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)
plot(clock,MRT_1F,type="o",pch=4,ylab="Response Time(sec.)",xlab="Time between Things requestes(seconds)",)
lines(clock,MRT_3F,type="o",pch=11,col="yellow")
lines(clock,MRT_5F,type="o",pch=1,col="red")
lines(clock,MRT_10F,type="o",pch=2,col="blue")
lines(clock,MRT_15F,type="o",pch=5,col="purple")
lines(clock,MRT_sem_F,type="o",pch=4,col="green")
legend("topright",pch=c(4,11,1,2,5,4),col=c("black","yellow","red","blue","purple","green"),legend=c("1 Fog","3 Fog","5 Fog","10 Fog","15 Fog","w/o Fog"))
layout(matrix(c(1, 2,
1, 2,
3, 4,
3, 4,
5, 6,
5, 6
), nrow=3, ncol=2, byrow=TRUE))
barplot(rbind(MRT_sem_F,MRT_1F),log="y",ylab="Response Time(s)",xlab="Time between Things requestes",names.arg=clock,col=c("#E6E6E6", "#666666"),beside = T)
legend("topright",pch=15,col=c("#E6E6E6", "#666666"),legend = c("w/o Fog","1 Fog"))
barplot(rbind(MRT_sem_F,MRT_3F),log="y",ylab="Response Time(s)",xlab="Time between Things requestes",names.arg=clock,col=c("#E6E6E6", "#666666"),beside = T)
legend("topright",pch=15,col=c("#E6E6E6", "#666666"),legend = c("w/o Fog","3 Fog"))
barplot(rbind(MRT_sem_F,MRT_5F),log="y",ylab="Response Time(s)",xlab="Time between Things requestes",names.arg=clock,col=c("#E6E6E6", "#666666"),beside = T)
legend("topright",pch=15,col=c("#E6E6E6", "#666666"),legend = c("w/o Fog","5 Fog"))
barplot(rbind(MRT_sem_F,MRT_10F),log="y",ylab="Response Time(s)",xlab="Time between Things requestes",names.arg=clock,col=c("#E6E6E6", "#666666"),beside = T)
legend("topright",pch=15,col=c("#E6E6E6", "#666666"),legend = c("w/o Fog","10 Fog"))
barplot(rbind(MRT_sem_F,MRT_5F),log="y",ylab="Response Time(s)",xlab="Time between Things requestes",names.arg=clock,col=c("#E6E6E6", "#666666"),beside = T)
legend("topright",pch=15,col=c("#E6E6E6", "#666666"),legend = c("w/o Fog","15 Fog"))
2
Meal_Price<-data.frame(QualityRanking= c("Good","Very Good","Excellent"),
S10a19= c(53.8,43.6,2.6),
S20a29= c(33.9,54.2,11.9),
S30a39= c(2.6,60.5,30.8),
S40a49= c(0,21.4,78.6))
MealPrice<-cbind(S10a19= c(53.8,43.6,2.6),S20a29= c(33.9,54.2,11.9), S30a39= c(2.6,60.5,30.8),S40a49= c(0,21.4,78.6))
colnames(MealPrice)<-c("$10-19","$20-29","$30-39","$40-49")
rownames(MealPrice)<-c("Good","Very Good","Excellent")
barplot(MealPrice,ylab="Preco",xlab="Qualidade",names.arg = colnames(MealPrice),legend.text = rownames(MealPrice))
3
AirQuality<- airquality
AirQuality$Temp <- (AirQuality$Temp-32)/1.8
hist(AirQuality$Temp[AirQuality$Month==5],main="Histograma Temperatura",col="Blue", xlab = "Temperatura",ylab = "Freaquencia",freq = F)
lines(density(AirQuality$Temp[AirQuality$Month==5]))
4
sales <- read.table("https://training-course-material.com/images/8/8f/Sales.txt",header=TRUE)
salesPercent<-round(sales[,2]/sum(sales[,2])*100)
salesPercent<-paste(sales[,1],salesPercent)
salesPercent<-paste(salesPercent,"%",sep="")
pie(sales[,2],salesPercent,main = "Total de Vendas por Pais",col=rainbow(length(sales[,1])))
legend("topright",pch = 15, col = rainbow(length(sales[,1])),legend = sales[,1])
5
insectcount<-InsectSprays
boxplot(insectcount$count~insectcount$spray,col = "yellow", outline = F,ylab="Unidades",xlab="Spray",main="Insect Sprays")
6
mcd0.1<-read.csv("C:/Users/Lucas Carneiro/Downloads/R_Scripts/Arquivos_exercicio12/monitoringCloudData_0.1.csv")
mcd0.5<-read.csv("C:/Users/Lucas Carneiro/Downloads/R_Scripts/Arquivos_exercicio12/monitoringCloudData_0.5.csv")
mcd1<-read.csv("C:/Users/Lucas Carneiro/Downloads/R_Scripts/Arquivos_exercicio12/monitoringCloudData_1.csv")
mcdNone<-read.csv("C:/Users/Lucas Carneiro/Downloads/R_Scripts/Arquivos_exercicio12/monitoringCloudData_NONE.csv")
mcd0.1$currentTime<-difftime(mcd0.1$currentTime,mcd0.1$currentTime[1],units="hours")
mcd0.5$currentTime<-difftime(mcd0.5$currentTime,min(mcd0.5$currentTime),units="hours")
mcd1$currentTime<-difftime(mcd1$currentTime,mcd1$currentTime[1],units="hours")
mcdNone$currentTime<-difftime(mcdNone$currentTime,mcdNone$currentTime[1],units="hours")
vetorAux<-grep(pattern = "GB$",mcd0.1$usedMemory, value = F, invert = F)
vetorAux2<-grep(pattern = "GB$",mcd0.1$usedMemory, value = T, invert = F)
vetorAux2<-paste0(1024*as.numeric(sub(("GB"),"",vetorAux2)),"MB")
mcd0.1$usedMemory[vetorAux]<-vetorAux2
vetorAux<-grep(pattern = "TB$",mcd0.1$usedMemory, value = F, invert = F)
vetorAux2<-grep(pattern = "TB$",mcd0.1$usedMemory, value = T, invert = F)
vetorAux2<-paste0(1000000*as.numeric(sub(("TB"),"",vetorAux2)),"MB")
mcd0.1$usedMemory[vetorAux]<-vetorAux2
mcd0.1Aux<-data.frame(currentTimeH=mcd0.1$currentTime,usedMemoryMB=mcd0.1$usedMemory)
mcd0.1Aux$currentTimeH<-as.numeric(sub((" hours"),"",mcd0.1Aux$currentTimeH))
mcd0.1Aux$usedMemoryMB<-as.numeric(sub(("MB"),"",mcd0.1Aux$usedMemoryMB))
vetorAux<-grep(pattern = "GB$",mcd0.5$usedMemory, value = F, invert = F)
vetorAux2<-grep(pattern = "GB$",mcd0.5$usedMemory, value = T, invert = F)
vetorAux2<-paste0(1024*as.numeric(sub(("GB"),"",vetorAux2)),"MB")
mcd0.5$usedMemory[vetorAux]<-vetorAux2
vetorAux<-grep(pattern = "TB$",mcd0.5$usedMemory, value = F, invert = F)
vetorAux2<-grep(pattern = "TB$",mcd0.5$usedMemory, value = T, invert = F)
vetorAux2<-paste0(1000000*as.numeric(sub(("TB"),"",vetorAux2)),"MB")
mcd0.5$usedMemory[vetorAux]<-vetorAux2
mcd0.5Aux<-data.frame(currentTimeH=mcd0.5$currentTime,usedMemoryMB=mcd0.5$usedMemory)
mcd0.5Aux$currentTimeH<-as.numeric(sub((" hours"),"",mcd0.5Aux$currentTimeH))
mcd0.5Aux$usedMemoryMB<-as.numeric(sub(("MB"),"",mcd0.5Aux$usedMemoryMB))
vetorAux<-grep(pattern = "GB$",mcd1$usedMemory, value = F, invert = F)
vetorAux2<-grep(pattern = "GB$",mcd1$usedMemory, value = T, invert = F)
vetorAux2<-paste0(1024*as.numeric(sub(("GB"),"",vetorAux2)),"MB")
mcd1$usedMemory[vetorAux]<-vetorAux2
vetorAux<-grep(pattern = "TB$",mcd1$usedMemory, value = F, invert = F)
vetorAux2<-grep(pattern = "TB$",mcd1$usedMemory, value = T, invert = F)
vetorAux2<-paste0(1000000*as.numeric(sub(("TB"),"",vetorAux2)),"MB")
mcd1$usedMemory[vetorAux]<-vetorAux2
mcd1Aux<-data.frame(currentTimeH=mcd1$currentTime,usedMemoryMB=mcd1$usedMemory)
mcd1Aux$currentTimeH<-as.numeric(sub((" hours"),"",mcd1Aux$currentTimeH))
mcd1Aux$usedMemoryMB<-as.numeric(sub(("MB"),"",mcd1Aux$usedMemoryMB))
vetorAux<-grep(pattern = "GB$",mcdNone$usedMemory, value = F, invert = F)
vetorAux2<-grep(pattern = "GB$",mcdNone$usedMemory, value = T, invert = F)
vetorAux2<-paste0(1024*as.numeric(sub(("GB"),"",vetorAux2)),"MB")
mcdNone$usedMemory[vetorAux]<-vetorAux2
vetorAux<-grep(pattern = "TB$",mcdNone$usedMemory, value = F, invert = F)
vetorAux2<-grep(pattern = "TB$",mcdNone$usedMemory, value = T, invert = F)
vetorAux2<-paste0(1000000*as.numeric(sub(("TB"),"",vetorAux2)),"MB")
mcdNone$usedMemory[vetorAux]<-vetorAux2
mcdNoneAux<-data.frame(currentTimeH=mcdNone$currentTime,usedMemoryMB=mcdNone$usedMemory)
mcdNoneAux$currentTimeH<-as.numeric(sub((" hours"),"",mcdNoneAux$currentTimeH))
mcdNoneAux$usedMemoryMB<-as.numeric(sub(("MB"),"",mcdNoneAux$usedMemoryMB))
par(mfrow=c(2,2))
plot(mcdNoneAux$currentTimeH,mcdNoneAux$usedMemoryMB,type="l",pch=4,ylab="Used Memory (MB)",xlab="Time (hour)",main="Memory Analysis (None Workload)")
plot(mcd0.1Aux$currentTimeH,mcd0.1Aux$usedMemoryMB,type="l",pch=4,ylab="Used Memory (MB)",xlab="Time (hour)",main="Memory Analysis (Workload of 0.1)")
plot(mcd0.5Aux$currentTimeH,mcd0.5Aux$usedMemoryMB,type="l",pch=4,ylab="Used Memory (MB)",xlab="Time (hour)",main="Memory Analysis (Workload of 0.5)")
plot(mcd1Aux$currentTimeH,mcd1Aux$usedMemoryMB,type="l",pch=4,ylab="Used Memory (MB)",xlab="Time (hour)",main="Memory Analysis (Workload of 1.0)")
7
netflix_titles <- read.csv(file = "C:/Users/Lucas Carneiro/Downloads/R_Scripts/Arquivos_exercicio12/netflix_titles.csv",
header = TRUE,
strip.white = TRUE,
na.strings = "")
netflix_titles <- netflix_titles %>%
filter(!is.na(country) & country != "" & !grepl(",", country)) %>%
filter(!is.na(country) & country != "" & !grepl(",", country))
top_countries <- netflix_titles %>%
group_by(country) %>%
summarise(count = n()) %>%
arrange(desc(count)) %>%
head(10)
plot_ly(labels = top_countries$country, values = top_countries$count, type = "pie",
textinfo = "label+percent", insidetextfont = list(color = "#FFFFFF"),
hoverinfo = "label+percent", hole = 0.6) %>%
layout(title = "10 Paises com Mais Conteudo na Netflix",
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
width = 900,
height = 800)
8
top_countries <- netflix_titles %>%
group_by(country) %>%
summarise(count = n()) %>%
arrange(desc(count)) %>%
head(10)
table <- plot_ly(
type = "table",
header = list(values = c("Pais", "Total de Conteudos"),
fill = list(color = "#a9a9a9"),
align = c("center"),
font = list(color = "white", size = 15)),
cells = list(values = list(top_countries$country, top_countries$count),
align = c("center"),
font = list(color = c("black", "black"), size = 12))
)
table
9
netflix <- read.csv(file = "C:/Users/Lucas Carneiro/Downloads/R_Scripts/Arquivos_exercicio12/netflix_titles.csv")
netflix <- netflix %>%
mutate(decade = 10 * (release_year %/% 10))
filmesPorDecada <- netflix %>%
filter(type == "Movie") %>%
group_by(decade) %>%
summarise(qtd_conteudo = n())
seriesPorDecada <- netflix %>%
filter(type == "TV Show") %>%
group_by(decade) %>%
summarise(num_series = n())
seriesFilmes <- left_join(filmesPorDecada, seriesPorDecada, by = "decade")
seriesFilmes$num_series[2] <- 1
fig <- plot_ly(
seriesFilmes,
x = ~decade
) %>%
add_trace(
y = ~num_series,
name = 'TV Series',
mode = 'lines+markers',
line = list(color = 'blue'),
marker = list(color = 'blue')
) %>%
add_trace(
y = ~qtd_conteudo,
name = 'Movies',
mode = 'lines+markers',
line = list(color = 'orange'),
marker = list(color = 'orange')
) %>%
layout(
xaxis = list(title = 'Decada'),
yaxis = list(title = 'Qtd. Conteúdo')
)
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 trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter
10
netflix <- read.csv("C:/Users/Lucas Carneiro/Downloads/R_Scripts/Arquivos_exercicio12/netflix_titles.csv")
df_filtrado <- netflix %>%
filter(between(release_year, 2000, 2010) & type == "Movie") %>%
select(release_year, listed_in)
contagem_categorias_por_ano <- df_filtrado %>%
mutate(primeira_categoria = ifelse(str_detect(listed_in, ","), word(listed_in, 1, sep = ", "), listed_in)) %>%
group_by(release_year, primeira_categoria) %>%
summarise(num_filmes = n())
## `summarise()` has grouped output by 'release_year'. You can override using the
## `.groups` argument.
categorias <- c("Action & Adventure", "Comedies", "Dramas")
df_final <- contagem_categorias_por_ano %>%
filter(primeira_categoria %in% categorias)
df_grafico <- data.frame(release_year = 2000:2010)
df_grafico <- df_grafico %>%
left_join(
df_final %>%
filter(str_detect(primeira_categoria, "Comedies")) %>%
select(release_year, num_filmes) %>%
rename(Comedies = num_filmes),
by = "release_year"
) %>%
left_join(
df_final %>%
filter(str_detect(primeira_categoria, "Dramas")) %>%
select(release_year, num_filmes) %>%
rename(Dramas = num_filmes),
by = "release_year"
) %>%
left_join(
df_final %>%
filter(str_detect(primeira_categoria, "Action & Adventure")) %>%
select(release_year, num_filmes) %>%
rename(`ActionAdventure` = num_filmes),
by = "release_year"
)
fig <- plot_ly(
df_grafico,
x = ~release_year,
type = 'bar',
y = ~Dramas,
name = 'Drama',
line = list(color = 'blue'),
marker = list(color = 'blue')
) %>%
add_trace(
y = ~ActionAdventure,
name = 'Acao e Aventura',
line = list(color = 'orange'),
marker = list(color = 'orange')
) %>%
add_trace(
y = ~Comedies,
name = 'Comédia',
line = list(color = 'green'),
marker = list(color = 'green')
) %>%
layout(
yaxis = list(title = 'Qnt. de Lancamentos'),
xaxis = list(title = 'Ano de Lancamento')
)
fig