# É er®ando que se aprende pt2
Uma introdução a linguagem R para análise de dados.
http://bit.ly/aprendeerrando2

## Instalando pacotes

install.packages("ggplot")
install.packages("ggplot2")
install.packages("gcookbook")

## Carregando pacotes instalados

library("ggplot")
library("ggplot2")
library("gcookbook")

## Gráficos

### Scatterplot

plot(mtcars$wt, mtcars$mpg)
qplot(mtcars$wt, mtcars$mpg)**


#Tipos de pontos diferentes
ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point(shape=3)

#Mapeando uma variavel continua por cor ou tamanho
heightweight[,c("sex", "ageYear", "heightIn", "weightLb")]
ggplot(heightweight, aes(x=ageYear, y=heightIn, size=weightLb)) + geom_point()

ggplot(heightweight, aes(x=ageYear, y=heightIn, colour=weightLb)) + geom_point()

### Gráfico de linhas

plot(pressure$temperature, pressure$pressure, type ="l")
points(pressure$temperature, pressure$pressure)
lines(pressure$temperature, pressure$pressure/2, col="red")
points(pressure$temperature, pressure$pressure/2, col = "red")

qplot(pressure$temperature, pressure$pressure, geom = c("line", "point"))

#Gráfico de área

ggplot(uspopage, aes(x=Year, y=Thousands, fill=AgeGroup)) + geom_area()
ggplot(uspopage, aes(x=Year, y=Thousands, fill=AgeGroup)) + geom_area(colour="black", size=2, alpha=.4)+ scale_fill_brewer(palette="Blues", breaks=rev(levels(uspopage$AgeGroup)))

### Gráfico de barras

barplot(BOD$demand, names.arg = BOD$Time, xlab = "Eixo X", ylab = "Eixo Y")
ggplot(pg_mean, aes(x = group, y = weight)) + geom_bar(stat = "identity")

str(BOD)

ggplot(BOD, aes(x = Time, y = demand)) + geom_bar(stat = "identity")

#Convertendo Time para variável categórica
ggplot(BOD, aes(x = factor(Time), y = demand)) + geom_bar(stat = "identity")

ggplot(pg_mean, aes(x = group, y = weight)) + geom_bar(stat = "identity", fill = "lightblue", colour = "black")

#Colorindo grafico de barras

upc <- subset(uspopchange, rank(Change)>40)
upc
ggplot(upc, aes(x=Abb, y=Change, fill=Region)) + geom_bar(stat="identity")

ggplot(upc, aes(x=reorder(Abb, Change), y=Change, fill=Region)) + geom_bar(stat="identity", colour="black") + scale_fill_manual(values=c("#669933", "#FFCC66")) + xlab("State")

#Colorindo barras 
csub <- subset(climate, Source=="Berkeley" & Year >= 1900)
csub$pos <-csub$Anomaly10y >= 0
csub
ggplot(csub, aes(x=Year, y=Anomaly10y, fill=pos)) + geom_bar(stat="identity", position="identity")

#Colorindo barras positivas e negativas
ggplot(csub, aes(x=Year, y=Anomaly10y, fill=pos)) + geom_bar(stat="identity", position="identity", colour="black", size=0.25) + scale_fill_manual(values=c("#CCEEFF", "#FFDDDD"), guide=FALSE)

### Histograma

hist(mtcars$mpg)
hist(mtcars$mpg, breaks = 10)

### BoxPlot

plot(ToothGrowth$supp, ToothGrowth$len)
qplot(ToothGrowth$supp, ToothGrowth$len, geom = "boxplot")
boxplot(len~supp+dose, data = ToothGrowth)

### Funções Curvas

curve(x^3 - 5*x, from= -4, to = 4)
teste <- function(xvar) {
1/(1 + exp(-xvar + 10))
}
curve(teste(x), from = 0, to = 20)
curve(1-teste(x), add = TRUE, col = "red")

### Editando legendas

p <- ggplot(PlantGrowth, aes(x=group, y=weight, fill=group)) + geom_boxplot() + scale_fill_brewer(palette="Pastel2")

p = theme(legend.position="top")
p = theme(legend.position=c(1,0))

p <- ggplot(PlantGrowth, aes(x=group, y=weight, fill=group)) + geom_boxplot()
p
p+labs(fill="Condicao")

p <- ggplot(PlantGrowth, aes(x=group, y=weight, fill=group)) + geom_boxplot()
p + scale_fill_discrete(labels=c("Controle", "Tratamento1", "Tratamento2"))