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#' 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) {
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#2012
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graph1 <- ggplot2::qplot(main = "Countries with above average number of companies certified with ISO 27001 (2012)",
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x = reorder(country_short,X2012),
y = X2012,
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xlab = "Country",
ylab = "Number of certifications",
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data = Cert_PerCountry[Cert_PerCountry$X2012 > mean(Cert_PerCountry$X2012),])
attacks2k12 <- Attacks[Attacks$Date < "2013-01-01" & Attacks$Date >= "2012-01-01",]
frameAttacks2k12 <- as.data.frame(table(attacks2k12$Country))
colnames(frameAttacks2k12) <- c("Country","Attacks")
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graph2 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2012)",
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x = reorder(Country,Attacks),
y = Attacks,
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xlab = "Country",
ylab = "Number of attacks",
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data = frameAttacks2k12[frameAttacks2k12$Attacks > mean(frameAttacks2k12$Attacks),])
Attacks2012ByMonth <- mutate(attacks2k12, month = format(attacks2k12$Date, "%m")) %>% group_by(month)
Attack2012FreqByMonth <- as.data.frame(table(Attacks2012ByMonth$month))
colnames(Attack2012FreqByMonth) <- c("Month", "Attacks")
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graph3 <- ggplot2::qplot(x = as.numeric(Month),
y = Attacks,
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main = "Global cyberattack progression by month (2012)",
data = Attack2012FreqByMonth,
geom = c("point", "smooth"),
xlim = c(1,12),
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xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12)
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#2013
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graph4 <- ggplot2::qplot(main = "Countries with above average number of companies certified with ISO 27001 (2013)",
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x = reorder(country_short,X2013),
y = X2013,
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xlab = "Country",
ylab = "Number of certifications",
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data = Cert_PerCountry[Cert_PerCountry$X2013 > mean(Cert_PerCountry$X2013),])
attacks2k13 <- Attacks[Attacks$Date < "2014-01-01" & Attacks$Date >= "2013-01-01",]
frameAttacks2k13 <- as.data.frame(table(attacks2k13$Country))
colnames(frameAttacks2k13) <- c("Country","Attacks")
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graph5 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2013)",
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x = reorder(Country,Attacks),
y = Attacks,
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xlab = "Country",
ylab = "Number of attacks",
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data = frameAttacks2k13[frameAttacks2k13$Attacks > mean(frameAttacks2k13$Attacks),])
Attacks2013ByMonth <- mutate(attacks2k13, month = format(attacks2k13$Date, "%m")) %>% group_by(month)
Attack2013FreqByMonth <- as.data.frame(table(Attacks2013ByMonth$month))
colnames(Attack2013FreqByMonth) <- c("Month", "Attacks")
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graph6 <- ggplot2::qplot(x = as.numeric(Month),
y = Attacks,
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main = "Global cyberattack progression by month (2013)",
data = Attack2013FreqByMonth,
geom = c("point", "smooth"),
xlim = c(1,12),
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xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12)
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#2014
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graph7 <- ggplot2::qplot(main = "Countries with above average number of companies certified with ISO 27001 (2014)",
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x = reorder(country_short,X2014),
y = X2014,
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xlab = "Country",
ylab = "Number of certifications",
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data = Cert_PerCountry[Cert_PerCountry$X2014 > mean(Cert_PerCountry$X2014),])
attacks2k14 <- Attacks[Attacks$Date < "2015-01-01" & Attacks$Date >= "2014-01-01",]
frameAttacks2k14 <- as.data.frame(table(attacks2k14$Country))
colnames(frameAttacks2k14) <- c("Country","Attacks")
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graph8 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2014)",
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x = reorder(Country,Attacks),
y = Attacks,
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xlab = "Country",
ylab = "Number of attacks",
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data = frameAttacks2k14[frameAttacks2k14$Attacks > mean(frameAttacks2k14$Attacks),])
Attacks2014ByMonth <- mutate(attacks2k14, month = format(attacks2k14$Date, "%m")) %>% group_by(month)
Attack2014FreqByMonth <- as.data.frame(table(Attacks2014ByMonth$month))
colnames(Attack2014FreqByMonth) <- c("Month", "Attacks")
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graph9 <- ggplot2::qplot(x = as.numeric(Month),
y = Attacks,
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main = "Global cyberattack progression by month (2014)",
data = Attack2014FreqByMonth,
geom = c("point", "smooth"),
xlim = c(1,12),
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xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12)
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#2015
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graph10 <- ggplot2::qplot(main = "Countries with above average number of companies certified with ISO 27001 (2015)",
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x = reorder(country_short,X2015),
y = X2015,
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xlab = "Country",
ylab = "Number of certifications",
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data = Cert_PerCountry[Cert_PerCountry$X2015 > mean(Cert_PerCountry$X2015),])
attacks2k15 <- Attacks[Attacks$Date < "2016-01-01" & Attacks$Date >= "2015-01-01",]
frameAttacks2k15 <- as.data.frame(table(attacks2k15$Country))
colnames(frameAttacks2k15) <- c("Country","Attacks")
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graph11 <- ggplot2::qplot(main = "Countries with above average number of cyberattacks (2015)",
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x = reorder(Country,Attacks),
y = Attacks,
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xlab = "Country",
ylab = "Number of attacks",
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data = frameAttacks2k15[frameAttacks2k15$Attacks > mean(frameAttacks2k15$Attacks),])
Attacks2015ByMonth <- mutate(attacks2k15, month = format(attacks2k15$Date, "%m")) %>% group_by(month)
Attack2015FreqByMonth <- as.data.frame(table(Attacks2015ByMonth$month))
colnames(Attack2015FreqByMonth) <- c("Month", "Attacks")
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graph12 <- ggplot2::qplot(x = as.numeric(Month),
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y = Attacks,
main = "Global cyberattack progression by month (2015)",
data = Attack2015FreqByMonth,
geom = c("point", "smooth"),
xlim = c(1,12),
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xlab = "Month") + ggplot2::scale_x_continuous(breaks = 1:12)
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list(graph1,graph2,graph3,graph4,graph5,graph6,graph7,graph8,graph9,graph10,graph11,graph12)
}
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