Introduction

Welcome to this RMarkdown project, where we delve into the realm of advanced data visualization using the powerful ggplot2 package in R. In this project, we aim to provide insightful and compelling visual representations of various socio-economic and health indicators. Our focus is on analyzing and comparing different aspects of well-being and life satisfaction across selected countries.

To achieve this, we have sourced a rich collection of datasets from Eurostat, encompassing a wide range of topics such as GDP, population dynamics, net greenhouse emissions, death rates due to cancer and suicide, BMI, and more. These datasets provide a comprehensive backdrop for our exploration into how these selected countries vary in their levels of well-being and quality of life.

Through a series of ggplot-based visualizations, we will uncover trends, draw comparisons, and unearth stories hidden within the data. Our goal is to not just present data, but to interpret it in a way that is both meaningful and insightful, providing a deeper understanding of how these countries fare in terms of health, economy, and environmental impact.

Selecting countries for analysis

For more detailed analysis we chose 5 countries. One of the main goals is to compare Poland with “West” and check whether Poland’s indicators are at the same level.

  1. PL: Poland
  2. DE: Germany
  3. IT: Italy
  4. FR: France
  5. SE: Sweden

Data Visulisation

Youth unemployment rates

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The youth unemployment rate is a crucial indicator when evaluating the best country to live in because it reflects the economic opportunities and job market health for younger generations. Additionally, low youth unemployment is often associated with better overall economic stability and social well-being, which are key factors in determining the quality of life in a country.

The below plots present the Youth Unemployment Rate trends from 2012 to 2022 for a selection of countries: Germany (DE), France (FR), Italy (IT), Poland (PL), and Sweden (SE). In 2012, Italy recorded the highest youth unemployment rate at 35%, followed by Poland and France at 30%, Sweden at 23%, and Germany at 9%. Over the years, Poland has seen a consistent decline in this rate, reaching approximately 10% in 2022. France and Italy have also experienced a steady decrease, with their rates dropping to 17.5% and 25%, respectively, in 2022. Sweden’s lowest rate was observed in 2018 at 17%, but it rose again to 23% in 2022. Notably, Germany has maintained the lowest Youth Unemployment Rate throughout the period, culminating in a low of 6% in 2022.

During the decade-long period, Poland experienced the most dramatic percentage decrease in Youth Unemployment Rate, with a substantial 60% reduction.

Across the chosen countries, General Unemployment Rates are notably lower than Youth Unemployment Rates, with Italy recording the highest rate at 7%. There is a consistent relationship observed between the Youth Unemployment Rate and the General Unemployment Rate, where countries with higher rates in youth unemployment also tend to have higher general unemployment rates. However, Poland presents a unique case in this pattern, displaying a slightly lower General Unemployment Rate than Germany, thus standing out from the trend seen in other countries.

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Death Rate Due to Cancer

Considering the death rate due to cancer is crucial when evaluating a country’s overall well-being because it reflects the effectiveness of the healthcare system, including access to early detection, treatment, and preventive care. Additionally, lower cancer mortality rates often indicate a higher standard of public health policies and living conditions, which are key factors in assessing the quality of life in a country.

Among the chosen countries, Poland is notable for its substantially higher Death Rate Due to Cancer, ranging between 300 and 275 from 2012 to 2020. France, Italy, and Germany exhibit a comparable trend, maintaining death rates approximately within the range of 265 to 225 over the same decade. Sweden consistently reports the lowest cancer mortality rate among these nations, reaching its lowest point at 210 in 2020.

In the selected countries, a consistent trend is observed where the number of female deaths due to cancer is lower than that of male deaths.

Death due to suicide

The rate of deaths due to suicide is an important metric when assessing the nicest country to live in because it serves as a key indicator of mental health and overall societal well-being. A lower suicide rate often suggests effective mental health support, strong community networks, and the presence of policies aimed at promoting mental health and preventing suicide, all of which contribute to a higher quality of life and well-being in a country.

Among the selected countries, Italy stands out for maintaining the lowest suicide death rate between 2012 and 2022, consistently keeping it under 7.5. In contrast, the other four countries exhibit a similar trend, each with a suicide rate exceeding 10.

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Across all the selected countries, the death rate due to suicide is notably lower for females than for males, a trend that is consistent in each country.

BMI

BMI can reflect nutritional status among the population. A healthy average BMI suggests a balance between undernutrition and obesity, both of which are linked to various health problems. A country with a healthy average BMI likely has better access to nutritious food and a more health-conscious population. A country’s average BMI can also indicate the effectiveness of its public health policies and education. Lower rates of obesity (high BMI) and underweight (low BMI) suggest successful health initiatives and a population that has access to resources for maintaining a healthy lifestyle.

In the selected countries, Poland exhibits the highest BMI between 2014 and 2019, uniquely showing an increase from 37.5 in 2014 to 39 in 2019. Contrarily, Germany stands out as the only country where BMI decreased during this five-year period. France maintains the lowest BMI at 32.

The accompanying plot also explores the potential relationship between BMI and unemployment rate. However, there seems to be no correlation between these two factors.

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Activity Rate

Another interesting approach is to see changes in indicators on maps. Here, there is a EU map in years 2010-2022 showing activity rate in each country. Even though it is not always the best tool to see intersting trends, we can spot some interesting facts. Firstly, in 2010 for most of the countries activity rate has the lowest values. Then, as the times goes, the colors become more intense for most countries. For example, ss the time goes, Poland’s population activity rate got closer to values for ‘West’. Sweden high activity rate remains one of the highest in EU. Additionally, for example, there is a visible decline in the coefficient in Europe in 2020, probably due to Covid.

Age group

To dig deeper into differences between age pyramid, we did for 5 selected countries. We chose step chart, as it is a very good tool to demonstrate such data. In the Eurostat dataset, population is divided into 6 age groups:

  1. 0-14
  2. 15-24
  3. 25-49
  4. 50-64
  5. 65-79
  6. 80+

At a first glance Sweden seems to be the youngest country. In Sweden there is the highest rate for children 0-14. On the other hand, Italy can be said to have the oldest society.

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Multiple plots

To explore the countries indicators and economic situation, we can add more factors to plots. Thanks to that it is possible to pick more dependences and trends.

plot 1

Let’s start looking for it with countries’ data such as: life expectancy, real GDP, unemployment rate and population density. At a first glance we cannot say that population density influences life expectancy or real gdp value in European countries as the size of the points varies across countries with lower and higher life expectancy. When it comes to unemployment rate, countries with high real GDP do not have a high unemployment rate. Poland and Czech Republic have a relatively low unemployment rate comparing their gdp value. Finally, overall, as gdp increases, life expectancy also increases, and in countries that have already achieved at least this value, the gdp value does not directly affect life expectancy.

For our selected group of European countries we can conclude more. In this case, the lower the population density the higher the real GDP. What’s more, it can also be considered that life expectancy increases with real GDP value. Unemployment rate does not affect GDP rate. Surprisingly, countries with lower employment rate have lower life expectancy.

plot 2

We can use the same data, but use them on different configuration. As before, countries with lower real GDP value have lower life expectancy.

plot 3

Now, let’s take a look for data using using unemployment rate instead of population density. We can observe that rather countries with lower real GDP have higher GDP rate.

For our selected countries there is a big disproportion between Poland and the rest. Poland has a very low unemployment rate, low life expectancy and relatively high GDP growth rate.

Summary - Key Observations

  • Youth Unemployment Trends (2012-2022): Poland showed a significant decrease in youth unemployment, dropping by about 60%, while Germany consistently had the lowest rate. Italy initially had the highest rate but saw a steady decline.

  • General vs. Youth Unemployment (2022): Generally, countries with higher youth unemployment also had higher general unemployment rates. However, Poland stood out with a lower general unemployment rate compared to Germany.

  • Death Rate Due to Cancer (2012-2020): Poland recorded a notably higher cancer death rate compared to other countries. While France, Italy, and Germany showed similar trends, Sweden had the lowest rate.

  • Cancer Death Rates by Sex (2020): The data revealed lower cancer death rates in females compared to males across all selected countries.

  • Suicide Death Rates (2012-2022): Italy maintained the lowest suicide death rate, while other countries exhibited higher rates exceeding 10.

  • BMI Trends (2014-2019): Poland showed an increase in BMI, unlike Germany, which recorded a decrease. France consistently had the lowest BMI among the countries.

  • Age Demographics (2022): Sweden appeared to have a younger population with a higher rate of children (0-14 years), while Italy had an older demographic profile.

  • Economic and Demographic Correlations: The analysis explored relationships between life expectancy, real GDP, unemployment rate, and population density, revealing no direct influence of population density on life expectancy or GDP. Higher GDP countries generally had lower unemployment rates and higher life expectancy.

  • Comparative Insights: The visualizations highlighted Poland’s unique position in several aspects, such as its rapid decrease in youth unemployment and distinct health indicators, when compared with Western European countries.

These bullet points summarize the key findings from the project, focusing on the comparative analysis of Poland with other selected European countries across various socio-economic and health dimensions.