GLOBAL EDUCATIONAL ANALYSIS

Education is a cornerstone of societal development, and literacy rates, educational proficiency, and employment trends offer a lens through which we can understand global progress. This analysis aims to explore key aspects of global education, with a focus on selected countries, to uncover patterns and correlations that shape the narrative of education worldwide.

1.Geospatial Analysis: Mapping Male Literacy Rates

To start our journey, we visualized male literacy rates across the globe using a geospatial map. The goal was to observe geographic patterns and identify regions where literacy rates are high or low. This analysis helps policymakers focus on specific areas requiring intervention.

Visualization: A scatterplot with longitude and latitude highlights literacy rates using a color gradient, where darker shades indicate higher literacy.

library(ggplot2)
library(viridis)
library(tidyr)

edu_data <- read.csv("U:/dataset/Global_Education.csv")

ggplot(edu_data, aes(x = Longitude, y = Latitude, color = Youth_15_24_Literacy_Rate_Male)) +
  geom_point(size = 3, alpha = 0.7) +
  scale_color_viridis_c() +
  labs(title = "Global Male Literacy Rates by Country",
       x = "Longitude", y = "Latitude", color = "Literacy Rate") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

2. Correlation Analysis: Relationships Between Key Variables

Education doesn’t exist in isolation—it intersects with various socioeconomic factors. Using a correlation matrix, we examined how literacy rates, birth rates, unemployment rates, and gross education enrollment influence each other.

Purpose: To identify relationships, such as whether higher education enrollment correlates with better literacy or lower unemployment.

Insight: This analysis provides a data-driven basis for prioritizing policies that focus on interconnected areas.

library(corrplot)
library(dplyr)

cor_matrix <- cor(edu_data %>% select(Youth_15_24_Literacy_Rate_Male,
                                      Youth_15_24_Literacy_Rate_Female,
                                      Gross_Primary_Education_Enrollment,
                                      Birth_Rate,
                                      Unemployment_Rate), use = "complete.obs")
corrplot(cor_matrix, method = "circle", type = "upper", order = "hclust")

3. Gender Gap Analysis: Male vs Female Literacy Rates

Literacy is not only a measure of educational success but also a reflection of gender equity. We analyzed the literacy rates of males and females across five selected countries: Afghanistan, Albania, Algeria, Brazil, and Colombia. A bar plot was used to highlight gender disparities.

Purpose: To bring attention to gender gaps in literacy and advocate for gender-focused interventions in education.

Finding: Variations between male and female literacy rates can indicate cultural, economic, or political barriers.

selected_countries <- c("Afghanistan", "Albania", "Algeria", "Brazil", "Col")
edu_data_filtered <- edu_data %>% filter(`Countries.and.areas` %in% selected_countries)

ggplot(edu_data_filtered, aes(x = `Countries.and.areas`)) +
  geom_bar(aes(y = Youth_15_24_Literacy_Rate_Male, fill = "Male"), stat = "identity", position = "dodge") +
  geom_bar(aes(y = Youth_15_24_Literacy_Rate_Female, fill = "Female"), stat = "identity", position = "dodge") +
  scale_fill_manual(values = c("Male" = "blue", "Female" = "red")) +
  labs(title = "Gender Gap in Literacy Rate by Selected Countries",
       x = "Country", y = "Literacy Rate") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

4. Proficiency Scores by Education Level

To explore how reading proficiency evolves at different education levels, we transformed the dataset into a long format and visualized boxplots for each grade. This helped uncover trends in how students perform as they progress through the education system.

Insight: Identifying grades where proficiency stagnates or declines can guide targeted curriculum improvements.

edu_data_long <- edu_data %>% pivot_longer(cols = starts_with("Grade"),
                                           names_to = "Education_Level",
                                           values_to = "Proficiency_Reading")

ggplot(edu_data_long, aes(x = Education_Level, y = Proficiency_Reading)) +
  geom_boxplot(fill = "lightblue") +
  labs(title = "Reading Proficiency by Education Level",
       x = "Education Level", y = "Proficiency Score") +
  theme_classic()

5. Exploring Literacy and Unemployment

Is there a relationship between literacy and unemployment? To answer this question, we visualized the correlation between male literacy rates and unemployment rates. The scatterplot with a trendline captures the broader relationship.

Purpose: To evaluate whether improving literacy could potentially lower unemployment rates, providing a compelling argument for investing in education.

ggplot(edu_data, aes(x = Unemployment_Rate, y = Youth_15_24_Literacy_Rate_Male)) +
  geom_point() +
  geom_smooth(method = "lm", color = "red") +
  labs(title = "Unemployment vs Male Literacy Rate",
       x = "Unemployment Rate", y = "Male Literacy Rate") +
  theme_minimal()

6. Animated Visualization: Male Literacy Rates Over Countries

To make data engaging and dynamic, we created an animated bar chart to showcase male literacy rates in the selected countries. Animation helps tell the story of how each country’s literacy compares to others in an interactive and visually appealing way.

Why Animation?: It draws attention to differences and trends while making data exploration more accessible and engaging.

library(gganimate)

p <- ggplot(edu_data_filtered, aes(x = `Countries.and.areas`, y = Youth_15_24_Literacy_Rate_Male, fill = `Countries.and.areas`)) +
  geom_bar(stat = "identity", show.legend = FALSE) +
  labs(title = "Male Literacy Rate by Selected Countries",
       x = "Countries", y = "Literacy Rate") +
  transition_states(`Countries.and.areas`, transition_length = 2, state_length = 1) +
  ease_aes('linear') +
  theme_minimal()

animate(p, nframes = 100, fps = 10)

anim_save("U:/dataset/literacy_rate_animation.gif", animation = last_animation())

7. Interactive Visualization: Comparing Literacy by Gender

Lastly, we created an interactive bar chart using Plotly to compare the average male and female literacy rates across the selected countries. This visualization allows users to interact with the data and explore insights at their own pace.

Purpose: To provide policymakers and stakeholders with an intuitive tool to understand gender disparities and focus on specific countries.

library(plotly)

regional_summary_filtered <- edu_data_filtered %>%
  group_by(`Countries.and.areas`) %>%
  summarise(Avg_Literacy_Male = mean(Youth_15_24_Literacy_Rate_Male, na.rm = TRUE),
            Avg_Literacy_Female = mean(Youth_15_24_Literacy_Rate_Female, na.rm = TRUE))

plot <- plot_ly(regional_summary_filtered, x = ~`Countries.and.areas`) %>%
  add_bars(y = ~Avg_Literacy_Male, name = "Male", marker = list(color = 'blue')) %>%
  add_bars(y = ~Avg_Literacy_Female, name = "Female", marker = list(color = 'pink')) %>%
  layout(
    title = "Literacy Rates in Selected Countries",
    xaxis = list(title = "Country"),
    yaxis = list(title = "Average Literacy Rate (%)"),
    barmode = "group"
  )

plot

Conclusion

Through these analyses and visualizations, we aimed to shed light on critical global education metrics. Each visualization serves a unique purpose, from identifying disparities and correlations to encouraging interactive exploration. Together, these insights can drive informed decision-making and policy formulation for a better-educated world.