Imanol-Mikel Barba Sabariego / DDS · Files

#----------------------------------------------------------------
#-------------------------General evolution----------------------
#----------------------------------------------------------------


#' Return graph representing the general attacks evolution by year
#'
#' @param Attacks data.frame with procesed source data
#' @param point.vjust Vector of vertical just to each point label
#' @param point.hjust Vector of Horizontal just to each point label
#' @param iso2013.y Vertical position to ISO27001:2013 label
#' @param iso2013.label.hjust Horizontal just to ISO27001:2013 label
#' @param smooth.x Horizontal position to smooth slope label
#' @param smooth.y Vertical position to smooth slope label
#' @param smooth.label.hjust Horizontal just to smooth slope label
#'
#' @return list(graph, slope)
#' @export
GetAttacksEvolution <- function(Attacks,
                                point.vjust = 0,
                                point.hjust = 0,
                                iso2013.y = 0,
                                iso2013.label.hjust = 0,
                                smooth.x = 0,
                                smooth.y = 0,
                                smooth.label.hjust = 0){

  #Extracting year from date
  attacks.evol <- dplyr::mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>%
                  dplyr::group_by(Year)#Grouping by year
  #Counting attacks each year
  attacks.evol <- as.data.frame(table(attacks.evol$Year))
  colnames(attacks.evol) <- c("Year","Attacks")

  #Slope of smooth
  attacks.evol.slope <- lm(formula = Attacks ~ as.numeric(Year),
                           data = attacks.evol)$coef[[2]]
  #Graph
  attacks.evol.graph <- ggplot2::qplot(main = "Cyberattacks evolution",
                                       x = attacks.evol$Year,
                                       y = attacks.evol$Attacks,
                                       group = 1,
                                       xlab = "Years",
                                       ylab = "Attacks",
                                       data = attacks.evol,
                                       geom = "line")  +
                        ggplot2::geom_point() +
                        ggplot2::geom_label(aes(label=attacks.evol$Attacks),
                                   vjust = point.vjust,
                                   hjust = point.hjust)+
                        ggplot2::theme(plot.title = element_text(hjust = 0.5)) +
                        ggplot2::geom_smooth(method = "lm", se = FALSE) +
                        ggplot2::geom_label(aes(x = smooth.x, y = smooth.y,
                                       label = paste("Slope =",
                                                     signif(attacks.evol.slope))),
                                   color = "blue",
                                   hjust = smooth.label.hjust) +
                        ggplot2::geom_vline(xintercept = 3,
                                            color = "Red",
                                            linetype = "longdash") +
                        ggplot2::geom_label(aes(x = "2014", y = iso2013.y,
                                                label = "ISO27001:2013 effects"),
                                            color = "Red",
                                            hjust = iso2013.label.hjust)

  list(attacks.evol.graph, attacks.evol.slope)

}

#' Return graph representing the general ISO27001 evolution
#'
#' @param Cert_PerCountry data.frame with procesed source data
#' @param point.vjust Vector of vertical just to each point label
#' @param point.hjust Vector of Horizontal just to each point label
#' @param iso2013.y Vertical position to ISO27001:2013 label
#' @param iso2013.label.hjust Horizontal just to ISO27001:2013 label
#' @param smooth.x Horizontal position to smooth slope label
#' @param smooth.y Vertical position to smooth slope label
#' @param smooth.label.hjust Horizontal just to smooth slope label
#'
#' @return list(graph, slope)
#' @export
GetCertsEvolution <- function(Cert_PerCountry,
                              point.vjust = 0,
                              point.hjust = 0,
                              iso2013.y = 0,
                              iso2013.label.hjust = 0,
                              smooth.x = 0,
                              smooth.y = 0,
                              smooth.label.hjust = 0){

  #Years from columns to rows in 1 column
  Certs.evol <- tidyr::gather(Cert_PerCountry, "Year", "Count", 2:6)
  #Group by year
  Certs.evol <- dplyr::group_by(Certs.evol, Year)
  #Sum counts to 1 row for each year
  Certs.evol <- dplyr::summarise(Certs.evol, Count = sum(Count))
  #Removing X from years
  Certs.evol$Year <- substr(Certs.evol$Year, 2, 5)

  #Slope of smooth
  Certs.evol.slope <- lm(formula = Count ~ as.numeric(Year),
                           data = Certs.evol)$coef[[2]]
  #Graph
  Certs.evol.graph <- ggplot2::qplot(main = "ISO 27001 evolution",
                           x = Certs.evol$Year,
                           y = Certs.evol$Count,
                           group = 1,
                           xlab = "Years",
                           ylab = "Certifications",
                           data = Certs.evol,
                          geom = "line") +
            geom_point() +
            geom_label(aes(label=Certs.evol$Count),
                       vjust = point.vjust,
                       hjust = point.hjust)+
            theme(plot.title = element_text(hjust = 0.5)) +
            ggplot2::geom_smooth(method = "lm", se = FALSE) +
            ggplot2::geom_label(aes(x = smooth.x, y = smooth.y,
                                    label = paste("Slope =",
                                                  signif(Certs.evol.slope))),
                                color = "blue",
                                hjust = smooth.label.hjust)+
            ggplot2::geom_vline(xintercept = 3,
                                color = "Red",
                                linetype = "longdash") +
            ggplot2::geom_label(aes(x = "2014", y = iso2013.y,
                                    label = "ISO27001:2013 changes"),
                                color = "Red",
                                hjust = iso2013.label.hjust)

  list(Certs.evol.graph, Certs.evol.slope)

}

#' Return graph representing the general attacks evolution by month
#'
#' @param Attacks data.frame with procesed source data
#'
#' @return graph
#' @export
GetAttacksMonthEvolution <- function(Attacks){

  #Extract Year - Month from date
  attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y-%m")) %>%
                  group_by(Year)
  #Group by Year - Month
  attacks.evol <- as.data.frame(table(attacks.evol$Year))
  colnames(attacks.evol) <- c("Year","Attacks")

  #Graph
  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("2012/01", "", "", "", "", "", "2012/07", "", "", "", "", "",
                                "2013/01", "", "", "", "", "", "2013/07", "", "", "", "", "",
                                "2014/01", "", "", "", "", "", "2014/07", "", "", "", "", "",
                                "2015/01", "", "", "", "", "", "2015/07", "", "", "", "", "",
                                "2016/01", "", "", "", "", "", "2016/07", "", "", "", "", ""))


}

#----------------------------------------------------------------
#-------------------------Attack type evolution------------------
#----------------------------------------------------------------


#' Return pie graph to show % of each attack type
#'
#' @param Attacks data.frame with procesed source data
#' @param label.vjust Vector of vertical just to each portion label
#' @param label.hjust Vector of horizontal just to each portion label
#'
#' @return list(graph, AttackTypeShowedList)
#' @export
GetAttackTypePie <- function (Attacks,
                              label.vjust = 0,
                              label.hjust = 0){

  #Group by attack type
  attack.pie <- group_by(Attacks, Attack.standar)
  #Counting rows for each attack type
  attack.pie <- as.data.frame(table(attack.pie$Attack.standar))
  attack.pie <- setNames(attack.pie, c("Attack", "Count"))

  #Removing rows without attack type defined
  attack.pie <- attack.pie[attack.pie$Attack != "",]
  #Removing attacks with less than 1% of representation from total
  attack.pie <- attack.pie[attack.pie$Count > (sum(attack.pie$Count) * 0.01),]
  #Calc % of each attack type
  attack.pie <- mutate(attack.pie, Perc=paste(round(100 * attack.pie$Count / sum(attack.pie$Count), 2), "%"))


  #Graph
  graph1 <- ggplot(data=attack.pie,
                  aes(x=factor(1),
                   y=Count,
                   fill=Attack)) +
            geom_col(width = 1, color='black') +
            geom_label(aes(label = attack.pie$Perc),
                        vjust = label.vjust,
                        hjust = label.hjust) +
            coord_polar(theta="y") +
            scale_x_discrete(labels = c("")) +
            scale_y_discrete(labels = c("")) +
            theme(plot.title = element_text(hjust = 0.5),
                  axis.title.x=element_blank(),
                  axis.title.y=element_blank()) +
            ggtitle("Attack types")

  #Returning graph and attack list
  list(graph1, unique(attack.pie$Attack))
}


#' Return graph to show the evolution of a attack types list
#'
#' @param Attacks data.frame with procesed source data
#' @param TypeList List with attack types to show
#'
#' @return list(graph, data.frame with TypeList attacks data by year)
#' @export
GetAttackTypeEvolution <- function(Attacks, TypeList){
  #Obtaining year from date
  Attacks.pre <- dplyr::mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>%
                group_by(Year, Attack.standar) #group by attack type
  #Count rows for each attack type and each year
  Attacks.pre <- as.data.frame(table(Attacks.pre$Year, Attacks.pre$Attack.standar))
  Attacks.pre <- setNames(Attacks.pre, c("Year", "Attack", "Count"))
  #Removing rows without an attack type specified
  Attacks.pre <- Attacks.pre[Attacks.pre$Attack %in% TypeList,]

  #Graph
  graph1 <- ggplot(data = Attacks.pre,
                   aes(x = Year,
                       y = Count,
                       color = Attack,
                       group = Attack)) +
            geom_point() +
            geom_line() +
            theme(plot.title = element_text(hjust = 0.5)) +
            ggtitle("Attack type evolution") +
            labs(colour = "Attack type") + xlab("Years") + ylab("Attacks")
  #Return graph and used data to separate representation
  list(graph1, Attacks.pre)

  }


#' Return graph to show the evolution of a single attack type and his smooth
#'
#' @param Attacks data.frame with procesed source data
#' @param AttackType Attack type to represent
#' @param smooth.x Horizontal position to smooth slope label
#' @param smooth.y Vertical position to smooth slope label
#' @param smooth.label.hjust Horizontal just to smooth slope label
#'
#' @return list(graph, slope)
#' @export
GetAttackTypeSigleEvolution <- function(Attacks,
                                        AttackType,
                                        smooth.x = 0,
                                        smooth.y = 0,
                                        smooth.label.hjust = 0){
  #Filtering for the AttackType specified
  attacks.evol <- Attacks[Attacks$Attack == AttackType,]

  #Cal slope
  slope1 <- lm(formula = Count ~ as.numeric(Year), data = attacks.evol)$coef[[2]]
  #Graph
  graph1 <- ggplot(data = attacks.evol,
                   aes(x = Year, y = Count, group = 1)) +
            geom_line() +
            geom_point() +
            theme(plot.title = element_text(hjust = 0.5)) +
            ggtitle(AttackType) +
            xlab("Years") + ylab("Attacks")+
            geom_smooth(method = "lm",
                        se = FALSE) +
            geom_label(aes(x = smooth.x, y = smooth.y,
                           label = paste("Slope =", signif(slope1, 5))),
                       color = "blue",
                       hjust = smooth.label.hjust)

  #Returning list(graph, slope)
  list(graph1, slope1)

}

#----------------------------------------------------------------
#-------------------------Geolocal evolution---------
#----------------------------------------------------------------

#' Return pie graph to show % of attacks and certifications for each continent
#'
#' @param Attacks data.frame with procesed source data of attacks
#' @param Attacks.label.vjust Vector of vertical just to each portion label
#' @param Attacks.label.hjust Vector of horizontal just to each portion label
#' @param Cert_PerCountry data.frame with procesed source data of certifications
#' @param Certs.label.vjust Vector of vertical just to each portion label
#' @param Certs.label.hjust Vector of horizontal just to each portion label
#'
#' @return list(AttacksGraph, CertsGraph, ContinentListToStudy)
#' @export
GetContinentPie <- function (Attacks,
                             Attacks.label.vjust = 0,
                             Attacks.label.hjust = 0,
                             Cert_PerCountry,
                             Certs.label.vjust = 0,
                             Certs.label.hjust = 0){

  #Group attacks by continent
  attack.pie <- group_by(Attacks, Continent)
  #Count attacks for each continent
  attack.pie <- as.data.frame(table(attack.pie$Continent))
  attack.pie <- setNames(attack.pie, c("Continent", "Count"))
  #Remove rows without Continent specified
  attack.pie <- attack.pie[attack.pie$Continent != "",]
  #Calc % of each continent
  attack.pie <- mutate(attack.pie, perc = round(100 * attack.pie$Count / sum(attack.pie$Count), 2))

  #Attacks graph
  graph1 <- ggplot(data=attack.pie,
                   aes(x=factor(1),
                       y=Count,
                       fill=Continent)) +
            geom_col(width = 1, color='black') +
            geom_label(aes(label=paste(attack.pie$perc, "%")),
                           vjust = Attacks.label.vjust,
                           hjust = Attacks.label.hjust) +
            coord_polar(theta="y") +
            scale_x_discrete(labels = c("")) +
            scale_y_discrete(labels = c("")) +
            theme(plot.title = element_text(hjust = 0.5),
                  axis.title.x=element_blank(),
                  axis.title.y=element_blank()) +
            ggtitle("Attacks")


  #Grouping certifications by continent
  cert.pie <- group_by(Cert_PerCountry, Continent)
  #Counting certificates for each continent
  cert.pie <- dplyr::summarise(cert.pie, Count = sum(X2011 + X2012 + X2013 + X2014 + X2015))
  #Remove rows without Continent specified
  cert.pie <- cert.pie[cert.pie$Continent != "",]
  #Calc % of each continent
  cert.pie <- mutate(cert.pie, perc = round(100 * cert.pie$Count / sum(cert.pie$Count), 2))

  #Certifications graph
  graph2 <- ggplot(data=cert.pie,
                   aes(x=factor(1),
                       y=Count,
                       fill=Continent)) +
            geom_col(width = 1, color='black') +
            geom_label(aes(label=paste(cert.pie$perc, "%")),
                           vjust = Certs.label.vjust,
                           hjust = Certs.label.hjust) +
            coord_polar(theta="y") +
            scale_x_discrete(labels = c("")) +
            scale_y_discrete(labels = c("")) +
            theme(plot.title = element_text(hjust = 0.5),
                  axis.title.x=element_blank(),
                  axis.title.y=element_blank()) +
            ggtitle("ISO 27001")

  #Return graphs and union of continent to study
  list(graph1, graph2,
       union(unique(attack.pie[attack.pie$perc > 5,]$Continent),
            unique(cert.pie[cert.pie$perc > 5,]$Continent)))
}


#' Return graph to show the evolution of a attack types list
#'
#' @param Attacks data.frame with procesed source data
#' @param ContinentList List with continents to show
#'
#' @return list(graph, data.frame with ContinentList attacks data by year)
#' @export
GetContinentAttacksEvolution <- function(Attacks,
                                         ContinentList){

  #Extract year from date
  attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>%
                group_by(Continent, Year) #Grouping atacks by continent and year
  #Counting attacks for each continent and year
  attacks.evol <- as.data.frame(table(attacks.evol$Continent, attacks.evol$Year))
  colnames(attacks.evol) <- c("Continent", "Year","Attacks")
  #Filtering by the ContinentList especified
  attacks.evol <- attacks.evol[attacks.evol$Continent %in% ContinentList,]

  #Graph
  graph1 <- ggplot2::qplot(main = "Cyberattacks evolution",
                           x = attacks.evol$Year,
                           y = attacks.evol$Attacks,
                           group = Continent,
                           xlab = "Years",
                           ylab = "Attacks",
                           data = attacks.evol,
                           geom = "point",
                           color = Continent)  +
            geom_line() +
            theme(plot.title = element_text(hjust = 0.5))

  #Return graph and data to represent 1b1
  list(graph1, attacks.evol)

}

#' Return graph to show the evolution of certifications in a continent list
#'
#' @param Cert_PerCountry data.frame with procesed source data
#' @param ContinentList List with continents to show
#'
#' @return list(graph, data.frame with ContinentList certifications data by year)
#' @export
GetContinentCertsEvolution <- function(Cert_PerCountry,
                                       ContinentList){
  #Collapsing year columns to 1 column with year value
  certs.evol <- gather(Cert_PerCountry, "Year", "Certs", 2:6) %>%
              group_by(Continent, Year) #Grouping by continent and year
  #Sum of certifications for echa continent and year
  certs.evol <- summarise(certs.evol, Certs = sum(Certs))
  #Removing the X from year values
  certs.evol$Year <- substr(certs.evol$Year,2,5)
  #Filtering for the specified continents
  certs.evol <- certs.evol[certs.evol$Continent %in% ContinentList,]

  #graph
  graph1 <- ggplot2::qplot(main = "ISO 27001 evolution",
                           x = certs.evol$Year,
                           y = certs.evol$Certs,
                           group = Continent,
                           xlab = "Years",
                           ylab = "Certifications",
                           data = certs.evol,
                           geom = "point",
                           color = Continent)  +
            geom_line() +
            theme(plot.title = element_text(hjust = 0.5))

  #Return the graph and data to represent continents 1b1
  list(graph1, certs.evol)

}
#' Return graph to show the evolution of attacks in a single continent and his smooth
#'
#' @param Attacks data.frame with procesed source data
#' @param Continent Continent to represent
#' @param smooth.x Horizontal position to smooth slope label
#' @param smooth.y Vertical position to smooth slope label
#' @param smooth.label.hjust Horizontal just to smooth slope label
#'
#' @return list(graph, slope)
#' @export
GetContinentAttacksSigleEvolution <- function(Attacks,
                                            Continent,
                                            smooth.x = 0,
                                            smooth.y = 0,
                                            smooth.label.hjust = 0){

  #Filtering for the continent specified
  attacks.evol <- Attacks[Attacks$Continent == Continent,]

  #Calc slope
  slope1 <- lm(formula = Attacks ~ as.numeric(Year), data = attacks.evol)$coef[[2]]
  #Graph
  graph1 <- ggplot(data = attacks.evol,
                   aes(x = Year, y = Attacks, group = 1)) +
            geom_line() +
            geom_point() +
            theme(plot.title = element_text(hjust = 0.5)) +
            ggtitle(Continent) +
            xlab("Years") + ylab("Attacks")+
            geom_smooth(method = "lm",
                        se = FALSE) +
            geom_label(aes(x = smooth.x, y = smooth.y,
                           label = paste("Slope =", signif(slope1, 5))),
                       color = "blue",
                       hjust = smooth.label.hjust)

  #Returning list(graph, slope)
  list(graph1, slope1)
}
#' Return graph to show the evolution of certifications in a single continent and his smooth
#'
#' @param Certs_byContinent data.frame with procesed source data
#' @param Continent Continent to represent
#' @param smooth.x Horizontal position to smooth slope label
#' @param smooth.y Vertical position to smooth slope label
#' @param smooth.label.hjust Horizontal just to smooth slope label
#'
#' @return list(graph, slope)
#' @export
GetContinentCertsSingleEvolution <- function(Certs_byContinent,
                                             Continent,
                                             smooth.x = 0,
                                             smooth.y = 0,
                                             smooth.label.hjust = 0){

  #Filtering for the continent specified
  certs.evol <- Certs_byContinent[Certs_byContinent$Continent == Continent,]

  #Calc slope
  slope1 <- lm(formula = Certs ~ as.numeric(Year), data = certs.evol)$coef[[2]]
  #Graph
  graph1 <- ggplot(data = certs.evol,
                   aes(x = Year, y = Certs, group = 1)) +
            geom_line() +
            geom_point() +
            theme(plot.title = element_text(hjust = 0.5)) +
            ggtitle(Continent) +
            xlab("Years") + ylab("Certifications")+
            geom_smooth(method = "lm",
                        se = FALSE) +
            geom_label(aes(x = smooth.x, y = smooth.y,
                           label = paste("Slope =", signif(slope1, 5))),
                       color = "blue",
                       hjust = smooth.label.hjust)

  #Returning list(graph, slope)
  list(graph1, slope1)
}

#' Return graph to show the attacks by country
#'
#' @param Attacks data.frame with procesed source data of attacks
#' @param Attacks.label.vjust Vector of vertical just to each portion label
#' @param Attacks.label.hjust Vector of horizontal just to each portion label
#' @param Cert_PerCountry data.frame with procesed source certifications
#' @param Certs.label.vjust Vector of vertical just to each portion label
#' @param Certs.label.hjust Vector of horizontal just to each portion label
#'
#' @return list(AttacksGraph, CertsGraph, CountryListToStudy)
GetCountriesCol <- function(Attacks,
                            Attacks.label.vjust = 0,
                            Attacks.label.hjust = 0,
                            Cert_PerCountry,
                            Certs.label.vjust = 0,
                            Certs.label.hjust = 0){

  #Group attacks by continent and country
  attacks.col <- group_by(Attacks, Continent, Country)
  #Count attacks for each country
  attacks.col <- as.data.frame(table(attacks.col$Continent, attacks.col$Country))
  attacks.col <- setNames(attacks.col, c("Continent", "Country", "Count"))
  #Remove rows without Continent/Country specified
  attacks.col <- attacks.col[attacks.col$Country != "",]
  attacks.col <- attacks.col[attacks.col$Country != "AU",] #Abnormal behaivour, corrupt??
  attacks.col <- attacks.col[attacks.col$Continent != "",]
  #Only the countries with more than 2% of total attacks
  attacks.col <- attacks.col[attacks.col$Count > (sum(attacks.col$Count) * 0.015), ]
  #Sort by attacks
  attacks.col <- arrange(attacks.col, desc(Count))

  #Attacks graph
  graph1 <- ggplot2::ggplot(aes(x = reorder(Country, Count),
                                y = Count),
                            data = attacks.col) +
            geom_col(aes(fill = Continent)) +
            geom_text(aes(label = attacks.col$Count),
                       vjust = Attacks.label.vjust,
                       hjust = Attacks.label.hjust,
                       size = 3) +
            theme(plot.title = element_text(hjust = 0.5)) +
            ggtitle("Attacks") +
            xlab("Country") + ylab("Attacks")

  #Grouping certifications by continent
  certs.col <- group_by(Cert_PerCountry, Continent, country_short)
  #Counting certificates for each continent
  certs.col <- dplyr::summarise(certs.col, Count = sum(X2011 + X2012 + X2013 + X2014 + X2015))
  #Remove rows without Continent specified
  certs.col <- certs.col[certs.col$Continent != "",]
  certs.col <- certs.col[certs.col$country_short != "",]
  #Only the countries with more than 2% of total certs
  certs.col <- certs.col[certs.col$Count > (sum(certs.col$Count) * 0.02), ]
  #Sort by certifications
  certs.col <- arrange(certs.col, desc(Count))


  #Certifications graph
  graph2 <- ggplot2::ggplot(aes(x = reorder(country_short, Count),
                                y = Count),
                            data = certs.col) +
            geom_col(aes(fill = Continent)) +
            geom_text(aes(label = certs.col$Count),
                       vjust = Certs.label.vjust,
                       hjust = Certs.label.hjust,
                       size = 3) +
            theme(plot.title = element_text(hjust = 0.5)) +
            ggtitle("ISO 27001") +
            xlab("Country") + ylab("Certifications")

  #Return graphs and union of countries to study
  list(graph1, graph2,
       union(unique(head(attacks.col$Country, 3)),
             unique(head(certs.col$country_short, 3))))

  }

#' Return graph to show the evolution of attacks in a single continent and his smooth
#'
#' @param Attacks data.frame with procesed source data
#' @param Country Country to represent
#' @param smooth.x Horizontal position to smooth slope label
#' @param smooth.y Vertical position to smooth slope label
#' @param smooth.label.hjust Horizontal just to smooth slope label
#'
#' @return list(graph, slope)
#' @export
GetCountriesAttacksSingleEvolution <- function(Attacks,
                                               Country,
                                               smooth.x = 0,
                                               smooth.y = 0,
                                               smooth.label.hjust = 0){
  #Extract year from date
  attacks.evol <- mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>%
                group_by(Year, Country) #Group by country
  #Count attacks for each year and country
  attacks.evol <- as.data.frame(table(attacks.evol$Year, attacks.evol$Country))
  attacks.evol <- setNames(attacks.evol, c("Year", "Country", "Count"))
  #Filter by the specified country
  attacks.evol <- attacks.evol[attacks.evol$Country == Country,]

  #Calc slope
  slope1 <- lm(formula = Count ~ as.numeric(Year), data = attacks.evol)$coef[[2]]
  #Graph
  graph1 <- ggplot(data = attacks.evol,
                   aes(x = Year, y = Count, group = 1)) +
            geom_line() +
            geom_point() +
            theme(plot.title = element_text(hjust = 0.5)) +
            ggtitle(paste("Cyberattacks -", Country)) +
            xlab("Years") + ylab("Attacks")+
            geom_smooth(method = "lm",
                        se = FALSE) +
            geom_label(aes(x = smooth.x, y = smooth.y,
                           label = paste("Slope =", signif(slope1, 5))),
                       color = "blue",
                       hjust = smooth.label.hjust)

  #Return list(graph, slope)
  list(graph1, slope1)

}

#' Return graph to show the evolution of certifications in a single continent and his smooth
#'
#' @param Attacks data.frame with procesed source data
#' @param Country Country to represent
#' @param smooth.x Horizontal position to smooth slope label
#' @param smooth.y Vertical position to smooth slope label
#' @param smooth.label.hjust Horizontal just to smooth slope label
#'
#' @return list(graph, slope)
#' @export
GetCountriesCertsSingleEvolution <- function(Cert_PerCountry,
                                          Country,
                                          smooth.x = 0,
                                          smooth.y = 0,
                                          smooth.label.hjust = 0){

  #Collapsing year columns to only one with the year value
  certs.evol <- gather(Cert_PerCountry, "Year", "Certs", 2:6) %>%
    group_by(country_short, Year) #Group by country and year
  #sum certificates for each country and year
  certs.evol <- summarise(certs.evol,
                          Certs = sum(Certs))
  #Removing the X of the year values
  certs.evol$Year <- substr(certs.evol$Year,2,5)
  #Filter by the specified Country
  certs.evol <- certs.evol[certs.evol$country_short == Country,]

  #Calc slope
  slope1 <- lm(formula = Certs ~ as.numeric(Year), data = certs.evol)$coef[[2]]
  #Graph
  graph1 <- ggplot(data = certs.evol,
                   aes(x = Year, y = Certs, group = 1)) +
    geom_line() +
    geom_point() +
    theme(plot.title = element_text(hjust = 0.5)) +
    ggtitle(paste("ISO 27001 -", Country)) +
    xlab("Years") + ylab("Certifications")+
    geom_smooth(method = "lm",
                se = FALSE) +
    geom_label(aes(x = smooth.x, y = smooth.y,
                   label = paste("Slope =", signif(slope1, 5))),
               color = "blue",
               hjust = smooth.label.hjust)

  #Return graph and slope
  list(graph1, slope1)

}


#----------------------------------------------------------------
#-------------------------gelocal/type---------------------------
#----------------------------------------------------------------

#' Return graph representing % of attacks type by continent
#'
#' @param Attacks data.frame with procesed source data
#' @param Country Country to represent on the left side
#' @param Country2 Country to represent on the right side
#'
#' @return Graph
#' @export
GetContinentAttackCol <- function (Attacks,
                                   Country,
                                   Country2){

  #Grouping attacks by attack type and country
  attack.pie <- group_by(Attacks, Attack.standar, Country)
  #Counting attacks for each attack type and country
  attack.pie <- as.data.frame(table(attack.pie$Country, attack.pie$Attack.standar))
  attack.pie <- setNames(attack.pie, c("Country", "Attack", "Count"))
  #Remove rows withouth a country specified
  attack.pie <- attack.pie[attack.pie$Country != "",]
  #Remove rows withouth attack type specified
  attack.pie <- attack.pie[attack.pie$Attack != "",]
  #grouping non 'important' attack types in 'Otros'
  attack.pie$Attack <- as.character(attack.pie$Attack)
  attack.pie[attack.pie$Attack != "DDoS" &
               attack.pie$Attack != "Defacement" &
               attack.pie$Attack != "Injection",]$Attack <- "Otros"
  attack.pie$Attack <- as.factor(attack.pie$Attack)
  #Grouping atatcks by attacky type and country
  attack.pie <- group_by(attack.pie, Attack, Country)
  #sum attacks for each attack type and country
  attack.pie <- summarise(attack.pie, Count = sum(Count))

  #Filtering by the desired countries
  attack.pie.C1 <- attack.pie[attack.pie$Country == Country,]
  attack.pie.C2 <- attack.pie[attack.pie$Country == Country2,]

  #Calc % of each attack type
  attack.pie.C1$perc <- round(100 * attack.pie.C1$Count / sum(attack.pie.C1$Count), 2)
  attack.pie.C2$perc <- round(100 * attack.pie.C2$Count / sum(attack.pie.C2$Count), 2)

  #Calc max % to draw gray background
  attack.pie.max <- data.frame(Attack = attack.pie.C1$Attack)
  percs <- c()
  for (AT in attack.pie.max$Attack){
    percs <- c(percs, max(attack.pie.C1[attack.pie.C1$Attack == AT,]$perc,
                          attack.pie.C2[attack.pie.C2$Attack == AT,]$perc))
  }
  attack.pie.max$perc <- percs

  graph1 <- ggplot() +
    geom_col(aes(x=Attack,
                 y=perc),
             width = 1,
             data = attack.pie.max,
             color = "Black",
             fill = "Grey") +
    geom_col(aes(x=1.25:4.25,
                 y=perc,
                 fill = Country),
             width = 0.4,
             data = attack.pie.C1) +
    geom_text(aes(x=1.25:4.25,
                  y=perc + 1,
                  label = paste(perc, "%")),
              data = attack.pie.C1) +
    geom_col(aes(x=0.75:3.75,
                 y=perc,
                 fill = Country2),
             width = 0.4,
             data = attack.pie.C2) +
    geom_text(aes(x=0.75:3.75,
                  y=perc + 1,
                  label = paste(perc, "%")),
              data = attack.pie.C2) +
    scale_x_discrete("Attack", attack.pie.C1$Attack) +
    ylab("% of total attacks") +
    theme(plot.title = element_text(hjust = 0.5)) +
    ggtitle(paste(Country, Country2, sep = " - "))

  graph1
}


#' Return graph representing the evolution of attack types in a country
#'
#' @param Attacks data.frame with procesed source data
#' @param Country Country to represent
#'
#' @return list(Graph, data to represent attacktypes of the country 1b1)
GetContinentAttackEvolution <- function(Attacks, Country){

  #Extract Year from date
  attack.evol <-mutate(Attacks, Year = format(Attacks$Date, "%Y")) %>%
              group_by(Attack.standar, Country, Year) #Group by attack type, country and year
  #Counting the attacks for each attack type, country and year
  attack.evol <- as.data.frame(table(attack.evol$Country, attack.evol$Attack.standar, attack.evol$Year))
  attack.evol <- setNames(attack.evol, c("Country", "Attack", "Year", "Count"))
  #Removing rows without country or attack type
  attack.evol <- attack.evol[attack.evol$Country != "",]
  attack.evol <- attack.evol[attack.evol$Attack != "",]
  #grouping non 'important' attack types in 'Otros'
  attack.evol$Attack <- as.character(attack.evol$Attack)
  attack.evol[attack.evol$Attack != "DDoS" &
               attack.evol$Attack != "Defacement" &
               attack.evol$Attack != "Injection",]$Attack <- "Otros"
  attack.evol$Attack <- as.factor(attack.evol$Attack)
  #Counting attacks of new tyoe
  attack.evol <- group_by(attack.evol, Attack, Country, Year)
  attack.evol <- summarise(attack.evol, Count = sum(Count))
  #Filtering by specified country
  attack.evol <- attack.evol[attack.evol$Country == Country,]

  #Graph
  graph1 <- ggplot2::qplot(main = Country,
                           x = attack.evol$Year,
                           y = attack.evol$Count,
                           group = attack.evol$Attack,
                           xlab = "Years",
                           ylab = "Attacks",
                           data = attack.evol,
                           geom = "point",
                           color = Attack)  +
            geom_line() +
            theme(plot.title = element_text(hjust = 0.5))

  #list with graph and processed data to represent 1b1
  list(graph1, attack.evol)

}