsk//ADxk\n/wC+DU99bTNeTEROQWOCFNN/tW7/AOe7VNe6lcx3cqrMwUMQBQBU+yT/APPGT/vg0fZJ/wDn\njJ/3wal/tW7/AOe7Uf2rd/8APdqAIvsk/wDzxk/74NH2Sf8A54yf98Gpf7Vu/wDnu1H9q3f/\nAD3agCL7JP8A88ZP++DR9kn/AOeMn/fBqX+1bv8A57tR/at3/wA92oAi+yT/APPGT/vg0fZJ\n/wDnjJ/3wal/tW7/AOe7Uf2rd/8APdqAIvsk/wDzxk/74NXre3lGlXamNwxZcDacnmq39q3f\n/PdquwX9w2mXUhlJdSoB9OaAM37JP/zxk/74NH2Sf/njJ/3wal/tW7/57tR/at3/AM92oAi+\nyT/88ZP++DR9kn/54yf98Gpf7Vu/+e7Uf2rd/wDPdqAIvsk//PGT/vg0fZJ/+eMn/fBqX+1b\nv/nu1H9q3f8Az3agCL7JP/zxk/74NH2Sf/njJ/3wal/tW7/57tR/at3/AM92oAi+yT/88ZP+\n+DR9kn/54yf98Gpf7Vu/+e7Uf2rd/wDPdqAHX1tM13KRE5BPUKag+yT/APPGT/vg1bvdSuY7\nqRVmYKDwKh/tW7/57tQBF9kn/wCeMn/fBo+yT/8APGT/AL4NS/2rd/8APdqP7Vu/+e7UARfZ\nJ/8AnjJ/3waPsk//ADxk/wC+DUv9q3f/AD3aj+1bv/nu1AEX2Sf/AJ4yf98Gj7JP/wA8ZP8A\nvg1L/at3/wA92o/tW7/57tQBF9kn/wCeMn/fBo+yT/8APGT/AL4NS/2rd/8APdqP7Vu/+e7U\nAWba3lGl3imNwxK4BU5PNUfsk/8Azxk/74NaVvf3D6bdSGUl0K7T6c1S/tW7/wCe7UARfZJ/\n+eMn/fBo+yT/APPGT/vg1L/at3/z3aj+1bv/AJ7tQBF9kn/54yf98Gj7JP8A88ZP++DUv9q3\nf/PdqP7Vu/8Anu1AEX2Sf/njJ/3waPsk/wDzxk/74NS/2rd/892o/tW7/wC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SIejAMp7ggggkHNfEv/AAUi/Yf+JHx0+K9n8QPANlb6/wCfp8NheaWbqK2mheMtiRDK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\nPeSvNc6DpGqtb2EjOcyYQDdGGOciNlHJxip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PrQs/+QVe/wDAf50faNP/AOfWT/vurdvLaGwuisDiMbdyluTzxQBiUVofaNP/AOfWT/vu\nj7Rp/wDz6yf990AZ9FaH2jT/APn1k/77o+0af/z6yf8AfdAGfRWh9o0//n1k/wC+6PtGn/8A\nPrJ/33QBn0VofaNP/wCfWT/vuj7Rp/8Az6yf990AZ9FaH2jT/wDn1k/77o+0af8A8+sn/fdA\nFe//AOPuT8P5Cq9a13NYrcOHt3ZuMkP7VD9o0/8A59ZP++6AK1j/AMftv/10X+ddfXO209ib\nmEJburl1wS/Q5roqACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAMHXvAXh\nnxTewXmteHNJ1e7gGIbi/sYp5I+/ysykj8K0n0ewk1G2v3srZr+2ieGC6aJTLFGxUuitjKqd\ni5A4O0egrifg/wDELUfiAfG/9ow2sP8AYfie90W2+yoy74YfL2M+5jlzvOSMDpwK5j4e/tHa\nVdfCLw14r8cX1npGoazPdwxWtjbzSGUw3Esf7uJfMkICIpY8gE84BAoatb+tynB6+X9foen2\nHgvw9pV3Fd2Wg6ZZ3UTzSxz29nGjo8xBmYMBkFyqlj/FtGc4qtc/DfwleXt/eT+FtFnvL9dl\n5cSafC0lyMg4kYrlxkDrnpXLn9pD4cf8I9b62niaKfTri7lsbd7e2nlknmjAMgjjVC7hQQSy\nqV5HPNWp/j74CgsNDvP+EgSaHW455NOFvbTTPc+SyrKqoiFt6lwCmN3Xj5ThtNaMi6Wp1Nj4\nN0DTLyK7s9D020uoXnkjngtI0dHmIMzBgMgyFVLEfeIGc4qtc/D3wve6beafdeHdLu9PvLt7\n64tLizjkiluHOXlZGBBcnJJxkkn1rnr74/8AgDTfD+ma1P4ji+w6nLJDaLHBNJPK8ZxIvkKh\nkBQ/eyo28ZxkVx/gf9qPw7qPh7WNa8RatY2tj/wkV7pWjtp1vNNJe28OxkcRpvd22uCzKABk\ncDNUk5Jvt/mv1aBJLY9qsNPtdKsobOytobO0hUJFBbxhI41HQKo4A9hWT4j/ANfD/un+dS+G\nfGOi+MfDkGvaNqMN9pE6syXakquFJDZ3YKkEEEEAgg5qnq19balJG1pcRXSqMMYXDgH3xUvz\nBeRnUU7Y390/lRsb+6fypDG0U7Y390/lRsb+6fyoAbRTtjf3T+VGxv7p/KgCbUP+P64/3z/O\nq9WtQRjfT/Kfvnt71X2N/dP5UANop2xv7p/KjY390/lQA2inbG/un8qNjf3T+VADaKdsb+6f\nyo2N/dP5UANop2xv7p/KjY390/lQBet/+QPd/wC+v8xWfWjbo39j3Qwc717e9UNjf3T+VADa\nKdsb+6fyo2N/dP5UANop2xv7p/KjY390/lQA2inbG/un8qNjf3T+VADaKdsb+6fyo2N/dP5U\nANqxqH/H7P8A7xqHY390/lVjUEY3s3yn7x7UAVaKdsb+6fyo2N/dP5UANop2xv7p/KjY390/\nlQA2inbG/un8qNjf3T+VADaKdsb+6fyo2N/dP5UANrQtv+QRef7y/wA6o7G/un8qv2yt/ZF4\nMHO5e3vQBnUU7Y390/lRsb+6fyoAbRTtjf3T+VGxv7p/KgBtFO2N/dP5UbG/un8qAG0U7Y39\n0/lRsb+6fyoAbRTtjf3T+VGxv7p/KgCbUP8Aj9m/3qr1av0Y3kvyn73pVfY390/lQA2inbG/\nun8qNjf3T+VADaKdsb+6fyo2N/dP5UANop2xv7p/KjY390/lQA2inbG/un8qNjf3T+VAF61/\n5BF79V/nWfWlaq39k3gwc5Xt71n7G/un8qAG0U7Y390/lXhP7Vf7XvhH9k3QdLuvENtearqu\nrM62GlWAUSShNu92ZiAiDcozySWGAeSAD3SivBf2Uv2xfCH7WWk6q+gWl7pGs6SUN7pV/tLo\nj52SI6nDoSpGeCCOQMgn3zY390/lQA2inbG/un8qNjf3T+VADaKdsb+6fyo2N/dP5UANqxqH\n/H5L9f6VDsb+6fyqxfoxvJPlPX09qAKtFO2N/dP5UbG/un8qAG0U7Y390/lRsb+6fyoAbRTt\njf3T+VGxv7p/KgBtFO2N/dP5UbG/un8qAG1oWf8AyCr3/gP86o7G/un8q0LRWGl3owc/L296\nAM2inbG/un8qNjf3T+VADaKdsb+6fyo2N/dP5UANop2xv7p/KjY390/lQA2inbG/un8qNjf3\nT+VADaKdsb+6fyo2N/dP5UATX/8Ax9yfh/IVXq1foxu3+U9u3tVfY390/lQBLY/8ftv/ANdF\n/nXX1yNkjC9t/lP+sXt7111ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA\nHhGi6T8QPg9r/jW30LwZD4z0vxBrc+u2t7Hq0Nmbd51QPFMkgzhWThk3ZB6A8Vw+h+ENU/Z0\n1X4a7pNP8S61Hoeo6dcaS1+lj80t2l1JNbPNhG2M3lsCVYrtYA8gfV9ZXiLwponi6zS013R9\nP1q1R/MWDUbVLhFb+8FcEZ96T6eX+Vi+Z6+f/DnyP8OPDvjvWtFj17RtKa70m68Ra9cSxeGt\nStrecPJcqqmK7mXJtsxyKfK2s2FPIAFdl8H/AIQ+M/DnjbwXca7oS29rotz4hllvf7SS7z9s\neGSFgzESMT+8UsVySpJxuGfpWysrfTrSG1tIIrW1hQRxQQoESNQMBVUcAD0FTValyu67W+Vr\nHPKmppX73Plvwh8M/Hfw8+JOr+OLHwv/AMJAZ9V1y1OktfxW8q21zeJcQXcLOdh3BdrKSGwR\nxkEVl6H8LfinoMst7J4eaKG+17V9TnsPDuqWkF3CbloWi23Uq5EOFkVlj2MSFOCABX1xRT5n\n+Fvy/wAjSx4v8GPhbqFv8I/Efgnx1o/l2uoXmopMi6kbn7XbXTu7fvgRJnEjKWbaxILcZrU+\nHvwZ8Ifs/wBnd6f4E0o6LaahIJ7mNrmW43uo2g5lZiOOwxXqlYPiP/Xw/wC6f51HRLsKMVHb\nzf3u5V/tq8/57f8Ajo/wo/tq8/57f+Oj/CqNFBRe/tq8/wCe3/jo/wAKP7avP+e3/jo/wqjR\nQBe/tq8/57f+Oj/Cj+2rz/nt/wCOj/CqNFAGpeatdRXcyLLhVYgDaP8ACof7avP+e3/jo/wq\nHUP+P64/3z/Oq9AF7+2rz/nt/wCOj/Cj+2rz/nt/46P8Ko0UAXv7avP+e3/jo/wo/tq8/wCe\n3/jo/wAKo0UAXv7avP8Ant/46P8ACj+2rz/nt/46P8Ko0UAXv7avP+e3/jo/wo/tq8/57f8A\njo/wqjRQBtQ6lcvptxKZMyIygHaOKp/21ef89v8Ax0f4U63/AOQPd/76/wAxWfQBe/tq8/57\nf+Oj/Cj+2rz/AJ7f+Oj/AAqjRQBe/tq8/wCe3/jo/wAKP7avP+e3/jo/wqjRQBe/tq8/57f+\nOj/Cj+2rz/nt/wCOj/CqNFAF7+2rz/nt/wCOj/Cj+2rz/nt/46P8Ko0UAXv7avP+e3/jo/wq\na81a6iupUWXCqxAG0f4Vl1Y1D/j9n/3jQBN/bV5/z2/8dH+FH9tXn/Pb/wAdH+FUaKAL39tX\nn/Pb/wAdH+FH9tXn/Pb/AMdH+FUaKAL39tXn/Pb/AMdH+FH9tXn/AD2/8dH+FUaKAL39tXn/\nAD2/8dH+FH9tXn/Pb/x0f4VRooAvf21ef89v/HR/hVyDUrl9OuZTJmRCoU7Rxk1i1oW3/IIv\nP95f50AN/tq8/wCe3/jo/wAKP7avP+e3/jo/wqjRQBe/tq8/57f+Oj/Cj+2rz/nt/wCOj/Cq\nNFAF7+2rz/nt/wCOj/Cj+2rz/nt/46P8Ko0UAXv7avP+e3/jo/wo/tq8/wCe3/jo/wAKo0UA\nXv7avP8Ant/46P8ACj+2rz/nt/46P8Ko0UAal5q11FcyIsuFBwBtH+FQ/wBtXn/Pb/x0f4VD\nqH/H7N/vVXoAvf21ef8APb/x0f4Uf21ef89v/HR/hVGigC9/bV5/z2/8dH+FH9tXn/Pb/wAd\nH+FUaKAL39tXn/Pb/wAdH+FH9tXn/Pb/AMdH+FUaKAL39tXn/Pb/AMdH+FH9tXn/AD2/8dH+\nFUaKANq31K5fTrqVpMuhXado4yap/wBtXn/Pb/x0f4U61/5BF79V/nWfQBe/tq8/57f+Oj/C\nvjX/AIKH/sa6/wDtaWPhnV/DOrWVr4k0FZoBa6iTHBdQyFWwHVTtdSvGRg7jkjAr68ooA+Ov\n+Cd/7F3iH9k//hJPEHinVbK48R63DHaLaaaxkhtoFYsdzso3OzbegwAvU54+0/7avP8Ant/4\n6P8ACqNFAF7+2rz/AJ7f+Oj/AAo/tq8/57f+Oj/CqNFAF7+2rz/nt/46P8KP7avP+e3/AI6P\n8Ko0UAXv7avP+e3/AI6P8KmvNWuorl0WXCg8DaP8Ky6sah/x+S/X+lAE39tXn/Pb/wAdH+FH\n9tXn/Pb/AMdH+FUaKAL39tXn/Pb/AMdH+FH9tXn/AD2/8dH+FUaKAL39tXn/AD2/8dH+FH9t\nXn/Pb/x0f4VRooAvf21ef89v/HR/hR/bV5/z2/8AHR/hVGigC9/bV5/z2/8AHR/hVy21K5ks\nLqVpMum3ado4yaxa0LP/AJBV7/wH+dADf7avP+e3/jo/wo/tq8/57f8Ajo/wqjRQBe/tq8/5\n7f8Ajo/wo/tq8/57f+Oj/CqNFAF7+2rz/nt/46P8KP7avP8Ant/46P8ACqNFAF7+2rz/AJ7f\n+Oj/AAo/tq8/57f+Oj/CqNFAF7+2rz/nt/46P8KP7avP+e3/AI6P8Ko0UAal5q11FcOqy4UY\nwNo9PpUP9tXn/Pb/AMdH+FQ3/wDx9yfh/IVXoA07XVrqW6hRpcqzqCNo9fpXR1yFj/x+2/8A\n10X+ddfQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4P+17rd7onhXwQLTUNesIbzxZZWl5/\nwjc0sd7PbtFPvij8ohmJwMKM5IHFe8V558aPhdffFLSvD0Wma8nh3UNE1q31q3u5LH7WpkiW\nQKpj3pxmTOc/w4xzxUWlJN7AeI+D/ind+AvFXjCazk8ZXXh3SfCU+sHSPH8s0V5dXUUmc27T\nqZBHsyrkAqCycV618Rfjt/wgNtpcv9h/b/tvh3U9fx9r8vZ9khil8r7hzv8ANxu427c4OcDM\nuf2e9U8Y3t9d+PvGjeJZn0e80azWw0tNPS0S6ULNIAHkLuVVQMnAx0NZ2o/s0654jtlXxB8Q\nX1WeDw/qHh61K6RHDHFHcxLH5pVZMs42gn5gGwAAmCSLbXt/8l/9qZT53K8dv/2f/tjmvEn7\nRt14T8UeEvFnjO0uPCeg3mi6lcWujR3/ANoe+BNibYug2oJjvkAU52DJLAbsdH8Q/ijqnhHU\n/CWva3o99p+qQ+Hdc1mbQrHXs2RNvDE4inAhxM+HGHGBG27G8HNdnq3wQ0zxDq/hm41eWHUr\nHR9Cu9DksprbIuVnWFGkDbvkO2JhgAnEh5GOeSv/ANme+1XwzpOj33jN7xdK0TWNAtbqbT8y\nm3vI0jiMhEoDNCsYGQBv4+6ckqKUW1/it8+a35r/AIFtYtO1+rav/wCS3/Jmvpfx61E6lpNv\nrPgu70+PXNHudY0hLO7W7ublYESR4XiCrskKyJtAZgScZBpPhv8AH258Y+O4vCuseHbfQtQu\nbCTULdbXWodQZURkDJOsYBhf94CAcg4bnin/ABA+AA8ex+F7eXXJbK20jRNQ0WV7ePbLKLmC\nKLzFO7C7fJztOchsZ45p+B/2e9R8I+M9A8RS+KrSZ9Kt57L7Bp2gQWNtJbyBMgKjEq+6NCXJ\nIwuAq5NXNRUvd2/4f/gDTqcmu+n6X/U4n4661L/w0PpukXmoePhoh8LG7Fh4Gmu/NNx9rKiS\nSOA/d2kqWI67RnpXVab45uPhr4G0p9A0PxX4g/tW6uJWl8e6o9k1iqBVImmugWjDbcom07ss\neK9Cb4c7vjLH49/tD7ugNof9n+R1zcLN5vmbv9nbt29857Vi/Fb4NTfEPxHoOu2Ws2+m3+kx\nTwJHqOlx6lblZShLrE7KElGwYcHoSCCKyV0kv66nVNpu67L8kca/7WMN5oXhefS/D1vNqutv\nexi3v9bgtLSNrWURyKt0QUlLMQUCD5l54FdTqPxt1NovC+n6P4Kvb7xbrdnLftod7dx2n2GC\nIqsjzSkMMb3VV2qd2c8Vy9t+zDrOlaN/Ztj4+8+1+13U7WusaHBe2kyzuJG8y3LKhkD7ysih\ncByu3Fa2m/s4y+E9F8Jr4T8WXGi6/wCH7a5sl1O4sY7mO6guJfNkieDKgKJACgVhsAxzSV7K\n/wA/u6fP8DON9b/1r/l+O50nhz402Gs/DjxB4rutLvrGXw613Bq+koqz3EFxbAmWJNp2yHGC\npBwwZenQZPww+Muj/H6wvdQ0Gw1bTIdPlEEqa5afZXZmG4FBk5GO9dZ8MPh1B8NdAubJb6bV\ndQv72bU9R1GdVR7u6lbMkmxflQcABRwAoHPU6PiP/Xw/7p/nVu3T+u4yv/Ys3/PSH/vuj+xZ\nv+ekP/fdZ9FIDQ/sWb/npD/33R/Ys3/PSH/vus+igDQ/sWb/AJ6Q/wDfdH9izf8APSH/AL7r\nPooA1rzSZZbqVw8QDMSAW5qH+xZv+ekP/fdV9Q/4/rj/AHz/ADqvQBof2LN/z0h/77o/sWb/\nAJ6Q/wDfdZ9FAGh/Ys3/AD0h/wC+6P7Fm/56Q/8AfdZ9FAGh/Ys3/PSH/vuj+xZv+ekP/fdZ\n9FAGh/Ys3/PSH/vuj+xZv+ekP/fdZ9FAG3DpsiadPEXj3OwIIbiqn9izf89If++6Lf8A5A93\n/vr/ADFZ9AGh/Ys3/PSH/vuj+xZv+ekP/fdZ9FAGh/Ys3/PSH/vuj+xZv+ekP/fdZ9FAGh/Y\ns3/PSH/vuj+xZv8AnpD/AN91n0UAaH9izf8APSH/AL7o/sWb/npD/wB91n0UAaH9izf89If+\n+6mu9JlluZXDxAMxOC3NZNWNQ/4/Z/8AeNAFj+xZv+ekP/fdH9izf89If++6z6KAND+xZv8A\nnpD/AN90f2LN/wA9If8Avus+igDQ/sWb/npD/wB90f2LN/z0h/77rPooA0P7Fm/56Q/990f2\nLN/z0h/77rPooA0P7Fm/56Q/991bg02RNOuIi8e5ypBDccGsStC2/wCQRef7y/zoAP7Fm/56\nQ/8AfdH9izf89If++6z6KAND+xZv+ekP/fdH9izf89If++6z6KAND+xZv+ekP/fdH9izf89I\nf++6z6KAND+xZv8AnpD/AN90f2LN/wA9If8Avus+igDQ/sWb/npD/wB90f2LN/z0h/77rPoo\nA1rvSZZbmRxJEATnluah/sWb/npD/wB91X1D/j9m/wB6q9AGh/Ys3/PSH/vuj+xZv+ekP/fd\nZ9FAGh/Ys3/PSH/vuj+xZv8AnpD/AN91n0UAaH9izf8APSH/AL7o/sWb/npD/wB91n0UAaH9\nizf89If++6P7Fm/56Q/991n0UAbcGmyJp9zEXjLOVwQ3HB71+d//AAU0/bI8ffs5eJ/Dfgrw\nJcW+kX2oad/al1rDWyXLhDK8aRRiRSg5icsSpPK4xzn9BbX/AJBF79V/nXhP7Rv7JXw9/ai0\n/T4fGVndx32nbhaappk4huoVbG5MlWVlJAOGU4PTGTkA8h/4Jn/tU+Mf2o/C/i7TvGbWt1rf\nhqS1b+1YoVg+1RTiXAdEAQOphblQoIYcZBJ+1P7Fm/56Q/8AfdeQfAD9nDwP+zT4Tn0HwVYS\nwRXUonvLy8l825upAMAyPgDgcBVAUZOBkkn0+gDQ/sWb/npD/wB90f2LN/z0h/77rPooA0P7\nFm/56Q/990f2LN/z0h/77rPooA0P7Fm/56Q/990f2LN/z0h/77rPooA0P7Fm/wCekP8A33U1\n3pMstw7h4gCehbmsmrGof8fkv1/pQBY/sWb/AJ6Q/wDfdH9izf8APSH/AL7rPooA0P7Fm/56\nQ/8AfdH9izf89If++6z6KAND+xZv+ekP/fdH9izf89If++6z6KAND+xZv+ekP/fdH9izf89I\nf++6z6KAND+xZv8AnpD/AN91bt9NkjsLqIvGWfbghuBg96xK0LP/AJBV7/wH+dAB/Ys3/PSH\n/vuj+xZv+ekP/fdZ9FAGh/Ys3/PSH/vuj+xZv+ekP/fdZ9FAGh/Ys3/PSH/vuj+xZv8AnpD/\nAN91n0UAaH9izf8APSH/AL7o/sWb/npD/wB91n0UAaH9izf89If++6P7Fm/56Q/991n0UAa1\n3pMstw7B4gDjq3tUP9izf89If++6r3//AB9yfh/IVXoA1LbSZYrmFzJEQrqSA3PWuirkLH/j\n9t/+ui/zrr6ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDmvCPxH\n8N+PL3XrPQtUjv7vQr59O1GAI6PbzoSGUhgCRlWAYZU7TgnBqjoPxh8I+Jp/D8OmaqbqTXhd\nnTgLWZROLZik/LIAm1gR82M9s180eC9NvfAOp+P/AIsaHBLcvpfjfWrPxFp8PJvdL+0BmdV6\nGSAkyL0yu8ZxgVzOhfEfxH4M+C/w81Lwpd3LOdE8W3i28TN5Tsl6pSZ4/uuYld3AYHHPqauc\neSVvK7/8Bv8A1815jjrC/W6S+bt+H9bn3hRXxh4+8XX3w28T6PL4D+JOseOJZPDGr6lLaXmr\nC+jEscCGK4KDjBy7BD8o8v5QMnOa3jLxpo2karJZ+LjPBe+EdWv7gDxa+sTlo7QvFdQbLeP7\nIVkKj7wU78AblqWrfdf81/7ayL3/AK9P80fcNYPiP/Xw/wC6f51yPgjw5r+g/BqZ9G1mfXPF\n+paedQhvfEly80RvpLddoYDlIQ4X5EGAM45NUPhgvxJSwvf+Ftv4efUfNH2H/hFvN8vysfN5\nnm87t3THanKLi3F9AjJSipLqdLRWh/xLP+nj9KP+JZ/08fpUlGfRWh/xLP8Ap4/Sj/iWf9PH\n6UAZ9FaH/Es/6eP0o/4ln/Tx+lAFfUP+P64/3z/Oq9a13/Z/2qXzPP37ju24xmof+JZ/08fp\nQBn0Vof8Sz/p4/Sj/iWf9PH6UAZ9FaH/ABLP+nj9KP8AiWf9PH6UAZ9FaH/Es/6eP0o/4ln/\nAE8fpQBn0Vof8Sz/AKeP0o/4ln/Tx+lABb/8ge7/AN9f5is+tuH7F/Z0+3zvJ3DdnGc9sVU/\n4ln/AE8fpQBn0Vof8Sz/AKeP0o/4ln/Tx+lAGfRWh/xLP+nj9KP+JZ/08fpQBn0Vof8AEs/6\neP0o/wCJZ/08fpQBn0Vof8Sz/p4/Sj/iWf8ATx+lAGfVjUP+P2f/AHjVj/iWf9PH6VNd/wBn\n/aZfM8/fuOduMZoAyaK0P+JZ/wBPH6Uf8Sz/AKeP0oAz6K0P+JZ/08fpR/xLP+nj9KAM+itD\n/iWf9PH6Uf8AEs/6eP0oAz6K0P8AiWf9PH6Uf8Sz/p4/SgDPrQtv+QRef7y/zo/4ln/Tx+lW\n4fsX9nXG3zvKyu/OM9eMUAYlFaH/ABLP+nj9KP8AiWf9PH6UAZ9FaH/Es/6eP0o/4ln/AE8f\npQBn0Vof8Sz/AKeP0o/4ln/Tx+lAGfRWh/xLP+nj9KP+JZ/08fpQBn0Vof8AEs/6eP0o/wCJ\nZ/08fpQBX1D/AI/Zv96q9a13/Z/2mTzPP35524xUP/Es/wCnj9KAM+itD/iWf9PH6Uf8Sz/p\n4/SgDPorQ/4ln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Rt3Vwd9+z141S6uZL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\n/fFAGfRWh5Gnf8/Mn/fFHka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hxe6o98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\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   ],
   "source": [
    "# Plot Happiness Score for last 50 Countries\n",
    "require(repr)\n",
    "options(repr.plot.width = 8, repr.plot.height = 10)\n",
    "library(ggplot2)\n",
    "\n",
    "ggplot(aes(HappScore_2017,reorder(Country,HappScore_2017)), data=last50) +\n",
    "    geom_point() +\n",
    "    xlab(\"Happiness Score\") +\n",
    "    ylab(\"Country\") +\n",
    "    ggtitle(\"Happiness Score for last 50 countries for 2017\")+\n",
    "    theme(plot.title = element_text(hjust = 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most African Countries are seen in last 50 places in terms of happiness score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 155\n",
      "[1] 147\n"
     ]
    }
   ],
   "source": [
    "# omit na values in Total_data\n",
    "Total_data_noNA <- na.omit(Total_data)\n",
    "print(nrow(Total_data))\n",
    "print(nrow(Total_data_noNA))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing happiness score among India, China, Bhutan and Russia for the years 2015, 2016 & 2017\n",
    "\n",
    "Why did I add Bhutan to this comparison along with the other three giants (in terms of population)?  \n",
    "As Bhutan is known to the world as the happiest country which could act as a bench mark in happiness score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in library(reshape2): there is no package called ‘reshape2’\n",
     "output_type": "error",
     "traceback": [
      "Error in library(reshape2): there is no package called ‘reshape2’\nTraceback:\n",
      "1. library(reshape2)",
      "2. stop(txt, domain = NA)"
     ]
    }
   ],
   "source": [
    "# Unpivot Total_data with countries and their happiness scores\n",
    "library(reshape2)\n",
    "library(stringr)\n",
    "\n",
    "Happ <- Total_data_noNA[,c(1,3,16,28)]\n",
    "\n",
    "Happ_unpivot <- melt(Happ, id=c(\"Country\"))\n",
    "\n",
    "Happ_unpivot$Year <- str_sub(Happ_unpivot$variable, start=-4)\n",
    "print(head(Happ_unpivot[order(Happ_unpivot$Country),]),row.names=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter only India, China, Bhutan and Russia\n",
    "\n",
    "library(dplyr)\n",
    "\n",
    "happ_filter <- Happ_unpivot %>%\n",
    "                dplyr::filter(Happ_unpivot$Country=='India' |Happ_unpivot$Country=='China'| Happ_unpivot$Country=='Bhutan'| Happ_unpivot$Country=='Russia')\n",
    "\n",
    "head(happ_filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just curious to compare India, China, Bhutan and Russia's happiness scores over years on a line chart\n",
    "library(ggplot2)\n",
    "library(directlabels)\n",
    "\n",
    "ggplot(aes(x=Year,y=value,group=Country,color=Country), data=happ_filter) +\n",
    "    geom_line() +\n",
    "    geom_point() +\n",
    "    scale_y_continuous(limits=c(0,6), breaks=seq(0,6,0.5)) +\n",
    "    ylab(\"happiness score\") +\n",
    "    geom_dl(aes(label=Country), method=\"last.points\", cex=0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Clearly **Russia** is doing much better than the other three countries. \n",
    "- Though **Bhutan** is known as the happiest country, its happiness score is slipping every year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing GDP among India, China, Bhutan and Russia for the years 2015, 2016 & 2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is GDP?\n",
    "\n",
    "https://www.thebalance.com/what-is-gdp-definition-of-gross-domestic-product-3306038\n",
    "\n",
    "**Definition:** Gross domestic product is the best way to measure a country's economy. GDP is the total value of everything produced by all the people and companies in the country. It doesn't matter if they are citizens or foreign-owned companies. If they are located within the country's boundaries, the government counts their production as GDP. \n",
    "\n",
    "**How to Calculate Gross Domestic Product:**\n",
    "\n",
    "The components of GDP are:\n",
    "\n",
    "***Personal Consumption Expenditures plus Business Investment plus Government Spending plus (Exports minus Imports).***\n",
    "\n",
    "Now that you know what the components are, it's easy to calculate a country's gross domestic product using this standard formula: C + I + G + (X-M)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unpivot Total_data with countries and their GDP\n",
    "library(reshape2)\n",
    "library(stringr)\n",
    "\n",
    "GDP <- Total_data_noNA[,c(1,6,19,30)]\n",
    "\n",
    "GDP_unpivot <- melt(GDP, id=c(\"Country\"))\n",
    "#print(Happ_unpivot[order(Happ_unpivot$Country),], row.names=FALSE)\n",
    "GDP_unpivot$Year <- str_sub(GDP_unpivot$variable, start=-4)\n",
    "print(head(GDP_unpivot[order(GDP_unpivot$Country),]),row.names=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter only India, China, Bhutan and Russia GDP data\n",
    "\n",
    "library(dplyr)\n",
    "\n",
    "GDP_filter <- GDP_unpivot %>%\n",
    "                dplyr::filter(GDP_unpivot$Country=='India' |GDP_unpivot$Country=='China'| GDP_unpivot$Country=='Bhutan'| GDP_unpivot$Country=='Russia')\n",
    "\n",
    "head(GDP_filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Comparing India, China, Bhutan and Russia's GDP over years on a line chart\n",
    "library(ggplot2)\n",
    "library(directlabels)\n",
    "\n",
    "ggplot(aes(x=Year,y=value,group=Country,color=Country), data=GDP_filter) +\n",
    "    geom_line() +\n",
    "    geom_point() +\n",
    "    scale_y_continuous(limits=c(0,1.5), breaks=seq(0,1.5,0.1)) +\n",
    "    ylab(\"GDP\") +\n",
    "    geom_dl(aes(label=Country), method=\"last.points\", cex=0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- What's interesting is **India is doing worse than Bhutan in GDP** taking into account its huge population. Is this because of recent GST launch in India? Need to investigate more on this data point.\n",
    "- The positive point here is all the four countries GDP is on the rise with Russia being a clear winner compared to the other three."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Generosity among India, China, Bhutan, United States and Russia for the years 2015, 2016 & 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unpivot Total_data with countries and their Generosity\n",
    "library(reshape2)\n",
    "library(stringr)\n",
    "\n",
    "Generosity <- Total_data_noNA[,c(1,10,24,35)]\n",
    "\n",
    "Gen_unpivot <- melt(Generosity, id=c(\"Country\"))\n",
    "Gen_unpivot$Year <- str_sub(Gen_unpivot$variable, start=-4)\n",
    "print(head(Gen_unpivot[order(Gen_unpivot$Country),]),row.names=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter only India, China, Bhutan, United States and Russia Generosity data\n",
    "\n",
    "library(dplyr)\n",
    "\n",
    "Gen_filter <- Gen_unpivot %>%\n",
    "                dplyr::filter(Gen_unpivot$Country=='India' |Gen_unpivot$Country=='China'| Gen_unpivot$Country=='Bhutan'| Gen_unpivot$Country=='Russia' | Gen_unpivot$Country=='United States')\n",
    "\n",
    "head(Gen_filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Comparing India, China, Bhutan, United States and Russia's Generosity over years on a line chart\n",
    "library(ggplot2)\n",
    "library(directlabels)\n",
    "\n",
    "ggplot(aes(x=Year,y=value,group=Country,color=Country), data=Gen_filter) +\n",
    "    geom_line() +\n",
    "    geom_point() +\n",
    "    scale_y_continuous(limits=c(0,1), breaks=seq(0,1,0.1)) +\n",
    "    ylab(\"Generosity\") +\n",
    "    geom_dl(aes(label=Country), method=\"last.points\", cex=0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- In terms of Generosity, **Bhutan is clearly on top followed by United States and India.** \n",
    "- **China's** score decreasing whereas **Russia** is improving."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing TrustGovt among India, China, Bhutan, United States and Russia for the years 2015, 2016 & 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unpivot Total_data with countries and TrustGovt\n",
    "library(reshape2)\n",
    "library(stringr)\n",
    "\n",
    "TrustGovt <- Total_data_noNA[,c(1,11,23,34)]\n",
    "\n",
    "TG_unpivot <- melt(TrustGovt, id=c(\"Country\"))\n",
    "TG_unpivot$Year <- str_sub(TG_unpivot$variable, start=-4)\n",
    "print(head(TG_unpivot[order(TG_unpivot$Country),]),row.names=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter only India, China, Bhutan, United States and Russia TrustGovt data\n",
    "\n",
    "library(dplyr)\n",
    "\n",
    "TG_filter <- TG_unpivot %>%\n",
    "                dplyr::filter(TG_unpivot$Country=='India' |TG_unpivot$Country=='China'| TG_unpivot$Country=='Bhutan'| TG_unpivot$Country=='Russia' | TG_unpivot$Country=='United States')\n",
    "\n",
    "head(TG_filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Comparing India, China, Bhutan, United States and Russia's TrustGovt over years on a line chart\n",
    "library(ggplot2)\n",
    "library(directlabels)\n",
    "\n",
    "ggplot(aes(x=Year,y=value,group=Country,color=Country), data=TG_filter) +\n",
    "    geom_line() +\n",
    "    geom_point() +\n",
    "    scale_y_continuous(limits=c(0,0.2), breaks=seq(0,0.2,0.01)) +\n",
    "    ylab(\"TrustGovt\") +\n",
    "    geom_dl(aes(label=Country), method=\"last.points\", cex=0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **India** and **Bhutan** TrustGovt scores improving where as **United States** score is declining. India's improved score is due to Narendra Modi's (Prime minister) policies?\n",
    "- **Bhutan** is clearly the winner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Freedom among India, China, Bhutan, United States and Russia for the years 2015, 2016 & 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unpivot Total_data with countries and Freedom\n",
    "library(reshape2)\n",
    "library(stringr)\n",
    "\n",
    "Freedom <- Total_data_noNA[,c(1,9,22,33)]\n",
    "\n",
    "Free_unpivot <- melt(Freedom, id=c(\"Country\"))\n",
    "Free_unpivot$Year <- str_sub(Free_unpivot$variable, start=-4)\n",
    "print(head(Free_unpivot[order(Free_unpivot$Country),]),row.names=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter only India, China, Bhutan, United States and Russia Freedom data\n",
    "\n",
    "library(dplyr)\n",
    "\n",
    "Free_filter <- Free_unpivot %>%\n",
    "                dplyr::filter(Free_unpivot$Country=='India' |Free_unpivot$Country=='China'| Free_unpivot$Country=='Bhutan'| Free_unpivot$Country=='Russia' | Free_unpivot$Country=='United States')\n",
    "\n",
    "head(Free_filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Comparing India, China, Bhutan, United States and Russia's TrustGovt over years on a line chart\n",
    "library(ggplot2)\n",
    "library(directlabels)\n",
    "\n",
    "ggplot(aes(x=Year,y=value,group=Country,color=Country), data=Free_filter) +\n",
    "    geom_line() +\n",
    "    geom_point() +\n",
    "    scale_y_continuous(limits=c(0,1), breaks=seq(0,1,0.1)) +\n",
    "    ylab(\"Freedom Score\") +\n",
    "    geom_dl(aes(label=Country), method=\"last.points\", cex=0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This is interesting. Except India, the rest of the countries freedom score declines in 2016 and improved in 2017.\n",
    "- India freedom score is consistent in 2016 and increased considerably in 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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