.Rapp.history

# This script intends to provide data on storms happened close to a selected location#
# R vesion 3.2.3 (2015-12-10)#
# This libraries are needed:#
library(XML)#
library(R.utils)#
library(plyr)#
library(dplyr)#
library(ggplot2)#
library(rgdal)#
library(rgeos)#
library(data.table)#
library(tidyr)#
library(ggmap)#
library(scales)#
library(dismo) # this package masks some of dplyr functions.
all_storms = read.csv('all_storms_3-16.csv')
initial.dir<-getwd()
all_storms = read.csv('all_storms_3-16.csv')
tornados = read.csv('tornados_3-16.csv')
tornados
tornados[tornados$BEGIN_LON > -13 & tornados$BEGIN_LON < 0 & !is.na(tornados$BEGIN_LON),]
tornados[tornados$BEGIN_LON > 0 & !is.na(tornados$BEGIN_LON),]
tornados[tornados$BEGIN_LON > -13 & tornados$BEGIN_LON < 0 & !is.na(tornados$BEGIN_LON),]
tornados_clean = tornados %>%dplyr::select(.id, YEAR, MONTH_NAME,BEGIN_DAY, BEGIN_YEARMONTH,
BEGIN_TIME, STATE, CZ_NAME, TOR_F_SCALE, TOR_LENGTH, TOR_WIDTH,BEGIN_LAT,BEGIN_LON,END_LAT,END_LON,
INJURIES_DIRECT, INJURIES_INDIRECT, DEATHS_DIRECT, DEATHS_INDIRECT, EVENT_ID)%>%mutate(BEGIN_LON = ifelse(BEGIN_LON < 0 & BEGIN_LON >-13 & !is.na(BEGIN_LON),
END_LON, BEGIN_LON))%>%mutate(BEGIN_LON = ifelse(BEGIN_LON > 0, -BEGIN_LON, BEGIN_LON), END_LON=ifelse(END_LON>0, -END_LON,END_LON))
tornados_clean[tornados_clean$BEGIN_LON < 0 & tornados_clean$BEGIN_LON > -13 & !is.na(tornados_clean$BEGIN_LON),]
tornados_clean[tornados_clean$BEGIN_LON > 0 & !is.na(tornados_clean$BEGIN_LON),]
tornados_clean = read.csv('tornados_clean_3-16.csv')
cat("According to NOAA, there has been",nrow(tornados_clean)," tornados reported in the US since 1951")
tor_Year = tornados_clean%>%group_by(YEAR)%>%summarise(total.count=n())
tor_Year
ave_tor= as.integer(tor_Year%>%summarise(average=mean(total.count, na.rm=T)))
ave_tor
cat("According to NOAA, there has been",nrow(tornados_clean)," tornados reported in the US since 1951. With an average of", ave_tor,"tornados per year.")
ggplot(tor_Year, aes(YEAR, total.count)) + geom_line()+
ggtitle("Tornados in the US (1951-2015)") +
xlab("YEAR") + ylab("No. of Tornados") +
geom_hline(yintercept = ave_tor)
geom_hline(yintercept = ave_tor, col ="blue")
ggplot(tor_Year, aes(YEAR, total.count)) + geom_line()+
ggtitle("Tornados in the US (1951-2015)") +
xlab("YEAR") + ylab("No. of Tornados") +
geom_hline(yintercept = ave_tor, col ="blue")
tornados_clean%>%group_by(TOR_F_SCALE)%>%summarise(total.count=n())
year = 2015
tornados_clean %>% filter(YEAR == year)%>% group_by(TOR_F_SCALE)%>%summarise(total.count=n())
year = 2014
tornados_clean %>% filter(YEAR == year)%>% group_by(TOR_F_SCALE)%>%summarise(total.count=n())
tor_cols <- c("YEAR", "TOR_F_SCALE")
col_com <- lapply(tor_cols, as.symbol)
tor_scale = tornados_clean %>%group_by_(.dots=col_com) %>%summarise(n = n())
tor_scale
ggplot(tor_scale,aes(x = YEAR ,y = n)) +
geom_bar(stat="identity", aes(fill = TOR_F_SCALE),position = "dodge") + guides(fill=FALSE)+
xlab("") + ylab("No. of Tornados")
year2="1965"
tor_scale_year = tornados_clean%>%filter(YEAR==year2)%>%group_by_(.dots=col_com)%>%summarise(n=n())
ggplot(tor_scale_year,aes(x = TOR_F_SCALE,y = n)) +
geom_bar(stat="identity", aes(fill = TOR_F_SCALE),position = "dodge")+ guides(fill=FALSE) +
xlab("") + ylab("No. of Tornados")
tor_DT = tornados_clean %>% mutate(DEATHS = tornados_clean$DEATHS_DIRECT + tornados_clean$DEATHS_INDIRECT)
tor_DT<-tor_DT%>%mutate(MONTH = substr(tor_DT$BEGIN_YEARMONTH, nchar(tor_DT$BEGIN_YEARMONTH)-1, nchar(tor_DT$BEGIN_YEARMONTH)))
t1 = distinct(tor_DT%>%dplyr::select(STATE, YEAR, MONTH, MONTH_NAME, DEATHS, DEATHS_DIRECT, DEATHS_INDIRECT)%>%gather(DEATH_TYPE, value, DEATHS:DEATHS_INDIRECT))
t1=t1%>% mutate(DECADE = ifelse(YEAR<=1959 & YEAR>=1950, 1950, ifelse(YEAR<=1969 & YEAR>=1960,1960,ifelse(YEAR<=1979 & YEAR>=1970,1970,ifelse(YEAR<=1989 & YEAR>=1980,1980,ifelse(YEAR<=1999 & YEAR>=1990,1990,ifelse(YEAR<=2009 & YEAR>=2000,2000, 2010)))))))
s_deaths = t1[t1$DEATH_TYPE!="DEATHS",]%>%dplyr::select(DEATH_TYPE, DECADE, value)%>%group_by(DEATH_TYPE, DECADE)%>%summarise_each(funs(mean, sum, max, min))
ggplot(s_deaths, aes(x=factor(DECADE), y=sum, fill = DEATH_TYPE )) +
geom_bar(stat="identity") +
ggtitle("Total number of deaths caused by \n TORNADOS in the USA per DECADE") +
xlab("DECADE") + ylab("No. of DEATHS")
ggplot(t1[t1$DEATH_TYPE!="DEATHS",], aes(x=factor(MONTH), y=value, fill = DEATH_TYPE )) +
geom_bar(stat="identity") +
facet_wrap(~DECADE) +
ggtitle("Average monthly Tornado related \n deaths in the USA per decade") +
xlab("Month") + ylab("No. of DEATHS")
ggplot(s_month_deaths[s_month_deaths$DEATH_TYPE!="DEATHS",], aes(x=factor(MONTH), y=sum, fill = DEATH_TYPE )) +
geom_bar(stat="identity") +
ggtitle("Average monthly Tornado-related \n deaths in the USA") +
xlab("Month") + ylab("No. of DEATHS")
ggplot(s_month_deaths[s_month_deaths$DEATH_TYPE!="DEATHS",], aes(x=factor(MONTH), y=sum, fill = DEATH_TYPE )) +
geom_bar(stat="identity") +
ggtitle("Average monthly Tornado-related \n deaths in the USA") +
xlab("Month") + ylab("No. of DEATHS")
s_month_deaths = t1[t1$DEATH_TYPE!="DEATHS",]%>%dplyr::select(DEATH_TYPE, MONTH, value)%>%group_by(DEATH_TYPE, MONTH)%>%summarise_each(funs(mean, sum, max, min))
ggplot(s_month_deaths[s_month_deaths$DEATH_TYPE!="DEATHS",], aes(x=factor(MONTH), y=sum, fill = DEATH_TYPE )) +
geom_bar(stat="identity") +
ggtitle("Average monthly Tornado-related \n deaths in the USA") +
xlab("Month") + ylab("No. of DEATHS")
# DEATHS PER STATE#
s_state = t1[t1$DEATH_TYPE!="DEATHS",]%>%dplyr::select(DEATH_TYPE, STATE, value)%>%group_by(DEATH_TYPE, STATE)%>%summarise_each(funs(mean, sum, max, min))#
# State(s) with maximum/minimum number of DIRECT_DEATHS:#
#
maxST_direct = as.character(s_state%>%filter(DEATH_TYPE=="DEATHS_DIRECT")%>%filter(sum==max(sum))%>%dplyr::select(STATE)%>%collect %>% .[["STATE"]])#
maxNo_direct =unique(as.character(s_state%>%filter(DEATH_TYPE=="DEATHS_DIRECT")%>%filter(sum==max(sum))%>%dplyr::select(sum)%>%collect %>% .[["sum"]]))#
#
minST_direct = as.character(s_state%>%filter(DEATH_TYPE=="DEATHS_DIRECT")%>%filter(sum==min(sum))%>%dplyr::select(STATE)%>%collect %>% .[["STATE"]])#
minNo_direct =unique(as.character(s_state%>%filter(DEATH_TYPE=="DEATHS_DIRECT")%>%filter(sum==min(sum))%>%dplyr::select(sum)%>%collect %>% .[["sum"]]))#
#
cat("The state(s) with higher mortalities (direct deaths) due to Tornados is/are",maxST, "with",maxNo,"deaths.")#
cat("The state(s) with lower mortalities (direct deaths) due to Tornados is/are",minST_direct, "with",minNo_direct,"deaths.")#
#
# Montlhy average DEATHS caused by Tordados in the USA#
ggplot(s_state[s_state$DEATH_TYPE!="DEATHS",], aes(x=factor(STATE), y=sum, fill = DEATH_TYPE )) + #
  geom_bar(stat="identity") + #
  ggtitle("Average monthly Tornado related \n deaths per State in the USA") +#
  xlab("STATE") + ylab("No. of DEATHS")+#
  coord_flip()#
#
# The same graph as above, but ordered by magnitude#
ggplot(s_state[s_state$DEATH_TYPE!="DEATHS",], aes(x=reorder(STATE,sum), y=sum, fill = DEATH_TYPE )) + #
  geom_bar(stat="identity") + #
  ggtitle("Average monthly Tornado related \n deaths per State in the USA") +#
  xlab("STATE") + ylab("No. of DEATHS")+#
  coord_flip()#
######################   CREATE MAPS LOCALIZING THE TORNADOS ####################################:#
#
# Upload the US MAP#
getTheMap <- suppressMessages(get_map(location = 'united states', zoom = 4, source = 'google'))#
#
# Plot the location of tornados for a particular year (Change the year)#
#
# Select the data for the map#
# Adjust the filter option at the end for another year:#
mapDT_year <- distinct(tor_DT%>%dplyr::select(YEAR,BEGIN_LON, BEGIN_LAT, DEATHS)%>%filter(YEAR==2015))#
#
# Create a map with the tornados for that year#
tor_one_year <- suppressMessages(ggmap(getTheMap) + #
                          geom_point(aes(x=BEGIN_LON, y=BEGIN_LAT), #
                                     data = mapDT_year,#
                                     size= mapDT_year $DEATHS*5, # size is proportional to the number. Their scale can be changed here. #
                                     colour="red", #
                                     alpha=.5)+#
                          ggtitle("Tornado related deaths in US (2015)")) # CHANGE YEAR HERE!!#
tor_one_year#
#
# For all years. Noticed the change in the size value.#
mapDT_All <- distinct(tor_DT%>%dplyr::select(YEAR,BEGIN_LON, BEGIN_LAT, DEATHS))#
tornados_US_50 <- suppressMessages(ggmap(getTheMap) + #
                                  geom_point(aes(x=BEGIN_LON, y=BEGIN_LAT),#
                                             data = mapDT_All,#
                                             size=mapDT_All$DEATHS*.1,#
                                             colour="red",#
                                             alpha=.5) +#
                                  ggtitle("Tornado related deaths in the US 1950-2015"))#
tornados_US_50#
################# PLOT TORNADOS AND RELATED DEATHS ON MAP OF AN AREA OF INTEREST ###########
# Isolating the coordinates for all tornados in the dataset#
locationCoords = tor_DT%>%dplyr::select(originalPlace=X, lon=BEGIN_LON, lat=BEGIN_LAT)#
head(tor_DT)#
# Coordinates for the City of Interest#
# Enter the name of the city or location#
city_of_interest = "Tulsa, OK"#
cityOI = mutate(suppressMessages(geocode(city_of_interest)), originalPlace = (city_of_interest[1]))#
cityOI = cityOI%>%dplyr::select(originalPlace, lon=longitude, lat=latitude)#
#
# Sorting the locations closer to the city of interest (cityOI)#
deg2rad <- function(deg) return(deg*pi/180)#
gcd.slc <- function(long1, lat1, long2, lat2) {#
  long1 <- deg2rad(long1)#
  lat1 <- deg2rad(lat1)#
  long2 <- deg2rad(long2)#
  lat2 <- deg2rad(lat2)#
  R <- 6371 # Earth mean radius [km]#
  d <- acos(sin(lat1)*sin(lat2) + cos(lat1)*cos(lat2) * cos(long2-long1)) * R#
  d <- d*0.621371 #in miles#
  return(d) # Distance in miles#
}#
#
distance <- gcd.slc(cityOI$lon, cityOI$lat, locationCoords$lon, locationCoords$lat)#
distance_matrix <- which( distance <= 75 ) # 75 miles from the center#
Area_COI = rbind((filter(locationCoords, originalPlace %in% distance_matrix)),cityOI)#
#
############### PLOTING THE DATA ON A MAP ###################
# Map of the area of interest#
cityMap <- gmap(city_of_interest, zoom=8, scale = 2)#
plot(cityMap)#
#
coordinates(cityOI) <- ~ lon + lat#
projection(cityOI) <- "+init=epsg:4326"#
centralLocation <- spTransform(cityOI, CRS = CRS(projection(cityMap)))#
#
mile2meter <- function(x) {  x * 1609.344 }#
distLayer100<- rgeos::gBuffer(centralLocation, width = mile2meter(100))#
distLayer50  <- rgeos::gBuffer(centralLocation, width = mile2meter(50))#
distLayer25  <- rgeos::gBuffer(centralLocation, width = mile2meter(25))#
#
plot(distLayer25, col = alpha("red", .0), add = TRUE)#
plot(distLayer50, col = alpha("red", .0), add = TRUE)#
plot(distLayer100, col = alpha("red", .0), add = TRUE)#
#
# Tornado locations around city of interest#
coordinates(Area_COI) <- ~ lon + lat#
projection(Area_COI) <- "+init=epsg:4326"#
tor_aroud_COI <- spTransform(Area_COI, CRS = CRS(projection(cityMap)))#
points(tor_aroud_COI, cex = 1, pch = 20, col='blue')#
#
# Add the Tornado related deaths around city of interest#
# Generate a list of locations with DEATHS in the defined area.#
DT_COI = filter(tor_DT, X %in% distance_matrix, DEATHS >0)#
#
coordinates(DT_COI) <- ~ BEGIN_LON + BEGIN_LAT#
projection(DT_COI) <- "+init=epsg:4326"#
allIncidents2 <- spTransform(DT_COI, CRS = CRS(projection(cityMap))) # coordinate reference system (CRS) #
points(allIncidents2, cex = 1, pch = 2, col='red')#
#
###### COMPARISON BETWEEN TWO CITIES #############
locationCoords = tor_DT%>%dplyr::select(originalPlace=X, lon=BEGIN_LON, lat=BEGIN_LAT)#
#
# Coordinates for the City of Interest (Change the city as much as you can!)#
yourcity = "GandPraire, TX"#
yourcity1 = "Dallas, TX"#
#
# Enter the distance around the cities to search for Tornados#
# This is the radius of a circle centered at the city#
distance_from_city = 10#
#
# Getting the geographic coordinates for each city#
your_city =  mutate(suppressMessages(geocode(yourcity)), originalPlace = (yourcity[1]))#
your_city = your_city%>%dplyr::select(originalPlace, lon=longitude, lat=latitude)#
your_city1 =  mutate(suppressMessages(geocode(yourcity1)), originalPlace = (yourcity1[1]))#
your_city1 = your_city1%>%dplyr::select(originalPlace, lon=longitude, lat=latitude)#
# Getting the events in a radios from your_city#
deg2rad <- function(deg) return(deg*pi/180)#
gcd.slc <- function(long1, lat1, long2, lat2) {#
  long1 <- deg2rad(long1)#
  lat1 <- deg2rad(lat1)#
  long2 <- deg2rad(long2)#
  lat2 <- deg2rad(lat2)#
  R <- 6371 # Earth mean radius [km]#
  d <- acos(sin(lat1)*sin(lat2) + cos(lat1)*cos(lat2) * cos(long2-long1)) * R#
  d <- d*0.621371 #in miles#
  return(d) # Distance in miles#
}#
#
# Creating a matrix with the locations araound the cities#
dist <- gcd.slc(your_city$lon, your_city$lat, locationCoords$lon, locationCoords$lat)#
#
dist_to_city <- which( dist <= distance_from_city) #
events_near_your_city = rbind((filter(locationCoords, originalPlace %in% dist_to_city)),your_city)#
#
events=tor_DT%>%filter(X %in% events_near_your_city$originalPlace)%>%dplyr::select(X,BEGIN_YEARMONTH, BEGIN_DAY, BEGIN_LAT, BEGIN_LON, DEATHS)#
#
n_e = nrow(events)#
#
dist1 <- gcd.slc(your_city1$lon, your_city1$lat, locationCoords$lon, locationCoords$lat)#
#
dist_to_city1 <- which( dist1 <= distance_from_city ) #
#
events_near_your_city1 = rbind((filter(locationCoords, originalPlace %in% dist_to_city1)),your_city1)#
#
events_1=tor_DT%>%filter(X %in% events_near_your_city1$originalPlace)%>%dplyr::select(X, BEGIN_YEARMONTH, BEGIN_DAY, BEGIN_LAT, BEGIN_LON, DEATHS)#
#
n_e1= nrow(events_1)#
common_events = merge(x=events, y=events_1)#
com_ev=nrow(common_events)#
#
# The result#
cat("There were",n_e, "tornados", distance_from_city, "miles around", yourcity, "and", n_e1, "in", yourcity1,", Of these,",com_ev,"were common.")#
# END!!!#
# THERE ARE MANY OTHER THINGS THAT CAN BE DONE ---- ENJOY !!!!
n_e1= nrow(events_1)
tornados_US_50 <- suppressMessages(ggmap(getTheMap) + #
                                  geom_point(aes(x=BEGIN_LON, y=BEGIN_LAT),#
                                             data = mapDT_All,#
                                             size=mapDT_All$DEATHS*.1,#
                                             colour="red",#
                                             alpha=.5) +#
                                  ggtitle("Tornado-related deaths in the US 1950-2015"))#
tornados_US_50
ggplot(s_state[s_state$DEATH_TYPE!="DEATHS",], aes(x=reorder(STATE,sum), y=sum, fill = DEATH_TYPE )) + #
  geom_bar(stat="identity") + #
  ggtitle("Average monthly Tornado-related \n deaths per State in the USA") +#
  xlab("STATE") + ylab("No. of DEATHS")+#
  coord_flip()
ggplot(s_state[s_state$DEATH_TYPE!="DEATHS",], aes(x=factor(STATE), y=sum, fill = DEATH_TYPE )) + #
  geom_bar(stat="identity") + #
  ggtitle("Average monthly Tornado-related \n deaths per State in the USA") +#
  xlab("STATE") + ylab("No. of DEATHS")+#
  coord_flip()
cat("The state(s) with higher mortalities (direct deaths) due to Tornados is/are",maxST, "with",maxNo,"deaths.")
maxST_direct = as.character(s_state%>%filter(DEATH_TYPE=="DEATHS_DIRECT")%>%filter(sum==max(sum))%>%dplyr::select(STATE)%>%collect %>% .[["STATE"]])
maxNo_direct =unique(as.character(s_state%>%filter(DEATH_TYPE=="DEATHS_DIRECT")%>%filter(sum==max(sum))%>%dplyr::select(sum)%>%collect %>% .[["sum"]]))
minST_direct = as.character(s_state%>%filter(DEATH_TYPE=="DEATHS_DIRECT")%>%filter(sum==min(sum))%>%dplyr::select(STATE)%>%collect %>% .[["STATE"]])
minNo_direct =unique(as.character(s_state%>%filter(DEATH_TYPE=="DEATHS_DIRECT")%>%filter(sum==min(sum))%>%dplyr::select(sum)%>%collect %>% .[["sum"]]))
cat("The state(s) with higher mortalities (direct deaths) due to Tornados is/are",maxST, "with",maxNo,"deaths.")
maxST_direct
cat("The state(s) with lower mortalities (direct deaths) due to Tornados is/are",minST_direct, "with",minNo_direct,"deaths.")
cat("The state(s) with higher mortalities (direct deaths) due to Tornados is/are", maxST_direct, "with", maxNo_direct,"deaths.")
ggplot(s_month_deaths[s_month_deaths$DEATH_TYPE!="DEATHS",], aes(x=factor(MONTH), y=sum, fill = DEATH_TYPE )) + #
  geom_bar(stat="identity") + #
  ggtitle("Average monthly Tornado-related \n deaths in the USA") +#
  xlab("Month") + ylab("No. of DEATHS")
s_state = t1[t1$DEATH_TYPE!="DEATHS",]%>%dplyr::select(DEATH_TYPE, STATE, value)%>%group_by(DEATH_TYPE, STATE)%>%summarise_each(funs(mean, sum, max, min))
maxST_direct = as.character(s_state%>%filter(DEATH_TYPE=="DEATHS_DIRECT")%>%filter(sum==max(sum))%>%dplyr::select(STATE)%>%collect %>% .[["STATE"]])
maxNo_direct =unique(as.character(s_state%>%filter(DEATH_TYPE=="DEATHS_DIRECT")%>%filter(sum==max(sum))%>%dplyr::select(sum)%>%collect %>% .[["sum"]]))
minST_direct = as.character(s_state%>%filter(DEATH_TYPE=="DEATHS_DIRECT")%>%filter(sum==min(sum))%>%dplyr::select(STATE)%>%collect %>% .[["STATE"]])
minNo_direct =unique(as.character(s_state%>%filter(DEATH_TYPE=="DEATHS_DIRECT")%>%filter(sum==min(sum))%>%dplyr::select(sum)%>%collect %>% .[["sum"]]))
cat("The state(s) with higher mortalities (direct deaths) due to Tornados is/are", maxST_direct, "with", maxNo_direct,"deaths.")
cat("The state(s) with lower mortalities (direct deaths) due to Tornados is/are",minST_direct, "with",minNo_direct,"deaths.")
ggplot(s_state[s_state$DEATH_TYPE!="DEATHS",], aes(x=factor(STATE), y=sum, fill = DEATH_TYPE )) + #
  geom_bar(stat="identity") + #
  ggtitle("Average monthly Tornado-related \n deaths per State in the USA") +#
  xlab("STATE") + ylab("No. of DEATHS")+#
  coord_flip()
ggplot(s_state[s_state$DEATH_TYPE!="DEATHS",], aes(x=reorder(STATE,sum), y=sum, fill = DEATH_TYPE )) + #
  geom_bar(stat="identity") + #
  ggtitle("Average monthly Tornado-related \n deaths per State in the USA") +#
  xlab("STATE") + ylab("No. of DEATHS")+#
  coord_flip()
url <- c("http://www1.ncdc.noaa.gov/pub/data/swdi/stormevents/csvfiles/")
site <- htmlParse(url)
a_nodes <- getNodeSet(site,"//a")
View(tornados)
cat("According to NOAA, there has been",nrow(tornados_clean)," tornados reported in the US since 1951")
tor_Year = tornados_clean%>%group_by(YEAR)%>%summarise(total.count=n())
tor_Year
as.data.table(tor_Year)
as.dat.table(ave_tor)
as.data.table(ave_tor)
ave_tor
cat("According to NOAA, there has been",nrow(tornados_clean)," tornados reported in the US since 1951. With an average of", ave_tor,"tornados per year.")
ggplot(tor_Year, aes(YEAR, total.count)) + geom_line()+#
  ggtitle("Tornados in the US (1951-2015)") +#
  xlab("YEAR") + ylab("No. of Tornados") +#
  geom_hline(yintercept = ave_tor, col ="blue")
ggplot(tor_Year, aes(YEAR, total.count)) + geom_line()+#
  ggtitle("Tornados in the US (1951-2015)") +#
  xlab("YEAR") + ylab("No. of Tornados") +#
  scale_x_continuous(breaks = c(1951, 1, 2015)+#
  geom_hline(yintercept = ave_tor, col ="blue")
ggplot(tor_Year, aes(YEAR, total.count)) + geom_line()+#
  ggtitle("Tornados in the US (1951-2015)") +#
  xlab("YEAR") + ylab("No. of Tornados") +#
  scale_x_continuous(breaks = c(1951, 1, 2015))+#
  geom_hline(yintercept = ave_tor, col ="blue")
ggplot(tor_Year, aes(YEAR, total.count)) + geom_line()+#
  ggtitle("Tornados in the US (1951-2015)") +#
  xlab("YEAR") + ylab("No. of Tornados") +#
  scale_x_continuous(breaks = c(1951, 0, 2015))+#
  geom_hline(yintercept = ave_tor, col ="blue")#
#
# Calculate the total number of tornados per scale:
ggplot(tor_Year, aes(YEAR, total.count)) + geom_line()+#
  ggtitle("Tornados in the US (1951-2015)") +#
  xlab("YEAR") + ylab("No. of Tornados") +#
  scale_x_continuous(breaks = c(1951, 10, 2015))+#
  geom_hline(yintercept = ave_tor, col ="blue")
ggplot(tor_Year, aes(YEAR, total.count)) + geom_line()+
ggtitle("Tornados in the US (1951-2015)") +
xlab("YEAR") + ylab("No. of Tornados") +
scale_x_discrete(breaks = c(1951, 0, 2015))+
geom_hline(yintercept = ave_tor, col ="blue")
ggplot(tor_Year, aes(YEAR, total.count)) + geom_line()+#
  ggtitle("Tornados in the US (1951-2015)") +#
  xlab("YEAR") + ylab("No. of Tornados") +#
  scale_x_discrete()+#
  geom_hline(yintercept = ave_tor, col ="blue")
tor_Year
ggplot(tor_Year, aes(YEAR, total.count)) + geom_line()+#
  ggtitle("Tornados in the US (1951-2015)") +#
  xlab("YEAR") + ylab("No. of Tornados") +#
  scale_x_discrete(YEAR)+#
  geom_hline(yintercept = ave_tor, col ="blue")
ggplot(tor_Year, aes(YEAR, total.count)) + geom_line()+#
  ggtitle("Tornados in the US (1951-2015)") +#
  xlab("YEAR") + ylab("No. of Tornados") +#
  scale_x_discrete(aes(YEAR))+#
  geom_hline(yintercept = ave_tor, col ="blue")
ggplot(tor_Year, aes(YEAR, total.count)) + geom_line()+#
  ggtitle("Tornados in the US (1951-2015)") +#
  xlab("YEAR") + ylab("No. of Tornados") +#
  scale_x_discrete(tor_Year$YEAR)+#
  geom_hline(yintercept = ave_tor, col ="blue")
scale_x_discrete(tor_Year$YEAR, labels= as.list(unique(tor_Year$YEAr))+
ggplot(tor_Year, aes(YEAR, total.count)) + geom_line()+#
  ggtitle("Tornados in the US (1951-2015)") +#
  xlab("YEAR") + ylab("No. of Tornados") +#
  scale_x_discrete(tor_Year$YEAR, labels= as.list(unique(tor_Year$Year))+#
  geom_hline(yintercept = ave_tor, col ="blue")
ggplot(tor_Year, aes(YEAR, total.count)) + geom_line()+#
  ggtitle("Tornados in the US (1951-2015)") +#
  xlab("YEAR") + ylab("No. of Tornados") +#
  scale_x_discrete(tor_Year$YEAR, labels= as.list(unique(tor_Year$Year)))+#
  geom_hline(yintercept = ave_tor, col ="blue")
ggplot(tor_Year, aes(YEAR, total.count)) + geom_line()+#
  ggtitle("Tornados in the US (1951-2015)") +#
  xlab("YEAR") + ylab("No. of Tornados") +#
  geom_hline(yintercept = ave_tor, col ="blue")