Luis Lemus 27 de octubre de 2016
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## Instrucci<c3><b3>n para agregar la base de datos.
setwd("~/Desktop/chino")
bici_data<-read.csv(file = "hour.csv")
## Gr<c3><a1>fica que relaciona la temperatura y humedad
with(bici_data, plot(temp, hum, main = "Relaci<c3><b3>n Temperatura vs Humedad"))

## Comportamiento de la demanda de bicicletas
hist(bici_data$cnt, main = "DEMANDA")

## Relaci<c3><b3>n de la demanda con los a<c3><b1>os
with(bici_data, plot(mnth, cnt, main = "Demanda por mes"))

boxplot(cnt ~ mnth, bici_data, xlab = "Mes", ylab = "Demanda", main = "Demanda por mes")

## Con esta gr<c3><a1>fica podemos determinar los meses con mayor demanda
boxplot(cnt ~ temp, bici_data, xlab = "Temperatura", ylab = "Demanda", main = "Demanda vs Temperatura" )

## Se evidencia una tendencia de comportamiento respecto a la temperatura
par(mfrow = c(1, 3), mar = c(4, 4, 2, 1), oma = c(0, 0, 2, 0))
with(bici_data, {
plot(temp, cnt, main = "Temperatura y Demanda")
plot(hum, cnt, main = "Humedad y Demanda")
plot(windspeed, cnt, main = "Velocida del viento y Demanda")
mtext("Relaci<c3><b3>n Clima vs Demanda", outer = TRUE)
})

bici_data_diciembre <- subset(bici_data, subset = mnth > 11 & mnth <= 12)
with(bici_data_diciembre, plot(temp,cnt, main = "Relaci<c3><b3>n Temperatura vs Demanda en Diciembre"))

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