EXERCÍCIO 11
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)
plot(clock,MRT_1F,type="o",pch=4,cex=1,
col="black", xlab="Time between Thing requests (seconds)",
ylab="Response Time (sec)",
main="Response Time",
cex.lab=1, cex.axis=1,
cex.main=2, xlim=c(0,max(clock)),
ylim=c(0,550))
lines(clock,MRT_3F, type="o",pch=11,cex=1,col="yellow")
lines(clock,MRT_5F, type="o",pch=1,cex=1,col="red")
lines(clock,MRT_10F, type="o",pch=2,cex=1,col="blue")
lines(clock,MRT_15F, type="o",pch=5,cex=1,col="purple")
lines(clock,MRT_sem_F, type="o",pch=4,cex=1,col="green")
legend("topright", pch = c(4,11,1,2,5,4),lty=1,cex=.8,
col =c("black","yellow","red","blue","purple","green"),
legend = c("1 Fog","3 Fogs","5 Fogs","10 Fogs", "15 Fogs","w/o Fog"))
colors = c("#E6E6E6", "#666666")
dados <- c("w/o F","1 Fog")
# Criar a matriz dos valores
Values <- matrix(c(MRT_sem_F,MRT_1F), nrow = 2, ncol = 7, byrow
= TRUE)
barplot(Values, main = "Rendimento
total", names.arg = clock,
xlab = "Time between Thing requests (seconds)", ylab =
"Response Time (sec)",log = 'y', col = colors,
beside = T)
legend("topright", pch=c(15,15), col=colors,
legend=dados,bty ="o")
layout.matrix<-matrix(c(1,2,3,4,5,0), nrow = 3, ncol = 2, byrow=TRUE)
layout(mat = layout.matrix,
heights = c(1,1,1),
widths = c(1,1))
colors = c("#E6E6E6", "#666666")
dados <- c("w/o Fog","1 Fog","3 Fog","5 Fog","10 Fog","15 Fog")
Fogs <- list(MRT_sem_F,MRT_1F,MRT_3F,MRT_5F,MRT_10F,MRT_15F)
par(mar = c(4, 8, 2.5, 0),xpd=TRUE)
for (i in 2:(length(Fogs))){
Values <- matrix(c(unlist(Fogs[1]),unlist(Fogs[i])), nrow = 2, ncol = 7, byrow
= TRUE)
barplot(Values, names.arg = clock,
xlab = "Time between Thing requests (seconds)", ylab =
"Response Time (sec)",log = 'y', col = colors,
beside = T)
legend("topright",bg = "white",
pch=c(15,15), col=colors,
legend=c(dados[1],dados[i]))
}
QUESTÃO 2
colors = c("brown","orange","green")
preco <- c("$10-19","$20-29","$30-39","$40-49")
quality <- c("Good","Very good","Excelent")
# Criar a matriz dos valores
Values <- matrix(c(53.8,33.9,2.6,0,43.6,54.2,60.5,21.4,2.6,11.9,36.8,78.6),
nrow = 3, ncol = 4, byrow
= TRUE)
par(mar = c(4, 15, 2.5, 8),xpd=TRUE)
barplot(Values, main = "Quality rating", names.arg = preco, cex=0.8,
xlab = "Meal Price", ylab =
"Percentage", space=1,cex.axis=0.8, col = colors)
legend("topright", pch=c(15,15,15), col =
colors,inset=c(-0.5,0),cex=1, legend=quality)
QUESTÃO 3
library(dplyr)
df<-filter(airquality,Month==5)
filter(df,is.na(df$Temp))
## [1] Ozone Solar.R Wind Temp Month Day
## <0 rows> (or 0-length row.names)
df<-mutate(df,Celsius=(df$Temp-32)/1.8)
Temperature<-df$Celsius
hist(Temperature, col='grey',main = "Histograma de Temperatura",xlab = "Temperatura", ylab="Densidade",probability=T)
densidade<-density(Temperature)
lines(densidade)
QUESTÃO 4
sales <- read.table("https://training-course-material.com/images/8/8f/Sales.txt",header=TRUE)
vendas <-sales$SALES
pais<-sales$COUNTRY
pct<-round(vendas/sum(vendas)*100)
lbls<-paste(pais,pct)
lbls<-paste(lbls,"%",sep="")
pie(vendas,lbls,main="Vendas",col=rainbow(length(pais)))
legend("topright",
legend=pais,
cex=0.8, fill=rainbow(length(pais)))
QUESTÃO 5
boxplot(count ~ spray, data=InsectSprays,
xlab="Tipo de spray",
ylab="Quantidade de insetos",main="Dados de Insetos",outline=FALSE,col="yellow")
QUESTÃO 6
plot(mtcars$mpg,mtcars$wt, main="Dados de carros",xlab="mpg",
ylab="Peso",pch=16, cex=0.8,col="blue")
legend("topright",
legend="Peso", col="blue",pch=16,
cex=0.8)
lm(formula=mtcars$wt ~ mtcars$mpg)
##
## Call:
## lm(formula = mtcars$wt ~ mtcars$mpg)
##
## Coefficients:
## (Intercept) mtcars$mpg
## 6.0473 -0.1409
abline(lm(mtcars$wt ~ mtcars$mpg),col="blue")
QUESTÃO 7
library(tidyr)
library(dplyr)
layout.matrix<-matrix(c(1,2,3,4), nrow = 2, ncol = 2, byrow=TRUE)
layout(mat = layout.matrix,
heights = c(1,1,1),
widths = c(1,1))
par(mar = c(4, 4, 2.5, 1),xpd=TRUE)
data_0.1<- read.csv("monitoringCloudData_0.1.csv",header=T,stringsAsFactors = FALSE)
data_0.5<- read.csv("monitoringCloudData_0.5.csv",header=T,stringsAsFactors = FALSE)
data_1<- read.csv("monitoringCloudData_1.csv",header=T,stringsAsFactors = FALSE)
data_none<- read.csv("monitoringCloudData_NONE.csv",header=T,stringsAsFactors = FALSE)
#None workload
data_none<-data_none%>%
separate(col="currentTime",
into=c("Date","Time"),
sep=" ")
data_none<-data_none%>%
separate(col="Date",
into=c("Ano","Mes","Dia"),
sep="-")
data_none<-data_none%>%
separate(col="Time",
into=c("Hora","Minuto","Segundo"),
sep=":")
data_none<-data_none%>%
separate(col="usedMemory",
into=c("Qnt_used","MB_GB"),
sep=-2)
data_none$Ano<-as.factor(data_none$Ano)
data_none$Mes<-as.factor(data_none$Mes)
data_none$Dia<-as.factor(data_none$Dia)
data_none$MB_GB<-as.factor(data_none$MB_GB)
data_none$Qnt_used<-as.numeric(data_none$Qnt_used)
data_none<-mutate(data_none,Qnt_used_MB=data_none$Qnt_used)
for (i in 1:length(data_none$MB_GB)){
if (data_none$MB_GB[i]=="GB"){
data_none$Qnt_used_MB[i]<-data_none$Qnt_used[i]*1024
}else{
data_none$Qnt_used_MB[i]<-data_none$Qnt_used[i]
}
}
data_none$Dia<-as.character(data_none$Dia)
data_none$Dia<-as.numeric(data_none$Dia)
data_none$Hora<-as.numeric(data_none$Hora)
data_none$Minuto<-as.numeric(data_none$Minuto)
data_none$Segundo<-as.numeric(data_none$Segundo)
data_none$Hora<-data_none$Hora+((data_none$Dia-min(data_none$Dia))*24)
data_none$Minuto<-(data_none$Minuto)/60
data_none$Segundo<-(data_none$Segundo)/3600
data_none<-mutate(data_none,Horat = data_none$Hora+data_none$Minuto+data_none$Segundo)
data_none<-mutate(data_none,Horat2 = data_none$Horat-min(data_none$Horat))
data_none<-arrange(data_none,Horat2)
plot(data_none$Horat2,data_none$Qnt_used_MB, main="Memory Analysis (None Workload)",xlab="Time(hour)",
ylab="Used Memory(MB)",xlim=c(-1,max(data_none$Horat2)),type="l",cex=2,col="black")
#Workload of 0.1
data_0.1<-data_0.1%>%
separate(col="currentTime",
into=c("Date","Time"),
sep=" ")
data_0.1<-data_0.1%>%
separate(col="Date",
into=c("Ano","Mes","Dia"),
sep="-")
data_0.1<-data_0.1%>%
separate(col="Time",
into=c("Hora","Minuto","Segundo"),
sep=":")
data_0.1<-data_0.1%>%
separate(col="usedMemory",
into=c("Qnt_used","MB_GB"),
sep=-2)
data_0.1$Ano<-as.factor(data_0.1$Ano)
data_0.1$Mes<-as.factor(data_0.1$Mes)
data_0.1$Dia<-as.factor(data_0.1$Dia)
data_0.1$MB_GB<-as.factor(data_0.1$MB_GB)
data_0.1$Qnt_used<-as.numeric(data_0.1$Qnt_used)
data_0.1<-mutate(data_0.1,Qnt_used_MB=data_0.1$Qnt_used)
for (i in 1:length(data_0.1$MB_GB)){
if (data_0.1$MB_GB[i]=="GB"){
data_0.1$Qnt_used_MB[i]<-data_0.1$Qnt_used[i]*1024
}else{
data_0.1$Qnt_used_MB[i]<-data_0.1$Qnt_used[i]
}
}
data_0.1$Dia<-as.character(data_0.1$Dia)
data_0.1$Dia<-as.numeric(data_0.1$Dia)
data_0.1$Hora<-as.numeric(data_0.1$Hora)
data_0.1$Minuto<-as.numeric(data_0.1$Minuto)
data_0.1$Segundo<-as.numeric(data_0.1$Segundo)
data_0.1$Hora<-data_0.1$Hora+((data_0.1$Dia-min(data_0.1$Dia))*24)
data_0.1$Minuto<-(data_0.1$Minuto)/60
data_0.1$Segundo<-(data_0.1$Segundo)/3600
data_0.1<-mutate(data_0.1,Horat = data_0.1$Hora+data_0.1$Minuto+data_0.1$Segundo)
data_0.1<-mutate(data_0.1,Horat2 = data_0.1$Horat-min(data_0.1$Horat))
data_01<-arrange(data_0.1,Horat2)
plot(data_0.1$Horat2,data_0.1$Qnt_used_MB, main="Memory Analysis (Workload of 0.1)",xlab="Time(hour)",
ylab="Used Memory(MB)",xlim=c(-1,max(data_0.1$Horat2)),type="l",cex=2,col="black")
#Workload of 0.5
data_0.5<-data_0.5%>%
separate(col="currentTime",
into=c("Date2","Time"),
sep=-15)
data_0.5<-data_0.5%>%
separate(col="Date2",
into=c("Date","ruido"),
sep=10)
data_0.5<-data_0.5%>%
separate(col="Date",
into=c("Ano","Mes","Dia"),
sep="-")
data_0.5<-data_0.5%>%
separate(col="Time",
into=c("Hora","Minuto","Segundo"),
sep=":")
data_0.5<-data_0.5%>%
separate(col="usedMemory",
into=c("Qnt_used","MB_GB"),
sep=-2)
data_0.5$Ano<-as.factor(data_0.5$Ano)
data_0.5$Mes<-as.factor(data_0.5$Mes)
data_0.5$Dia<-as.factor(data_0.5$Dia)
data_0.5$MB_GB<-as.factor(data_0.5$MB_GB)
data_0.5$Qnt_used<-as.numeric(data_0.5$Qnt_used)
data_0.5<-mutate(data_0.5,Qnt_used_MB=data_0.5$Qnt_used)
for (i in 1:length(data_0.5$MB_GB)){
if (data_0.5$MB_GB[i]=="GB"){
data_0.5$Qnt_used_MB[i]<-data_0.5$Qnt_used[i]*1024
}else{
data_0.5$Qnt_used_MB[i]<-data_0.5$Qnt_used[i]
}
}
data_0.5$Dia<-as.character(data_0.5$Dia)
data_0.5$Dia<-as.numeric(data_0.5$Dia)
data_0.5$Hora<-as.numeric(data_0.5$Hora)
data_0.5$Minuto<-as.numeric(data_0.5$Minuto)
data_0.5$Segundo<-as.numeric(data_0.5$Segundo)
data_0.5$Hora<-data_0.5$Hora+((data_0.5$Dia-min(data_0.5$Dia))*24)
data_0.5$Minuto<-(data_0.5$Minuto)/60
data_0.5$Segundo<-(data_0.5$Segundo)/3600
data_0.5<-mutate(data_0.5,Horat = data_0.5$Hora+data_0.5$Minuto+data_0.5$Segundo)
data_0.5<-mutate(data_0.5,Horat2 = data_0.5$Horat-min(data_0.5$Horat))
data_0.5<-arrange(data_0.5,Horat2)
plot(data_0.5$Horat2,data_0.5$Qnt_used_MB, main="Memory Analysis (Workload of 0.5)",xlab="Time(hour)",
ylab="Used Memory(MB)",xlim=c(-1,max(data_0.5$Horat2)),type="l",cex.axis=0.8,col="black")
#Workload of 1.0
data_1<-data_1%>%
separate(col="currentTime",
into=c("Date","Time"),
sep=" ")
data_1<-data_1%>%
separate(col="Date",
into=c("Ano","Mes","Dia"),
sep="-")
data_1<-data_1%>%
separate(col="Time",
into=c("Hora","Minuto","Segundo"),
sep=":")
data_1<-data_1%>%
separate(col="usedMemory",
into=c("Qnt_used","MB_GB"),
sep=-2)
data_1$Ano<-as.factor(data_1$Ano)
data_1$Mes<-as.factor(data_1$Mes)
data_1$Dia<-as.factor(data_1$Dia)
data_1$MB_GB<-as.factor(data_1$MB_GB)
data_1$Qnt_used<-as.numeric(data_1$Qnt_used)
data_1<-mutate(data_1,Qnt_used_MB=data_1$Qnt_used)
for (i in 1:length(data_1$MB_GB)){
if (data_1$MB_GB[i]=="GB"){
data_1$Qnt_used_MB[i]<-data_1$Qnt_used[i]*1024
}else{
data_1$Qnt_used_MB[i]<-data_1$Qnt_used[i]
}
}
data_1$Dia<-as.character(data_1$Dia)
data_1$Dia<-as.numeric(data_1$Dia)
data_1$Hora<-as.numeric(data_1$Hora)
data_1$Minuto<-as.numeric(data_1$Minuto)
data_1$Segundo<-as.numeric(data_1$Segundo)
data_1$Hora<-data_1$Hora+((data_1$Dia-min(data_1$Dia))*24)
data_1$Minuto<-(data_1$Minuto)/60
data_1$Segundo<-(data_1$Segundo)/3600
data_1<-mutate(data_1,Horat = data_1$Hora+data_1$Minuto+data_1$Segundo)
data_1<-mutate(data_1,Horat2 = data_1$Horat-min(data_1$Horat))
data_1<-arrange(data_1,Horat2)
plot(data_1$Horat2,data_1$Qnt_used_MB, main="Memory Analysis (Workload of 1)",xlab="Time(hour)",
ylab="Used Memory(MB)",xlim=c(-1,max(data_1$Horat2)),type="l",cex.axis=0.8
,col="black")
