Questão 1 - Gráficos com layout()
# Dados fornecidos
MRT_1F <-c(517.1468515630205, 85.13094142168089, 30.333207896694553, 12.694776264558937, 3.3041601673945418, 1.1823111717498882, 1.1892293502386786)
MRT_3F <-c(156.68929936163462, 11.540837783562276, 0.4512835621696538, 0.4509797929766453, 0.4502068233039181, 0.4496185276300172, 0.4543157082191288)
MRT_5F <-c(83.90319666471157, 0.3068151086494968, 0.30522314133037304, 0.3072588968084928, 0.30655265997285697, 0.3055812715727718, 0.3053297166713006)
MRT_10F <-c(29.55430642951759, 0.19832832665772515, 0.1971923924717474, 0.19796648905716516, 0.19615594370806338, 0.2034569237883263, 0.19617420889447737)
MRT_15F <-c(11.317736530583566, 0.167364215666193, 0.16172168266811013, 0.16701085329580515, 0.1598052657153692, 0.1645934043532696, 0.16216563797118075)
MRT_sem_F <-c(11.93430909937736, 0.6095414637034009, 0.6060645101029295, 0.612167181646899, 0.6146761002685637, 0.6096747087200697, 0.6125810476877268)
clock <- c(0.1, 0.5, 1, 1.5, 2, 2.5, 3)
# Layout com 3 linhas e 2 colunas
layout(matrix(1:6, nrow=3, ncol=2, byrow=TRUE))
par(mar=c(4,4,2,1))
plot(clock, MRT_1F, type="b", col="red", main="MRT_1F", xlab="Clock", ylab="Valor")
plot(clock, MRT_3F, type="b", col="blue", main="MRT_3F", xlab="Clock", ylab="Valor")
plot(clock, MRT_5F, type="b", col="green", main="MRT_5F", xlab="Clock", ylab="Valor")
plot(clock, MRT_10F, type="b", col="orange", main="MRT_10F", xlab="Clock", ylab="Valor")
plot(clock, MRT_15F, type="b", col="purple", main="MRT_15F", xlab="Clock", ylab="Valor")
plot(clock, MRT_sem_F, type="b", col="brown", main="MRT_sem_F", xlab="Clock", ylab="Valor")
valores <- c(50, 100, 1000, 5000, 20000)
categorias <- c("A", "B", "C", "D", "E")
barplot(valores, names.arg=categorias, log="y",
col=c("#E6E6E6", "#666666", "#E6E6E6", "#666666", "#E6E6E6"),
main="Gráfico de Barras (Escala Logarítmica)",
ylab="Valores (log)", xlab="Categorias")
# Tabela simulada de qualidade de refeição
dados <- matrix(c(20, 15, 10, 25, 30, 5), nrow=2, byrow=TRUE)
colnames(dados) <- c("Baixa", "Média", "Alta")
rownames(dados) <- c("Barato", "Caro")
barplot(dados, beside=FALSE, col=c("skyblue", "orange"),
main="Qualidade da Refeição por Categoria de Preço",
ylab="Frequência", xlab="Qualidade")
legend("topright", legend=rownames(dados), fill=c("skyblue", "orange"))
data("airquality")
temp_c <- (airquality$Temp - 32) / 1.8
hist(temp_c, col="lightgreen", main="Histograma das Temperaturas em Maio (°C)",
xlab="Temperatura (°C)", ylab="Frequência", freq=FALSE)
lines(density(temp_c, na.rm=TRUE), col="red", lwd=2)
# Simulação de dados de vendas
sales <- data.frame(
Country = c("Brazil", "USA", "France", "Japan", "Canada"),
Total = c(120, 300, 180, 220, 150)
)
pct <- round(sales$Total/sum(sales$Total)*100, 1)
lbls <- paste(sales$Country, "-", pct, "%")
pie(sales$Total, labels=lbls, col=rainbow(length(lbls)),
main="Participação nas Vendas por País")
legend("topright", legend=sales$Country, fill=rainbow(length(lbls)))
data("InsectSprays")
boxplot(count ~ spray, data=InsectSprays, main="Contagem de Insetos por Inseticida",
ylab="Contagem", xlab="Tipo de Inseticida", col="yellow", outline=FALSE)
library(plotly)
## Loading required package: ggplot2
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
# Simulação de dados Netflix
netflix <- data.frame(
country = c("USA","India","UK","Brazil","France","Canada","Japan","Germany","Mexico","Italy"),
total = c(3200,1200,800,700,600,500,400,300,200,100)
)
fig <- plot_ly(netflix, labels = ~country, values = ~total, type = 'pie') %>%
layout(title = "Top 10 Países com Mais Conteúdos na Netflix (Simulado)")
fig
fig <- plot_ly(
type = 'table',
header = list(values = c("País", "Total de Conteúdos"),
fill = list(color = "grey"),
font = list(color = "white", size = 14),
align = c("center", "center")),
cells = list(values = rbind(netflix$country, netflix$total),
align = "center")
)
fig
# Simulação de dados
decadas <- c("1980", "1990", "2000", "2010", "2020")
series <- c(50, 100, 300, 800, 1200)
filmes <- c(200, 400, 900, 1500, 2000)
fig <- plot_ly() %>%
add_trace(x=decadas, y=series, type='scatter', mode='lines+markers', name='Séries', line=list(color='blue')) %>%
add_trace(x=decadas, y=filmes, type='scatter', mode='lines+markers', name='Filmes', line=list(color='yellow')) %>%
layout(title="Quantidade de Conteúdo por Década",
xaxis=list(title="Década"), yaxis=list(title="Quantidade"))
fig
anos <- 2000:2010
dramas <- sample(50:200, 11)
acao <- sample(60:220, 11)
comedias <- sample(40:180, 11)
fig <- plot_ly(x = anos, y = dramas, type = 'bar', name = 'Dramas') %>%
add_trace(y = acao, name = 'Action & Adventure') %>%
add_trace(y = comedias, name = 'Comedies') %>%
layout(barmode = 'group',
title = "Filmes por Gênero (2000–2010)",
xaxis = list(title = "Ano"),
yaxis = list(title = "Quantidade"))
fig
