The World Happiness Report 2021 dataset, sourced from Kaggle, offers a detailed analysis of happiness levels across different countries. Published annually by the United Nations Sustainable Development Solutions Network (UNSDSN), this dataset provides valuable insights into global well-being trends. It encompasses various factors contributing to happiness, including economic prosperity, social support, healthy life expectancy, freedom to make life choices, generosity, and perceptions of corruption.
Variables Description
The dataset includes multiple variables, such as the country name and regional indicator, as well as:
- Social Support: Measures the extent of social support within the country.
- Healthy Life Expectancy: Represents the average life expectancy within the country.
- Freedom to Make Life Choices: Reflects the degree of freedom individuals have within the country in life choices.
- Generosity: Measures the level of generosity within the country.
- Perceptions of Corruption: Measures the perceived level of corruption within the country.
Observation Characteristics
- The dataset covers multiple countries worldwide.
- Each observation represents a specific country.
- Data for the most recent year (2021) is included.
The World Happiness Report dataset holds relevance for both business and social science disciplines. Businesses can use insights from the dataset to inform corporate social responsibility initiatives, employee well-being programs, and marketing strategies. Social scientists can leverage the dataset to study global happiness trends, explore the impact of socio-economic factors on well-being, and inform policy interventions.
Objectives:
To evaluate the relationship between economic indicators and happiness scores to understand how economic well-being contributes to overall happiness levels globally.
Assess the distribution of social factors across different regions to identify trends in societal well being.
Explore the Relationship Between Economic Indicators and Happiness Scores
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.2 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ ggplot2 3.4.2 ✔ tibble 3.2.1
✔ lubridate 1.9.2 ✔ tidyr 1.3.0
✔ purrr 1.0.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(janitor)
Attaching package: 'janitor'
The following objects are masked from 'package:stats':
chisq.test, fisher.test
library(gt)library(dplyr)#dataset World Happiness Report 2021happiness_data <-read.csv("~/Downloads/world-happiness-report-2021.csv")str(happiness_data)
ggplot(happiness_data, aes(x =`Logged.GDP.per.capita`, y =`Ladder.score`)) +geom_point() +labs(x ="GDP per Capita", y ="Happiness Score") +ggtitle("Scatter Plot: GDP per Capita vs. Happiness Score")
Interpretation: The x-axis represents the GDP per Capita (economic prosperity) of different regions across the world. Higher values on the x-axis indicate a higher GDP per capita. The y-axis represents the Happiness Score (life satisfaction) of the regions across the world. Higher values on the y-axis indicate higher happiness scores. Each black dot represents a specific region or country. There is a visible trend, as GDP per Capita increases, so does the Happiness score. This depicts a positive correlation between econonmic prosperity and perceived happiness. Regions with higher economic well-being tend to have happier populations.
# 2. Bar Chart: “Economic Prosperity and Happiness”
ggplot(happiness_data, aes(x =`Logged.GDP.per.capita`, y =`Social.support`)) +geom_bar(stat ="identity", color ="red") +labs(x ="GDP per Capita", y ="Social Support") +ggtitle("Bar Chart: GDP per Capita vs. Social Support")
Interpretation: The x-axis represents the GDP per Capita of different regions. The Y-axis represents the Social Support available in the same regions. Each red bar corresponds to a specific data point, and the height of each bar represents the level of social support at a given GDP per Capita value. The bar chart visually shows the relationship between economic prosperity and the availability of social support. Regions with higher GDP per Capita tend to have better social support systems.
#3. Box Plot: Distribution of Healthy Life Expectancy by Region
ggplot(happiness_data, aes(x = Regional.indicator, y =`Ladder.score`, fill = Regional.indicator)) +geom_boxplot() +labs(x ="Healthy Life Expectancy", y ="Happiness Score") +ggtitle("Box Plot: Healthy Life Expectancy vs. Happiness Score")
Interpretation: The x-axis represents the Healthy Life Expectancy of different regions. The y-axis represents the Happiness score in the same regions. Each colored box corresponds to a specific country and region. The box plot visually shows the relationship between both variables across different regions. The position of the box indicates the median happiness score for that region.
#4. Descriptive Table: Summary Statistics of Economic Indicators
Logged.GDP.per.capita Ladder.score
Min. : 6.635 Min. :2.523
1st Qu.: 8.541 1st Qu.:4.852
Median : 9.569 Median :5.534
Mean : 9.432 Mean :5.533
3rd Qu.:10.421 3rd Qu.:6.255
Max. :11.647 Max. :7.842
#Descriptive Table: Summary Statistics of Social Factors
Social.support Freedom.to.make.life.choices Generosity
Min. :0.4630 Min. :0.3820 Min. :-0.28800
1st Qu.:0.7500 1st Qu.:0.7180 1st Qu.:-0.12600
Median :0.8320 Median :0.8040 Median :-0.03600
Mean :0.8147 Mean :0.7916 Mean :-0.01513
3rd Qu.:0.9050 3rd Qu.:0.8770 3rd Qu.: 0.07900
Max. :0.9830 Max. :0.9700 Max. : 0.54200
Descriptive Table: To analyze and visualize the average levels of generosity and social support across different regions in the dataset.
Bar Chart: Happiness Scores vs. Social Support by Region
ggplot(happiness_data, aes(x = Regional.indicator, y = Ladder.score, fill = Regional.indicator)) +geom_bar(stat ="identity") +labs(x ="Social Support", y ="Happiness Score", title ="Happiness Scores vs. Social Support by Region")
Interpretation: The x-axis represents the social support levels across different regions around the world and the y-axis represents the happiness score for each region. Each colored bar represents a specific region, the taller the bar, the higher the happiness score for that region. Western Europe and North America & ANZ exhibit higher happiness scores, whereas South Asia has a lower score comparatively.
iv) Data Wrangling techniques
Cleaning Dataset
To remove unnecessary columns (Explained.by..Log.GDP.per.capita, Explained.by..Social.support, etc.) from the dataset to focus only on the relevant variables for analysis.
Country.name Regional.indicator Ladder.score
1 Finland Western Europe 7.842
2 Denmark Western Europe 7.620
3 Switzerland Western Europe 7.571
4 Iceland Western Europe 7.554
5 Netherlands Western Europe 7.464
6 Norway Western Europe 7.392
7 Sweden Western Europe 7.363
8 Luxembourg Western Europe 7.324
9 New Zealand North America and ANZ 7.277
10 Austria Western Europe 7.268
11 Australia North America and ANZ 7.183
12 Israel Middle East and North Africa 7.157
13 Germany Western Europe 7.155
14 Canada North America and ANZ 7.103
15 Ireland Western Europe 7.085
16 Costa Rica Latin America and Caribbean 7.069
17 United Kingdom Western Europe 7.064
18 Czech Republic Central and Eastern Europe 6.965
19 United States North America and ANZ 6.951
20 Belgium Western Europe 6.834
21 France Western Europe 6.690
22 Bahrain Middle East and North Africa 6.647
23 Malta Western Europe 6.602
24 Taiwan Province of China East Asia 6.584
25 United Arab Emirates Middle East and North Africa 6.561
26 Saudi Arabia Middle East and North Africa 6.494
27 Spain Western Europe 6.491
28 Italy Western Europe 6.483
29 Slovenia Central and Eastern Europe 6.461
30 Guatemala Latin America and Caribbean 6.435
31 Uruguay Latin America and Caribbean 6.431
32 Singapore Southeast Asia 6.377
33 Kosovo Central and Eastern Europe 6.372
34 Slovakia Central and Eastern Europe 6.331
35 Brazil Latin America and Caribbean 6.330
36 Mexico Latin America and Caribbean 6.317
37 Jamaica Latin America and Caribbean 6.309
38 Lithuania Central and Eastern Europe 6.255
39 Cyprus Western Europe 6.223
40 Estonia Central and Eastern Europe 6.189
41 Panama Latin America and Caribbean 6.180
42 Uzbekistan Commonwealth of Independent States 6.179
43 Chile Latin America and Caribbean 6.172
44 Poland Central and Eastern Europe 6.166
45 Kazakhstan Commonwealth of Independent States 6.152
46 Romania Central and Eastern Europe 6.140
47 Kuwait Middle East and North Africa 6.106
48 Serbia Central and Eastern Europe 6.078
49 El Salvador Latin America and Caribbean 6.061
50 Mauritius Sub-Saharan Africa 6.049
51 Latvia Central and Eastern Europe 6.032
52 Colombia Latin America and Caribbean 6.012
53 Hungary Central and Eastern Europe 5.992
54 Thailand Southeast Asia 5.985
55 Nicaragua Latin America and Caribbean 5.972
56 Japan East Asia 5.940
57 Argentina Latin America and Caribbean 5.929
58 Portugal Western Europe 5.929
59 Honduras Latin America and Caribbean 5.919
60 Croatia Central and Eastern Europe 5.882
61 Philippines Southeast Asia 5.880
62 South Korea East Asia 5.845
63 Peru Latin America and Caribbean 5.840
64 Bosnia and Herzegovina Central and Eastern Europe 5.813
65 Moldova Commonwealth of Independent States 5.766
66 Ecuador Latin America and Caribbean 5.764
67 Kyrgyzstan Commonwealth of Independent States 5.744
68 Greece Western Europe 5.723
69 Bolivia Latin America and Caribbean 5.716
70 Mongolia East Asia 5.677
71 Paraguay Latin America and Caribbean 5.653
72 Montenegro Central and Eastern Europe 5.581
73 Dominican Republic Latin America and Caribbean 5.545
74 North Cyprus Western Europe 5.536
75 Belarus Commonwealth of Independent States 5.534
76 Russia Commonwealth of Independent States 5.477
77 Hong Kong S.A.R. of China East Asia 5.477
78 Tajikistan Commonwealth of Independent States 5.466
79 Vietnam Southeast Asia 5.411
80 Libya Middle East and North Africa 5.410
81 Malaysia Southeast Asia 5.384
82 Indonesia Southeast Asia 5.345
83 Congo (Brazzaville) Sub-Saharan Africa 5.342
84 China East Asia 5.339
85 Ivory Coast Sub-Saharan Africa 5.306
86 Armenia Commonwealth of Independent States 5.283
87 Nepal South Asia 5.269
88 Bulgaria Central and Eastern Europe 5.266
89 Maldives South Asia 5.198
90 Azerbaijan Commonwealth of Independent States 5.171
91 Cameroon Sub-Saharan Africa 5.142
92 Senegal Sub-Saharan Africa 5.132
93 Albania Central and Eastern Europe 5.117
94 North Macedonia Central and Eastern Europe 5.101
95 Ghana Sub-Saharan Africa 5.088
96 Niger Sub-Saharan Africa 5.074
97 Turkmenistan Commonwealth of Independent States 5.066
98 Gambia Sub-Saharan Africa 5.051
99 Benin Sub-Saharan Africa 5.045
100 Laos Southeast Asia 5.030
101 Bangladesh South Asia 5.025
102 Guinea Sub-Saharan Africa 4.984
103 South Africa Sub-Saharan Africa 4.956
104 Turkey Middle East and North Africa 4.948
105 Pakistan South Asia 4.934
106 Morocco Middle East and North Africa 4.918
107 Venezuela Latin America and Caribbean 4.892
108 Georgia Commonwealth of Independent States 4.891
109 Algeria Middle East and North Africa 4.887
110 Ukraine Commonwealth of Independent States 4.875
111 Iraq Middle East and North Africa 4.854
112 Gabon Sub-Saharan Africa 4.852
113 Burkina Faso Sub-Saharan Africa 4.834
114 Cambodia Southeast Asia 4.830
115 Mozambique Sub-Saharan Africa 4.794
116 Nigeria Sub-Saharan Africa 4.759
117 Mali Sub-Saharan Africa 4.723
118 Iran Middle East and North Africa 4.721
119 Uganda Sub-Saharan Africa 4.636
120 Liberia Sub-Saharan Africa 4.625
121 Kenya Sub-Saharan Africa 4.607
122 Tunisia Middle East and North Africa 4.596
123 Lebanon Middle East and North Africa 4.584
124 Namibia Sub-Saharan Africa 4.574
125 Palestinian Territories Middle East and North Africa 4.517
126 Myanmar Southeast Asia 4.426
127 Jordan Middle East and North Africa 4.395
128 Chad Sub-Saharan Africa 4.355
129 Sri Lanka South Asia 4.325
130 Swaziland Sub-Saharan Africa 4.308
131 Comoros Sub-Saharan Africa 4.289
132 Egypt Middle East and North Africa 4.283
133 Ethiopia Sub-Saharan Africa 4.275
134 Mauritania Sub-Saharan Africa 4.227
135 Madagascar Sub-Saharan Africa 4.208
136 Togo Sub-Saharan Africa 4.107
137 Zambia Sub-Saharan Africa 4.073
138 Sierra Leone Sub-Saharan Africa 3.849
139 India South Asia 3.819
140 Burundi Sub-Saharan Africa 3.775
141 Yemen Middle East and North Africa 3.658
142 Tanzania Sub-Saharan Africa 3.623
143 Haiti Latin America and Caribbean 3.615
144 Malawi Sub-Saharan Africa 3.600
145 Lesotho Sub-Saharan Africa 3.512
146 Botswana Sub-Saharan Africa 3.467
147 Rwanda Sub-Saharan Africa 3.415
148 Zimbabwe Sub-Saharan Africa 3.145
149 Afghanistan South Asia 2.523
Standard.error.of.ladder.score upperwhisker lowerwhisker
1 0.032 7.904 7.780
2 0.035 7.687 7.552
3 0.036 7.643 7.500
4 0.059 7.670 7.438
5 0.027 7.518 7.410
6 0.035 7.462 7.323
7 0.036 7.433 7.293
8 0.037 7.396 7.252
9 0.040 7.355 7.198
10 0.036 7.337 7.198
11 0.041 7.265 7.102
12 0.034 7.224 7.090
13 0.040 7.232 7.077
14 0.042 7.185 7.021
15 0.040 7.164 7.006
16 0.056 7.179 6.960
17 0.038 7.138 6.990
18 0.049 7.062 6.868
19 0.049 7.047 6.856
20 0.034 6.901 6.767
21 0.037 6.762 6.618
22 0.068 6.779 6.514
23 0.044 6.688 6.516
24 0.038 6.659 6.510
25 0.039 6.637 6.484
26 0.056 6.604 6.384
27 0.042 6.574 6.408
28 0.045 6.572 6.395
29 0.043 6.546 6.376
30 0.073 6.577 6.292
31 0.046 6.521 6.341
32 0.043 6.460 6.293
33 0.059 6.487 6.257
34 0.041 6.411 6.251
35 0.043 6.415 6.245
36 0.053 6.420 6.213
37 0.156 6.615 6.004
38 0.045 6.344 6.167
39 0.049 6.319 6.128
40 0.038 6.263 6.115
41 0.073 6.323 6.036
42 0.068 6.312 6.045
43 0.046 6.262 6.081
44 0.040 6.245 6.087
45 0.047 6.243 6.060
46 0.057 6.253 6.027
47 0.066 6.235 5.977
48 0.053 6.181 5.974
49 0.065 6.188 5.933
50 0.059 6.165 5.933
51 0.036 6.103 5.961
52 0.061 6.132 5.892
53 0.047 6.085 5.899
54 0.047 6.077 5.893
55 0.083 6.134 5.810
56 0.040 6.020 5.861
57 0.056 6.040 5.819
58 0.055 6.037 5.821
59 0.082 6.081 5.758
60 0.048 5.975 5.788
61 0.052 5.982 5.778
62 0.042 5.928 5.763
63 0.075 5.988 5.692
64 0.050 5.911 5.715
65 0.046 5.856 5.677
66 0.057 5.875 5.653
67 0.046 5.834 5.653
68 0.046 5.813 5.632
69 0.053 5.819 5.613
70 0.042 5.760 5.595
71 0.092 5.832 5.473
72 0.054 5.686 5.475
73 0.071 5.685 5.405
74 0.051 5.636 5.435
75 0.047 5.625 5.442
76 0.033 5.541 5.413
77 0.049 5.573 5.380
78 0.034 5.532 5.400
79 0.039 5.488 5.334
80 0.076 5.558 5.262
81 0.049 5.480 5.289
82 0.056 5.454 5.235
83 0.097 5.533 5.151
84 0.029 5.397 5.281
85 0.078 5.460 5.152
86 0.058 5.397 5.168
87 0.070 5.406 5.132
88 0.054 5.371 5.160
89 0.072 5.339 5.057
90 0.040 5.250 5.091
91 0.074 5.288 4.996
92 0.068 5.266 4.998
93 0.059 5.234 5.001
94 0.051 5.202 5.001
95 0.067 5.219 4.958
96 0.102 5.273 4.875
97 0.036 5.136 4.996
98 0.089 5.225 4.877
99 0.073 5.189 4.901
100 0.045 5.119 4.941
101 0.046 5.115 4.934
102 0.090 5.160 4.808
103 0.060 5.074 4.839
104 0.046 5.038 4.857
105 0.068 5.066 4.802
106 0.060 5.036 4.800
107 0.064 5.017 4.767
108 0.054 4.998 4.785
109 0.053 4.991 4.783
110 0.052 4.977 4.773
111 0.059 4.970 4.738
112 0.075 4.998 4.706
113 0.081 4.993 4.675
114 0.067 4.963 4.698
115 0.103 4.997 4.592
116 0.052 4.861 4.658
117 0.082 4.884 4.563
118 0.055 4.828 4.614
119 0.073 4.780 4.493
120 0.106 4.833 4.417
121 0.072 4.747 4.466
122 0.058 4.709 4.484
123 0.055 4.691 4.477
124 0.064 4.700 4.448
125 0.067 4.649 4.384
126 0.052 4.527 4.324
127 0.062 4.516 4.273
128 0.094 4.540 4.171
129 0.066 4.454 4.196
130 0.071 4.448 4.168
131 0.084 4.454 4.123
132 0.045 4.371 4.195
133 0.051 4.374 4.175
134 0.070 4.365 4.090
135 0.072 4.349 4.068
136 0.077 4.258 3.956
137 0.069 4.209 3.938
138 0.077 4.001 3.698
139 0.026 3.869 3.769
140 0.107 3.985 3.565
141 0.070 3.794 3.521
142 0.071 3.762 3.485
143 0.173 3.953 3.276
144 0.092 3.781 3.419
145 0.120 3.748 3.276
146 0.074 3.611 3.322
147 0.068 3.548 3.282
148 0.058 3.259 3.030
149 0.038 2.596 2.449
Logged.GDP.per.capita Social.support Healthy.life.expectancy
1 10.775 0.954 72.000
2 10.933 0.954 72.700
3 11.117 0.942 74.400
4 10.878 0.983 73.000
5 10.932 0.942 72.400
6 11.053 0.954 73.300
7 10.867 0.934 72.700
8 11.647 0.908 72.600
9 10.643 0.948 73.400
10 10.906 0.934 73.300
11 10.796 0.940 73.900
12 10.575 0.939 73.503
13 10.873 0.903 72.500
14 10.776 0.926 73.800
15 11.342 0.947 72.400
16 9.880 0.891 71.400
17 10.707 0.934 72.500
18 10.556 0.947 70.807
19 11.023 0.920 68.200
20 10.823 0.906 72.199
21 10.704 0.942 74.000
22 10.669 0.862 69.495
23 10.674 0.931 72.200
24 10.871 0.898 69.600
25 11.085 0.844 67.333
26 10.743 0.891 66.603
27 10.571 0.932 74.700
28 10.623 0.880 73.800
29 10.529 0.948 71.400
30 9.053 0.813 64.958
31 9.966 0.925 69.100
32 11.488 0.915 76.953
33 9.318 0.821 63.813
34 10.369 0.936 69.201
35 9.577 0.882 66.601
36 9.859 0.831 68.597
37 9.186 0.877 67.500
38 10.499 0.935 67.906
39 10.576 0.802 73.898
40 10.481 0.941 68.800
41 10.350 0.896 69.652
42 8.836 0.918 65.255
43 10.071 0.882 70.000
44 10.382 0.898 69.702
45 10.155 0.952 65.200
46 10.284 0.832 67.355
47 10.817 0.843 66.900
48 9.787 0.873 68.600
49 9.054 0.762 66.402
50 10.008 0.905 66.701
51 10.315 0.927 67.100
52 9.557 0.847 68.001
53 10.358 0.943 68.000
54 9.805 0.888 67.401
55 8.620 0.864 67.657
56 10.611 0.884 75.100
57 9.962 0.898 69.000
58 10.421 0.879 72.600
59 8.648 0.812 67.300
60 10.217 0.924 70.799
61 9.076 0.830 62.000
62 10.651 0.799 73.900
63 9.458 0.832 68.250
64 9.590 0.870 68.098
65 9.454 0.857 65.699
66 9.313 0.821 68.800
67 8.538 0.893 64.401
68 10.279 0.823 72.600
69 9.046 0.810 63.901
70 9.400 0.935 62.500
71 9.448 0.893 65.900
72 9.940 0.858 68.699
73 9.802 0.853 66.102
74 10.576 0.820 73.898
75 9.853 0.910 66.253
76 10.189 0.903 64.703
77 11.000 0.836 76.820
78 8.091 0.860 64.281
79 8.973 0.850 68.034
80 9.622 0.827 62.300
81 10.238 0.817 67.102
82 9.365 0.811 62.236
83 8.117 0.636 58.221
84 9.673 0.811 69.593
85 8.551 0.644 50.114
86 9.487 0.799 67.055
87 8.120 0.774 64.233
88 10.016 0.931 67.000
89 9.826 0.913 70.600
90 9.569 0.836 65.656
91 8.189 0.710 53.515
92 8.118 0.710 59.802
93 9.520 0.697 68.999
94 9.693 0.805 65.474
95 8.580 0.727 57.586
96 7.098 0.641 53.780
97 9.629 0.983 62.409
98 7.686 0.690 55.160
99 8.087 0.489 54.713
100 8.947 0.728 58.968
101 8.454 0.693 64.800
102 7.838 0.639 55.008
103 9.403 0.860 56.904
104 10.240 0.822 67.199
105 8.458 0.651 58.709
106 8.903 0.560 66.208
107 9.073 0.861 66.700
108 9.585 0.671 64.300
109 9.342 0.802 66.005
110 9.436 0.888 64.902
111 9.240 0.746 60.583
112 9.603 0.776 59.962
113 7.678 0.672 54.151
114 8.360 0.765 62.000
115 7.158 0.744 54.706
116 8.533 0.740 50.102
117 7.744 0.724 51.969
118 9.584 0.710 66.300
119 7.677 0.781 56.101
120 7.288 0.720 56.498
121 8.361 0.688 60.704
122 9.266 0.691 67.201
123 9.626 0.848 67.355
124 9.161 0.818 56.799
125 8.485 0.826 62.250
126 8.541 0.779 59.302
127 9.182 0.767 67.000
128 7.364 0.619 48.478
129 9.470 0.827 67.299
130 9.065 0.770 50.833
131 8.031 0.626 57.349
132 9.367 0.750 61.998
133 7.694 0.764 59.000
134 8.542 0.795 57.161
135 7.396 0.686 59.305
136 7.362 0.569 54.914
137 8.145 0.708 55.809
138 7.434 0.630 51.651
139 8.755 0.603 60.633
140 6.635 0.490 53.400
141 7.578 0.832 57.122
142 7.876 0.702 57.999
143 7.477 0.540 55.700
144 6.958 0.537 57.948
145 7.926 0.787 48.700
146 9.782 0.784 59.269
147 7.676 0.552 61.400
148 7.943 0.750 56.201
149 7.695 0.463 52.493
Freedom.to.make.life.choices Generosity Perceptions.of.corruption
1 0.949 -0.098 0.186
2 0.946 0.030 0.179
3 0.919 0.025 0.292
4 0.955 0.160 0.673
5 0.913 0.175 0.338
6 0.960 0.093 0.270
7 0.945 0.086 0.237
8 0.907 -0.034 0.386
9 0.929 0.134 0.242
10 0.908 0.042 0.481
11 0.914 0.159 0.442
12 0.800 0.031 0.753
13 0.875 0.011 0.460
14 0.915 0.089 0.415
15 0.879 0.077 0.363
16 0.934 -0.126 0.809
17 0.859 0.233 0.459
18 0.858 -0.208 0.868
19 0.837 0.098 0.698
20 0.783 -0.153 0.646
21 0.822 -0.147 0.571
22 0.925 0.089 0.722
23 0.927 0.133 0.653
24 0.784 -0.070 0.721
25 0.932 0.074 0.589
26 0.877 -0.149 0.684
27 0.761 -0.081 0.745
28 0.693 -0.084 0.866
29 0.949 -0.101 0.806
30 0.906 -0.038 0.775
31 0.896 -0.092 0.590
32 0.927 -0.018 0.082
33 0.869 0.257 0.917
34 0.766 -0.124 0.911
35 0.804 -0.071 0.756
36 0.862 -0.147 0.799
37 0.890 -0.137 0.884
38 0.773 -0.203 0.826
39 0.763 -0.015 0.844
40 0.909 -0.106 0.527
41 0.872 -0.166 0.856
42 0.970 0.311 0.515
43 0.742 -0.044 0.830
44 0.841 -0.165 0.735
45 0.853 -0.069 0.733
46 0.845 -0.219 0.938
47 0.867 -0.104 0.736
48 0.778 0.002 0.835
49 0.888 -0.110 0.688
50 0.867 -0.054 0.789
51 0.715 -0.162 0.800
52 0.837 -0.135 0.841
53 0.755 -0.186 0.876
54 0.884 0.287 0.895
55 0.836 0.020 0.664
56 0.796 -0.258 0.638
57 0.828 -0.182 0.834
58 0.892 -0.244 0.887
59 0.857 0.081 0.809
60 0.754 -0.118 0.939
61 0.917 -0.097 0.742
62 0.672 -0.083 0.727
63 0.822 -0.154 0.891
64 0.706 0.113 0.931
65 0.822 -0.079 0.918
66 0.842 -0.124 0.843
67 0.935 0.119 0.908
68 0.582 -0.288 0.823
69 0.875 -0.077 0.839
70 0.708 0.116 0.856
71 0.876 0.028 0.882
72 0.708 -0.034 0.812
73 0.860 -0.133 0.714
74 0.795 0.012 0.626
75 0.650 -0.180 0.627
76 0.718 -0.111 0.845
77 0.717 0.067 0.403
78 0.832 -0.056 0.553
79 0.940 -0.098 0.796
80 0.771 -0.087 0.667
81 0.895 0.125 0.839
82 0.873 0.542 0.867
83 0.695 -0.068 0.745
84 0.904 -0.146 0.755
85 0.741 -0.016 0.794
86 0.825 -0.168 0.629
87 0.782 0.152 0.727
88 0.788 -0.096 0.932
89 0.854 0.024 0.825
90 0.814 -0.223 0.506
91 0.731 0.026 0.848
92 0.695 -0.046 0.801
93 0.785 -0.030 0.901
94 0.751 0.038 0.905
95 0.807 0.123 0.848
96 0.806 0.018 0.693
97 0.877 0.273 0.888
98 0.697 0.424 0.746
99 0.757 -0.034 0.661
100 0.910 0.123 0.658
101 0.877 -0.041 0.682
102 0.697 0.095 0.766
103 0.749 -0.067 0.860
104 0.576 -0.139 0.776
105 0.726 0.098 0.787
106 0.774 -0.236 0.801
107 0.615 -0.169 0.827
108 0.783 -0.238 0.655
109 0.480 -0.067 0.752
110 0.724 -0.011 0.924
111 0.630 -0.053 0.875
112 0.731 -0.200 0.840
113 0.695 -0.009 0.748
114 0.959 0.034 0.843
115 0.882 0.061 0.684
116 0.737 0.037 0.878
117 0.697 -0.036 0.827
118 0.608 0.218 0.714
119 0.709 0.122 0.855
120 0.735 0.050 0.850
121 0.779 0.287 0.825
122 0.656 -0.201 0.870
123 0.525 -0.073 0.898
124 0.719 -0.149 0.847
125 0.653 -0.163 0.821
126 0.876 0.509 0.660
127 0.755 -0.167 0.705
128 0.579 0.041 0.807
129 0.841 0.079 0.863
130 0.647 -0.185 0.708
131 0.548 0.082 0.781
132 0.749 -0.182 0.795
133 0.752 0.082 0.761
134 0.561 -0.106 0.731
135 0.552 -0.005 0.803
136 0.619 0.032 0.772
137 0.782 0.061 0.823
138 0.717 0.084 0.866
139 0.893 0.089 0.774
140 0.626 -0.024 0.607
141 0.602 -0.147 0.800
142 0.833 0.183 0.577
143 0.593 0.422 0.721
144 0.780 0.038 0.729
145 0.715 -0.131 0.915
146 0.824 -0.246 0.801
147 0.897 0.061 0.167
148 0.677 -0.047 0.821
149 0.382 -0.102 0.924
Ladder.score.in.Dystopia Dystopia...residual
1 2.43 3.253
2 2.43 2.868
3 2.43 2.839
4 2.43 2.967
5 2.43 2.798
6 2.43 2.580
7 2.43 2.683
8 2.43 2.653
9 2.43 2.612
10 2.43 2.784
11 2.43 2.598
12 2.43 3.083
13 2.43 2.824
14 2.43 2.585
15 2.43 2.384
16 2.43 3.387
17 2.43 2.596
18 2.43 3.124
19 2.43 2.807
20 2.43 2.862
21 2.43 2.521
22 2.43 2.631
23 2.43 2.268
24 2.43 2.687
25 2.43 2.422
26 2.43 2.668
27 2.43 2.513
28 2.43 2.794
29 2.43 2.388
30 2.43 3.375
31 2.43 2.600
32 2.43 1.379
33 2.43 3.182
34 2.43 2.714
35 2.43 3.015
36 2.43 2.961
37 2.43 3.135
38 2.43 2.624
39 2.43 2.578
40 2.43 2.103
41 2.43 2.509
42 2.43 2.477
43 2.43 2.682
44 2.43 2.438
45 2.43 2.446
46 2.43 2.830
47 2.43 2.368
48 2.43 2.682
49 2.43 3.085
50 2.43 2.462
51 2.43 2.536
52 2.43 2.794
53 2.43 2.432
54 2.43 2.309
55 2.43 2.841
56 2.43 2.048
57 2.43 2.461
58 2.43 2.225
59 2.43 2.934
60 2.43 2.325
61 2.43 2.872
62 2.43 2.262
63 2.43 2.744
64 2.43 2.583
65 2.43 2.665
66 2.43 2.653
67 2.43 2.648
68 2.43 2.561
69 2.43 2.805
70 2.43 2.492
71 2.43 2.306
72 2.43 2.254
73 2.43 2.178
74 2.43 1.653
75 2.43 2.247
76 2.43 2.148
77 2.43 1.236
78 2.43 2.619
79 2.43 2.211
80 2.43 2.331
81 2.43 1.784
82 2.43 1.987
83 2.43 3.476
84 2.43 1.982
85 2.43 3.469
86 2.43 2.127
87 2.43 2.642
88 2.43 1.823
89 2.43 1.520
90 2.43 1.919
91 2.43 3.195
92 2.43 3.071
93 2.43 2.250
94 2.43 2.042
95 2.43 2.684
96 2.43 3.470
97 2.43 1.409
98 2.43 2.990
99 2.43 3.482
100 2.43 2.204
101 2.43 2.427
102 2.43 3.216
103 2.43 2.187
104 2.43 1.852
105 2.43 2.784
106 2.43 2.749
107 2.43 2.135
108 2.43 2.191
109 2.43 2.242
110 2.43 1.813
111 2.43 2.429
112 2.43 2.201
113 2.43 3.133
114 2.43 2.148
115 2.43 2.783
116 2.43 2.736
117 2.43 3.016
118 2.43 1.823
119 2.43 2.596
120 2.43 2.857
121 2.43 2.180
122 2.43 2.138
123 2.43 1.736
124 2.43 2.068
125 2.43 2.131
126 2.43 1.407
127 2.43 1.553
128 2.43 3.209
129 2.43 1.075
130 2.43 2.155
131 2.43 2.610
132 2.43 1.648
133 2.43 2.089
134 2.43 2.069
135 2.43 2.620
136 2.43 2.806
137 2.43 1.975
138 2.43 2.396
139 2.43 1.405
140 2.43 2.876
141 2.43 1.776
142 2.43 1.263
143 2.43 2.060
144 2.43 2.190
145 2.43 1.800
146 2.43 0.648
147 2.43 1.095
148 2.43 1.205
149 2.43 1.895
Renaming columns to have more descriptive names
renamed_dataset <- cleaned_dataset %>%rename(Region = Regional.indicator,`Happiness Score`= Ladder.score,`Freedom To Make Life Choices`= Freedom.to.make.life.choices )print (renamed_dataset)
Country.name Region
1 Finland Western Europe
2 Denmark Western Europe
3 Switzerland Western Europe
4 Iceland Western Europe
5 Netherlands Western Europe
6 Norway Western Europe
7 Sweden Western Europe
8 Luxembourg Western Europe
9 New Zealand North America and ANZ
10 Austria Western Europe
11 Australia North America and ANZ
12 Israel Middle East and North Africa
13 Germany Western Europe
14 Canada North America and ANZ
15 Ireland Western Europe
16 Costa Rica Latin America and Caribbean
17 United Kingdom Western Europe
18 Czech Republic Central and Eastern Europe
19 United States North America and ANZ
20 Belgium Western Europe
21 France Western Europe
22 Bahrain Middle East and North Africa
23 Malta Western Europe
24 Taiwan Province of China East Asia
25 United Arab Emirates Middle East and North Africa
26 Saudi Arabia Middle East and North Africa
27 Spain Western Europe
28 Italy Western Europe
29 Slovenia Central and Eastern Europe
30 Guatemala Latin America and Caribbean
31 Uruguay Latin America and Caribbean
32 Singapore Southeast Asia
33 Kosovo Central and Eastern Europe
34 Slovakia Central and Eastern Europe
35 Brazil Latin America and Caribbean
36 Mexico Latin America and Caribbean
37 Jamaica Latin America and Caribbean
38 Lithuania Central and Eastern Europe
39 Cyprus Western Europe
40 Estonia Central and Eastern Europe
41 Panama Latin America and Caribbean
42 Uzbekistan Commonwealth of Independent States
43 Chile Latin America and Caribbean
44 Poland Central and Eastern Europe
45 Kazakhstan Commonwealth of Independent States
46 Romania Central and Eastern Europe
47 Kuwait Middle East and North Africa
48 Serbia Central and Eastern Europe
49 El Salvador Latin America and Caribbean
50 Mauritius Sub-Saharan Africa
51 Latvia Central and Eastern Europe
52 Colombia Latin America and Caribbean
53 Hungary Central and Eastern Europe
54 Thailand Southeast Asia
55 Nicaragua Latin America and Caribbean
56 Japan East Asia
57 Argentina Latin America and Caribbean
58 Portugal Western Europe
59 Honduras Latin America and Caribbean
60 Croatia Central and Eastern Europe
61 Philippines Southeast Asia
62 South Korea East Asia
63 Peru Latin America and Caribbean
64 Bosnia and Herzegovina Central and Eastern Europe
65 Moldova Commonwealth of Independent States
66 Ecuador Latin America and Caribbean
67 Kyrgyzstan Commonwealth of Independent States
68 Greece Western Europe
69 Bolivia Latin America and Caribbean
70 Mongolia East Asia
71 Paraguay Latin America and Caribbean
72 Montenegro Central and Eastern Europe
73 Dominican Republic Latin America and Caribbean
74 North Cyprus Western Europe
75 Belarus Commonwealth of Independent States
76 Russia Commonwealth of Independent States
77 Hong Kong S.A.R. of China East Asia
78 Tajikistan Commonwealth of Independent States
79 Vietnam Southeast Asia
80 Libya Middle East and North Africa
81 Malaysia Southeast Asia
82 Indonesia Southeast Asia
83 Congo (Brazzaville) Sub-Saharan Africa
84 China East Asia
85 Ivory Coast Sub-Saharan Africa
86 Armenia Commonwealth of Independent States
87 Nepal South Asia
88 Bulgaria Central and Eastern Europe
89 Maldives South Asia
90 Azerbaijan Commonwealth of Independent States
91 Cameroon Sub-Saharan Africa
92 Senegal Sub-Saharan Africa
93 Albania Central and Eastern Europe
94 North Macedonia Central and Eastern Europe
95 Ghana Sub-Saharan Africa
96 Niger Sub-Saharan Africa
97 Turkmenistan Commonwealth of Independent States
98 Gambia Sub-Saharan Africa
99 Benin Sub-Saharan Africa
100 Laos Southeast Asia
101 Bangladesh South Asia
102 Guinea Sub-Saharan Africa
103 South Africa Sub-Saharan Africa
104 Turkey Middle East and North Africa
105 Pakistan South Asia
106 Morocco Middle East and North Africa
107 Venezuela Latin America and Caribbean
108 Georgia Commonwealth of Independent States
109 Algeria Middle East and North Africa
110 Ukraine Commonwealth of Independent States
111 Iraq Middle East and North Africa
112 Gabon Sub-Saharan Africa
113 Burkina Faso Sub-Saharan Africa
114 Cambodia Southeast Asia
115 Mozambique Sub-Saharan Africa
116 Nigeria Sub-Saharan Africa
117 Mali Sub-Saharan Africa
118 Iran Middle East and North Africa
119 Uganda Sub-Saharan Africa
120 Liberia Sub-Saharan Africa
121 Kenya Sub-Saharan Africa
122 Tunisia Middle East and North Africa
123 Lebanon Middle East and North Africa
124 Namibia Sub-Saharan Africa
125 Palestinian Territories Middle East and North Africa
126 Myanmar Southeast Asia
127 Jordan Middle East and North Africa
128 Chad Sub-Saharan Africa
129 Sri Lanka South Asia
130 Swaziland Sub-Saharan Africa
131 Comoros Sub-Saharan Africa
132 Egypt Middle East and North Africa
133 Ethiopia Sub-Saharan Africa
134 Mauritania Sub-Saharan Africa
135 Madagascar Sub-Saharan Africa
136 Togo Sub-Saharan Africa
137 Zambia Sub-Saharan Africa
138 Sierra Leone Sub-Saharan Africa
139 India South Asia
140 Burundi Sub-Saharan Africa
141 Yemen Middle East and North Africa
142 Tanzania Sub-Saharan Africa
143 Haiti Latin America and Caribbean
144 Malawi Sub-Saharan Africa
145 Lesotho Sub-Saharan Africa
146 Botswana Sub-Saharan Africa
147 Rwanda Sub-Saharan Africa
148 Zimbabwe Sub-Saharan Africa
149 Afghanistan South Asia
Happiness Score Standard.error.of.ladder.score upperwhisker lowerwhisker
1 7.842 0.032 7.904 7.780
2 7.620 0.035 7.687 7.552
3 7.571 0.036 7.643 7.500
4 7.554 0.059 7.670 7.438
5 7.464 0.027 7.518 7.410
6 7.392 0.035 7.462 7.323
7 7.363 0.036 7.433 7.293
8 7.324 0.037 7.396 7.252
9 7.277 0.040 7.355 7.198
10 7.268 0.036 7.337 7.198
11 7.183 0.041 7.265 7.102
12 7.157 0.034 7.224 7.090
13 7.155 0.040 7.232 7.077
14 7.103 0.042 7.185 7.021
15 7.085 0.040 7.164 7.006
16 7.069 0.056 7.179 6.960
17 7.064 0.038 7.138 6.990
18 6.965 0.049 7.062 6.868
19 6.951 0.049 7.047 6.856
20 6.834 0.034 6.901 6.767
21 6.690 0.037 6.762 6.618
22 6.647 0.068 6.779 6.514
23 6.602 0.044 6.688 6.516
24 6.584 0.038 6.659 6.510
25 6.561 0.039 6.637 6.484
26 6.494 0.056 6.604 6.384
27 6.491 0.042 6.574 6.408
28 6.483 0.045 6.572 6.395
29 6.461 0.043 6.546 6.376
30 6.435 0.073 6.577 6.292
31 6.431 0.046 6.521 6.341
32 6.377 0.043 6.460 6.293
33 6.372 0.059 6.487 6.257
34 6.331 0.041 6.411 6.251
35 6.330 0.043 6.415 6.245
36 6.317 0.053 6.420 6.213
37 6.309 0.156 6.615 6.004
38 6.255 0.045 6.344 6.167
39 6.223 0.049 6.319 6.128
40 6.189 0.038 6.263 6.115
41 6.180 0.073 6.323 6.036
42 6.179 0.068 6.312 6.045
43 6.172 0.046 6.262 6.081
44 6.166 0.040 6.245 6.087
45 6.152 0.047 6.243 6.060
46 6.140 0.057 6.253 6.027
47 6.106 0.066 6.235 5.977
48 6.078 0.053 6.181 5.974
49 6.061 0.065 6.188 5.933
50 6.049 0.059 6.165 5.933
51 6.032 0.036 6.103 5.961
52 6.012 0.061 6.132 5.892
53 5.992 0.047 6.085 5.899
54 5.985 0.047 6.077 5.893
55 5.972 0.083 6.134 5.810
56 5.940 0.040 6.020 5.861
57 5.929 0.056 6.040 5.819
58 5.929 0.055 6.037 5.821
59 5.919 0.082 6.081 5.758
60 5.882 0.048 5.975 5.788
61 5.880 0.052 5.982 5.778
62 5.845 0.042 5.928 5.763
63 5.840 0.075 5.988 5.692
64 5.813 0.050 5.911 5.715
65 5.766 0.046 5.856 5.677
66 5.764 0.057 5.875 5.653
67 5.744 0.046 5.834 5.653
68 5.723 0.046 5.813 5.632
69 5.716 0.053 5.819 5.613
70 5.677 0.042 5.760 5.595
71 5.653 0.092 5.832 5.473
72 5.581 0.054 5.686 5.475
73 5.545 0.071 5.685 5.405
74 5.536 0.051 5.636 5.435
75 5.534 0.047 5.625 5.442
76 5.477 0.033 5.541 5.413
77 5.477 0.049 5.573 5.380
78 5.466 0.034 5.532 5.400
79 5.411 0.039 5.488 5.334
80 5.410 0.076 5.558 5.262
81 5.384 0.049 5.480 5.289
82 5.345 0.056 5.454 5.235
83 5.342 0.097 5.533 5.151
84 5.339 0.029 5.397 5.281
85 5.306 0.078 5.460 5.152
86 5.283 0.058 5.397 5.168
87 5.269 0.070 5.406 5.132
88 5.266 0.054 5.371 5.160
89 5.198 0.072 5.339 5.057
90 5.171 0.040 5.250 5.091
91 5.142 0.074 5.288 4.996
92 5.132 0.068 5.266 4.998
93 5.117 0.059 5.234 5.001
94 5.101 0.051 5.202 5.001
95 5.088 0.067 5.219 4.958
96 5.074 0.102 5.273 4.875
97 5.066 0.036 5.136 4.996
98 5.051 0.089 5.225 4.877
99 5.045 0.073 5.189 4.901
100 5.030 0.045 5.119 4.941
101 5.025 0.046 5.115 4.934
102 4.984 0.090 5.160 4.808
103 4.956 0.060 5.074 4.839
104 4.948 0.046 5.038 4.857
105 4.934 0.068 5.066 4.802
106 4.918 0.060 5.036 4.800
107 4.892 0.064 5.017 4.767
108 4.891 0.054 4.998 4.785
109 4.887 0.053 4.991 4.783
110 4.875 0.052 4.977 4.773
111 4.854 0.059 4.970 4.738
112 4.852 0.075 4.998 4.706
113 4.834 0.081 4.993 4.675
114 4.830 0.067 4.963 4.698
115 4.794 0.103 4.997 4.592
116 4.759 0.052 4.861 4.658
117 4.723 0.082 4.884 4.563
118 4.721 0.055 4.828 4.614
119 4.636 0.073 4.780 4.493
120 4.625 0.106 4.833 4.417
121 4.607 0.072 4.747 4.466
122 4.596 0.058 4.709 4.484
123 4.584 0.055 4.691 4.477
124 4.574 0.064 4.700 4.448
125 4.517 0.067 4.649 4.384
126 4.426 0.052 4.527 4.324
127 4.395 0.062 4.516 4.273
128 4.355 0.094 4.540 4.171
129 4.325 0.066 4.454 4.196
130 4.308 0.071 4.448 4.168
131 4.289 0.084 4.454 4.123
132 4.283 0.045 4.371 4.195
133 4.275 0.051 4.374 4.175
134 4.227 0.070 4.365 4.090
135 4.208 0.072 4.349 4.068
136 4.107 0.077 4.258 3.956
137 4.073 0.069 4.209 3.938
138 3.849 0.077 4.001 3.698
139 3.819 0.026 3.869 3.769
140 3.775 0.107 3.985 3.565
141 3.658 0.070 3.794 3.521
142 3.623 0.071 3.762 3.485
143 3.615 0.173 3.953 3.276
144 3.600 0.092 3.781 3.419
145 3.512 0.120 3.748 3.276
146 3.467 0.074 3.611 3.322
147 3.415 0.068 3.548 3.282
148 3.145 0.058 3.259 3.030
149 2.523 0.038 2.596 2.449
Logged.GDP.per.capita Social.support Healthy.life.expectancy
1 10.775 0.954 72.000
2 10.933 0.954 72.700
3 11.117 0.942 74.400
4 10.878 0.983 73.000
5 10.932 0.942 72.400
6 11.053 0.954 73.300
7 10.867 0.934 72.700
8 11.647 0.908 72.600
9 10.643 0.948 73.400
10 10.906 0.934 73.300
11 10.796 0.940 73.900
12 10.575 0.939 73.503
13 10.873 0.903 72.500
14 10.776 0.926 73.800
15 11.342 0.947 72.400
16 9.880 0.891 71.400
17 10.707 0.934 72.500
18 10.556 0.947 70.807
19 11.023 0.920 68.200
20 10.823 0.906 72.199
21 10.704 0.942 74.000
22 10.669 0.862 69.495
23 10.674 0.931 72.200
24 10.871 0.898 69.600
25 11.085 0.844 67.333
26 10.743 0.891 66.603
27 10.571 0.932 74.700
28 10.623 0.880 73.800
29 10.529 0.948 71.400
30 9.053 0.813 64.958
31 9.966 0.925 69.100
32 11.488 0.915 76.953
33 9.318 0.821 63.813
34 10.369 0.936 69.201
35 9.577 0.882 66.601
36 9.859 0.831 68.597
37 9.186 0.877 67.500
38 10.499 0.935 67.906
39 10.576 0.802 73.898
40 10.481 0.941 68.800
41 10.350 0.896 69.652
42 8.836 0.918 65.255
43 10.071 0.882 70.000
44 10.382 0.898 69.702
45 10.155 0.952 65.200
46 10.284 0.832 67.355
47 10.817 0.843 66.900
48 9.787 0.873 68.600
49 9.054 0.762 66.402
50 10.008 0.905 66.701
51 10.315 0.927 67.100
52 9.557 0.847 68.001
53 10.358 0.943 68.000
54 9.805 0.888 67.401
55 8.620 0.864 67.657
56 10.611 0.884 75.100
57 9.962 0.898 69.000
58 10.421 0.879 72.600
59 8.648 0.812 67.300
60 10.217 0.924 70.799
61 9.076 0.830 62.000
62 10.651 0.799 73.900
63 9.458 0.832 68.250
64 9.590 0.870 68.098
65 9.454 0.857 65.699
66 9.313 0.821 68.800
67 8.538 0.893 64.401
68 10.279 0.823 72.600
69 9.046 0.810 63.901
70 9.400 0.935 62.500
71 9.448 0.893 65.900
72 9.940 0.858 68.699
73 9.802 0.853 66.102
74 10.576 0.820 73.898
75 9.853 0.910 66.253
76 10.189 0.903 64.703
77 11.000 0.836 76.820
78 8.091 0.860 64.281
79 8.973 0.850 68.034
80 9.622 0.827 62.300
81 10.238 0.817 67.102
82 9.365 0.811 62.236
83 8.117 0.636 58.221
84 9.673 0.811 69.593
85 8.551 0.644 50.114
86 9.487 0.799 67.055
87 8.120 0.774 64.233
88 10.016 0.931 67.000
89 9.826 0.913 70.600
90 9.569 0.836 65.656
91 8.189 0.710 53.515
92 8.118 0.710 59.802
93 9.520 0.697 68.999
94 9.693 0.805 65.474
95 8.580 0.727 57.586
96 7.098 0.641 53.780
97 9.629 0.983 62.409
98 7.686 0.690 55.160
99 8.087 0.489 54.713
100 8.947 0.728 58.968
101 8.454 0.693 64.800
102 7.838 0.639 55.008
103 9.403 0.860 56.904
104 10.240 0.822 67.199
105 8.458 0.651 58.709
106 8.903 0.560 66.208
107 9.073 0.861 66.700
108 9.585 0.671 64.300
109 9.342 0.802 66.005
110 9.436 0.888 64.902
111 9.240 0.746 60.583
112 9.603 0.776 59.962
113 7.678 0.672 54.151
114 8.360 0.765 62.000
115 7.158 0.744 54.706
116 8.533 0.740 50.102
117 7.744 0.724 51.969
118 9.584 0.710 66.300
119 7.677 0.781 56.101
120 7.288 0.720 56.498
121 8.361 0.688 60.704
122 9.266 0.691 67.201
123 9.626 0.848 67.355
124 9.161 0.818 56.799
125 8.485 0.826 62.250
126 8.541 0.779 59.302
127 9.182 0.767 67.000
128 7.364 0.619 48.478
129 9.470 0.827 67.299
130 9.065 0.770 50.833
131 8.031 0.626 57.349
132 9.367 0.750 61.998
133 7.694 0.764 59.000
134 8.542 0.795 57.161
135 7.396 0.686 59.305
136 7.362 0.569 54.914
137 8.145 0.708 55.809
138 7.434 0.630 51.651
139 8.755 0.603 60.633
140 6.635 0.490 53.400
141 7.578 0.832 57.122
142 7.876 0.702 57.999
143 7.477 0.540 55.700
144 6.958 0.537 57.948
145 7.926 0.787 48.700
146 9.782 0.784 59.269
147 7.676 0.552 61.400
148 7.943 0.750 56.201
149 7.695 0.463 52.493
Freedom To Make Life Choices Generosity Perceptions.of.corruption
1 0.949 -0.098 0.186
2 0.946 0.030 0.179
3 0.919 0.025 0.292
4 0.955 0.160 0.673
5 0.913 0.175 0.338
6 0.960 0.093 0.270
7 0.945 0.086 0.237
8 0.907 -0.034 0.386
9 0.929 0.134 0.242
10 0.908 0.042 0.481
11 0.914 0.159 0.442
12 0.800 0.031 0.753
13 0.875 0.011 0.460
14 0.915 0.089 0.415
15 0.879 0.077 0.363
16 0.934 -0.126 0.809
17 0.859 0.233 0.459
18 0.858 -0.208 0.868
19 0.837 0.098 0.698
20 0.783 -0.153 0.646
21 0.822 -0.147 0.571
22 0.925 0.089 0.722
23 0.927 0.133 0.653
24 0.784 -0.070 0.721
25 0.932 0.074 0.589
26 0.877 -0.149 0.684
27 0.761 -0.081 0.745
28 0.693 -0.084 0.866
29 0.949 -0.101 0.806
30 0.906 -0.038 0.775
31 0.896 -0.092 0.590
32 0.927 -0.018 0.082
33 0.869 0.257 0.917
34 0.766 -0.124 0.911
35 0.804 -0.071 0.756
36 0.862 -0.147 0.799
37 0.890 -0.137 0.884
38 0.773 -0.203 0.826
39 0.763 -0.015 0.844
40 0.909 -0.106 0.527
41 0.872 -0.166 0.856
42 0.970 0.311 0.515
43 0.742 -0.044 0.830
44 0.841 -0.165 0.735
45 0.853 -0.069 0.733
46 0.845 -0.219 0.938
47 0.867 -0.104 0.736
48 0.778 0.002 0.835
49 0.888 -0.110 0.688
50 0.867 -0.054 0.789
51 0.715 -0.162 0.800
52 0.837 -0.135 0.841
53 0.755 -0.186 0.876
54 0.884 0.287 0.895
55 0.836 0.020 0.664
56 0.796 -0.258 0.638
57 0.828 -0.182 0.834
58 0.892 -0.244 0.887
59 0.857 0.081 0.809
60 0.754 -0.118 0.939
61 0.917 -0.097 0.742
62 0.672 -0.083 0.727
63 0.822 -0.154 0.891
64 0.706 0.113 0.931
65 0.822 -0.079 0.918
66 0.842 -0.124 0.843
67 0.935 0.119 0.908
68 0.582 -0.288 0.823
69 0.875 -0.077 0.839
70 0.708 0.116 0.856
71 0.876 0.028 0.882
72 0.708 -0.034 0.812
73 0.860 -0.133 0.714
74 0.795 0.012 0.626
75 0.650 -0.180 0.627
76 0.718 -0.111 0.845
77 0.717 0.067 0.403
78 0.832 -0.056 0.553
79 0.940 -0.098 0.796
80 0.771 -0.087 0.667
81 0.895 0.125 0.839
82 0.873 0.542 0.867
83 0.695 -0.068 0.745
84 0.904 -0.146 0.755
85 0.741 -0.016 0.794
86 0.825 -0.168 0.629
87 0.782 0.152 0.727
88 0.788 -0.096 0.932
89 0.854 0.024 0.825
90 0.814 -0.223 0.506
91 0.731 0.026 0.848
92 0.695 -0.046 0.801
93 0.785 -0.030 0.901
94 0.751 0.038 0.905
95 0.807 0.123 0.848
96 0.806 0.018 0.693
97 0.877 0.273 0.888
98 0.697 0.424 0.746
99 0.757 -0.034 0.661
100 0.910 0.123 0.658
101 0.877 -0.041 0.682
102 0.697 0.095 0.766
103 0.749 -0.067 0.860
104 0.576 -0.139 0.776
105 0.726 0.098 0.787
106 0.774 -0.236 0.801
107 0.615 -0.169 0.827
108 0.783 -0.238 0.655
109 0.480 -0.067 0.752
110 0.724 -0.011 0.924
111 0.630 -0.053 0.875
112 0.731 -0.200 0.840
113 0.695 -0.009 0.748
114 0.959 0.034 0.843
115 0.882 0.061 0.684
116 0.737 0.037 0.878
117 0.697 -0.036 0.827
118 0.608 0.218 0.714
119 0.709 0.122 0.855
120 0.735 0.050 0.850
121 0.779 0.287 0.825
122 0.656 -0.201 0.870
123 0.525 -0.073 0.898
124 0.719 -0.149 0.847
125 0.653 -0.163 0.821
126 0.876 0.509 0.660
127 0.755 -0.167 0.705
128 0.579 0.041 0.807
129 0.841 0.079 0.863
130 0.647 -0.185 0.708
131 0.548 0.082 0.781
132 0.749 -0.182 0.795
133 0.752 0.082 0.761
134 0.561 -0.106 0.731
135 0.552 -0.005 0.803
136 0.619 0.032 0.772
137 0.782 0.061 0.823
138 0.717 0.084 0.866
139 0.893 0.089 0.774
140 0.626 -0.024 0.607
141 0.602 -0.147 0.800
142 0.833 0.183 0.577
143 0.593 0.422 0.721
144 0.780 0.038 0.729
145 0.715 -0.131 0.915
146 0.824 -0.246 0.801
147 0.897 0.061 0.167
148 0.677 -0.047 0.821
149 0.382 -0.102 0.924
Ladder.score.in.Dystopia Dystopia...residual
1 2.43 3.253
2 2.43 2.868
3 2.43 2.839
4 2.43 2.967
5 2.43 2.798
6 2.43 2.580
7 2.43 2.683
8 2.43 2.653
9 2.43 2.612
10 2.43 2.784
11 2.43 2.598
12 2.43 3.083
13 2.43 2.824
14 2.43 2.585
15 2.43 2.384
16 2.43 3.387
17 2.43 2.596
18 2.43 3.124
19 2.43 2.807
20 2.43 2.862
21 2.43 2.521
22 2.43 2.631
23 2.43 2.268
24 2.43 2.687
25 2.43 2.422
26 2.43 2.668
27 2.43 2.513
28 2.43 2.794
29 2.43 2.388
30 2.43 3.375
31 2.43 2.600
32 2.43 1.379
33 2.43 3.182
34 2.43 2.714
35 2.43 3.015
36 2.43 2.961
37 2.43 3.135
38 2.43 2.624
39 2.43 2.578
40 2.43 2.103
41 2.43 2.509
42 2.43 2.477
43 2.43 2.682
44 2.43 2.438
45 2.43 2.446
46 2.43 2.830
47 2.43 2.368
48 2.43 2.682
49 2.43 3.085
50 2.43 2.462
51 2.43 2.536
52 2.43 2.794
53 2.43 2.432
54 2.43 2.309
55 2.43 2.841
56 2.43 2.048
57 2.43 2.461
58 2.43 2.225
59 2.43 2.934
60 2.43 2.325
61 2.43 2.872
62 2.43 2.262
63 2.43 2.744
64 2.43 2.583
65 2.43 2.665
66 2.43 2.653
67 2.43 2.648
68 2.43 2.561
69 2.43 2.805
70 2.43 2.492
71 2.43 2.306
72 2.43 2.254
73 2.43 2.178
74 2.43 1.653
75 2.43 2.247
76 2.43 2.148
77 2.43 1.236
78 2.43 2.619
79 2.43 2.211
80 2.43 2.331
81 2.43 1.784
82 2.43 1.987
83 2.43 3.476
84 2.43 1.982
85 2.43 3.469
86 2.43 2.127
87 2.43 2.642
88 2.43 1.823
89 2.43 1.520
90 2.43 1.919
91 2.43 3.195
92 2.43 3.071
93 2.43 2.250
94 2.43 2.042
95 2.43 2.684
96 2.43 3.470
97 2.43 1.409
98 2.43 2.990
99 2.43 3.482
100 2.43 2.204
101 2.43 2.427
102 2.43 3.216
103 2.43 2.187
104 2.43 1.852
105 2.43 2.784
106 2.43 2.749
107 2.43 2.135
108 2.43 2.191
109 2.43 2.242
110 2.43 1.813
111 2.43 2.429
112 2.43 2.201
113 2.43 3.133
114 2.43 2.148
115 2.43 2.783
116 2.43 2.736
117 2.43 3.016
118 2.43 1.823
119 2.43 2.596
120 2.43 2.857
121 2.43 2.180
122 2.43 2.138
123 2.43 1.736
124 2.43 2.068
125 2.43 2.131
126 2.43 1.407
127 2.43 1.553
128 2.43 3.209
129 2.43 1.075
130 2.43 2.155
131 2.43 2.610
132 2.43 1.648
133 2.43 2.089
134 2.43 2.069
135 2.43 2.620
136 2.43 2.806
137 2.43 1.975
138 2.43 2.396
139 2.43 1.405
140 2.43 2.876
141 2.43 1.776
142 2.43 1.263
143 2.43 2.060
144 2.43 2.190
145 2.43 1.800
146 2.43 0.648
147 2.43 1.095
148 2.43 1.205
149 2.43 1.895
Obtaining a frequency table using the janitor package
To understand the distribution of observations across different regions, providing an overview of how data is distributed among different categories.
Region n percent
Central and Eastern Europe 17 0.11409396
Commonwealth of Independent States 12 0.08053691
East Asia 6 0.04026846
Latin America and Caribbean 20 0.13422819
Middle East and North Africa 17 0.11409396
North America and ANZ 4 0.02684564
South Asia 7 0.04697987
Southeast Asia 9 0.06040268
Sub-Saharan Africa 36 0.24161074
Western Europe 21 0.14093960
Calculate mean happiness score and social support by region
To summarize and analyze the average happiness score and social support within each region, enabling comparison and identification of regional trends.
Creating a new variable to represent the economic freedom score, happiness index, health index and using mutation function providing additional insights and facilitating further analysis of the dataset.
mutated_dataset <- renamed_dataset %>%mutate(Economic_Freedom_Score =`Logged.GDP.per.capita`*`Freedom To Make Life Choices`,Happiness_Index =`Happiness Score`/max(`Happiness Score`),Health_Index = (`Social.support`+`Healthy.life.expectancy`) /2 )
Forcats Package
Applying fct_rev
Utilization of fct_rev to reverse the order of factor levels within the categorical variable “Region”. By reversing the factor levels, we can highlight different regions characteristics.
[1] "Western Europe" "Sub-Saharan Africa"
[3] "Southeast Asia" "South Asia"
[5] "North America and ANZ" "Middle East and North Africa"
[7] "Latin America and Caribbean" "East Asia"
[9] "Commonwealth of Independent States" "Central and Eastern Europe"
Reorder levels of Region based on median Happiness Score
Utilization of fct_reorder was to reorder the levels of the “region” variable based on the median “Happiness score”. By prioritizing regions with higher median happiness scores, we can emphasize the importance of understanding the factors contributing to well-being in these regions.
[1] "Sub-Saharan Africa" "Middle East and North Africa"
[3] "South Asia" "Southeast Asia"
[5] "Commonwealth of Independent States" "East Asia"
[7] "Latin America and Caribbean" "Central and Eastern Europe"
[9] "Western Europe" "North America and ANZ"
