Introduction

In this report, we explore the relationship between public sector innovation and AI capability. Various factors such as research and development funding, data governance, ethics, and international collaboration play significant roles in shaping AI development across countries. This report aims to identify key trends and impacts.

Data Loading and Cleaning

```{r} # Load necessary libraries library(dplyr) library(ggplot2)

Exploratory Data Analysis (EDA)

data <- read.csv(“path_to_your_merged_data.csv”)

Visualize the Distribution of Key Variables

head(data)

Advanced Analysis and Modeling

Correlation Analysis

# Correlation analysis between selected variables cor(data$AI_Capability, data$R_and_D_Funding)

Regression Analysis

# Perform regression analysis model <- lm(AI_Capability ~ R_and_D_Funding + Data_Governance + Ethics, data = data) summary(model)

Clustering Analysis

# Perform clustering analysis using k-means kmeans_result <- kmeans(scale(data[, c(“AI_Capability”, “R_and_D_Funding”)]), centers = 3)

Plot clustering results

clusplot(data, kmeans_result$cluster, color = TRUE, shade = TRUE, labels = 2, lines = 0)

Trend Analysis

# Trend analysis over time (if time variable is present) ggplot(data, aes(x = Date, y = AI_Capability)) + geom_line() + labs(title = “AI Capability over Time”)

Policy Impact Evaluation

# Visualize the impact of specific policies on AI Capability ggplot(data, aes(x = Policy, y = AI_Capability)) + geom_bar(stat = “identity”, fill = “lightblue”) + labs(title = “Policy Impact on AI Capability”)

Conclusion