World Happiness Report Analysis in R With Code

This project presents a World Happiness Report Analysis in R using the 2019 dataset. We’ve used Google Colab for this project to run the R code. 

The analysis looks at the global happiness scores and their relationship with key factors like GDP, social support, life expectancy, and corruption. Through data cleaning, visualization, and correlation analysis, the project identifies trends and patterns that influence national well-being. 

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What You Must Understand Before Starting the World Happiness Report Analysis in R

Here are the key concepts and basics you should know:

• Basic R Syntax: Understand how to write and run R code, especially in Google Colab.
• Working with Data Frames: Learn how to load, view, and manipulate tabular data.
• Data Cleaning: Know how to check for and handle missing or inconsistent data.
• Exploratory Data Analysis (EDA): Be familiar with summarizing and visualizing data to uncover insights.
• Correlation Analysis: Understand how different variables relate to each other statistically.
• Plotting with ggplot2: Learn how to create clear and informative visualizations using this powerful R package.

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The Tools and R Libraries Used In This Analysis

Before starting the analysis, it's important to know the tools and libraries that make this project possible. Each component plays a specific role, making it easier to work with and understand the World Happiness dataset.

How Long Does It Take and How Hard Is This Project

To help you plan better, here's a quick overview of the time commitment, complexity, and who this project is best suited for:

A Breakdown of the World Happiness Report Analysis Project Using R

The section below will explain the step-by-step process of building this World Happiness Report Analysis, along with the code and output for each step.

Step 1: Getting R Ready in Google Colab

Google Colab runs Python by default, so a quick setup is needed before writing any R code. The good news is, it only takes a few clicks to switch the environment.

To enable R in your notebook:

• Launch a new notebook at Google Colab
• Navigate to the top menu and select Runtime
• Choose Change runtime type
• In the dialog that appears, switch the language to R
• Click Save, and you're all set to start coding in R

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Step 2: Install and Load the Required R Packages

Before we can begin analyzing the data, we need to make sure the right tools are in place. R uses packages (similar to plugins) for various functions, like reading files, visualizing data, or creating charts. In this step, you'll install and load all the packages necessary for the project. The code is given below:

# Install the required packages (this needs to be done only once)
install.packages("tidyverse")   # Includes packages for data manipulation and visualization
install.packages("ggplot2")     # Used to create high-quality visualizations
install.packages("readr")       # Helps in reading CSV and other flat files
install.packages("corrplot")    # For creating correlation matrix plots

# Load the libraries into your R session
library(tidyverse)   # Loads dplyr, ggplot2, readr, etc.
library(ggplot2)     # For plotting data
library(readr)       # For reading CSV files
library(corrplot)    # For plotting correlations

The output for the above code confirms the installation and loading of the libraries and packages:

Step 3: Load the World Happiness Dataset into R

With the packages ready, it's time to bring in the dataset. In this step, you'll read the CSV file into R so you can start exploring and analyzing the data. We'll also take a quick look at the contents and structure. Here’s the code:

# Read the CSV file and store it in a variable called 'happiness'
happiness <- read_csv("Happiness Report - 2019.csv")

# Display the first few rows of the dataset to understand how it looks
head(happiness)

# Show the names of all columns in the dataset
colnames(happiness)

The output of this code gives us an overview of the dataset:

Step 4: Explore the Structure and Quality of the Data

Before diving into analysis, it’s important to understand what the dataset contains. This step helps you examine the structure, check basic statistics, and identify any missing values that could affect the results. Here’s the code for this step:

# Check the structure of the dataset: data types and format of each column
str(happiness)

# Get summary statistics: min, max, mean, median for each numeric column
summary(happiness)

# Check for missing values in each column
colSums(is.na(happiness))

The output for this code is:

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Step 5: Simplify Column Names for Easier Use

Some of the column names in the dataset are long or contain spaces, which can make them harder to work with in code. This step renames those columns to shorter, cleaner versions for simplicity and readability. Here’s the code:

# Rename long or complex column names to simpler, more usable ones
happiness <- happiness %>%
  rename(
    Country = `Country or region`,
    Score = `Score`,
    GDP = `GDP per capita`,
    Social = `Social support`,
    Life = `Healthy life expectancy`,
    Freedom = `Freedom to make life choices`,
    Generosity = `Generosity`,
    Corruption = `Perceptions of corruption`
  )

# Confirm the updated column names
colnames(happiness)

The output for this code is:

Step 6: Visualize the Top 10 Happiest Countries

Now that the data is clean, let’s highlight the countries with the highest happiness scores. This bar plot visually compares the top 10 happiest nations based on their overall score in the 2019 report. Here’s the code:

# Select and sort the top 10 countries with the highest happiness scores
top10 <- happiness %>% arrange(desc(Score)) %>% head(10)

# Create a horizontal bar chart to display the top 10 happiest countries
ggplot(top10, aes(x = reorder(Country, Score), y = Score, fill = Country)) +
  geom_bar(stat = "identity") +            # Draw bars based on the Score
  coord_flip() +                           # Flip the chart to make it horizontal
  labs(title = "Top 10 Happiest Countries in 2019", 
       x = "Country", 
       y = "Happiness Score") +
  theme_minimal()                          # Use a clean, minimal chart theme

The output for the above code gives us a graph of the top 10 happiest countries in 2019:

Step 7: Analyze Correlations Between Happiness Factors

To understand how different factors (like GDP, social support, and life expectancy) relate to happiness, we’ll create a correlation matrix. This step helps us see which variables are positively or negatively associated with the happiness score. Here’s the code:

# Select only the numeric columns needed for correlation analysis
numeric_data <- happiness %>% select(Score, GDP, Social, Life, Freedom, Generosity, Corruption)

# Calculate the correlation matrix between selected numeric columns
cor_matrix <- cor(numeric_data)

# Visualize the correlation matrix using color-coded blocks and numeric values
corrplot(cor_matrix, method = "color",     # Use colored squares to show strength of correlation
         type = "upper",                   # Show only the upper triangle of the matrix
         addCoef.col = "black",            # Add correlation values in black text
         tl.cex = 0.8)                     # Adjust label size

The output gives a correlation plot between various happiness factors:

The above plot means that:

• Happiness is strongly linked to GDP, social support, and life expectancy; countries with higher values in these areas tend to have higher happiness scores.
• Freedom and low corruption also have a noticeable positive effect on happiness, but not as strong as GDP or social support.
• Generosity has a very weak connection to overall happiness in this dataset.
• Darker blue boxes mean stronger relationships between factors, while lighter ones show weaker or no relationship.

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Step 8: Visualize the Relationship Between GDP and Happiness Score

Now let’s look into one of the strongest correlations we saw earlier, between GDP per capita and happiness score. In this step, we’ll use a scatter plot with a trend line to show how happiness changes with economic prosperity. Here’s the code for this step:

# Create a scatter plot to visualize the relationship between GDP and happiness score
ggplot(happiness, aes(x = GDP, y = Score)) +
  geom_point(color = "blue") +                # Plot each country as a blue dot
  geom_smooth(method = "lm", se = FALSE, color = "red") +  # Add a red trend line without confidence band
  labs(title = "Happiness Score vs GDP per Capita", 
       x = "GDP per Capita", 
       y = "Happiness Score") +
  theme_minimal()                            # Use a clean chart style

The output for the above step gives us a graphical representation of the Happiness Score vs GDP Per Capita

The above graph shows that:

• There is a clear upward trend: as GDP per capita increases, the happiness score also tends to increase.
• The red line confirms a strong positive linear relationship between economic wealth and happiness.
• Most points cluster around the trend line, showing that GDP is a reliable predictor of happiness in this dataset.

Conclusion

In this World Happiness Report Analysis in R, we explored the 2019 dataset using Google Colab to understand how various factors impact a country's happiness score. 

After cleaning and simplifying the data, we visualized top-performing countries and examined correlations between variables such as GDP, social support, life expectancy, and perceptions of corruption.

Our analysis revealed strong positive relationships between happiness and GDP, social support, and life expectancy. A scatter plot confirmed GDP as a key influencer of happiness.

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Colab Link:
https://colab.research.google.com/drive/10WLUd3o68yQuFvViEqL5nYS2Wmv_NiNA#scrollTo=5whGKTOtjcRL