# Análise Exploratória de dados - Guia de comandos

############### Análise gráfica ###################

# Gráfico de setores
# pie(X)

# Gráfico de caixa
# boxplot(X, main="")

# Gráfico de linhas
# plot(X, ylab="", xlab="", main="") 

# Histograma
# hist(X, col="", ylab="", xlab="", freq=FALSE, main="")
# rug(lat)
# lines(density(lat))

# Gráficos de pontos
# stripchart(X, pch = 20, method ="stack", main="")
# library(plotrix)
# dotplot.mtb(X, main="")

# Gráfico de barras
# barplot(table(X))

# Tabelas de frequências
# table(x)

# Tabelas de frequências relativas
# n <- length(x)
# table(x) / n

# Gráfico de Pareto
# library(qcc)
# pareto.chart(freqa, main = "", xlab = "", ylab = "")

# Gráfico de ramos-e-folhas
# stem(x, scale = 2)

############### Medidas de posição ###################

# Média 
# mean(x)

# Mediana
# median(x)

# Trimédia ou Média aparada
# mean(x, trim = 0.05)

# Quantis
# summary(x)  
# quantile(x)

# Decis
# (alfa = seq(0.1, 0.9, by = 0.1))
# (decis = quantile(x, probs = alfa))

############### Medidas de dispersão ###################

# Amplitude 
# As = (max(x) - min(x)) 

# Gráfico de caixa
# boxplot(X, main="")
# identify(bx$out, rep(1, length(bx$out)), match(bx$out, x))

# Variância
# var(x)

# Desvio padrão
# sd(x)

# Desvio absoluto médio
# dm = mean(abs(x - xb))

# Desvio absoluto mediano
# M = median(x)
# (MAD = median(abs(x - M)))

# Coeficiente de variação
# cv = sd(x) / mean(x)

# Amplitude studentizada
# As = (max(x) - min(x)) / sd(x)

# Limites de x
# range(x)


############### Medidas de assimetria ###################

# Momentos
# library(moments)
# moment(x, order = 3, central = FALSE)

# momc = c(1,  0,  2.7, -1.4, 13)
# xb =  3.8
# (mom0 = central2raw(momc, xb))
# mom0 = c(1.0,  1.5,  3.1,  8.4, 27.0)
# (momc = raw2central(mom0))

# mom4 = all.moments(x, order.max = 4, central = TRUE)

# Medidas de assimetria
# skewness(x)

############### Medidas de curtose ###################

# kurtosis(x)
# hist(x, main = "", xlab = "x", freq = FALSE, ylab = "Densidade") 
# rug(x)
# lines(density(x), col = "blue")

# Curva de Lorenz
# library(ineq)
# clorenz = Lc(x)
# plot(clorenz, main = "", ylab = "q")
# jmax = which.max(clorenz$p - clorenz$L)
# segments(clorenz$p[jmax], clorenz$L[jmax],  clorenz$p[jmax], clorenz$p[jmax],lty = 2)

# Índice de Gini
# library(ineq)
# Gini(x)