EFNEP enrollments per year

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# Calculate mean returns mean_returns <- colMeans(returns_date_droped_dividened2) # Function to calculate VaR (assuming normal distribution) VaR <- function(x, alpha = 0.05) { mean(x) - qnorm(1 - alpha) * sd(x) } # Calculate VaR for each stock VaR_values <- apply(returns_date_droped_dividened2, 2, VaR) # Create a dataframe for plotting plot_data <- data.frame(Stock = names(mean_returns), Mean_Return = mean_returns, VaR = VaR_values) # Create the ggplot p <- ggplot(plot_data, aes(x = Mean_Return, y = VaR, text = paste("Stock:", Stock, "<br>Return:", round(Mean_Return, 4), "<br>VaR:", round(VaR, 4)))) + geom_point(color = 'blue', size = 3) + labs(title = "Mean Return vs VaR for Each Stock", x = "Mean Return", y = "Value at Risk (VaR)") + theme_minimal() # Convert ggplot to plotly for interactivity interactive_plot <- ggplotly(p, tooltip = "text") # Display the interactive plot interactive_plot

Análisis descriptivo básico de la base de datos "Accidentalidad_en_Barranquilla.csv".

initial_portfolio <- portfolio.spec(assets = colnames(returns_date_droped_dividened2)) # Add objectives - here we minimize risk (VAR) initial_portfolio <- add.objective(portfolio = initial_portfolio, type = "risk", name = "VaR") initial_portfolio <- add.objective(portfolio = initial_portfolio, type = "risk-adjusted", name = "SortinoRatio") # Add constraints - fully invested portfolio with no short sales initial_portfolio <- add.constraint(portfolio = initial_portfolio, type = "long_only") initial_portfolio <- add.constraint(portfolio = initial_portfolio, type = "full_investment") initial_portfolio <- add.constraint(portfolio = initial_portfolio, type = "box", min = 0, max = 1) # Optimize the portfolio to minimize risk optimal_portfolio <- optimize.portfolio(R = returns_date_droped_dividened2, portfolio = initial_portfolio, optimize_method = "DEoptim") # View the optimal weights optimal_weights <- extractWeights(optimal_portfolio) asset_names <- names(optimal_weights) weights_df <- data.frame(Asset = asset_names, Weight = optimal_weights) plot_ly(data = weights_df, x = ~Asset, y = ~Weight, type = 'bar') %>% layout(title = "Interactive Portfolio Weights", xaxis = list(title = "Asset"), yaxis = list(title = "Weight"))

This report presents the geocoding of Zenith Bank branches in Lagos State, Nigeria, to enhance spatial analysis and inform business strategies.

Leveraging historical trials to predict Fusarium head blight resistance in wheat breeding programs