ejercicioC.R

rm(list=ls())
dev.off()
library(easypackages)
library(xts)
library(lattice)
library(ggplot2)
pdiaria <- read.csv(file.choose(),header=TRUE, check.names = F, stringsAsFactors = F) # almacenar los datos csv en pdiaria
str(pdiaria)
idx <- as.Date(pdiaria[,1])
data.matrix <- pdiaria[,-1]
data.xts <- xts(data.matrix, order.by = idx )
str(data.xts)
plot(data.xts)
plot(data.matrix[,4], type="l")
plot(data.xts[,2])
data.zoo <- as.zoo(data.xts)
str(data.zoo)
plot(data.zoo, main = "Series de tiempo de precipitación")
summary(data.zoo)
max(data.zoo, na.rm = T)
plot(data.zoo, main = "Series de tiempo de precipitación", ylim = c(0,80))
xyplot(data.xts,xlab = "Fecha",ylab = "Precipitación [mm/dia]",ylim=c(0,100))
autoplot(data.xts[,1:4]) +theme_bw()+xlab('Fecha')+ylab('Precipitación [mm/dia]')
boxplot(coredata(data.xts))
hist(coredata(data.xts[,2]), freq = T)
histogram(coredata(data.xts[,3]))

# Heatmap de valores faltantes
library(visdat)
vis_miss(pdiaria, sort_miss = TRUE)