options(OutDec = ",", round = 1, digits = c(5,2))

Referências

plotly

Análise Exploratória de Dados

Simulaçao com t-student

Medida Estimativa
Percentil 0,1% -5,76
Percentil 1% -3,33
Percentil 5% -1,99
Percentil 10% -1,46
1o quartil -0,72
Média 0,01
Mediana 0
3o quartil 0,73
Percentil 90% 1,48
Percentil 95% 2,01
Percentil 99% 3,36
Percentil 99,9% 6,02
Desvio-padrão 1,28
Variância 1,64
Assimetria 0,04
Curtose 4,47

Histograma da t

Simulação da Normal

Medida Estimativa
Percentil 0,1% -12,57
Percentil 1% -9,28
Percentil 5% -6,58
Percentil 10% -5,12
1o quartil -2,67
Média 0,03
Mediana 0,01
3o quartil 2,76
Percentil 90% 5,17
Percentil 95% 6,61
Percentil 99% 9,3
Percentil 99,9% 12,44
Desvio-padrão 4,01
Variância 16,07
Assimetria -0,01
Curtose -0,02

Histograma da normal

Medida Estimativa
Percentil 0,1% -12,57
Percentil 1% -9,28
Percentil 5% -6,58
Percentil 10% -5,12
1o quartil -2,67
Média 0,03
Mediana 0,01
3o quartil 2,76
Percentil 90% 5,17
Percentil 95% 6,61
Percentil 99% 9,3
Percentil 99,9% 12,44
Desvio-padrão 4,01
Variância 16,07
Assimetria -0,01
Curtose -0,02

Supondo que os meus retornos tenham outra distribuição:

rate = 1
z = -1*rgamma(10000, shape = 2, rate = 1) + 2.5
Medida Estimativa
Percentil 0,1% -12,57
Percentil 1% -9,28
Percentil 5% -6,58
Percentil 10% -5,12
1o quartil -2,67
Média 0,03
Mediana 0,01
3o quartil 2,76
Percentil 90% 5,17
Percentil 95% 6,61
Percentil 99% 9,3
Percentil 99,9% 12,44
Desvio-padrão 4,01
Variância 16,07
Assimetria -0,01
Curtose -0,02
Medida Estimativa
Percentil 0,1% -6,66
Percentil 1% -4,18
Percentil 5% -2,27
Percentil 10% -1,41
1o quartil -0,21
Média 0,49
Mediana 0,82
3o quartil 1,52
Percentil 90% 1,97
Percentil 95% 2,15
Percentil 99% 2,34
Percentil 99,9% 2,44
Desvio-padrão 1,42
Variância 2,01
Assimetria -1,39
Curtose 2,66

Como a média estimada, \(\bar{z}\), é 0,4913.

Visualização de dados com o Plotly

Line Plots (Gráficos de Linha)

Scatter Plot Básico

fig <- plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length)
fig
retornos = cbind.data.frame(x, z)
fig <- plot_ly(data = retornos, x = ~x, y = ~z)
fig
## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode
fig <- plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length, color = ~Species)
fig
## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode
fig <- plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length, color = ~Species, colors = "Set1")
fig
fig <- plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length, color = ~Species, colors = "Dark2")
fig
## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode
d <- diamonds[sample(nrow(diamonds), 1000), ]
fig <- plot_ly(
  d, x = ~carat, y = ~price,
  color = ~carat, size = ~carat
)
fig
## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode
## Warning: `line.width` does not currently support multiple values.
fig <- plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length, type = 'scatter',
  mode = 'markers', symbol = ~Species, symbols = c('circle','x','A'),
  color = ~Species, marker = list(size = 10))
fig

Referências

Line Plots (Gráficos de Linha)

x <- c(1:100)
random_y <- rnorm(100, mean = 0)
data <- data.frame(x, random_y)
fig <- plot_ly(data, x = ~x, y = ~random_y, type = 'scatter', mode = 'lines')
fig

Gráfico de barras

Gráfico de pizza