<file_sep># Mapping Data
Mymapping <- read.csv(file.choose())

YearsMymapping <- Mymapping[ , c('Year.of.publication')] 
hist(YearsMymapping, main = "Publications over the years", xlab = "Years", ylab = "Density", xlim=c(2000,2020), ylim=c(0,0.7), prob=T, cex.axis=1.5, cex.main=2, cex.lab=1.5, cex.sub=1.5)
box(bty="l")

#NFV Data
NFVIEEE <- read.csv(file.choose())
NFVACM <- read.csv(file.choose())
NFVElsevier <- read.csv(file.choose())
NFVSpringer <- read.csv(file.choose())
YearsNFVIEEE <- NFVIEEE[ , c('Publication_Year')]
YearsNFVACM <- NFVACM[ , c('year')]
YearsNFVElsevier <- unlist(c(NFVElsevier))
YearsNFVSpringer <- unlist(c(NFVSpringer))
YearsNFV <- c(YearsNFVIEEE, YearsNFVACM,YearsNFVElsevier,YearsNFVSpringer)
lines(density(YearsNFV[YearsNFV < 2020],na.rm=T), col="red", lwd=2)
legend("topleft",legend="NFV", cex=2, lty=1, col="red", lwd=2, bty="n")

#SDN Data
SDNIEEE <- read.csv(file.choose())
SDNACM <- read.csv(file.choose())
SDNElsevier <- read.csv(file.choose())
SDNSpringer <- read.csv(file.choose())
YearsSDNIEEE <- SDNIEEE[ , c('Publication_Year')]
YearsSDNACM <- SDNACM[ , c('year')]
YearsSDNElsevier <- unlist(c(SDNElsevier))
YearsSDNSpringer <- SDNSpringer[ , c('Publication.Year')]
YearsSDN <- c(YearsSDNIEEE, YearsSDNACM,YearsSDNElsevier,YearsSDNSpringer)
lines(density(YearsSDN[YearsSDN < 2020],na.rm=T), col="blue", lwd=2)
legend("topleft",legend="SDN", cex=2, lty=1, col="blue", lwd=2, bty="n")

#Cloud Data
CloudIEEE <- read.csv(file.choose())
CloudACM <- read.csv(file.choose())
CloudElsevier <- read.csv(file.choose())
CloudSpringer <- read.csv(file.choose())
YearsCloudIEEE <- CloudIEEE[ , c('Publication_Year')]
YearsCloudIEEE <- YearsCloudIEEE[-141]
YearsCloudACM <- CloudACM[ , c('year')]
YearsCloudElsevier <- unlist(c(CloudElsevier))
YearsCloudSpringer <- CloudSpringer[ , c('Publication.Year')]
YearsCloud <- c(YearsCloudIEEE,YearsCloudACM, YearsCloudElsevier, YearsCloudSpringer)
lines(density(YearsCloud,na.rm=T), col="green", lwd=2)
legend("topleft", legend="Cloud Computing", cex=2, lty=1, col="green", lwd=2, bty="n")


#Virtualization Data
VirtIEEE <- read.csv(file.choose())
VirtACM <- read.csv(file.choose())
VirtElsevier <- read.csv(file.choose())
VirtSpringer <- read.csv(file.choose())
YearsVirtIEEE <- VirtIEEE[ , c('Publication_Year')]
YearsVirtACM <- VirtACM[ , c('year')]
YearsVirtElsevier <- unlist(c(VirtElsevier))
YearsVirtSpringer <- VirtSpringer[ , c('Publication.Year')]
YearsVirt <- c(YearsVirtIEEE, YearsVirtACM,YearsVirtElsevier,YearsVirtSpringer)
lines(density(YearsVirt[YearsVirt < 2020],na.rm=T), col="orange", lwd=2)
legend("topleft", legend="Virtualization", cex=2, lty=1, col="orange", lwd=2, bty="n")


#DTN Data
DTNIEEE <- read.csv(file.choose())
DTNACM <- read.csv(file.choose())
DTNElsevier <- read.csv(file.choose())
DTNSpringer <- read.csv(file.choose())
YearsDTNIEEE <- DTNIEEE[ , c('Publication_Year')]
YearsDTNACM <- DTNACM[ , c('year')]
YearsDTNElsevier <- unlist(c(DTNElsevier))
YearsDTNSpringer <- DTNSpringer[ , c('Publication.Year')]
YearsDTN <- c(YearsDTNIEEE, YearsDTNACM,YearsDTNElsevier,YearsDTNSpringer)
lines(density(YearsDTN[YearsDTN < 2020],na.rm=T), col="purple", lwd=2)
legend("topleft", legend="DTN", cex = 2, lty=1, col="purple", lwd=2, bty="n")

#Grid Data
GridIEEE <- read.csv(file.choose())
GridACM <- read.csv(file.choose())
GridElsevier <- read.csv(file.choose())
GridSpringer <- read.csv(file.choose())
YearsGridIEEE <- GridIEEE[ , c('Publication_Year')]
YearsGridACM <- GridACM[ , c('year')]
YearsGridElsevier <- unlist(c(GridElsevier))
YearsGridSpringer <- GridSpringer[ , c('Publication.Year')]
YearsGrid <- c(YearsGridIEEE, YearsGridACM,YearsGridElsevier,YearsGridSpringer)
lines(density(YearsGrid[YearsGrid < 2020],na.rm=T), col="brown", lwd=2)
legend("topleft", legend="Grid Computing", cex=2, lty=1, col="brown", lwd=2, bty="n")

#P2P Data
P2PIEEE <- read.csv(file.choose())
P2PACM <- read.csv(file.choose())
P2PElsevier <- read.csv(file.choose())
P2PSpringer <- read.csv(file.choose())
YearsP2PIEEE <- P2PIEEE[ , c('Publication_Year')]
YearsP2PACM <- P2PACM[ , c('year')]
YearsP2PElsevier <- unlist(c(P2PElsevier))
YearsP2PSpringer <- P2PSpringer[ , c('Publication.Year')]
YearsP2P <- c(YearsP2PIEEE, YearsP2PACM,YearsP2PElsevier,YearsP2PSpringer)
lines(density(YearsP2P[YearsP2P < 2020],na.rm=T), col="cyan", lwd=2)
legend("topleft", legend="Peer-to-Peer", cex=2, lty=1, col="cyan", lwd=2, bty="n")
<file_sep>library(ggplot2)

#Gráfico de Barras Empilhados
#Management
Management <- read.csv(file.choose(), sep = ';')
Management <- as.data.frame(Management)
colnames(Management) <- c("Years", "Topics", "Quantity")
ggplot(data = Management, aes(x= Years, y= Quantity, fill= Topics)) + 
  geom_bar(stat = "identity") + 
  scale_fill_brewer(palette = 18) +
  scale_x_continuous(breaks = seq(2010,2019,1)) +
  theme(panel.grid.major=element_line(linetype=2,color="gray90"),
        panel.background = element_rect(fill = "white", colour = "black", size = 1),
        axis.text = element_text(size = 17), 
        axis.title = element_text(size=20), 
        legend.text = element_text(size=17),
        legend.title = element_text(size=17)
  )

#Orchestration
Orchestration <- read.csv(file.choose(), sep = ';')
Orchestration <- as.data.frame(Orchestration)
colnames(Orchestration) <- c("Years", "Topics", "Quantity")
ggplot(data = Orchestration, aes(x= Years, y= Quantity, fill= Topics)) + 
  geom_bar(stat = "identity") + 
  scale_fill_brewer(palette = 10) +
  scale_x_continuous(breaks = seq(2010,2019,1)) +
  theme(panel.grid.major=element_line(linetype=2,color="gray90"),
        panel.background = element_rect(fill = "white", colour = "black", size = 1),
        axis.text = element_text(size = 17), 
        axis.title = element_text(size=20), 
        legend.text = element_text(size=17),
        legend.title = element_text(size=17)
  )

#5G
FiveG <- read.csv(file.choose(), sep = ';')
FiveG <- as.data.frame(FiveG)
colnames(FiveG) <- c("Years", "Topics", "Quantity")
ggplot(data = FiveG, aes(x= Years, y= Quantity, fill= Topics)) + 
  geom_bar(stat = "identity") + 
  scale_fill_brewer(palette = 11) +
  scale_x_continuous(breaks = seq(2010,2019,1)) +
  theme(panel.grid.major=element_line(linetype=2,color="gray90"),
        panel.background = element_rect(fill = "white", colour = "black", size = 1),
        axis.text = element_text(size = 17), 
        axis.title = element_text(size=20), 
        legend.text = element_text(size=17),
        legend.title = element_text(size=17)
  )

#Architecture
Architecture <- read.csv(file.choose(), sep = ';')
Architecture <- as.data.frame(Architecture)
colnames(Architecture) <- c("Years", "Topics", "Quantity")
ggplot(data = Architecture, aes(x= Years, y= Quantity, fill= Topics)) + 
  geom_bar(stat = "identity") + 
  scale_fill_brewer(palette = 8) +
  scale_x_continuous(breaks = seq(2010,2019,1)) +
  theme(panel.grid.major=element_line(linetype=2,color="gray90"),
        panel.background = element_rect(fill = "white", colour = "black", size = 1),
        axis.text = element_text(size = 17), 
        axis.title = element_text(size=20), 
        legend.text = element_text(size=17),
        legend.title = element_text(size=17)
  )

#BillingPrice
BillingPrice <- read.csv(file.choose(), sep = ';')
BillingPrice <- as.data.frame(BillingPrice)
colnames(BillingPrice) <- c("Years", "Topics", "Quantity")
ggplot(data = BillingPrice, aes(x= Years, y= Quantity, fill= Topics)) + 
  geom_bar(stat = "identity") + 
  scale_fill_brewer(palette = 12) +
  scale_x_continuous(breaks = seq(2010,2019,1)) +
  theme(panel.grid.major=element_line(linetype=2,color="gray90"),
        panel.background = element_rect(fill = "white", colour = "black", size = 1),
        axis.text = element_text(size = 17), 
        axis.title = element_text(size=20), 
        legend.text = element_text(size=17),
        legend.title = element_text(size=17)
        )

<file_sep>library(ggplot2)
library(dplyr)
library(scales)
library(plotly)

#Gráfico de Bolha
data <- read.csv(file.choose(), sep =';')
data$Value <- sub(",", ".",data$Value)
data$radius <- sqrt(as.numeric(data$Value)/pi)
colnames(data) <- c("TF", "RF", "Value", "Radius")
data$RF <- tools::toTitleCase(as.vector(data$RF))
data$RF <- as.character(data$RF)
data$RF <- factor(data$RF, levels=unique(data$RF))
data$RF <- factor(data$RF, levels=c("Evaluation Research","Opinion Papers","Personal Experience Papers","Solution Proposal","Philosophical Papers","Validation Research","Total (Technological Facet)"))
data$TF <- as.character(data$TF)
data$TF <- factor(data$TF, levels=unique(data$TF))
data$TF <- factor(data$TF, levels=c("Total (Research Facet)","5G","Architecture","Management","Orchestration","Pricing Model"))
ggplot(data,aes(RF,TF))+
  geom_point(aes(size=as.numeric(data$Radius)*25, colour =factor(data$RF)), shape=20, alpha = 0.4)+
  geom_text(aes(label = paste0(data$Value,"%")),size=5.5)+
  scale_size_identity()+
  annotate("label", x="Total (Technological Facet)", y="Total (Research Facet)", label="100%")+
  annotate("label", x="Evaluation Research", y="Total (Research Facet)", label="2,54%")+
  annotate("label", x="Solution Proposal", y="Total (Research Facet)", label="51,54%")+
  annotate("label", x="Validation Research", y="Total (Research Facet)", label="38,59%")+
  annotate("label", x="Philosophical Papers", y="Total (Research Facet)", label="4,54%")+
  annotate("label", x="Personal Experience Papers", y="Total (Research Facet)", label="0,73%")+
  annotate("label", x="Opinion Papers", y="Total (Research Facet)", label="2,05%")+
  annotate("label", x="Total (Technological Facet)", y="Management", label="45,04%")+
  annotate("label", x="Total (Technological Facet)", y="Orchestration", label="7,77%")+
  annotate("label", x="Total (Technological Facet)", y="5G", label="32,49%")+
  annotate("label", x="Total (Technological Facet)", y="Architecture", label="12,16%")+
  annotate("label", x="Total (Technological Facet)", y="Pricing Model", label="2,56%")+
  theme(panel.grid.major=element_line(linetype=2,color="gray90"),
        axis.text.x = element_text(angle=30, hjust = 1, colour = "black", size = rel(1.5)), 
        axis.text.y = element_text(colour = "black", size = rel(1.5)),
        panel.background = element_rect(fill = "white", colour = "black", size = 1),
        plot.margin = margin(2, 2, 0, 0, "cm"),
        axis.title.x=element_blank(),
        axis.title.y=element_blank(),
        legend.position = "none"
        )
