GetAttacksEvolution <- function(Attacks){ attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>% group_by(Year) attacks.evol <- as.data.frame(table(attacks.evol$Year)) colnames(attacks.evol) <- c("Year","Attacks") graph1 <- ggplot2::qplot(main = "Cyberattacks evolution", x = attacks.evol$Year, y = attacks.evol$Attacks, group = 1, xlab = "Years", ylab = "Attacks", data = attacks.evol, geom = "line") + geom_point() + geom_label(aes(label=attacks.evol$Attacks), vjust=c(1.5, 0, 0, -0.5, -0.5), hjust=c(0.5, -0.5, 1.5, 0, 0))+ theme(plot.title = element_text(hjust = 0.5)) graph1 } GetCertsEvolution <- function(Certs){ Certs.evol <- data.frame(Year = c(2011, 2012, 2013, 2014, 2015), Certs = c(sum(Certs$X2011), sum(Certs$X2012), sum(Certs$X2013), sum(Certs$X2014), sum(Certs$X2015))) graph1 <- ggplot2::qplot(main = "ISO 27001 evolution", x = Certs.evol$Year, y = Certs.evol$Certs, group = 1, xlab = "Years", ylab = "Certifications", data = Certs.evol, geom = "line") + geom_point() + geom_label(aes(label=Certs.evol$Certs), vjust=c(0.2, -0.7, 0, 0, 0.5), hjust=c(-0.6, 1, 1.2, 1.2, 1.2))+ theme(plot.title = element_text(hjust = 0.5)) graph1 } GetAttacksMonthEvolution <- function(Attacks){ attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y-%m")) %>% group_by(Year) attacks.evol <- as.data.frame(table(attacks.evol$Year)) colnames(attacks.evol) <- c("Year","Attacks") graph1 <- ggplot2::qplot(main = "Cyberattacks evolution", x = attacks.evol$Year, y = attacks.evol$Attacks, group = 1, xlab = "Months", ylab = "Attacks", data = attacks.evol, geom = "line") + geom_point() + theme(plot.title = element_text(hjust = 0.5)) + geom_smooth(method = 'loess') + scale_x_discrete(labels = c("2012", "", "", "", "", "", "", "", "", "", "", "", "2013", "", "", "", "", "", "", "", "", "", "", "", "2014", "", "", "", "", "", "", "", "", "", "", "", "2015", "", "", "", "", "", "", "", "", "", "", "", "2016", "", "", "", "", "", "", "", "", "", "", "")) graph1 } GetBaseCertsGraph <- function(Cert_PerCountry, year){ graph1 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2012)", x = reorder(country_short,X2012), y = X2012, xlab = "Country", ylab = "Number of certifications", data = Cert_PerCountry[Cert_PerCountry$X2012 > mean(Cert_PerCountry$X2012),], geom = "col", fill = Continent) graph1 } #' Return every graph used in the report file #' #' @param Cert_PerCountry data.frame with the processed data of ISO 27001 certifications #' @param Attacks data.frame with the processed data of cyberattacks #' #' @return data.frame #' @export GetReportGraphs <- function(Cert_PerCountry,Attacks) { #2012 graph1 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2012)", x = reorder(country_short,X2012), y = X2012, xlab = "Country", ylab = "Number of certifications", data = Cert_PerCountry[Cert_PerCountry$X2012 > mean(Cert_PerCountry$X2012),], geom = "col", fill = Continent) attacks2k12 <- Attacks[Attacks$Date < "2013-01-01" & Attacks$Date >= "2012-01-01",] frameAttacks2k12 <- as.data.frame(table(attacks2k12$Country)) colnames(frameAttacks2k12) <- c("Country","Attacks") graph2 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2012)", x = reorder(Country,Attacks), y = Attacks, xlab = "Country", ylab = "Number of attacks", data = frameAttacks2k12[frameAttacks2k12$Attacks > mean(frameAttacks2k12$Attacks),], geom = "col", fill = Continent) Attacks2012ByMonth <- mutate(attacks2k12, month = format(attacks2k12$Date, "%m")) %>% group_by(month) Attack2012FreqByMonth <- as.data.frame(table(Attacks2012ByMonth$month)) colnames(Attack2012FreqByMonth) <- c("Month", "Attacks") graph3 <- ggplot2::qplot(x = as.numeric(Month), y = Attacks, main = "Global cyberattack progression by month (2012)", data = Attack2012FreqByMonth, geom = c("point", "smooth"), xlim = c(1,12), xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12) #2013 graph4 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2013)", x = reorder(country_short,X2013), y = X2013, xlab = "Country", ylab = "Number of certifications", data = Cert_PerCountry[Cert_PerCountry$X2013 > mean(Cert_PerCountry$X2013),] , geom = "col", fill = Continent) attacks2k13 <- Attacks[Attacks$Date < "2014-01-01" & Attacks$Date >= "2013-01-01",] frameAttacks2k13 <- as.data.frame(table(attacks2k13$Country)) colnames(frameAttacks2k13) <- c("Country","Attacks") graph5 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2013)", x = reorder(Country,Attacks), y = Attacks, xlab = "Country", ylab = "Number of attacks", data = frameAttacks2k13[frameAttacks2k13$Attacks > mean(frameAttacks2k13$Attacks),] , geom = "col", fill = Continent) Attacks2013ByMonth <- mutate(attacks2k13, month = format(attacks2k13$Date, "%m")) %>% group_by(month) Attack2013FreqByMonth <- as.data.frame(table(Attacks2013ByMonth$month)) colnames(Attack2013FreqByMonth) <- c("Month", "Attacks") graph6 <- ggplot2::qplot(x = as.numeric(Month), y = Attacks, main = "Global cyberattack progression by month (2013)", data = Attack2013FreqByMonth, geom = c("point", "smooth"), xlim = c(1,12), xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12) #2014 graph7 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2014)", x = reorder(country_short,X2014), y = X2014, xlab = "Country", ylab = "Number of certifications", data = Cert_PerCountry[Cert_PerCountry$X2014 > mean(Cert_PerCountry$X2014),] , geom = "col", fill = Continent) attacks2k14 <- Attacks[Attacks$Date < "2015-01-01" & Attacks$Date >= "2014-01-01",] frameAttacks2k14 <- as.data.frame(table(attacks2k14$Country)) colnames(frameAttacks2k14) <- c("Country","Attacks") graph8 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2014)", x = reorder(Country,Attacks), y = Attacks, xlab = "Country", ylab = "Number of attacks", data = frameAttacks2k14[frameAttacks2k14$Attacks > mean(frameAttacks2k14$Attacks),] , geom = "col", fill = Continent) Attacks2014ByMonth <- mutate(attacks2k14, month = format(attacks2k14$Date, "%m")) %>% group_by(month) Attack2014FreqByMonth <- as.data.frame(table(Attacks2014ByMonth$month)) colnames(Attack2014FreqByMonth) <- c("Month", "Attacks") graph9 <- ggplot2::qplot(x = as.numeric(Month), y = Attacks, main = "Global cyberattack progression by month (2014)", data = Attack2014FreqByMonth, geom = c("point", "smooth"), xlim = c(1,12), xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12) #2015 graph10 <- ggplot2::qplot(main = "Countries with above average number of companies certified with 27001 (2015)", x = reorder(country_short,X2015), y = X2015, xlab = "Country", ylab = "Number of certifications", data = Cert_PerCountry[Cert_PerCountry$X2015 > mean(Cert_PerCountry$X2015),] , geom = "col", fill = Continent) attacks2k15 <- Attacks[Attacks$Date < "2016-01-01" & Attacks$Date >= "2015-01-01",] frameAttacks2k15 <- as.data.frame(table(attacks2k15$Country)) colnames(frameAttacks2k15) <- c("Country","Attacks") graph11 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2015)", x = reorder(Country,Attacks), y = Attacks, xlab = "Country", ylab = "Number of attacks", data = frameAttacks2k15[frameAttacks2k15$Attacks > mean(frameAttacks2k15$Attacks),] , geom = "col", fill = Continent) Attacks2015ByMonth <- mutate(attacks2k15, month = format(attacks2k15$Date, "%m")) %>% group_by(month) Attack2015FreqByMonth <- as.data.frame(table(Attacks2015ByMonth$month)) colnames(Attack2015FreqByMonth) <- c("Month", "Attacks") graph12 <- ggplot2::qplot(x = as.numeric(Month), y = Attacks, main = "Global cyberattack progression by month (2015)", data = Attack2015FreqByMonth, geom = c("point", "smooth"), xlim = c(1,12), xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12) list(graph1,graph2,graph3,graph4,graph5,graph6,graph7,graph8,graph9,graph10,graph11,graph12) }