Data Visualization
World Happiness Report 2020
The World Happiness Report is a landmark survey of the state of global happiness. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness. Data collecting from http://worldhappiness.report and http://www.gallup.com/corporate/212381/who-we-are.aspx
Dataset have a columns following the happiness score estimate the extent to which each of six factors – economic production / GDP per capita, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors.
Below description of the column in world happiness 2020 dataset :
- Ladder score: Happiness score or subjective well-being. This is the national average response to the question of life evaluations.
- Logged GDP per capita: Extent to which GDP contributes to the calculation of the Ladder score.
- Social support: Social support refers to assistance or support provided by members of social networks (like government) to an individual.
- Healthy life expectancy: Healthy life expectancy is the average life in good health - that is to say without irreversible limitation of activity in daily life or incapacities of a fictitious generation subject to the conditions of mortality and morbidity prevailing that year.
- Freedom to make life choices: Freedom to make life choices is the national average of binary responses question “Are you satisfied or dissatisfied with your freedom to choose what you do with your life?”
- Generosity is the residual of regressing national average of response to the GWP question “Have you donated money to a charity in the past month?” on GDP per capita.
- Perceptions of corruption: The measure is the national average of the survey responses to two questions : “Is corruption widespread throughout the government or not” and “Is corruption widespread within businesses or not?”
- Dystopia.residual: It has values equal to the world’s lowest national averages. Dystopia as a benchmark against which to compare contributions from each of the six factors. Dystopia is an imaginary country that has the world’s least-happy people.
Import Library
library(tidyverse)
library(plotly)
library(scales)
library(hrbrthemes)
library(dplyr)
library(tidyr)
library(reshape2)
library(ggcorrplot)
library(rnaturalearth)
library(rnaturalearthdata)
library(sf)
library(tmap)Read Data
whr <- read.csv("data/world-happiness-report-2021.csv")
# Check structure data
glimpse(whr)Rows: 149
Columns: 20
$ ï..Country.name <chr> "Finland", "Denmark", "Swit~
$ Regional.indicator <chr> "Western Europe", "Western ~
$ Ladder.score <dbl> 7.842, 7.620, 7.571, 7.554,~
$ Standard.error.of.ladder.score <dbl> 0.032, 0.035, 0.036, 0.059,~
$ upperwhisker <dbl> 7.904, 7.687, 7.643, 7.670,~
$ lowerwhisker <dbl> 7.780, 7.552, 7.500, 7.438,~
$ Logged.GDP.per.capita <dbl> 10.775, 10.933, 11.117, 10.~
$ Social.support <dbl> 0.954, 0.954, 0.942, 0.983,~
$ Healthy.life.expectancy <dbl> 72.000, 72.700, 74.400, 73.~
$ Freedom.to.make.life.choices <dbl> 0.949, 0.946, 0.919, 0.955,~
$ Generosity <dbl> -0.098, 0.030, 0.025, 0.160~
$ Perceptions.of.corruption <dbl> 0.186, 0.179, 0.292, 0.673,~
$ Ladder.score.in.Dystopia <dbl> 2.43, 2.43, 2.43, 2.43, 2.4~
$ Explained.by..Log.GDP.per.capita <dbl> 1.446, 1.502, 1.566, 1.482,~
$ Explained.by..Social.support <dbl> 1.106, 1.108, 1.079, 1.172,~
$ Explained.by..Healthy.life.expectancy <dbl> 0.741, 0.763, 0.816, 0.772,~
$ Explained.by..Freedom.to.make.life.choices <dbl> 0.691, 0.686, 0.653, 0.698,~
$ Explained.by..Generosity <dbl> 0.124, 0.208, 0.204, 0.293,~
$ Explained.by..Perceptions.of.corruption <dbl> 0.481, 0.485, 0.413, 0.170,~
$ Dystopia...residual <dbl> 3.253, 2.868, 2.839, 2.967,~Preprocessing Data
Data Cleaning and Wrangling
# Check whether any missing data in dataset column :
anyNA(whr)[1] FALSEthere is no missing value in whr dataset column, then I want to modify some column name in dataset, to make it easier for the analysis.
whr <- whr %>%
rename(
Country = ï..Country.name,
Region = Regional.indicator,
Score = Ladder.score,
GDP.per.capita = Logged.GDP.per.capita,
Dystopia.Residual = Dystopia...residual
)remove some column which not use in analysis processing
# remove column by index column number:
whr <- whr[-c(4,5,6,13,14,15,16,17,18,19)] names(whr) [1] "Country" "Region"
[3] "Score" "GDP.per.capita"
[5] "Social.support" "Healthy.life.expectancy"
[7] "Freedom.to.make.life.choices" "Generosity"
[9] "Perceptions.of.corruption" "Dystopia.Residual" There is one data type of the column still not accurate, which is Region column , it should be modify into factor data type :
# modifying Region column data type into factor
whr$Region <- as.factor(whr$Region)
glimpse(whr)Rows: 149
Columns: 10
$ Country <chr> "Finland", "Denmark", "Switzerland", "Ice~
$ Region <fct> Western Europe, Western Europe, Western E~
$ Score <dbl> 7.842, 7.620, 7.571, 7.554, 7.464, 7.392,~
$ GDP.per.capita <dbl> 10.775, 10.933, 11.117, 10.878, 10.932, 1~
$ Social.support <dbl> 0.954, 0.954, 0.942, 0.983, 0.942, 0.954,~
$ Healthy.life.expectancy <dbl> 72.000, 72.700, 74.400, 73.000, 72.400, 7~
$ Freedom.to.make.life.choices <dbl> 0.949, 0.946, 0.919, 0.955, 0.913, 0.960,~
$ Generosity <dbl> -0.098, 0.030, 0.025, 0.160, 0.175, 0.093~
$ Perceptions.of.corruption <dbl> 0.186, 0.179, 0.292, 0.673, 0.338, 0.270,~
$ Dystopia.Residual <dbl> 3.253, 2.868, 2.839, 2.967, 2.798, 2.580,~Region column its already change now , lets get more information from the data using summary. Summary is used to get a description of statistical values from numeric data by using the summary() function, it will help to get quick insight from the data we have.
summary(whr) Country Region Score
Length:149 Sub-Saharan Africa :36 Min. :2.523
Class :character Western Europe :21 1st Qu.:4.852
Mode :character Latin America and Caribbean :20 Median :5.534
Central and Eastern Europe :17 Mean :5.533
Middle East and North Africa :17 3rd Qu.:6.255
Commonwealth of Independent States:12 Max. :7.842
(Other) :26
GDP.per.capita Social.support Healthy.life.expectancy
Min. : 6.635 Min. :0.4630 Min. :48.48
1st Qu.: 8.541 1st Qu.:0.7500 1st Qu.:59.80
Median : 9.569 Median :0.8320 Median :66.60
Mean : 9.432 Mean :0.8147 Mean :64.99
3rd Qu.:10.421 3rd Qu.:0.9050 3rd Qu.:69.60
Max. :11.647 Max. :0.9830 Max. :76.95
Freedom.to.make.life.choices Generosity Perceptions.of.corruption
Min. :0.3820 Min. :-0.28800 Min. :0.0820
1st Qu.:0.7180 1st Qu.:-0.12600 1st Qu.:0.6670
Median :0.8040 Median :-0.03600 Median :0.7810
Mean :0.7916 Mean :-0.01513 Mean :0.7274
3rd Qu.:0.8770 3rd Qu.: 0.07900 3rd Qu.:0.8450
Max. :0.9700 Max. : 0.54200 Max. :0.9390
Dystopia.Residual
Min. :0.648
1st Qu.:2.138
Median :2.509
Mean :2.430
3rd Qu.:2.794
Max. :3.482
Interpretation :
- Length Country: 149 , describe total country in dataset , it means also the data have 149 total rows
- For the region : Sub-Saharan Africa have the most countries , 36 countries, meanwhile Commonwealth of Independent States only have 12 countries
- For the happines score minimum values is 2.523 and maximum value is 7.842, and mean value is about 5.533
- We can find out also the minimum GDP per capita is 6.635 and maximum GDP per capita is 11.647, mean : 9.432
- Social.support Min value is :0.4630, and Max value :0.9830
- Healthy.life.expectancy Min value is :48.48, Max value is :76.95
- Freedom.to.make.life.choice Min value :0.3820, Max value :0.9700
- its interesting value in Generosity it have minus for Min value :-0.28800, and Max value is: 0.54200
- Perceptions.of.corruption Min value is :0.0820 , Max value is:0.9390
Processing Data and Plotting
# Create manual color for plotting :
pallete <- c("#5bdcdc", "#7a0651", "#06517a",
"#808080", "#bc8f8f", "#8fa6bc",
"#bc8fbc", "#8fbcbc","#e3aaa3","#9695a4")Check how many countries in each region :
# Check how many country in each region :
ggplot(whr, aes(y = Region,fill = Region))+
geom_bar()+
scale_fill_manual(values = pallete)+
theme_ipsum()
Correlation
First, check the correlation between happiness score and the six factor : GDP.per.capita, Social.support, Healthy.life.expectancy, Freedom.to.make.life.choices, Generosity, Perceptions.of.corruption,from the visualization we can find out which factor is the strongest .
# plot correlation Happiness Score & GDp per capita
gdp_plot <- ggplot(data = whr, aes(x = GDP.per.capita, y = Score)) +
geom_point(colour = "gray", shape = 21, size =4, aes(fill = Region)) +
scale_fill_brewer(type = "seq", palette = "Spectral") +
theme_minimal() +
labs(title = "Does GDP bring Happiness?")
gdp_plot
# plot correlation Happiness Score & Social Life Expectancy
ss_plot <- ggplot(data = whr, aes(x = Social.support, y = Score)) +
geom_point(colour = "gray", shape = 21, size =4, aes(fill = Region)) +
scale_fill_brewer(type = "seq", palette = "Spectral") +
theme_minimal() +
labs(title = "Does Social Support bring Happiness?")
ss_plot
# plot correlation Happiness Score & Healthy.life.expectancy
healthy_plot <- ggplot(data = whr, aes(x = Healthy.life.expectancy, y = Score)) +
geom_point(colour = "gray", shape = 21, size =4, aes(fill = Region)) +
scale_fill_brewer(type = "seq", palette = "Spectral") +
theme_minimal() +
labs(title = "Does Healthy life bring Happiness?")
healthy_plot
# plot correlation Happiness Score & Freedom
freedom_plot <- ggplot(data = whr, aes(x = Freedom.to.make.life.choices, y = Score)) +
geom_point(colour = "gray", shape = 21, size =4, aes(fill = Region)) +
scale_fill_brewer(type = "seq", palette = "Spectral") +
theme_minimal() +
labs(title = "Does Freedom bring Happiness?")
freedom_plot
# plot correlation Happiness Score & Generosity
generosity_plot <- ggplot(data = whr, aes(x = Generosity, y = Score)) +
geom_point(colour = "gray", shape = 21, size =4, aes(fill = Region)) +
scale_fill_brewer(type = "seq", palette = "Spectral") +
theme_minimal() +
labs(title = "Does Generosity bring Happiness?")
generosity_plot
# plot correlation Happiness Score & Corruption
corruption_plot <- ggplot(data = whr, aes(x = Perceptions.of.corruption, y = Score)) +
geom_point(colour = "gray", shape = 21, size =4, aes(fill = Region)) +
scale_fill_brewer(type = "seq", palette = "Spectral") +
theme_minimal() +
labs(title = "Does Corruption bring Happiness?")
corruption_plot
# Select numeric column & create object corr_data
corr_data <- whr %>%
select(Score,GDP.per.capita,Social.support,Healthy.life.expectancy,Freedom.to.make.life.choices,Generosity,Perceptions.of.corruption)
corr_data Score GDP.per.capita Social.support Healthy.life.expectancy
1 7.842 10.775 0.954 72.0
2 7.620 10.933 0.954 72.7
3 7.571 11.117 0.942 74.4
4 7.554 10.878 0.983 73.0
5 7.464 10.932 0.942 72.4
6 7.392 11.053 0.954 73.3
7 7.363 10.867 0.934 72.7
8 7.324 11.647 0.908 72.6
9 7.277 10.643 0.948 73.4
10 7.268 10.906 0.934 73.3
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
[ reached 'max' / getOption("max.print") -- omitted 139 rows ]# modifying data into matrix
corr_data <- as.matrix(corr_data)# create correlation object data
corr_data <- cor(corr_data)# Plotting data correlation
corrplot <- ggcorrplot(corr_data, method = "circle",lab=TRUE,lab_size = 3, colors = c("brown","pink","blue")) +
ggtitle("Correlation Graph between variables")
corrplot
Interpretations :
From the above output we can check which one have a strong correlation with the happiness score :
- Happiness score has strong level correlation with GDP per capita, Healthy life Expectancy, and Social Support
- GDP per capita score has strong correlation with Social support and Healthy Life expectancy.
- Happiness score and the value of Freedom to make life choice have middle level correlation between them.
- Between happiness score and the value of Generosity and perception of corruption have weak correlation, and both have a weak negative level correlation .
Distribution
Boxplot
I want to use boxplot for visualizing “Social support”,“Freedom to make life choices”,“Generosity”,and “Perception of corruption”, I take these 4 columns because of distribution of the numeric values are similar to each other ( all of them have the range between 0 and 1), and by using box plot we can see outliers better.
# Check the value of the column and assign into one object distribution
dist <- whr %>%
select(Social.support,Freedom.to.make.life.choices,Generosity,Perceptions.of.corruption)
dist Social.support Freedom.to.make.life.choices Generosity
1 0.954 0.949 -0.098
2 0.954 0.946 0.030
3 0.942 0.919 0.025
4 0.983 0.955 0.160
5 0.942 0.913 0.175
6 0.954 0.960 0.093
7 0.934 0.945 0.086
8 0.908 0.907 -0.034
9 0.948 0.929 0.134
10 0.934 0.908 0.042
11 0.940 0.914 0.159
12 0.939 0.800 0.031
13 0.903 0.875 0.011
14 0.926 0.915 0.089
15 0.947 0.879 0.077
16 0.891 0.934 -0.126
17 0.934 0.859 0.233
18 0.947 0.858 -0.208
Perceptions.of.corruption
1 0.186
2 0.179
3 0.292
4 0.673
5 0.338
6 0.270
7 0.237
8 0.386
9 0.242
10 0.481
11 0.442
12 0.753
13 0.460
14 0.415
15 0.363
16 0.809
17 0.459
18 0.868
[ reached 'max' / getOption("max.print") -- omitted 131 rows ]- Before plotting into boxplot we have to transform the data first , from wide to long using pivot longer function , as below :
# transform data distribution from wide to long :
dist <- pivot_longer(data = dist,
cols =Social.support:Freedom.to.make.life.choices:Generosity:Perceptions.of.corruption,
names_to = "variabel",
values_to = "value")
dist# A tibble: 596 x 2
variabel value
<chr> <dbl>
1 Social.support 0.954
2 Freedom.to.make.life.choices 0.949
3 Generosity -0.098
4 Perceptions.of.corruption 0.186
5 Social.support 0.954
6 Freedom.to.make.life.choices 0.946
7 Generosity 0.03
8 Perceptions.of.corruption 0.179
9 Social.support 0.942
10 Freedom.to.make.life.choices 0.919
# ... with 586 more rows# Plotting the data
ggplot(dist) +
geom_boxplot(aes(x=variabel, y=value, fill=variabel))+
scale_fill_manual(values = pallete)+
coord_flip()+
theme_ipsum()
- It is noticed the presence of some outliers, mainly in the Perceptions of corruption and Generosity. “Perceptron of corruption” variable has higher than the others meaning that only some countries have indicators above the majority in these indicators. - Generosity has a little bit different range when we compare with other three variables.
# distribution between happiness Score & GDP per capita
dist2 <- whr %>%
select(Score,GDP.per.capita)
dist2 Score GDP.per.capita
1 7.842 10.775
2 7.620 10.933
3 7.571 11.117
4 7.554 10.878
5 7.464 10.932
6 7.392 11.053
7 7.363 10.867
8 7.324 11.647
9 7.277 10.643
10 7.268 10.906
11 7.183 10.796
12 7.157 10.575
13 7.155 10.873
14 7.103 10.776
15 7.085 11.342
16 7.069 9.880
17 7.064 10.707
18 6.965 10.556
19 6.951 11.023
20 6.834 10.823
21 6.690 10.704
22 6.647 10.669
23 6.602 10.674
24 6.584 10.871
25 6.561 11.085
26 6.494 10.743
27 6.491 10.571
28 6.483 10.623
29 6.461 10.529
30 6.435 9.053
31 6.431 9.966
32 6.377 11.488
33 6.372 9.318
34 6.331 10.369
35 6.330 9.577
36 6.317 9.859
37 6.309 9.186
[ reached 'max' / getOption("max.print") -- omitted 112 rows ] dist2 <- pivot_longer(data = dist2,
cols =Score:GDP.per.capita,
names_to = "variabel",
values_to = "value")
dist2# A tibble: 298 x 2
variabel value
<chr> <dbl>
1 Score 7.84
2 GDP.per.capita 10.8
3 Score 7.62
4 GDP.per.capita 10.9
5 Score 7.57
6 GDP.per.capita 11.1
7 Score 7.55
8 GDP.per.capita 10.9
9 Score 7.46
10 GDP.per.capita 10.9
# ... with 288 more rows# Plotting the data
ggplot(dist2) +
geom_boxplot(aes(x=variabel, y=value, fill=variabel))+
scale_fill_manual(values = pallete)+
theme_ipsum()
- There is no outliers between these two variable and GDP per capita have a high of mean value ~ 9.00
dist3 <- whr %>%
select(Healthy.life.expectancy)
dist3 Healthy.life.expectancy
1 72.000
2 72.700
3 74.400
4 73.000
5 72.400
6 73.300
7 72.700
8 72.600
9 73.400
10 73.300
11 73.900
12 73.503
13 72.500
14 73.800
15 72.400
16 71.400
17 72.500
18 70.807
19 68.200
20 72.199
21 74.000
22 69.495
23 72.200
24 69.600
25 67.333
26 66.603
27 74.700
28 73.800
29 71.400
30 64.958
31 69.100
32 76.953
33 63.813
34 69.201
35 66.601
36 68.597
37 67.500
38 67.906
39 73.898
40 68.800
41 69.652
42 65.255
43 70.000
44 69.702
45 65.200
46 67.355
47 66.900
48 68.600
49 66.402
50 66.701
51 67.100
52 68.001
53 68.000
54 67.401
55 67.657
56 75.100
57 69.000
58 72.600
59 67.300
60 70.799
61 62.000
62 73.900
63 68.250
64 68.098
65 65.699
66 68.800
67 64.401
68 72.600
69 63.901
70 62.500
71 65.900
72 68.699
73 66.102
74 73.898
75 66.253
[ reached 'max' / getOption("max.print") -- omitted 74 rows ]dist3 <- pivot_longer(data = dist3,
cols =Healthy.life.expectancy,
names_to = "variabel",
values_to = "value")
dist3# A tibble: 149 x 2
variabel value
<chr> <dbl>
1 Healthy.life.expectancy 72
2 Healthy.life.expectancy 72.7
3 Healthy.life.expectancy 74.4
4 Healthy.life.expectancy 73
5 Healthy.life.expectancy 72.4
6 Healthy.life.expectancy 73.3
7 Healthy.life.expectancy 72.7
8 Healthy.life.expectancy 72.6
9 Healthy.life.expectancy 73.4
10 Healthy.life.expectancy 73.3
# ... with 139 more rows# Plotting the data
ggplot(dist3) +
geom_boxplot(aes(x=variabel, y=value, fill=variabel))+
scale_fill_manual(values = pallete)+
theme_ipsum()
- I created the boxplot separately because of the distribution of the numeric values higher than the others. From the output , there is no outliers also in healthy life variable, and the median value around ~ 66.
Density
I am going to use density plot to show the happiness score distribution according to the geographical location , region of the countries.
ggplot(whr, aes(Score, fill = Region)) +
scale_fill_manual(values = pallete)+
geom_density() +
xlim(1, 9)+
labs(title = "Happiness Score Distribution by Region")+
theme_ipsum()+
theme(plot.title = element_text(size = 14))
- According to distribution plot, we can say that the happiest region is Western Europe because the range value of the happiness score between 5 until around 7.8 and have a lot of distribution also from the plot density, then follow by North America and ANZ. - Plot also shows us the unhappiest region is South Asia and follow by Sub-Saharan Africa
Top 10 Happiest & Unhappiest Countries
# Subset top 10 happiest countries
happiest <- whr %>%
select(Country,Region,Score) %>%
arrange(desc(Score)) %>%
head(10)# Plotting top 10 Hapiest Countries
top10_happiest <- happiest %>%
ggplot(aes(x = Score,
y = reorder(Country, Score)))+
geom_col(fill = "#993366") +
labs(title = "Top 10 Happiest Countries",
x = "Score",
y = NULL) +
theme_ipsum() +
theme(axis.title.x = element_text(size = 8 ))
top10_happiest
- From the output we find out that the happiest country is Findland and follow by Denmark and Switzerland
# Subset unhappiest countries data :
unhappiest <- whr %>%
select(Country,Region, Score) %>%
arrange(Score) %>%
head(10)# Plotting top 10 Unhapiest Countries
top10_unhappiest <- unhappiest %>%
ggplot(aes(x = Score,
y = reorder(Country, Score))) +
geom_col(fill = "#d396a3") +
labs(title = "Top 10 Unhappiest Countries",
x = "Score",
y = NULL) +
theme_ipsum() +
theme(axis.title.x = element_text(size = 10 ))
top10_unhappiest
- From the output we find out that the unhappiest country is Afghanistan and follow by Zimbabwe and Rwanda
Map Visualisation
The tmap package was loaded to create the below charts. In this visualization , I just mapping the happiness score by each countries . With the help of the tm_text() function, we were able to load the respective countries’ names over the area of the map.
# create data for map location
maplocation <-
read_sf("Longitude_Graticules_and_World_Countries_Boundaries-shp/99bfd9e7-bb42-4728-87b5-07f8c8ac631c2020328-1-1vef4ev.lu5nk.shp")
str(maplocation)sf [251 x 3] (S3: sf/tbl_df/tbl/data.frame)
$ OBJECTID : int [1:251] 1 2 3 4 5 6 7 8 9 10 ...
$ CNTRY_NAME: chr [1:251] "Aruba" "Antigua and Barbuda" "Afghanistan" "Algeria" ...
$ geometry :sfc_MULTIPOLYGON of length 251; first list element: List of 1
..$ :List of 1
.. ..$ : num [1:11, 1:2] -69.9 -69.9 -70.1 -70.1 -70 ...
..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"
- attr(*, "sf_column")= chr "geometry"
- attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA
..- attr(*, "names")= chr [1:2] "OBJECTID" "CNTRY_NAME"# combined dataset & map location
combined_dataset <- left_join(maplocation,whr ,
by = c("CNTRY_NAME" = "Country"))
names(combined_dataset) [1] "OBJECTID" "CNTRY_NAME"
[3] "geometry" "Region"
[5] "Score" "GDP.per.capita"
[7] "Social.support" "Healthy.life.expectancy"
[9] "Freedom.to.make.life.choices" "Generosity"
[11] "Perceptions.of.corruption" "Dystopia.Residual" # check na value and select some column
combined_dataset <- combined_dataset[!is.na(combined_dataset$Score),] %>%
select(CNTRY_NAME,Region,Score,GDP.per.capita,Social.support,
Healthy.life.expectancy,Freedom.to.make.life.choices,
Generosity,Perceptions.of.corruption)# plotting map
tmap_mode("view")
tm_shape(combined_dataset) +
tm_fill("Score",
style = "quantile",
palette = "Greens") +
tm_borders(alpha = 0.5) +
tm_text("CNTRY_NAME", size="CNTRY_NAME")+
tmap_style("classic")Conclusion
- Happiness score has strong level correlation with GDP,Healthy life Expectancy, and Social Support . High scores among these categories speak to the likelihood of having a high overall Happiness Score.
- The variable that look like interesting is between Generosity and Corruption , this two variable have a lowest point at this analysis, It is a bigger concern, it means generosity didn’t effect to much for the happiness.
- Happiest countries tend to have longer life expectancy and a higher value of GDP ,which are also most of Western Europe.
- Finland is one of the happiest countries to live in with a high score reported, follow by Denmark and Switzerland
- Meanwhile Afghanistan is the unhappiest country with lowest score reported.
- Most of the Sub-Saharan African countries have lower level of happiness.
- From the analysing reports, we are able to interpret what makes countries and their citizens happier, thus allowing us to focus on prioritizing and improving these aspects of each nation.