library(ggcorrplot)
library(tidyverse)
library(visdat)
library(highcharter)
library(plotly)
library(forecast)
euro_tc <- as.tibble(read_csv("EURO_TC.csv"))
colnames(euro_tc) <- map_chr(str_extract_all(colnames(euro_tc), "\\w+|\\\\"), ~ paste(.x, collapse = "_"))
euro_tc <- euro_tc %>% rename("Period" = "Period_\\_Unit")
euro_tc[,2:33] <- lapply(euro_tc[,2:33], as.numeric)
euro_tc$Period <- as.Date(euro_tc$Period, "%d/%m/%Y", offset = 0)
(euro_tc %>%
ggplot(aes(x = Period, y = Brazilian_real)) +
geom_line(color = "blue") +
labs(x = "Tiempo", y = "Tipo de Cambio") +
labs(title = "Tipo de cambio Euro a Real Brasileño")) %>%
ggplotly()
correlacion <- cor(euro_tc[2:33], method = "spearman")
ggcorrplot(correlacion)
EuroTC <- euro_tc %>%
select(Period, Chinese_yuan_renminbi,
US_dollar, Indian_rupee,
Japanese_yen, Russian_rouble,
Korean_won, Mexican_peso,
Brazilian_real, UK_pound_sterling,
Iceland_krona)
Correlacion <- cor(EuroTC[2:11], method = "spearman")
ggcorrplot(Correlacion)
