Introduction

This project is based on the real-life data from World Happiness Report, year 2015, 2016, and 2017. The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012 and made a huge impact worldwide. The most up-to-date report, 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 consistently gains global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions.

Data was found at Sustainable Development Solutions Network. Data is collected from people in over 150 countries. Each variable measured reveals a populated-weighted average score on a scale running from 0 to 10 that is tracked over time and compared against other countries. Variables in the data set include - GDP per capita, family, healthy life expectancy, freedom to make life choices, generosity, and perceptions of corruption.

I genuinely care about this topic because I want to develop my global mindset, especially from a humanity aspect. I consider myself living in a happy life, and I am very grateful. However, I was shocked to see my home country’s rank was below average, and the expert team of the report even include a chapter discussing the dropping happiness in China. This made me realized that my country may be fast in economic growth, building infrastructure, and gaining global recognition - I am proud of all the achievements - but did we go too fast? I believe some of us need to pause and take a look back. And I am doing this EDA (Exploratory Data Analysis) to deepen and reinforce my understanding.

To my audience, I hope this topic interests you and my analysis convey some insights you might find meaningful. This project is not just about data analysis, it aims to bring extra considerations to the table - globalization, economics, sociology, psychology, politics, ethics, religions, etc. I hope this is a good start for further exploration.

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Packages Required

library(devtools) 
library(DT) 
library(tidyverse) 
library(plotly)
library(formattable) 
library(plyr)
library(corrplot)
library(rworldmap)
library(GGally)

devtools <- Include the session information

DT <- Interactive HTML display of data

tidyverse <- A set of packages includes readr, ggplot2, dplyr

readr <- Read rectangular text data

gglot2 <- Create elegant data visualizations using the grammar of graphics

dplyr <- A grammar of data manipulation

plotly <- Create interactive plots

formattable <- Apply formatting on vectors and data frames

plyr <- A split-apply-combine paradigm

corrplot <- Visualization of a correlation matrix

rworldmap <- For mapping global data

GGally <- Extend ggplot2 by adding several functions to reduce the complexity of combining geometric objects with transformed data

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Data Preparation

Data Importing

Source of data: Sustainable Development Solutions Network

This source contains three years of data, 2015, 2016, and 2017.

readr package allows us to read csv data. First, I imported three original data sets, and named them with Original.

Year2015_Original <- read_csv("2015.csv")
Year2016_Original <- read_csv("2016.csv")
Year2017_Original <- read_csv("2017.csv")

After importing the original data sets, we can read column names, number of observations and variables as following. We can see that each year has different format, so the next step is to clean each data set into tidy data.

colnames(Year2015_Original)
##  [1] "Country"                       "Region"                       
##  [3] "Happiness Rank"                "Happiness Score"              
##  [5] "Standard Error"                "Economy (GDP per Capita)"     
##  [7] "Family"                        "Health (Life Expectancy)"     
##  [9] "Freedom"                       "Trust (Government Corruption)"
## [11] "Generosity"                    "Dystopia Residual"
dim(Year2015_Original)
## [1] 158  12
colnames(Year2016_Original)
##  [1] "Country"                       "Region"                       
##  [3] "Happiness Rank"                "Happiness Score"              
##  [5] "Lower Confidence Interval"     "Upper Confidence Interval"    
##  [7] "Economy (GDP per Capita)"      "Family"                       
##  [9] "Health (Life Expectancy)"      "Freedom"                      
## [11] "Trust (Government Corruption)" "Generosity"                   
## [13] "Dystopia Residual"
dim(Year2016_Original)
## [1] 157  13
colnames(Year2017_Original)
##  [1] "Country"                       "Happiness.Rank"               
##  [3] "Happiness.Score"               "Whisker.high"                 
##  [5] "Whisker.low"                   "Economy..GDP.per.Capita."     
##  [7] "Family"                        "Health..Life.Expectancy."     
##  [9] "Freedom"                       "Generosity"                   
## [11] "Trust..Government.Corruption." "Dystopia.Residual"
dim(Year2017_Original)
## [1] 155  12

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Data Cleaning

To make my data tidy, here are several things need to be addressed in the data cleaning process:

  • Data set for year 2017 does not have a Region column
  • There are a few columns that are less relevant to this analysis
  • Data set for year 2017 does not have same variable names as year 2016 and 2015
  • Three data sets do not have accordant variable order

Firstly, select the Region column from year 2016, left_join to year 2017 by Country, and sort(select) columns to make the Region column next to the Country column.

region <- Year2016_Original %>%
  select(Country, Region)

Year2017 <- Year2017_Original %>% 
  left_join(region, by = "Country") %>%
  select(Country, Region, everything())

Secondly, deselect irrelevant variables in each data set.

Year2015 <-
  select(Year2015_Original, -(`Standard Error`))

Year2016 <- 
  select(Year2016_Original, -(`Lower Confidence Interval`:`Upper Confidence Interval`))

Year2017 <-
  select(Year2017, -(`Whisker.high`:`Whisker.low`))

Thirdly, rename the year2017 data set to make it consistent with other two data sets.

Year2017 <- 
  rename(Year2017, c("Happiness.Rank"="Happiness Rank",
                     "Happiness.Score"="Happiness Score",
                     "Economy..GDP.per.Capita."="Economy (GDP per Capita)",
                     "Health..Life.Expectancy."="Health (Life Expectancy)",
                     "Trust..Government.Corruption."="Trust (Government Corruption)",
                     "Dystopia.Residual"="Dystopia Residual"))

Last but not least, reorder(select) year 2017 data set.

Year2017 <-
  select(Year2017, Country, Region, `Happiness Rank`, `Happiness Score`, 
         `Economy (GDP per Capita)`, `Family`, `Health (Life Expectancy)`,
         `Freedom`, `Trust (Government Corruption)`, `Generosity`, `Dystopia Residual`)

At this point, all three data sets have 11 variables, with slightly different numbers of observations (because every year the report conducts surveys in different number of countries, but the difference is very minimal). Data frames are completely tidy now.

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Data Preview

2015

Year2015 %>%
  arrange(desc(`Happiness Score`)) %>%
  mutate_each(funs(round(., 2)), -c(Country, Region)) %>%
  head(158) %>%
  formattable(list(
    'Happiness Score' = color_bar("lightpink"),
    'Economy (GDP per Capita)' = color_bar("paleturquoise"),
    'Family' = color_bar("powderblue"),
    'Health (Life Expectancy)' = color_bar("palegreen"),
    'Freedom' = color_bar("lightgreen"),
    'Generosity' = color_bar("thistle"),
    'Trust (Government Corruption)' = color_bar("lavender"),
    'Generosity' = color_bar("thistle"),
    'Dystopia Residual' = color_bar("navajowhite")
  ), align = "l")
Country Region Happiness Rank Happiness Score Economy (GDP per Capita) Family Health (Life Expectancy) Freedom Trust (Government Corruption) Generosity Dystopia Residual
Switzerland Western Europe 1 7.59 1.40 1.35 0.94 0.67 0.42 0.30 2.52
Iceland Western Europe 2 7.56 1.30 1.40 0.95 0.63 0.14 0.44 2.70
Denmark Western Europe 3 7.53 1.33 1.36 0.87 0.65 0.48 0.34 2.49
Norway Western Europe 4 7.52 1.46 1.33 0.89 0.67 0.37 0.35 2.47
Canada North America 5 7.43 1.33 1.32 0.91 0.63 0.33 0.46 2.45
Finland Western Europe 6 7.41 1.29 1.32 0.89 0.64 0.41 0.23 2.62
Netherlands Western Europe 7 7.38 1.33 1.28 0.89 0.62 0.32 0.48 2.47
Sweden Western Europe 8 7.36 1.33 1.29 0.91 0.66 0.44 0.36 2.37
New Zealand Australia and New Zealand 9 7.29 1.25 1.32 0.91 0.64 0.43 0.48 2.26
Australia Australia and New Zealand 10 7.28 1.33 1.31 0.93 0.65 0.36 0.44 2.27
Israel Middle East and Northern Africa 11 7.28 1.23 1.22 0.91 0.41 0.08 0.33 3.09
Costa Rica Latin America and Caribbean 12 7.23 0.96 1.24 0.86 0.63 0.11 0.25 3.18
Austria Western Europe 13 7.20 1.34 1.30 0.89 0.62 0.19 0.33 2.53
Mexico Latin America and Caribbean 14 7.19 1.02 0.91 0.81 0.48 0.21 0.14 3.60
United States North America 15 7.12 1.39 1.25 0.86 0.55 0.16 0.40 2.51
Brazil Latin America and Caribbean 16 6.98 0.98 1.23 0.70 0.49 0.18 0.15 3.26
Luxembourg Western Europe 17 6.95 1.56 1.22 0.92 0.62 0.38 0.28 1.97
Ireland Western Europe 18 6.94 1.34 1.37 0.90 0.62 0.29 0.46 1.98
Belgium Western Europe 19 6.94 1.31 1.29 0.90 0.58 0.23 0.22 2.41
United Arab Emirates Middle East and Northern Africa 20 6.90 1.43 1.13 0.81 0.64 0.39 0.26 2.25
United Kingdom Western Europe 21 6.87 1.27 1.29 0.91 0.60 0.32 0.52 1.97
Oman Middle East and Northern Africa 22 6.85 1.36 1.08 0.76 0.63 0.33 0.22 2.47
Venezuela Latin America and Caribbean 23 6.81 1.04 1.26 0.72 0.43 0.11 0.06 3.19
Singapore Southeastern Asia 24 6.80 1.52 1.02 1.03 0.54 0.49 0.31 1.89
Panama Latin America and Caribbean 25 6.79 1.06 1.20 0.80 0.54 0.09 0.24 2.85
Germany Western Europe 26 6.75 1.33 1.30 0.89 0.61 0.22 0.28 2.12
Chile Latin America and Caribbean 27 6.67 1.11 1.12 0.86 0.44 0.13 0.33 2.68
Qatar Middle East and Northern Africa 28 6.61 1.69 1.08 0.80 0.64 0.52 0.33 1.56
France Western Europe 29 6.58 1.28 1.26 0.95 0.55 0.21 0.12 2.21
Argentina Latin America and Caribbean 30 6.57 1.05 1.25 0.79 0.45 0.08 0.11 2.84
Czech Republic Central and Eastern Europe 31 6.50 1.18 1.21 0.84 0.46 0.03 0.11 2.68
Uruguay Latin America and Caribbean 32 6.49 1.06 1.21 0.81 0.60 0.25 0.23 2.32
Colombia Latin America and Caribbean 33 6.48 0.92 1.24 0.69 0.53 0.05 0.18 2.86
Thailand Southeastern Asia 34 6.46 0.97 1.27 0.74 0.56 0.03 0.58 2.32
Saudi Arabia Middle East and Northern Africa 35 6.41 1.40 1.08 0.72 0.31 0.33 0.14 2.44
Spain Western Europe 36 6.33 1.23 1.31 0.96 0.46 0.06 0.18 2.12
Malta Western Europe 37 6.30 1.21 1.30 0.89 0.60 0.14 0.52 1.65
Taiwan Eastern Asia 38 6.30 1.29 1.08 0.88 0.40 0.08 0.25 2.32
Kuwait Middle East and Northern Africa 39 6.29 1.55 1.17 0.72 0.55 0.26 0.16 1.88
Suriname Latin America and Caribbean 40 6.27 1.00 0.97 0.61 0.60 0.14 0.17 2.79
Trinidad and Tobago Latin America and Caribbean 41 6.17 1.21 1.18 0.61 0.56 0.01 0.32 2.27
El Salvador Latin America and Caribbean 42 6.13 0.76 1.03 0.68 0.40 0.12 0.11 3.04
Guatemala Latin America and Caribbean 43 6.12 0.75 1.04 0.64 0.58 0.09 0.27 2.74
Uzbekistan Central and Eastern Europe 44 6.00 0.63 1.34 0.60 0.66 0.31 0.23 2.24
Slovakia Central and Eastern Europe 45 6.00 1.17 1.27 0.79 0.32 0.03 0.17 2.25
Japan Eastern Asia 46 5.99 1.27 1.26 0.99 0.50 0.18 0.11 1.68
South Korea Eastern Asia 47 5.98 1.24 0.96 0.97 0.33 0.08 0.19 2.22
Ecuador Latin America and Caribbean 48 5.97 0.86 1.00 0.79 0.49 0.18 0.12 2.54
Bahrain Middle East and Northern Africa 49 5.96 1.32 1.22 0.75 0.45 0.31 0.17 1.74
Italy Western Europe 50 5.95 1.25 1.20 0.95 0.26 0.03 0.23 2.03
Bolivia Latin America and Caribbean 51 5.89 0.68 0.98 0.54 0.57 0.09 0.21 2.82
Moldova Central and Eastern Europe 52 5.89 0.59 1.02 0.62 0.33 0.02 0.21 3.11
Paraguay Latin America and Caribbean 53 5.88 0.76 1.30 0.66 0.54 0.08 0.34 2.19
Kazakhstan Central and Eastern Europe 54 5.86 1.12 1.12 0.64 0.52 0.08 0.12 2.25
Slovenia Central and Eastern Europe 55 5.85 1.18 1.27 0.87 0.61 0.04 0.25 1.62
Lithuania Central and Eastern Europe 56 5.83 1.15 1.26 0.73 0.21 0.01 0.03 2.45
Nicaragua Latin America and Caribbean 57 5.83 0.59 1.14 0.74 0.55 0.19 0.28 2.32
Peru Latin America and Caribbean 58 5.82 0.90 0.97 0.73 0.41 0.06 0.15 2.59
Belarus Central and Eastern Europe 59 5.81 1.03 1.23 0.74 0.38 0.19 0.11 2.13
Poland Central and Eastern Europe 60 5.79 1.13 1.28 0.78 0.53 0.04 0.17 1.87
Malaysia Southeastern Asia 61 5.77 1.12 1.07 0.72 0.53 0.11 0.33 1.89
Croatia Central and Eastern Europe 62 5.76 1.08 0.80 0.79 0.26 0.02 0.05 2.75
Libya Middle East and Northern Africa 63 5.75 1.13 1.12 0.70 0.42 0.11 0.18 2.09
Russia Central and Eastern Europe 64 5.72 1.14 1.24 0.67 0.37 0.03 0.00 2.27
Jamaica Latin America and Caribbean 65 5.71 0.81 1.15 0.69 0.50 0.02 0.21 2.32
North Cyprus Western Europe 66 5.70 1.21 1.07 0.92 0.49 0.14 0.26 1.60
Cyprus Western Europe 67 5.69 1.21 0.89 0.92 0.41 0.06 0.31 1.89
Algeria Middle East and Northern Africa 68 5.61 0.94 1.08 0.62 0.29 0.17 0.08 2.43
Kosovo Central and Eastern Europe 69 5.59 0.80 0.81 0.63 0.25 0.05 0.28 2.77
Turkmenistan Central and Eastern Europe 70 5.55 0.96 1.23 0.54 0.48 0.31 0.17 1.87
Mauritius Sub-Saharan Africa 71 5.48 1.01 0.99 0.71 0.56 0.08 0.38 1.76
Hong Kong Eastern Asia 72 5.47 1.39 1.06 1.01 0.60 0.37 0.39 0.65
Estonia Central and Eastern Europe 73 5.43 1.15 1.23 0.77 0.45 0.15 0.09 1.59
Indonesia Southeastern Asia 74 5.40 0.83 1.09 0.64 0.47 0.00 0.52 1.86
Vietnam Southeastern Asia 75 5.36 0.63 0.91 0.75 0.59 0.10 0.17 2.20
Turkey Middle East and Northern Africa 76 5.33 1.06 0.95 0.73 0.23 0.16 0.12 2.09
Kyrgyzstan Central and Eastern Europe 77 5.29 0.47 1.15 0.65 0.43 0.04 0.30 2.23
Nigeria Sub-Saharan Africa 78 5.27 0.65 0.90 0.16 0.34 0.04 0.27 2.89
Bhutan Southern Asia 79 5.25 0.77 1.10 0.57 0.53 0.15 0.48 1.64
Azerbaijan Central and Eastern Europe 80 5.21 1.02 0.94 0.64 0.37 0.16 0.08 2.00
Pakistan Southern Asia 81 5.19 0.60 0.41 0.51 0.12 0.10 0.34 3.11
Jordan Middle East and Northern Africa 82 5.19 0.90 1.05 0.70 0.41 0.14 0.11 1.88
Montenegro Central and Eastern Europe 82 5.19 0.97 0.91 0.73 0.18 0.14 0.16 2.10
China Eastern Asia 84 5.14 0.89 0.95 0.82 0.52 0.03 0.08 1.86
Zambia Sub-Saharan Africa 85 5.13 0.47 0.92 0.30 0.49 0.12 0.20 2.63
Romania Central and Eastern Europe 86 5.12 1.04 0.89 0.77 0.35 0.01 0.14 1.93
Serbia Central and Eastern Europe 87 5.12 0.92 1.01 0.75 0.20 0.03 0.19 2.02
Portugal Western Europe 88 5.10 1.16 1.14 0.88 0.51 0.01 0.14 1.26
Latvia Central and Eastern Europe 89 5.10 1.11 1.10 0.72 0.30 0.06 0.18 1.62
Philippines Southeastern Asia 90 5.07 0.71 1.04 0.58 0.63 0.12 0.25 1.75
Somaliland region Sub-Saharan Africa 91 5.06 0.19 0.95 0.44 0.47 0.40 0.50 2.11
Morocco Middle East and Northern Africa 92 5.01 0.73 0.64 0.61 0.42 0.09 0.07 2.45
Macedonia Central and Eastern Europe 93 5.01 0.92 1.00 0.74 0.33 0.05 0.22 1.74
Mozambique Sub-Saharan Africa 94 4.97 0.08 1.03 0.09 0.34 0.16 0.22 3.05
Albania Central and Eastern Europe 95 4.96 0.88 0.80 0.81 0.36 0.06 0.14 1.90
Bosnia and Herzegovina Central and Eastern Europe 96 4.95 0.83 0.92 0.79 0.09 0.00 0.25 2.06
Lesotho Sub-Saharan Africa 97 4.90 0.38 1.04 0.08 0.32 0.13 0.16 2.80
Dominican Republic Latin America and Caribbean 98 4.88 0.90 1.17 0.67 0.58 0.14 0.22 1.21
Laos Southeastern Asia 99 4.88 0.59 0.74 0.55 0.60 0.24 0.42 1.74
Mongolia Eastern Asia 100 4.87 0.83 1.30 0.60 0.44 0.03 0.33 1.35
Swaziland Sub-Saharan Africa 101 4.87 0.71 1.07 0.08 0.31 0.03 0.18 2.49
Greece Western Europe 102 4.86 1.15 0.93 0.88 0.08 0.01 0.00 1.80
Lebanon Middle East and Northern Africa 103 4.84 1.03 0.80 0.84 0.34 0.05 0.22 1.57
Hungary Central and Eastern Europe 104 4.80 1.12 1.20 0.76 0.32 0.03 0.13 1.24
Honduras Latin America and Caribbean 105 4.79 0.60 0.95 0.70 0.40 0.07 0.23 1.84
Tajikistan Central and Eastern Europe 106 4.79 0.39 0.86 0.57 0.47 0.15 0.23 2.11
Tunisia Middle East and Northern Africa 107 4.74 0.88 0.60 0.74 0.26 0.06 0.06 2.12
Palestinian Territories Middle East and Northern Africa 108 4.71 0.60 0.93 0.66 0.24 0.13 0.11 2.04
Bangladesh Southern Asia 109 4.69 0.40 0.43 0.60 0.41 0.13 0.21 2.52
Iran Middle East and Northern Africa 110 4.69 1.01 0.54 0.70 0.30 0.06 0.38 1.69
Ukraine Central and Eastern Europe 111 4.68 0.80 1.20 0.67 0.25 0.03 0.15 1.57
Iraq Middle East and Northern Africa 112 4.68 0.99 0.82 0.60 0.00 0.14 0.18 1.95
South Africa Sub-Saharan Africa 113 4.64 0.92 1.18 0.28 0.33 0.09 0.12 1.72
Ghana Sub-Saharan Africa 114 4.63 0.55 0.68 0.40 0.42 0.04 0.23 2.31
Zimbabwe Sub-Saharan Africa 115 4.61 0.27 1.03 0.33 0.26 0.08 0.19 2.44
Liberia Sub-Saharan Africa 116 4.57 0.07 0.79 0.34 0.29 0.06 0.24 2.78
India Southern Asia 117 4.57 0.64 0.38 0.52 0.40 0.08 0.26 2.28
Sudan Sub-Saharan Africa 118 4.55 0.52 1.01 0.37 0.10 0.15 0.19 2.21
Haiti Latin America and Caribbean 119 4.52 0.27 0.74 0.39 0.24 0.17 0.46 2.24
Congo (Kinshasa) Sub-Saharan Africa 120 4.52 0.00 1.00 0.10 0.23 0.08 0.25 2.87
Nepal Southern Asia 121 4.51 0.36 0.86 0.57 0.38 0.06 0.32 1.96
Ethiopia Sub-Saharan Africa 122 4.51 0.19 0.60 0.44 0.43 0.15 0.24 2.45
Sierra Leone Sub-Saharan Africa 123 4.51 0.33 0.96 0.00 0.41 0.09 0.21 2.51
Mauritania Sub-Saharan Africa 124 4.44 0.45 0.87 0.36 0.24 0.17 0.22 2.12
Kenya Sub-Saharan Africa 125 4.42 0.36 1.00 0.41 0.42 0.06 0.38 1.79
Djibouti Sub-Saharan Africa 126 4.37 0.44 0.59 0.36 0.46 0.28 0.18 2.05
Armenia Central and Eastern Europe 127 4.35 0.77 0.78 0.73 0.20 0.04 0.08 1.76
Botswana Sub-Saharan Africa 128 4.33 0.99 1.10 0.05 0.49 0.12 0.10 1.46
Myanmar Southeastern Asia 129 4.31 0.27 0.71 0.48 0.44 0.19 0.80 1.42
Georgia Central and Eastern Europe 130 4.30 0.74 0.39 0.73 0.41 0.38 0.06 1.60
Malawi Sub-Saharan Africa 131 4.29 0.02 0.41 0.23 0.43 0.07 0.33 2.81
Sri Lanka Southern Asia 132 4.27 0.84 1.02 0.71 0.54 0.09 0.41 0.67
Cameroon Sub-Saharan Africa 133 4.25 0.42 0.89 0.23 0.49 0.06 0.21 1.95
Bulgaria Central and Eastern Europe 134 4.22 1.01 1.11 0.77 0.31 0.01 0.12 0.90
Egypt Middle East and Northern Africa 135 4.19 0.88 0.75 0.62 0.17 0.06 0.11 1.60
Yemen Middle East and Northern Africa 136 4.08 0.55 0.68 0.40 0.36 0.08 0.09 1.92
Angola Sub-Saharan Africa 137 4.03 0.76 0.86 0.17 0.10 0.07 0.12 1.95
Mali Sub-Saharan Africa 138 4.00 0.26 1.04 0.21 0.39 0.12 0.19 1.79
Congo (Brazzaville) Sub-Saharan Africa 139 3.99 0.68 0.66 0.31 0.41 0.12 0.12 1.68
Comoros Sub-Saharan Africa 140 3.96 0.24 0.79 0.36 0.23 0.20 0.17 1.96
Uganda Sub-Saharan Africa 141 3.93 0.21 1.13 0.34 0.46 0.07 0.29 1.43
Senegal Sub-Saharan Africa 142 3.90 0.36 0.98 0.44 0.37 0.11 0.21 1.44
Gabon Sub-Saharan Africa 143 3.90 1.06 0.91 0.43 0.32 0.11 0.07 1.00
Niger Sub-Saharan Africa 144 3.85 0.07 0.77 0.30 0.48 0.16 0.19 1.88
Cambodia Southeastern Asia 145 3.82 0.46 0.63 0.61 0.66 0.07 0.40 0.98
Tanzania Sub-Saharan Africa 146 3.78 0.29 1.00 0.38 0.33 0.06 0.34 1.38
Madagascar Sub-Saharan Africa 147 3.68 0.21 0.67 0.47 0.19 0.08 0.21 1.85
Central African Republic Sub-Saharan Africa 148 3.68 0.08 0.00 0.07 0.49 0.08 0.24 2.72
Chad Sub-Saharan Africa 149 3.67 0.34 0.76 0.15 0.24 0.05 0.18 1.94
Guinea Sub-Saharan Africa 150 3.66 0.17 0.46 0.24 0.38 0.12 0.29 1.99
Ivory Coast Sub-Saharan Africa 151 3.65 0.47 0.77 0.15 0.47 0.18 0.20 1.42
Burkina Faso Sub-Saharan Africa 152 3.59 0.26 0.85 0.27 0.39 0.13 0.22 1.46
Afghanistan Southern Asia 153 3.58 0.32 0.30 0.30 0.23 0.10 0.37 1.95
Rwanda Sub-Saharan Africa 154 3.46 0.22 0.77 0.43 0.59 0.55 0.23 0.67
Benin Sub-Saharan Africa 155 3.34 0.29 0.35 0.32 0.48 0.08 0.18 1.63
Syria Middle East and Northern Africa 156 3.01 0.66 0.47 0.72 0.16 0.19 0.47 0.33
Burundi Sub-Saharan Africa 157 2.90 0.02 0.42 0.22 0.12 0.10 0.20 1.83
Togo Sub-Saharan Africa 158 2.84 0.21 0.14 0.28 0.36 0.11 0.17 1.57

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2016

Year2016 %>%
  arrange(desc(`Happiness Score`)) %>%
  mutate_each(funs(round(., 2)), -c(Country, Region)) %>%
  head(157) %>%
  formattable(list(
    'Happiness Score' = color_bar("lightpink"),
    'Economy (GDP per Capita)' = color_bar("paleturquoise"),
    'Family' = color_bar("powderblue"),
    'Health (Life Expectancy)' = color_bar("palegreen"),
    'Freedom' = color_bar("lightgreen"),
    'Trust (Government Corruption)' = color_bar("lavender"),
    'Generosity' = color_bar("thistle"),
    'Dystopia Residual' = color_bar("navajowhite")
  ), align = "l")
Country Region Happiness Rank Happiness Score Economy (GDP per Capita) Family Health (Life Expectancy) Freedom Trust (Government Corruption) Generosity Dystopia Residual
Denmark Western Europe 1 7.53 1.44 1.16 0.80 0.58 0.44 0.36 2.74
Switzerland Western Europe 2 7.51 1.53 1.15 0.86 0.59 0.41 0.28 2.69
Iceland Western Europe 3 7.50 1.43 1.18 0.87 0.57 0.15 0.48 2.83
Norway Western Europe 4 7.50 1.58 1.13 0.80 0.60 0.36 0.38 2.66
Finland Western Europe 5 7.41 1.41 1.13 0.81 0.57 0.41 0.25 2.83
Canada North America 6 7.40 1.44 1.10 0.83 0.57 0.31 0.45 2.70
Netherlands Western Europe 7 7.34 1.46 1.03 0.81 0.55 0.30 0.47 2.71
New Zealand Australia and New Zealand 8 7.33 1.36 1.17 0.83 0.58 0.42 0.49 2.48
Australia Australia and New Zealand 9 7.31 1.44 1.10 0.85 0.57 0.32 0.47 2.55
Sweden Western Europe 10 7.29 1.45 1.09 0.83 0.58 0.41 0.38 2.55
Israel Middle East and Northern Africa 11 7.27 1.34 1.00 0.85 0.36 0.09 0.32 3.31
Austria Western Europe 12 7.12 1.45 1.08 0.81 0.54 0.21 0.33 2.69
United States North America 13 7.10 1.51 1.05 0.78 0.48 0.15 0.41 2.73
Costa Rica Latin America and Caribbean 14 7.09 1.07 1.02 0.76 0.55 0.11 0.23 3.35
Puerto Rico Latin America and Caribbean 15 7.04 1.36 1.08 0.78 0.47 0.12 0.22 3.01
Germany Western Europe 16 6.99 1.45 1.10 0.81 0.53 0.29 0.30 2.51
Brazil Latin America and Caribbean 17 6.95 1.09 1.04 0.61 0.40 0.14 0.16 3.51
Belgium Western Europe 18 6.93 1.43 1.05 0.82 0.51 0.26 0.24 2.61
Ireland Western Europe 19 6.91 1.48 1.16 0.81 0.54 0.30 0.45 2.16
Luxembourg Western Europe 20 6.87 1.70 1.04 0.85 0.55 0.35 0.28 2.11
Mexico Latin America and Caribbean 21 6.78 1.12 0.71 0.71 0.38 0.18 0.12 3.56
Singapore Southeastern Asia 22 6.74 1.65 0.87 0.95 0.49 0.47 0.33 1.99
United Kingdom Western Europe 23 6.72 1.40 1.09 0.81 0.50 0.27 0.50 2.15
Chile Latin America and Caribbean 24 6.70 1.22 0.91 0.82 0.38 0.11 0.32 2.96
Panama Latin America and Caribbean 25 6.70 1.18 0.99 0.71 0.49 0.08 0.24 3.01
Argentina Latin America and Caribbean 26 6.65 1.15 1.07 0.70 0.42 0.07 0.11 3.13
Czech Republic Central and Eastern Europe 27 6.60 1.31 1.01 0.76 0.41 0.04 0.10 2.96
United Arab Emirates Middle East and Northern Africa 28 6.57 1.57 0.87 0.73 0.56 0.36 0.27 2.22
Uruguay Latin America and Caribbean 29 6.54 1.18 1.03 0.72 0.54 0.21 0.18 2.67
Malta Western Europe 30 6.49 1.31 1.10 0.80 0.55 0.18 0.56 1.99
Colombia Latin America and Caribbean 31 6.48 1.03 1.02 0.60 0.45 0.05 0.16 3.17
France Western Europe 32 6.48 1.39 1.01 0.84 0.47 0.18 0.12 2.47
Thailand Southeastern Asia 33 6.47 1.09 1.04 0.65 0.50 0.03 0.59 2.58
Saudi Arabia Middle East and Northern Africa 34 6.38 1.49 0.85 0.59 0.38 0.30 0.15 2.61
Taiwan Eastern Asia 34 6.38 1.40 0.93 0.80 0.32 0.07 0.25 2.62
Qatar Middle East and Northern Africa 36 6.38 1.82 0.88 0.72 0.57 0.48 0.32 1.58
Spain Western Europe 37 6.36 1.34 1.13 0.88 0.38 0.06 0.18 2.40
Algeria Middle East and Northern Africa 38 6.36 1.05 0.83 0.62 0.21 0.16 0.07 3.41
Guatemala Latin America and Caribbean 39 6.32 0.83 0.87 0.54 0.50 0.09 0.29 3.20
Suriname Latin America and Caribbean 40 6.27 1.10 0.78 0.51 0.52 0.13 0.17 3.07
Kuwait Middle East and Northern Africa 41 6.24 1.62 0.88 0.64 0.43 0.24 0.16 2.28
Bahrain Middle East and Northern Africa 42 6.22 1.44 0.94 0.66 0.47 0.26 0.17 2.27
Trinidad and Tobago Latin America and Caribbean 43 6.17 1.33 0.99 0.53 0.48 0.01 0.32 2.51
Venezuela Latin America and Caribbean 44 6.08 1.13 1.03 0.62 0.20 0.08 0.04 2.97
Slovakia Central and Eastern Europe 45 6.08 1.28 1.08 0.70 0.23 0.03 0.14 2.61
El Salvador Latin America and Caribbean 46 6.07 0.87 0.81 0.60 0.37 0.11 0.09 3.22
Malaysia Southeastern Asia 47 6.00 1.25 0.88 0.62 0.39 0.09 0.41 2.35
Nicaragua Latin America and Caribbean 48 5.99 0.69 0.90 0.65 0.47 0.16 0.30 2.82
Uzbekistan Central and Eastern Europe 49 5.99 0.74 1.17 0.50 0.61 0.28 0.34 2.35
Italy Western Europe 50 5.98 1.35 1.04 0.85 0.19 0.03 0.17 2.35
Ecuador Latin America and Caribbean 51 5.98 0.97 0.86 0.69 0.40 0.18 0.10 2.77
Belize Latin America and Caribbean 52 5.96 0.88 0.69 0.46 0.51 0.11 0.24 3.08
Japan Eastern Asia 53 5.92 1.38 1.06 0.91 0.47 0.19 0.10 1.81
Kazakhstan Central and Eastern Europe 54 5.92 1.23 0.96 0.57 0.41 0.11 0.15 2.49
Moldova Central and Eastern Europe 55 5.90 0.69 0.83 0.52 0.25 0.02 0.20 3.38
Russia Central and Eastern Europe 56 5.86 1.23 1.05 0.59 0.33 0.04 0.03 2.59
Poland Central and Eastern Europe 57 5.84 1.25 1.05 0.69 0.45 0.06 0.14 2.20
South Korea Eastern Asia 57 5.84 1.36 0.72 0.89 0.25 0.08 0.19 2.35
Bolivia Latin America and Caribbean 59 5.82 0.79 0.84 0.47 0.51 0.08 0.22 2.92
Lithuania Central and Eastern Europe 60 5.81 1.27 1.06 0.65 0.19 0.02 0.02 2.61
Belarus Central and Eastern Europe 61 5.80 1.13 1.05 0.63 0.29 0.17 0.14 2.39
North Cyprus Western Europe 62 5.77 1.31 0.82 0.84 0.44 0.17 0.26 1.93
Slovenia Central and Eastern Europe 63 5.77 1.30 1.06 0.79 0.53 0.04 0.26 1.80
Peru Latin America and Caribbean 64 5.74 1.00 0.81 0.63 0.38 0.05 0.15 2.73
Turkmenistan Central and Eastern Europe 65 5.66 1.08 1.04 0.44 0.37 0.28 0.23 2.21
Mauritius Sub-Saharan Africa 66 5.65 1.14 0.76 0.66 0.46 0.05 0.37 2.20
Libya Middle East and Northern Africa 67 5.62 1.07 0.95 0.52 0.41 0.10 0.17 2.39
Latvia Central and Eastern Europe 68 5.56 1.22 0.95 0.64 0.28 0.09 0.17 2.21
Cyprus Western Europe 69 5.55 1.32 0.71 0.85 0.30 0.05 0.28 2.04
Paraguay Latin America and Caribbean 70 5.54 0.89 1.11 0.58 0.46 0.07 0.25 2.16
Romania Central and Eastern Europe 71 5.53 1.17 0.73 0.68 0.37 0.01 0.13 2.45
Estonia Central and Eastern Europe 72 5.52 1.28 1.05 0.68 0.42 0.19 0.08 1.82
Jamaica Latin America and Caribbean 73 5.51 0.89 0.96 0.59 0.44 0.04 0.22 2.36
Croatia Central and Eastern Europe 74 5.49 1.19 0.61 0.71 0.24 0.04 0.18 2.52
Hong Kong Eastern Asia 75 5.46 1.51 0.87 0.95 0.48 0.32 0.40 0.93
Somalia Sub-Saharan Africa 76 5.44 0.00 0.34 0.11 0.57 0.31 0.27 3.84
Kosovo Central and Eastern Europe 77 5.40 0.90 0.66 0.54 0.14 0.07 0.28 2.81
Turkey Middle East and Northern Africa 78 5.39 1.16 0.88 0.65 0.24 0.12 0.05 2.29
Indonesia Southeastern Asia 79 5.31 0.95 0.88 0.49 0.39 0.00 0.57 2.03
Jordan Middle East and Northern Africa 80 5.30 1.00 0.86 0.61 0.36 0.13 0.14 2.20
Azerbaijan Central and Eastern Europe 81 5.29 1.12 0.76 0.55 0.35 0.18 0.06 2.27
Philippines Southeastern Asia 82 5.28 0.81 0.88 0.47 0.55 0.12 0.22 2.23
China Eastern Asia 83 5.25 1.03 0.79 0.74 0.44 0.03 0.05 2.17
Bhutan Southern Asia 84 5.20 0.85 0.91 0.50 0.46 0.16 0.49 1.83
Kyrgyzstan Central and Eastern Europe 85 5.18 0.56 0.95 0.55 0.40 0.05 0.38 2.28
Serbia Central and Eastern Europe 86 5.18 1.03 0.81 0.65 0.16 0.04 0.21 2.28
Bosnia and Herzegovina Central and Eastern Europe 87 5.16 0.93 0.64 0.71 0.10 0.00 0.30 2.48
Montenegro Central and Eastern Europe 88 5.16 1.08 0.74 0.64 0.15 0.13 0.17 2.26
Dominican Republic Latin America and Caribbean 89 5.16 1.03 0.99 0.58 0.52 0.12 0.21 1.70
Morocco Middle East and Northern Africa 90 5.15 0.84 0.39 0.59 0.26 0.08 0.04 2.95
Hungary Central and Eastern Europe 91 5.14 1.24 0.93 0.68 0.20 0.04 0.10 1.95
Pakistan Southern Asia 92 5.13 0.69 0.26 0.40 0.15 0.14 0.31 3.18
Lebanon Middle East and Northern Africa 93 5.13 1.12 0.64 0.76 0.26 0.03 0.24 2.07
Portugal Western Europe 94 5.12 1.28 0.94 0.79 0.45 0.02 0.12 1.53
Macedonia Central and Eastern Europe 95 5.12 1.02 0.78 0.65 0.28 0.07 0.24 2.09
Vietnam Southeastern Asia 96 5.06 0.74 0.79 0.66 0.56 0.12 0.25 1.94
Somaliland Region Sub-Saharan Africa 97 5.06 0.26 0.76 0.33 0.39 0.37 0.51 2.44
Tunisia Middle East and Northern Africa 98 5.04 0.98 0.43 0.60 0.24 0.08 0.04 2.68
Greece Western Europe 99 5.03 1.25 0.75 0.80 0.06 0.04 0.00 2.13
Tajikistan Central and Eastern Europe 100 5.00 0.49 0.76 0.53 0.43 0.14 0.26 2.39
Mongolia Eastern Asia 101 4.91 0.99 1.09 0.55 0.36 0.03 0.35 1.54
Laos Southeastern Asia 102 4.88 0.68 0.55 0.38 0.52 0.22 0.43 2.09
Nigeria Sub-Saharan Africa 103 4.88 0.75 0.64 0.05 0.28 0.03 0.23 2.89
Honduras Latin America and Caribbean 104 4.87 0.69 0.76 0.58 0.27 0.07 0.20 2.30
Iran Middle East and Northern Africa 105 4.81 1.12 0.39 0.64 0.23 0.06 0.39 2.00
Zambia Sub-Saharan Africa 106 4.80 0.61 0.64 0.24 0.43 0.11 0.18 2.59
Nepal Southern Asia 107 4.79 0.45 0.70 0.50 0.37 0.07 0.38 2.33
Palestinian Territories Middle East and Northern Africa 108 4.75 0.67 0.72 0.57 0.18 0.11 0.11 2.40
Albania Central and Eastern Europe 109 4.66 0.96 0.50 0.73 0.32 0.05 0.17 1.93
Bangladesh Southern Asia 110 4.64 0.54 0.25 0.53 0.40 0.13 0.19 2.61
Sierra Leone Sub-Saharan Africa 111 4.63 0.36 0.63 0.00 0.31 0.08 0.24 3.01
Iraq Middle East and Northern Africa 112 4.58 1.07 0.59 0.51 0.25 0.14 0.20 1.82
Namibia Sub-Saharan Africa 113 4.57 0.93 0.70 0.35 0.49 0.10 0.08 1.92
Cameroon Sub-Saharan Africa 114 4.51 0.52 0.63 0.13 0.43 0.06 0.23 2.52
Ethiopia Sub-Saharan Africa 115 4.51 0.29 0.38 0.35 0.37 0.17 0.30 2.66
South Africa Sub-Saharan Africa 116 4.46 1.02 0.96 0.19 0.42 0.08 0.14 1.64
Sri Lanka Southern Asia 117 4.42 0.97 0.85 0.62 0.51 0.08 0.47 0.92
India Southern Asia 118 4.40 0.74 0.29 0.45 0.40 0.09 0.25 2.18
Myanmar Southeastern Asia 119 4.39 0.34 0.70 0.40 0.43 0.20 0.82 1.51
Egypt Middle East and Northern Africa 120 4.36 0.95 0.50 0.52 0.19 0.10 0.13 1.97
Armenia Central and Eastern Europe 121 4.36 0.86 0.62 0.64 0.14 0.04 0.08 1.98
Kenya Sub-Saharan Africa 122 4.36 0.52 0.76 0.30 0.41 0.07 0.41 1.88
Ukraine Central and Eastern Europe 123 4.32 0.87 1.01 0.59 0.13 0.02 0.20 1.50
Ghana Sub-Saharan Africa 124 4.28 0.63 0.49 0.30 0.41 0.03 0.21 2.20
Congo (Kinshasa) Sub-Saharan Africa 125 4.27 0.06 0.81 0.19 0.16 0.06 0.25 2.75
Georgia Central and Eastern Europe 126 4.25 0.84 0.19 0.64 0.32 0.32 0.07 1.87
Congo (Brazzaville) Sub-Saharan Africa 127 4.24 0.77 0.48 0.28 0.38 0.10 0.12 2.11
Senegal Sub-Saharan Africa 128 4.22 0.44 0.77 0.40 0.31 0.12 0.19 1.98
Bulgaria Central and Eastern Europe 129 4.22 1.11 0.93 0.68 0.21 0.01 0.13 1.15
Mauritania Sub-Saharan Africa 130 4.20 0.61 0.84 0.29 0.13 0.18 0.23 1.93
Zimbabwe Sub-Saharan Africa 131 4.19 0.35 0.71 0.16 0.25 0.09 0.19 2.44
Malawi Sub-Saharan Africa 132 4.16 0.09 0.15 0.29 0.41 0.08 0.31 2.83
Sudan Sub-Saharan Africa 133 4.14 0.63 0.82 0.30 0.00 0.10 0.18 2.11
Gabon Sub-Saharan Africa 134 4.12 1.16 0.72 0.35 0.28 0.09 0.06 1.45
Mali Sub-Saharan Africa 135 4.07 0.31 0.86 0.16 0.28 0.14 0.21 2.11
Haiti Latin America and Caribbean 136 4.03 0.34 0.30 0.27 0.12 0.14 0.48 2.37
Botswana Sub-Saharan Africa 137 3.97 1.09 0.89 0.35 0.44 0.11 0.12 0.97
Comoros Sub-Saharan Africa 138 3.96 0.28 0.60 0.30 0.15 0.18 0.18 2.26
Ivory Coast Sub-Saharan Africa 139 3.92 0.56 0.58 0.04 0.41 0.16 0.20 1.97
Cambodia Southeastern Asia 140 3.91 0.56 0.54 0.42 0.59 0.08 0.40 1.32
Angola Sub-Saharan Africa 141 3.87 0.85 0.66 0.05 0.01 0.08 0.12 2.09
Niger Sub-Saharan Africa 142 3.86 0.13 0.61 0.26 0.38 0.17 0.21 2.09
South Sudan Sub-Saharan Africa 143 3.83 0.39 0.19 0.16 0.20 0.13 0.26 2.51
Chad Sub-Saharan Africa 144 3.76 0.42 0.63 0.04 0.13 0.05 0.19 2.31
Burkina Faso Sub-Saharan Africa 145 3.74 0.32 0.63 0.21 0.33 0.13 0.24 1.87
Uganda Sub-Saharan Africa 145 3.74 0.35 0.91 0.20 0.44 0.06 0.27 1.51
Yemen Middle East and Northern Africa 147 3.72 0.58 0.47 0.31 0.23 0.06 0.10 1.97
Madagascar Sub-Saharan Africa 148 3.69 0.28 0.46 0.37 0.14 0.08 0.22 2.15
Tanzania Sub-Saharan Africa 149 3.67 0.47 0.78 0.36 0.32 0.05 0.31 1.38
Liberia Sub-Saharan Africa 150 3.62 0.11 0.50 0.23 0.26 0.05 0.24 2.23
Guinea Sub-Saharan Africa 151 3.61 0.22 0.31 0.19 0.31 0.12 0.30 2.16
Rwanda Sub-Saharan Africa 152 3.52 0.33 0.62 0.32 0.54 0.51 0.24 0.97
Benin Sub-Saharan Africa 153 3.48 0.39 0.10 0.21 0.40 0.07 0.20 2.11
Afghanistan Southern Asia 154 3.36 0.38 0.11 0.17 0.16 0.07 0.31 2.15
Togo Sub-Saharan Africa 155 3.30 0.28 0.00 0.25 0.35 0.12 0.18 2.14
Syria Middle East and Northern Africa 156 3.07 0.75 0.15 0.63 0.07 0.17 0.48 0.82
Burundi Sub-Saharan Africa 157 2.90 0.07 0.23 0.16 0.04 0.09 0.20 2.10

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2017

Year2017 %>%
  arrange(desc(`Happiness Score`)) %>%
  mutate_each(funs(round(., 2)), -c(Country, Region)) %>%
  head(155) %>%
  formattable(list(
    'Happiness Score' = color_bar("lightpink"),
    'Economy (GDP per Capita)' = color_bar("paleturquoise"),
    'Family' = color_bar("powderblue"),
    'Health (Life Expectancy)' = color_bar("palegreen"),
    'Freedom' = color_bar("lightgreen"),
    'Trust (Government Corruption)' = color_bar("lavender"),
    'Generosity' = color_bar("thistle"),
    'Dystopia Residual' = color_bar("navajowhite")
  ), align = "l")
Country Region Happiness Rank Happiness Score Economy (GDP per Capita) Family Health (Life Expectancy) Freedom Trust (Government Corruption) Generosity Dystopia Residual
Norway Western Europe 1 7.54 1.62 1.53 0.80 0.64 0.32 0.36 2.28
Denmark Western Europe 2 7.52 1.48 1.55 0.79 0.63 0.40 0.36 2.31
Iceland Western Europe 3 7.50 1.48 1.61 0.83 0.63 0.15 0.48 2.32
Switzerland Western Europe 4 7.49 1.56 1.52 0.86 0.62 0.37 0.29 2.28
Finland Western Europe 5 7.47 1.44 1.54 0.81 0.62 0.38 0.25 2.43
Netherlands Western Europe 6 7.38 1.50 1.43 0.81 0.59 0.28 0.47 2.29
Canada North America 7 7.32 1.48 1.48 0.83 0.61 0.29 0.44 2.19
New Zealand Australia and New Zealand 8 7.31 1.41 1.55 0.82 0.61 0.38 0.50 2.05
Sweden Western Europe 9 7.28 1.49 1.48 0.83 0.61 0.38 0.39 2.10
Australia Australia and New Zealand 10 7.28 1.48 1.51 0.84 0.60 0.30 0.48 2.07
Israel Middle East and Northern Africa 11 7.21 1.38 1.38 0.84 0.41 0.09 0.33 2.80
Costa Rica Latin America and Caribbean 12 7.08 1.11 1.42 0.76 0.58 0.10 0.21 2.90
Austria Western Europe 13 7.01 1.49 1.46 0.82 0.57 0.22 0.32 2.14
United States North America 14 6.99 1.55 1.42 0.77 0.51 0.14 0.39 2.22
Ireland Western Europe 15 6.98 1.54 1.56 0.81 0.57 0.30 0.43 1.77
Germany Western Europe 16 6.95 1.49 1.47 0.80 0.56 0.28 0.34 2.02
Belgium Western Europe 17 6.89 1.46 1.46 0.82 0.54 0.25 0.23 2.12
Luxembourg Western Europe 18 6.86 1.74 1.46 0.85 0.60 0.32 0.28 1.62
United Kingdom Western Europe 19 6.71 1.44 1.50 0.81 0.51 0.27 0.49 1.70
Chile Latin America and Caribbean 20 6.65 1.25 1.28 0.82 0.38 0.08 0.33 2.51
United Arab Emirates Middle East and Northern Africa 21 6.65 1.63 1.27 0.73 0.61 0.32 0.36 1.73
Brazil Latin America and Caribbean 22 6.64 1.11 1.43 0.62 0.44 0.11 0.16 2.77
Czech Republic Central and Eastern Europe 23 6.61 1.35 1.43 0.75 0.49 0.04 0.09 2.45
Argentina Latin America and Caribbean 24 6.60 1.19 1.44 0.70 0.49 0.06 0.11 2.61
Mexico Latin America and Caribbean 25 6.58 1.15 1.21 0.71 0.41 0.13 0.12 2.84
Singapore Southeastern Asia 26 6.57 1.69 1.35 0.95 0.55 0.46 0.35 1.22
Malta Western Europe 27 6.53 1.34 1.49 0.82 0.59 0.15 0.57 1.56
Uruguay Latin America and Caribbean 28 6.45 1.22 1.41 0.72 0.58 0.18 0.18 2.17
Guatemala Latin America and Caribbean 29 6.45 0.87 1.26 0.54 0.53 0.08 0.28 2.89
Panama Latin America and Caribbean 30 6.45 1.23 1.37 0.71 0.55 0.07 0.21 2.31
France Western Europe 31 6.44 1.43 1.39 0.84 0.47 0.17 0.13 2.01
Thailand Southeastern Asia 32 6.42 1.13 1.43 0.65 0.58 0.03 0.57 2.04
Taiwan Province of China NA 33 6.42 1.43 1.38 0.79 0.36 0.06 0.26 2.13
Spain Western Europe 34 6.40 1.38 1.53 0.89 0.41 0.07 0.19 1.93
Qatar Middle East and Northern Africa 35 6.38 1.87 1.27 0.71 0.60 0.44 0.33 1.15
Colombia Latin America and Caribbean 36 6.36 1.07 1.40 0.60 0.48 0.05 0.15 2.62
Saudi Arabia Middle East and Northern Africa 37 6.34 1.53 1.29 0.59 0.45 0.27 0.15 2.07
Trinidad and Tobago Latin America and Caribbean 38 6.17 1.36 1.38 0.52 0.52 0.01 0.33 2.05
Kuwait Middle East and Northern Africa 39 6.11 1.63 1.26 0.63 0.50 0.22 0.23 1.64
Slovakia Central and Eastern Europe 40 6.10 1.33 1.51 0.71 0.30 0.02 0.14 2.10
Bahrain Middle East and Northern Africa 41 6.09 1.49 1.32 0.65 0.54 0.26 0.17 1.66
Malaysia Southeastern Asia 42 6.08 1.29 1.28 0.62 0.40 0.07 0.42 2.00
Nicaragua Latin America and Caribbean 43 6.07 0.74 1.29 0.65 0.45 0.13 0.30 2.51
Ecuador Latin America and Caribbean 44 6.01 1.00 1.29 0.69 0.46 0.14 0.15 2.29
El Salvador Latin America and Caribbean 45 6.00 0.91 1.18 0.60 0.43 0.09 0.08 2.71
Poland Central and Eastern Europe 46 5.97 1.29 1.45 0.70 0.52 0.06 0.16 1.80
Uzbekistan Central and Eastern Europe 47 5.97 0.79 1.55 0.50 0.66 0.25 0.42 1.82
Italy Western Europe 48 5.96 1.40 1.44 0.85 0.26 0.03 0.17 1.81
Russia Central and Eastern Europe 49 5.96 1.28 1.47 0.55 0.37 0.03 0.05 2.21
Belize Latin America and Caribbean 50 5.96 0.91 1.08 0.45 0.55 0.10 0.24 2.63
Japan Eastern Asia 51 5.92 1.42 1.44 0.91 0.51 0.16 0.12 1.36
Lithuania Central and Eastern Europe 52 5.90 1.31 1.47 0.63 0.23 0.01 0.01 2.23
Algeria Middle East and Northern Africa 53 5.87 1.09 1.15 0.62 0.23 0.15 0.07 2.57
Latvia Central and Eastern Europe 54 5.85 1.26 1.40 0.64 0.33 0.07 0.15 1.99
South Korea Eastern Asia 55 5.84 1.40 1.13 0.90 0.26 0.06 0.21 1.88
Moldova Central and Eastern Europe 56 5.84 0.73 1.25 0.59 0.24 0.01 0.21 2.81
Romania Central and Eastern Europe 57 5.82 1.22 1.15 0.69 0.46 0.00 0.13 2.18
Bolivia Latin America and Caribbean 58 5.82 0.83 1.23 0.47 0.56 0.06 0.23 2.44
Turkmenistan Central and Eastern Europe 59 5.82 1.13 1.49 0.44 0.42 0.26 0.25 1.83
Kazakhstan Central and Eastern Europe 60 5.82 1.28 1.38 0.61 0.44 0.12 0.20 1.78
North Cyprus Western Europe 61 5.81 1.35 1.19 0.83 0.47 0.16 0.27 1.55
Slovenia Central and Eastern Europe 62 5.76 1.34 1.45 0.79 0.57 0.05 0.24 1.31
Peru Latin America and Caribbean 63 5.72 1.04 1.22 0.63 0.45 0.05 0.13 2.21
Mauritius Sub-Saharan Africa 64 5.63 1.19 1.21 0.64 0.49 0.04 0.36 1.70
Cyprus Western Europe 65 5.62 1.36 1.13 0.84 0.36 0.04 0.27 1.62
Estonia Central and Eastern Europe 66 5.61 1.32 1.48 0.70 0.48 0.18 0.10 1.36
Belarus Central and Eastern Europe 67 5.57 1.16 1.44 0.64 0.30 0.16 0.16 1.72
Libya Middle East and Northern Africa 68 5.53 1.10 1.36 0.52 0.47 0.09 0.15 1.84
Turkey Middle East and Northern Africa 69 5.50 1.20 1.34 0.64 0.30 0.10 0.05 1.88
Paraguay Latin America and Caribbean 70 5.49 0.93 1.51 0.58 0.47 0.09 0.22 1.69
Hong Kong S.A.R., China NA 71 5.47 1.55 1.26 0.94 0.49 0.29 0.37 0.55
Philippines Southeastern Asia 72 5.43 0.86 1.25 0.47 0.59 0.10 0.19 1.97
Serbia Central and Eastern Europe 73 5.39 1.07 1.26 0.65 0.21 0.04 0.22 1.95
Jordan Middle East and Northern Africa 74 5.34 0.99 1.24 0.60 0.42 0.12 0.17 1.79
Hungary Central and Eastern Europe 75 5.32 1.29 1.34 0.69 0.18 0.04 0.08 1.72
Jamaica Latin America and Caribbean 76 5.31 0.93 1.37 0.64 0.47 0.06 0.23 1.61
Croatia Central and Eastern Europe 77 5.29 1.22 0.97 0.70 0.26 0.04 0.25 1.85
Kosovo Central and Eastern Europe 78 5.28 0.95 1.14 0.54 0.26 0.06 0.32 2.01
China Eastern Asia 79 5.27 1.08 1.16 0.74 0.47 0.02 0.03 1.76
Pakistan Southern Asia 80 5.27 0.73 0.67 0.40 0.24 0.12 0.32 2.79
Indonesia Southeastern Asia 81 5.26 1.00 1.27 0.49 0.44 0.02 0.61 1.43
Venezuela Latin America and Caribbean 82 5.25 1.13 1.43 0.62 0.15 0.06 0.07 1.79
Montenegro Central and Eastern Europe 83 5.24 1.12 1.24 0.67 0.19 0.09 0.20 1.73
Morocco Middle East and Northern Africa 84 5.24 0.88 0.77 0.60 0.41 0.09 0.03 2.46
Azerbaijan Central and Eastern Europe 85 5.23 1.15 1.15 0.54 0.40 0.18 0.05 1.76
Dominican Republic Latin America and Caribbean 86 5.23 1.08 1.40 0.57 0.55 0.11 0.19 1.32
Greece Western Europe 87 5.23 1.29 1.24 0.81 0.10 0.04 0.00 1.75
Lebanon Middle East and Northern Africa 88 5.22 1.07 1.13 0.74 0.29 0.04 0.26 1.70
Portugal Western Europe 89 5.20 1.32 1.37 0.80 0.50 0.02 0.10 1.11
Bosnia and Herzegovina Central and Eastern Europe 90 5.18 0.98 1.07 0.71 0.20 0.00 0.33 1.89
Honduras Latin America and Caribbean 91 5.18 0.73 1.14 0.58 0.35 0.07 0.24 2.07
Macedonia Central and Eastern Europe 92 5.18 1.06 1.21 0.64 0.33 0.06 0.25 1.62
Somalia Sub-Saharan Africa 93 5.15 0.02 0.72 0.11 0.60 0.28 0.29 3.12
Vietnam Southeastern Asia 94 5.07 0.79 1.28 0.65 0.57 0.09 0.23 1.46
Nigeria Sub-Saharan Africa 95 5.07 0.78 1.22 0.06 0.39 0.03 0.23 2.37
Tajikistan Central and Eastern Europe 96 5.04 0.52 1.27 0.53 0.47 0.15 0.25 1.85
Bhutan Southern Asia 97 5.01 0.89 1.34 0.50 0.50 0.17 0.47 1.14
Kyrgyzstan Central and Eastern Europe 98 5.00 0.60 1.39 0.55 0.45 0.04 0.43 1.54
Nepal Southern Asia 99 4.96 0.48 1.18 0.50 0.44 0.07 0.39 1.89
Mongolia Eastern Asia 100 4.95 1.03 1.49 0.56 0.39 0.03 0.34 1.11
South Africa Sub-Saharan Africa 101 4.83 1.05 1.38 0.19 0.48 0.07 0.14 1.51
Tunisia Middle East and Northern Africa 102 4.80 1.01 0.87 0.61 0.29 0.09 0.05 1.89
Palestinian Territories Middle East and Northern Africa 103 4.78 0.72 1.16 0.57 0.25 0.09 0.11 1.88
Egypt Middle East and Northern Africa 104 4.74 0.99 1.00 0.52 0.28 0.11 0.13 1.70
Bulgaria Central and Eastern Europe 105 4.71 1.16 1.43 0.71 0.29 0.01 0.11 1.00
Sierra Leone Sub-Saharan Africa 106 4.71 0.37 0.98 0.01 0.32 0.07 0.29 2.67
Cameroon Sub-Saharan Africa 107 4.70 0.56 0.95 0.13 0.43 0.05 0.24 2.33
Iran Middle East and Northern Africa 108 4.69 1.16 0.71 0.64 0.25 0.05 0.39 1.50
Albania Central and Eastern Europe 109 4.64 1.00 0.80 0.73 0.38 0.04 0.20 1.49
Bangladesh Southern Asia 110 4.61 0.59 0.74 0.53 0.48 0.12 0.17 1.98
Namibia Sub-Saharan Africa 111 4.57 0.96 1.10 0.34 0.52 0.09 0.08 1.48
Kenya Sub-Saharan Africa 112 4.55 0.56 1.07 0.31 0.45 0.06 0.44 1.65
Mozambique NA 113 4.55 0.23 0.87 0.11 0.48 0.18 0.32 2.36
Myanmar Southeastern Asia 114 4.55 0.37 1.12 0.40 0.51 0.19 0.84 1.12
Senegal Sub-Saharan Africa 115 4.53 0.48 1.18 0.41 0.38 0.12 0.18 1.79
Zambia Sub-Saharan Africa 116 4.51 0.64 1.00 0.26 0.46 0.08 0.25 1.83
Iraq Middle East and Northern Africa 117 4.50 1.10 0.98 0.50 0.29 0.11 0.20 1.32
Gabon Sub-Saharan Africa 118 4.47 1.20 1.16 0.36 0.31 0.08 0.04 1.32
Ethiopia Sub-Saharan Africa 119 4.46 0.34 0.86 0.35 0.41 0.17 0.31 2.02
Sri Lanka Southern Asia 120 4.44 1.01 1.26 0.63 0.56 0.07 0.49 0.42
Armenia Central and Eastern Europe 121 4.38 0.90 1.01 0.64 0.20 0.03 0.08 1.52
India Southern Asia 122 4.32 0.79 0.75 0.46 0.47 0.09 0.23 1.52
Mauritania Sub-Saharan Africa 123 4.29 0.65 1.27 0.29 0.10 0.14 0.20 1.65
Congo (Brazzaville) Sub-Saharan Africa 124 4.29 0.81 0.83 0.29 0.44 0.08 0.12 1.72
Georgia Central and Eastern Europe 125 4.29 0.95 0.57 0.65 0.31 0.25 0.05 1.50
Congo (Kinshasa) Sub-Saharan Africa 126 4.28 0.09 1.23 0.19 0.24 0.06 0.25 2.22
Mali Sub-Saharan Africa 127 4.19 0.48 1.28 0.17 0.31 0.10 0.18 1.67
Ivory Coast Sub-Saharan Africa 128 4.18 0.60 0.90 0.05 0.45 0.13 0.20 1.84
Cambodia Southeastern Asia 129 4.17 0.60 1.01 0.43 0.63 0.07 0.39 1.04
Sudan Sub-Saharan Africa 130 4.14 0.66 1.21 0.29 0.01 0.09 0.18 1.69
Ghana Sub-Saharan Africa 131 4.12 0.67 0.87 0.30 0.42 0.03 0.26 1.58
Ukraine Central and Eastern Europe 132 4.10 0.89 1.39 0.58 0.12 0.02 0.27 0.81
Uganda Sub-Saharan Africa 133 4.08 0.38 1.13 0.22 0.44 0.06 0.33 1.53
Burkina Faso Sub-Saharan Africa 134 4.03 0.35 1.04 0.22 0.32 0.12 0.25 1.73
Niger Sub-Saharan Africa 135 4.03 0.16 0.99 0.27 0.36 0.14 0.23 1.87
Malawi Sub-Saharan Africa 136 3.97 0.23 0.51 0.32 0.47 0.07 0.29 2.08
Chad Sub-Saharan Africa 137 3.94 0.44 0.95 0.04 0.16 0.05 0.22 2.07
Zimbabwe Sub-Saharan Africa 138 3.88 0.38 1.08 0.20 0.34 0.10 0.19 1.60
Lesotho NA 139 3.81 0.52 1.19 0.00 0.39 0.12 0.16 1.43
Angola Sub-Saharan Africa 140 3.80 0.86 1.10 0.05 0.00 0.07 0.10 1.61
Afghanistan Southern Asia 141 3.79 0.40 0.58 0.18 0.11 0.06 0.31 2.15
Botswana Sub-Saharan Africa 142 3.77 1.12 1.22 0.34 0.51 0.10 0.10 0.38
Benin Sub-Saharan Africa 143 3.66 0.43 0.44 0.21 0.43 0.06 0.21 1.89
Madagascar Sub-Saharan Africa 144 3.64 0.31 0.91 0.38 0.19 0.07 0.21 1.58
Haiti Latin America and Caribbean 145 3.60 0.37 0.64 0.28 0.03 0.10 0.49 1.70
Yemen Middle East and Northern Africa 146 3.59 0.59 0.94 0.31 0.25 0.06 0.10 1.35
South Sudan Sub-Saharan Africa 147 3.59 0.40 0.60 0.16 0.15 0.12 0.29 1.88
Liberia Sub-Saharan Africa 148 3.53 0.12 0.87 0.23 0.33 0.04 0.27 1.67
Guinea Sub-Saharan Africa 149 3.51 0.24 0.79 0.19 0.35 0.11 0.26 1.55
Togo Sub-Saharan Africa 150 3.49 0.31 0.43 0.25 0.38 0.10 0.20 1.84
Rwanda Sub-Saharan Africa 151 3.47 0.37 0.95 0.33 0.58 0.46 0.25 0.54
Syria Middle East and Northern Africa 152 3.46 0.78 0.40 0.50 0.08 0.15 0.49 1.06
Tanzania Sub-Saharan Africa 153 3.35 0.51 1.04 0.36 0.39 0.07 0.35 0.62
Burundi Sub-Saharan Africa 154 2.90 0.09 0.63 0.15 0.06 0.08 0.20 1.68
Central African Republic NA 155 2.69 0.00 0.00 0.02 0.27 0.06 0.28 2.07

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Data Description

Measuments are defined as in World Happiness Report 2017

  • Hapiness Rank: rank of country’s happiness score from high to low

  • Happiness Score: measured by each variable; reveals a populated-weighted average score on a scale running from 0 to 10 that is tracked over time and compared against other countries

There are 6 variables:

  • Economy (GDP per Capita): a measure of the total output of a country that takes gross domestic product (GDP) and divides it by the number of people in the country

  • Family: an important factor of social support

  • Helth (Life Expectancy): healthy years of life expectancy

  • Freedom: perceived freedom to make life decisions

  • Trust (Government Corruption): as measured by a perceived absence of corruption in government and business

  • Generosity: as measured by recent donations

These factors describe the extent to which they contribute in evaluating the happiness in each country.

  • Dystopia Residual: the Dystopia Happiness Score(1.85) + the Residual value or the unexplained value for each country

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Exploratory Data Analysis

By Map

Maps of World Happiness Score

Following Happiness Score maps show us an overview of Happiness Score across the globe. Since there were different number of countries conducted the survey each year, the blue-shaded countries may vary. There are also countries that never participated in the report, thus those countries are shaded in grey. But the majority countries are consistent.

At the first glance, North America, Australia and New Zealand are most recognizable regions that are in light blue. If we look closer to Europe, we can also see the light blue area, which includes many of the top rankers. It is also pretty obvious that Asia is around the score-5 range. However, if we look at Africa and Middle East, the extremely dark blue shading shows that people living in these places are truly suffering.

2015

world <- map_data("world")
colnames(world)[5] <- "Country"
Year2015$Country <- as.character(Year2015$Country)
Year2015Map <- Year2015[which(!is.na(Year2015$Country)),]
ChangeName <- c("Congo (Brazzaville)"="Democratic Republic of the Congo","Congo (Kinshasa)"="Republic of Congo","Somaliland region" = "Somalia", "United States"="USA","United Kingdom"= "UK")
for(i in names(ChangeName)){
  Year2015Map[Year2015Map$Country==i,"Country"] <- ChangeName[i]}
Map15 <- left_join(world, Year2015Map)

ggplot()  +
  geom_polygon( aes(x = Map15$long, y = Map15$lat, group = Map15$group,fill= Map15$`Happiness Score`)) + 
  coord_equal() +scale_fill_gradient(breaks=c(3,5,7,9)) +
  xlab("") + ylab("") + guides(shape=FALSE) + labs(fill="Happiness Score")

2016

world <- map_data("world")
colnames(world)[5] <- "Country"
Year2016$Country <- as.character(Year2016$Country)
Year2016Map <- Year2016[which(!is.na(Year2016$Country)),]
ChangeName <- c("Congo (Brazzaville)"="Democratic Republic of the Congo","Congo (Kinshasa)"="Republic of Congo", "Somaliland region" = "Somalia", "United States"="USA","United Kingdom"= "UK")
for(i in names(ChangeName)){
  Year2016Map[Year2016Map$Country==i,"Country"] <- ChangeName[i]}
Map16 <- left_join(world, Year2016Map)

ggplot()  +
  geom_polygon( aes(x = Map16$long, y = Map16$lat, group = Map16$group,fill= Map16$`Happiness Score`)) + 
  coord_equal() +scale_fill_gradient(breaks=c(3,5,7,9)) +
  xlab("") + ylab("") + guides(shape=FALSE) + labs(fill="Happiness Score")

2017

world <- map_data("world")
colnames(world)[5] <- "Country"
Year2017$Country <- as.character(Year2017$Country)
Year2017Map <- Year2017[which(!is.na(Year2017$Country)),]
ChangeName <- c("Congo (Brazzaville)"="Democratic Republic of the Congo","Congo (Kinshasa)"="Republic of Congo", "Somaliland region" = "Somalia", "United States"="USA","United Kingdom"= "UK")
for(i in names(ChangeName)){
  Year2017Map[Year2017Map$Country==i,"Country"] <- ChangeName[i]}
Map17 <- left_join(world, Year2017Map)

ggplot()  +
  geom_polygon( aes(x = Map17$long, y = Map17$lat, group = Map17$group,fill= Map17$`Happiness Score`)) + 
  coord_equal() +scale_fill_gradient(breaks=c(3,5,7,9)) +
  xlab("") + ylab("") + guides(shape=FALSE) + labs(fill="Happiness Score")

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By Region

Horizontal Bar for Number of Countries in 10 regions

Following bar chart shows the number of countries in World Happiness Report 2016. The reason of using this year is that it contains the most number of countries among these three years. We can see the number of countries distribution here.

Num_Countries <- ggplot(data = Year2016, aes(x = Region)) +
  geom_bar() +
  coord_flip()

ggplotly(Num_Countries)

Box Plots of Happiness Score across Regions

We can see from these box plots that Happiness Scores in Australia and New Zealand and North America are the only two regions that has median score greater than 7. However, we need to note that these two regions only contain 4 countries (Australia, New Zealand, Canada, and United States). Most of the Western Europe countries rank top in the data set, but with lower outliers, the region median is slightly below 7. Middle East and Northern Africa has the most noticeable whisker. Sub-Saharan Africa has the largest number of countries and their Happiness Scores are the lowest score across regions.

var_boxplots <- ggplot(data = Year2016, aes(Region , `Happiness Score`)) +
  geom_boxplot() +
  geom_jitter(width = .25, alpha = .25) +
  theme(axis.text.x = element_text(angle = -45, hjust = -0.002))
ggplotly(var_boxplots)

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By Variable

Correlations between Each Variable

To quickly approach to multiple relationships, scatter plot matrices can visualize and compare relationships across the entire data set. Following matrices are generated by ggpairs from the GGally. Following matrices are based on World Happiness Report 2017.

Year2017 %>%
  select('Happiness Score', 'Economy (GDP per Capita)', 'Family', 'Health (Life Expectancy)', 'Freedom', 'Trust (Government Corruption)', 'Generosity', 'Dystopia Residual') %>%
  ggpairs()

To better visualized, in the following corrplot, we can see the correlation between each variable. Larger the circle, greater the correlation. With scale from -1 to 1, positive correlations are shown in blue colors. Darker the blue, greater the correlation.

In the first row of Happiness Score, we can see that the top three contributors are Economy (GDP per Capita), Family, and Health (Life Expectancy). Other noticeable correlations are Economy vs. Health and Economy vs. Family, which are not surprising because these are the top three contributors. Additionally, Family vs. Health also has a notable correlation, especially this correlation grows year-by-year.

2015
corr2015 <- cor(as.matrix(Year2015[,-c(1,2,3)]))
corrplot(corr2015,method = "circle",type = "upper",mar = c(0,0,1,0))

2016
corr2016 <- cor(as.matrix(Year2016[,-c(1,2,3)]))
corrplot(corr2016,method = "circle",type = "upper",mar = c(0,0,1,0))

2017
corr2017 <- cor(as.matrix(Year2017[,-c(1,2,3)]))
corrplot(corr2017,method = "circle",type = "upper",mar = c(0,0,1,0))

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By Rank

Following two tables show Rank Change within two-year time frame. Default tables are sorted alphabetically. You can select columns to sort your result.

It appears that top 5 happiest countries basically stay the same, with slightly up or down in rank change. Countries that experienced significant rank change are mostly rank lower, even in some cases, rank bottom.

You can type in specific country you want to see in the Search box; you can also select 10/25/50/100 entries to show per page.

Rank Change 2015 to 2016

Y1516 <- merge(Year2015[,c(1,3)],
           Year2016[,c(1,3)],
           by.x = "Country",
           by.y = "Country")
colnames(Y1516)<-c("Country","Happiness Rank 2015","Happiness Rank 2016")

Y1516 <- Y1516 %>%
  mutate(`Rank Change`=`Happiness Rank 2015`-`Happiness Rank 2016`)

Y1516RC <- formattable(Y1516, list(
  `Rank Change` = formatter("span",
                            style = ~ style(color = ifelse(`Rank Change` >= 0, "green", "red")))))

as.datatable(Y1516RC)

Rank Change 2016 to 2017

Y1617 <- merge(Year2016[,c(1,3)],
           Year2017[,c(1,3)],
           by.x = "Country",
           by.y = "Country")
colnames(Y1617)<-c("Country","Happiness Rank 2016","Happiness Rank 2017")

Y1617 <- Y1617 %>%
  mutate(`Rank Change`=`Happiness Rank 2016`-`Happiness Rank 2017`)

Y1617RC <- formattable(Y1617, list(
  `Rank Change` = formatter("span",
                            style = ~ style(color = ifelse(`Rank Change` >= 0, "green", "red")))))

as.datatable(Y1617RC)

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Summary

This exploratory data analysis addressed problems and findings associated with World Happiness Report from year 2015 to 2017.

Problem Statements

  • What are the happiest/unhappiest countries/regions in the world?

  • What is the Happiness Score distribution across the globe?

  • Is the Happiness Score changing over the three-year time frame?

  • How do the Happiness Score and each individual variable score visualized? Can we understand individual scores without just looking at numbers?

  • How do six variables that make up final score correlate with the Happiness Score? Is there any strong relationship?

  • How do six variables correlate with each other? Is there any strong relationship?

  • Is the correlation between each variable changing over the three-year time frame?

  • Is there any Happiness Rank change in these available reported three years?

Problem-solving Procedures

  • Cleaned data to fully tidy

  • Visualized numeric values in Data Preview

  • Utilized various packages (details as shown in the Packages Required section) to access self-explanatory maps, graphics, matrices, and tables

  • Added helpful comments to enhance audiences’ understanding of the content

  • Created a reader-friendly analytical report

Notable Findings

  • People live in Western Europe, North America, Australia and New Zealand regions are more likely to have a happier life

  • People live in Sub-Saharan Africa, Middle East and Northern Africa regions are suffering that need worldwide humanistic concern

  • Top three main factors contributed to the Happiness Score are Economy (GDP per Capita), Family, and Health (Life Expectancy)

  • Top five happiest countries are fairly consistent every year, with slightly rank change

  • Countries that experienced significant rank change are mostly rank lower, even in some cases, rank bottom

Prospective Improvements

  • As World Happiness Report keeps coming out every year, we will have more data, and possibly more countries to analyze

  • Since every report has chapters that discussing specific regions and countries, if you want to deeply analyze one specific place, you can find additional information in the report

  • Beyond the report itself, there are many relative resources and subjects that can be a nice combination to work on

Implications to My Audience

Thank you for reading! I hope my EDA is helpful, or even make me happier, meaningful to you. As I addressed in my introduction, this project is not just about data analysis, it aims to bring extra considerations to the table. Happiness seems like an abstract thing, but we never stop pursuing it. At the very last, I want to quote Aristotle to conclude my analysis:

The simply complete thing, then, is that which is always chosen for itself and never on account of something else. Happiness above all seems to be of this character, for we always choose it on account of itself and never on account of something else. Yet honor, pleasure, intellect, and every virtue we choose on their own account…but we choose them also for the sake of happiness, because we suppose that, through them, we will be happy. Aristotle, The Nicomachean Ethics

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