Correlacion

Correlacion Spearman

# Filtrar la tabla final y seleccionar solo las columnas específicas
tabla_filtrada <- tabla_final %>%
  select(Pais, ISO_code, year, `Geographical Particularity`, `ECONOMIC FREEDOM`,
         access_sea_bin, trade_usd, `3_sound_money`, `5c_business_reg`, `2_property_rights`, `4_trade`)

# Convertir las columnas a numéricas
tabla_filtrada <- tabla_filtrada %>%
  mutate(
    trade_usd = as.numeric(trade_usd),
    `ECONOMIC FREEDOM` = as.numeric(`ECONOMIC FREEDOM`),
    `3_sound_money` = as.numeric(`3_sound_money`),
    `5c_business_reg` = as.numeric(`5c_business_reg`),
    `2_property_rights` = as.numeric(`2_property_rights`),
    `4_trade` = as.numeric(`4_trade`)
  )

# Filtrar tabla a solo las columnas necesarias
tabla_cor <- tabla_filtrada %>%
  select(`2_property_rights`, `4_trade`, `3_sound_money`, `5c_business_reg`, trade_usd)

# Calcular la matriz de correlación de Spearman
cor_matrix <- cor(tabla_cor, use = "complete.obs", method = "spearman")

# Mostrar la matriz de correlación
print(cor_matrix)

##                   2_property_rights   4_trade 3_sound_money 5c_business_reg
## 2_property_rights         1.0000000 0.6964299     0.6867933       0.7570379
## 4_trade                   0.6964299 1.0000000     0.6918219       0.6386557
## 3_sound_money             0.6867933 0.6918219     1.0000000       0.5114182
## 5c_business_reg           0.7570379 0.6386557     0.5114182       1.0000000
## trade_usd                 0.3885797 0.1758924     0.3612481       0.3002451
##                   trade_usd
## 2_property_rights 0.3885797
## 4_trade           0.1758924
## 3_sound_money     0.3612481
## 5c_business_reg   0.3002451
## trade_usd         1.0000000

# Visualizar la matriz con colores
corrplot(cor_matrix, method = "color",
         col = colorRampPalette(c("red", "white", "blue"))(200),
         type = "full", addCoef.col = "black",
         number.cex = 0.8, tl.cex = 1, tl.col = "black")