Exercício 12 [Visualização de Dados]
2025-11-10
Questão 1
Carregamento dos dados
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)GRÁFICO 1: Linhas
layout(matrix(c(1,2), 1, 2))
plot(clock, MRT_1F, type = "l", col = "red", lwd = 2,
ylim = c(0, max(MRT_1F)), xlab = "Clock (GHz)", ylab = "MRT", main = "Tempo Médio de Resposta")
lines(clock, MRT_3F, col = "blue", lwd = 2)
lines(clock, MRT_5F, col = "green", lwd = 2)
lines(clock, MRT_10F, col = "purple", lwd = 2)
lines(clock, MRT_15F, col = "orange", lwd = 2)
lines(clock, MRT_sem_F, col = "black", lwd = 2)
legend("topright", legend = c("1F", "3F", "5F", "10F", "15F", "Sem F"),
col = c("red", "blue", "green", "purple", "orange", "black"),
lty = 1, cex = 0.8)
GRÁFICO(s) 2: Barras
bar_colors <- c("#E6E6E6", "#666666")
# 2.1.) 1F vs Sem Fog
barplot(rbind(MRT_1F, MRT_sem_F),
beside = TRUE, log = "y", col = bar_colors,
names.arg = clock,
xlab = "Clock (GHz)", ylab = "MRT (log)",
main = "MRT - 1F vs Sem Fog")
legend("topright", legend = c("Com Fog", "Sem Fog"), fill = bar_colors, cex = 0.8)
# 2.2.) 3F vs Sem Fog
barplot(rbind(MRT_3F, MRT_sem_F),
beside = TRUE, log = "y", col = bar_colors,
names.arg = clock,
xlab = "Clock (GHz)", ylab = "MRT (log)",
main = "MRT - 3F vs Sem Fog")
# 2.3.) 5F vs Sem Fog
barplot(rbind(MRT_5F, MRT_sem_F),
beside = TRUE, log = "y", col = bar_colors,
names.arg = clock,
xlab = "Clock (GHz)", ylab = "MRT (log)",
main = "MRT - 5F vs Sem Fog")
# 2.4.) 10F vs Sem Fog
barplot(rbind(MRT_10F, MRT_sem_F),
beside = TRUE, log = "y", col = bar_colors,
names.arg = clock,
xlab = "Clock (GHz)", ylab = "MRT (log)",
main = "MRT - 10F vs Sem Fog")
# 2.5.) 15F vs Sem Fog
barplot(rbind(MRT_15F, MRT_sem_F),
beside = TRUE, log = "y", col = bar_colors,
names.arg = clock,
xlab = "Clock (GHz)", ylab = "MRT (log)",
main = "MRT - 15F vs Sem Fog")
Questão 2
Carregamento dos dados
meal_price <- c("$10–19", "$20–29", "$30–39", "$40–49")
quality_rating <- c("Good", "Very Good", "Excellent")dados <- matrix(
c(53.8, 33.9, 2.6, 0.0, # Good
43.6, 54.2, 60.5, 21.4, # Very Good
2.6, 11.9, 36.8, 78.6), # Excellent
nrow = 3, byrow = TRUE
)
rownames(dados) <- quality_rating
colnames(dados) <- meal_priceMontagem do Barplot
barplot(dados,
beside = TRUE,
col = c("#AED6F1", "#5DADE2", "#2E86C1"),
legend.text = rownames(dados),
args.legend = list(x = "topright", title = "Quality Rating"),
xlab = "Meal Price",
ylab = "Percentage (%)",
main = "Meal Quality by Price Range")
abline(h = seq(0, 100, 10), col = "gray90", lty = 2)
Questão 3
Carrega os dados
data("airquality")Realiza as manipulações pedidas
airquality$Temp_C <- (airquality$Temp - 32) / 1.8
maio <- subset(airquality, Month == 5)Monta o Histograma
hist(maio$Temp_C,
breaks = 10,
col = "lightblue",
border = "white",
main = "Histograma das Temperaturas em Maio (°C)",
xlab = "Temperatura (°C)",
ylab = "Frequência",
prob = TRUE)
lines(density(maio$Temp_C, na.rm = TRUE),
col = "red",
lwd = 2)
Questão 4
Carrega os dados
sales <- read.table("https://training-course-material.com/images/8/8f/Sales.txt",header=TRUE)Monta o gráfico
porcentagem <- round(sales$SALES/sum(sales$SALES)*100)
labels <- paste(sales$COUNTRY, porcentagem)
labels <- paste(labels, "%", sep="")
pie(sales$SALES,
labels = labels,
col = rainbow(length(sales$COUNTRY)),
main="Vendas por país")
str(sales)
Questão 5
Carregar os dados
data("InsectSprays")Montar Boxplot
boxplot(
count ~ spray,
data = InsectSprays,
main = "Contagem de insetos por tipo de inseticida",
xlab = "Tipo de Inseticida",
ylab = "Número de Insetos",
col = "yellow",
outline = FALSE
)
Questão 6
Função auxiliar de conversão
convert_to_mb <- function(mem_str) {
mem_str <- trimws(mem_str) # remove espaços
if (grepl("TB", mem_str, ignore.case = TRUE)) {
as.numeric(gsub("[^0-9.]", "", mem_str)) * 1000000
} else if (grepl("GB", mem_str, ignore.case = TRUE)) {
as.numeric(gsub("[^0-9.]", "", mem_str)) * 1024
} else if (grepl("MB", mem_str, ignore.case = TRUE)) {
as.numeric(gsub("[^0-9.]", "", mem_str))
} else {
NA # caso não tenha unidade reconhecida
}
}Função para processar arquivos
process_file <- function(filepath) {
df <- read.csv(filepath)
# Converter currentTime em POSIXct e normalizar para horas
df$currentTime <- as.POSIXct(df$currentTime)
df$time_hours <- as.numeric(difftime(df$currentTime, df$currentTime[1], units = "hours"))
# Converter memória para MB
df$usedMemory_MB <- sapply(df$usedMemory, convert_to_mb)
return(df)
}Carregar dados
# Caminhos dos arquivos
files <- c(
"/home/felipe/Downloads/monitoringCloudData/monitoringCloudData_NONE.csv",
"/home/felipe/Downloads/monitoringCloudData/monitoringCloudData_0.1.csv",
"/home/felipe/Downloads/monitoringCloudData/monitoringCloudData_0.5.csv",
"/home/felipe/Downloads/monitoringCloudData/monitoringCloudData_1.csv"
)Processar os dados
# Lê e processa todos
data_list <- lapply(files, process_file)
names(data_list) <- c("NONE", "0.1", "0.5", "1.0")Monta os gŕaficos
layout(matrix(1:4, ncol = 2, byrow = TRUE))
# Gera os gráficos
for (name in names(data_list)) {
df <- data_list[[name]]
plot(df$time_hours, df$usedMemory_MB, type = "l", col = "blue",
main = paste("Uso de Memória -", name),
xlab = "Tempo (horas)",
ylab = "Memória usada (MB)")
}