# É er®ando que se aprende - 2
Uma introdução a linguagem R para análise de dados - 2.
Parte 2: http://bit.ly/aprendeerrando2
Parte 3: http://bit.ly/aprendeerrando3
Parte 4: http://bit.ly/aprendeerrando4
Parte 1: http://bit.ly/aprendeerrando
## Instalando pacotes
```r
install.packages("ggplot")
install.packages("ggplot2")
install.packages("gcookbook")
```
## Carregando pacotes instalados
```r
library("ggplot")
library("ggplot2")
library("gcookbook")
```
## Gráficos
### Scatterplot
```r
plot(mtcars$wt, mtcars$mpg)
qplot(mtcars$wt, mtcars$mpg)
```
```r
#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
```r
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$AgeGroup)))
```
### Gráfico de barras
```r
barplot(BOD$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$Anomaly10y >= 0
csub
ggplot(csub, aes(x=Year, y=Anomaly10y, fill=pos)) + geom_bar(stat="identity", position="identity")
#Colorindo barras positivas e negativas
ggplot(csub, aes(x=Year, y=Anomaly10y, fill=pos)) + geom_bar(stat="identity", position="identity", colour="black", size=0.25) + scale_fill_manual(values=c("#CCEEFF", "#FFDDDD"), guide=FALSE)
```
### Histograma
```r
hist(mtcars$mpg)
```
### BoxPlot
```r
plot(ToothGrowth$supp, ToothGrowth$len)
qplot(ToothGrowth$supp, ToothGrowth$len, geom = "boxplot")
boxplot(len~supp+dose, data = ToothGrowth)
```
### Funções Curvas
```r
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
```r
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"))
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