Atividades
Questão 1
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)
dados <- c(MRT_1F,MRT_3F, MRT_5F, MRT_10F, MRT_15F, MRT_sem_F)
nomes <- c("1 Frog", "3 Frogs", "5 Frogs", "10 Frogs", "15 Frogs", "W/O Frogs")
layout(matrix(c(1, 1,
2, 3,
4, 5,
6, 0), nrow=4, byrow=TRUE)
)
par(mar = c(1, 1, 1, 1))
plot(clock, MRT_1F , type= "o",
pch = 1,
col = 1,
xlim = range(clock),
ylim = range(dados),
ylab = "Response time (seconds)",
xlab = "Time between things requests (seconds)")
lines(clock, MRT_3F, type= "o", pch =2, col=2)
lines(clock, MRT_5F, type= "o", pch =3, col=3)
lines(clock, MRT_10F, type= "o", pch =4, col=4)
lines(clock, MRT_15F, type= "o", pch =5, col=5)
lines(clock, MRT_sem_F, type= "o", pch =6, col=6)
legend("topright",
legend = nomes,
pch = 1:6,
col = 1:6)
barplot(rbind(MRT_sem_F, MRT_1F),
beside = TRUE,
names.arg = clock,
log = "y",
col = c("#E6E6E6", "#666666"),
xlab = "Time between things requests",
ylab = "Response time (seconds)",
legend.text = c("W/o Frog", "1 Frog"),
args.legend = list(x = "topright")
)
barplot(rbind(MRT_sem_F, MRT_3F),
beside = TRUE,
names.arg = clock,
log = "y",
col = c("#E6E6E6", "#666666"),
xlab = "Time between things requests",
ylab = "Response time (seconds)",
legend.text = c("W/o Frog", "3 Frog"),
args.legend = list(x = "topright")
)
barplot(rbind(MRT_sem_F, MRT_5F),
beside = TRUE,
names.arg = clock,
log = "y",
col = c("#E6E6E6", "#666666"),
xlab = "Time between things requests",
ylab = "Response time (seconds)",
legend.text = c("W/o Frog", "5 Frog"),
args.legend = list(x = "topright")
)
barplot(rbind(MRT_sem_F, MRT_10F),
beside = TRUE,
names.arg = clock,
log = "y",
col = c("#E6E6E6", "#666666"),
xlab = "Time between things requests",
ylab = "Response time (seconds)",
legend.text = c("W/o Frog", "10 Frog"),
args.legend = list(x = "topright")
)
barplot(rbind(MRT_sem_F, MRT_15F),
beside = TRUE,
names.arg = clock,
log = "y",
col = c("#E6E6E6", "#666666"),
xlab = "Time between things requests",
ylab = "Response time (seconds)",
legend.text = c("W/o Frog", "15 Frog"),
args.legend = list(x = "topright")
)
Questão 2
dados <- matrix(
c(53.8, 43.6, 2.6,
33.9, 54.2, 11.9,
2.6, 60.5, 36.8,
0.0, 21.4, 78.6),
nrow = 3,
byrow = FALSE
)
rownames(dados) <- c("Bom", "Muito bom", "Exelente")
colnames(dados) <- c("$10-19", "$20-29", "$30-39", "$40-49")
cores <- c("lightblue", "lightgreen", "lightyellow")
barplot(
dados,
main = "Qualidade da refeição por categoria de preço",
xlab = "Preço da refeição",
ylab = "Percentual (%)",
col = cores,
legend.text = rownames(dados),
ylim = c(0, 120),
args.legend = list(x = "top", bty = "n", ncol = 3),
space = 0.5
)
Questão 3
temp <- airquality[airquality$Month == 5, "Temp"]
temp <- temp[!is.na(temp)]
temp <- (temp - 32) / 1.8
hist(
temp,
main = "Histograma das temperaturas de maio (Cº)",
xlab = "Temperatura (Cº)",
ylab = "Densidade",
col = "blue",
freq = FALSE,
ylim = c(0, 0.2)
)
lines(
density(temp),
col = "red",
lwd = 2
)
Questão 4
sales <- read.table("https://training-course-material.com/images/8/8f/Sales.txt", header = TRUE)
pct <- round(sales$SALES/sum(sales$SALES)*100)
lbls <- paste(sales$COUNTRY, pct)
lbls <- paste(lbls, "%", sep="")
pie(sales$SALES, labels = lbls,
main = "Vendas por país",
col=rainbow(nrow(sales)))
Questão 5
boxplot(
count ~ spray,
data = InsectSprays,
main = "Insetos por inseticida",
xlab = "Tipo de inseticida",
ylab = "Número de insetos",
col = "yellow",
outline = FALSE
)
Questão 6
processar_dados <- function(filepath) {
dados <- read.csv(filepath, stringsAsFactors = FALSE)
valores_num <- as.numeric(gsub("([0-9.]+).*", "\\1", dados$usedMemory))
unidades <- gsub("[0-9.]+(.*)", "\\1", dados$usedMemory)
dados$memoria_mb <- ifelse(
unidades == "TB", valores_num * 1000000,
ifelse(
unidades == "GB", valores_num * 1024,
valores_num
)
)
dados$currentTime <- as.POSIXct(dados$currentTime)
tempo_inicio <- min(dados$currentTime)
dados$tempo_horas <- as.numeric(
difftime(dados$currentTime, tempo_inicio, units = "hours")
)
return(dados)
}
arquivos <- c(
"/home/rick/Downloads/monitoringCloudData/monitoringCloudData_0.1.csv",
"/home/rick/Downloads/monitoringCloudData/monitoringCloudData_0.5.csv",
"/home/rick/Downloads/monitoringCloudData/monitoringCloudData_1.csv",
"/home/rick/Downloads/monitoringCloudData/monitoringCloudData_NONE.csv"
)
titulos <- c(
"Cenário: 0.1",
"Cenário: 0.5",
"Cenário: 1.0",
"Cenário: NONE"
)
layout(matrix(c(1, 2, 3, 4), nrow = 2, byrow = TRUE))
par(mar = c(4.5, 4.5, 2.5, 1.5))
for (i in 1:length(arquivos)) {
dados_plot <- processar_dados(arquivos[i])
plot(
dados_plot$tempo_horas,
dados_plot$memoria_mb,
type = "l",
main = titulos[i],
xlab = "Tempo (horas)",
ylab = "Memória Usada (MB)",
col = "blue",
lwd = 2
)
}
Questão 7
library(plotly)
library(dplyr)
df_netflix <- read.csv("netflix_titles.csv")
top_10_countries <- df_netflix %>%
filter(!is.na(country) & country != "") %>%
filter(!grepl(",", country)) %>%
group_by(country) %>%
summarise(total_conteudo = n()) %>%
arrange(desc(total_conteudo)) %>%
slice_head(n = 10)
fig <- plot_ly(
data = top_10_countries,
labels = ~country,
values = ~total_conteudo,
type = 'pie',
textinfo = 'percent',
textposition = 'inside',
insidetextorientation = 'radial'
)
fig <- fig %>% layout(
title = "10 Países com mais conteúdo na netflix",
showlegend = TRUE,
width = 500,
height = 300
)
## Warning: Specifying width/height in layout() is now deprecated.
## Please specify in ggplotly() or plot_ly()
fig
Questão 8
library(plotly)
library(dplyr)
df_netflix <- read.csv("netflix_titles.csv")
top_10_countries <- df_netflix %>%
filter(!is.na(country) & country != "") %>%
filter(!grepl(",", country)) %>%
group_by(country) %>%
summarise(total_conteudo = n()) %>%
arrange(desc(total_conteudo)) %>%
slice_head(n = 10)
fig <- plot_ly(
type = 'table',
header = list(
values = c("País", "Total de conteúdos"),
align = "center",
fill = list(color = 'grey'),
font = list(color = 'white', size = 12)
),
cells = list(
values = list(top_10_countries$country, top_10_countries$total_conteudo),
align = "center",
fill = list(color = c("white", "lightgrey")),
height = 30
)
)
fig <- fig %>% layout(
title = "10 Países com mais conteúdo"
)
fig
Questão 9
library(plotly)
library(dplyr)
df_netflix <- read.csv("netflix_titles.csv")
data_processed <- df_netflix %>%
filter(!is.na(release_year) & release_year != "") %>%
mutate(decade = floor(release_year / 10) * 10) %>%
group_by(decade, type) %>%
summarise(total_conteudo = n()) %>%
ungroup() %>%
filter(type %in% c("Movie", "TV Show")) %>%
arrange(decade) %>%
mutate(decade = as.factor(decade))
data_movies <- data_processed %>%
filter(type == "Movie")
data_tv_shows <- data_processed %>%
filter(type == "TV Show")
fig <- plot_ly() %>%
add_trace(
data = data_movies,
x = ~decade,
y = ~total_conteudo,
type = 'scatter',
mode = 'lines+markers',
name = 'Filmes',
line = list(color = 'gold'),
marker = list(color = 'gold')
) %>%
add_trace(
data = data_tv_shows,
x = ~decade,
y = ~total_conteudo,
type = 'scatter',
mode = 'lines+markers',
name = 'Séries',
line = list(color = 'blue'),
marker = list(color = 'blue')
) %>%
layout(
title = "Quantidade de conteúdo por década",
xaxis = list(title = "Década"),
yaxis = list(title = "Quantidade de conteúdo")
)
fig
Questão 10
library(plotly)
library(dplyr)
df_netflix <- read.csv("netflix_titles.csv")
generos_alvo <- c("Dramas", "Action & Adventure", "Comedies")
data_plot <- df_netflix %>%
filter(type == "Movie") %>%
filter(release_year >= 2000 & release_year <= 2010) %>%
mutate(primeiro_genero = sapply(strsplit(listed_in, ", "), function(x) x[1])) %>%
filter(primeiro_genero %in% generos_alvo) %>%
group_by(release_year, primeiro_genero) %>%
summarise(total_filmes = n(), .groups = 'drop') %>%
mutate(release_year = as.factor(release_year))
fig <- plot_ly(
data = data_plot,
x = ~release_year,
y = ~total_filmes,
color = ~primeiro_genero,
type = 'bar'
)
fig <- fig %>% layout(
title = "Filmes lançados por ano e gênero (2000-2010)",
xaxis = list(title = "Ano de lançamento"),
yaxis = list(title = "Quantidade de filmes"),
barmode = 'group'
)
fig