LearnPlotting:

library(dplyr)
library(ggplot2)
library(Hmisc)

## To make graphs with ggplot2, the data must be in a data frame, and in “long” (as opposed to wide) format. If your data needs to be restructured,
##see this page for more information.  http://www.cookbook-r.com/Graphs/Bar_and_line_graphs_(ggplot2)
##The count of cases for each group – typically, each x value represents one group. This is done with stat_bin,
##which calculates the number of cases in each group (if x is discrete, then each x value is a group; if x is continuous,
##then all the data is automatically in one group, unless you specifiy grouping with group=xx).
##The value of a column in the data set. This is done with stat_identity, which leaves the y values unchanged


library(knitr)

#See solution here:  http://www.rpubs.com/kagklis/87570

storm <- tbl_df(read.csv("./data/rawData.csv.bz2", as.is=T))


#Select only useful for our analysis columns.
storm_subs <- subset(storm, select = c("EVTYPE", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP", "FATALITIES"))


#Clean data. Convert letters to capitals and change the value that indicates missing value
with(storm_subs, {
     EVTYPE <- factor(EVTYPE)
     PROPDMGEXP <- toupper(PROPDMGEXP)
     PROPDMGEXP[PROPDMGEXP == ""] <- "0"
     CROPDMGEXP <- toupper(CROPDMGEXP)
     CROPDMGEXP[CROPDMGEXP == ""] <- "0"
})
################ORIGINAL######################################
health_impact <- aggregate(cbind(FATALITIES, INJURIES) ~ EVTYPE, storm_subs, sum, na.rm= TRUE)

health_impact.top <- health_impact[order(-health_impact$FATALITIES)[1:20], ]

health_impact.top$INJURIES <- cut2(health_impact.top$INJURIES, g = 7)

ggplot(health_impact.top, aes(x = reorder(EVTYPE, -FATALITIES), y = FATALITIES, fill = INJURIES)) +
     geom_bar(stat = "identity") + scale_fill_brewer(palette = 1) + guides(fill = guide_legend(reverse = T)) +
     theme(axis.text.x = element_text(angle = 90, hjust = 1)) + xlab(NULL) +
     ggtitle(paste("Top 20 most harmful weather events in the United States")) + labs(colour = "red")
################ORIGINAL######################################

##############MY CHANGES################################WORKING COPY ORIGINAL DESIGN
#totalsPlot1 <- totals[order(-totals$totalDeathInjury)[1:10],]
totalsPlot1 <- totals %>% arrange(desc(totalDeathInjury))
totalsPlotNEW <- totalsPlot1[1:20,]
totalsPlotNEW$INJURIES <- cut2(totalsPlotNEW$INJURIES, g = 4)

ggplot(totalsPlotNEW, aes(x = reorder(EVTYPE, -FATALITIES), y = FATALITIES, fill = INJURIES)) +
     geom_bar(stat = "identity") + scale_fill_brewer(palette = 1) + guides(fill = guide_legend(reverse = T)) +
     theme(axis.text.x = element_text(angle = 90, hjust = 1)) + xlab(NULL) +
     ggtitle(paste("Top 20 most harmful weather events in the United States")) + labs(colour = "red")

##############MY CHANGES################################
##############MORE CHANGES#############################WORKING STACKED PLOT
totalsPlotNEWGather <- totals %>% arrange(desc(totalDeathInjury))
totalsPlotNEWGather <- totalsPlot1[1:10,]

totalsPlotNEWGather <- totalsPlotNEWGather %>%
     gather(PhysicalHarm, Injuries, FATALITIES:INJURIES)


ggplot(totalsPlotNEWGather, aes(x=reorder(EVTYPE, -totalDeathInjury), y=Injuries, fill=PhysicalHarm, order=desc(PhysicalHarm))) + #works
     geom_bar(stat="identity") +
     theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
     #geom_text(aes(label=totalDeathInjury)) +
     ggtitle(paste("Most Harmful Weather Events ")) +
     xlab("Weather Event") + ylab("Total Deaths and Injuries") +
     theme(legend.position=c(.9,.9))  +
     scale_fill_brewer(palette = "Dark2", labels=c("Fatalities", "Injuries")) + #http://www.cookbook-r.com/Graphs/Colors_(ggplot2)
     labs(fill="Physical Harm")
##############MORE CHANGES#############################

##############MORE CHANGES#############################SIDE BY SIDE BAR CHART WORKING
totalsPlotNEW2 <- totalsPlot1[1:10,]
totalsPlotNEW2 <- totalsPlotNEW2 %>%
     gather(PhysicalHarm, Injuries, FATALITIES:INJURIES)
ggplot(totalsPlotNEW2, aes(x=reorder(EVTYPE, -totalDeathInjury), y=Injuries, fill=PhysicalHarm)) +
     geom_bar(stat="identity", position = position_dodge()) +
     theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
     #geom_text(aes(label=totalDeathInjury)) +
     ggtitle(paste("Most Harmful Weather Events ")) +
     xlab("Weather Event") + ylab("Total Deaths and Injuries") +
     theme(legend.position=c(.9,.9))  +
     scale_fill_brewer(palette = "Dark2", labels=c("Fatalities", "Injuries")) + #http://www.cookbook-r.com/Graphs/Colors_(ggplot2)
     labs(fill="Physical Harm")

##############MORE CHANGES#############################

################ORIGINAL######################################
decode.units <- function(d) {
     switch(d, H = 100, K = 1000, M = 1e+06, B = 1e+09, `0` = 1, `1` = 10, `2` = 100, `3` = 1000,
            `4` = 10000,  `5` = 1e+05, `6` = 1e+06, `7` = 1e+07, `8` = 1e+08, `9` = 1e+09, 0)}

#Next, we calculate the total economic damage.
storm_subs$DAMAGE <- storm_subs$PROPDMG * sapply(storm_subs$PROPDMGEXP, decode.units) + storm_subs$CROPDMG * sapply(storm_subs$CROPDMGEXP, decode.units)

damage <- aggregate(DAMAGE ~ EVTYPE, storm_subs, sum, na.rm = T)

damage.top <- damage[order(-damage$DAMAGE)[1:20], ]

ggplot(damage.top, aes(x = reorder(EVTYPE, -DAMAGE), y = DAMAGE)) + geom_bar(stat = "identity", fill = "grey") +
     theme(axis.text.x = element_text(angle = 90, hjust = 1)) + xlab(NULL) +
     ylab("Damage in $") + ggtitle(paste("Top 20 events which have the greatest economic consequences in the United States"))
################ORIGINAL######################################

################MY CODE###################################WORKING
totalsPlot3 <- totals %>% arrange(desc(totalDollarLost))
totalsPlotNEW3 <- totalsPlot2[1:10,]
totalsPlotNEW3 <- totalsPlotNEW3 %>%
     gather(dollarHarm, dollarDamage, PROPDMG:CROPDMG)
ggplot(totalsPlotNEW3, aes(x=reorder(EVTYPE, -totalDollarLost), y=dollarDamage/1000000000, fill=dollarHarm)) + #Good side by side
     geom_bar(stat="identity", position = position_dodge()) +
     theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
     #geom_text(aes(label=totalDeathInjury)) +
     ggtitle(paste("Most Costly Weather Events ")) +
     xlab("Weather Event") + ylab("Total Economic Cost - Billions $") +
     theme(legend.position=c(.9,.9))  +
     scale_fill_brewer(palette = "Dark2", labels=c("Property", "Crop")) + #http://www.cookbook-r.com/Graphs/Colors_(ggplot2)
     labs(fill="Cost")

ggplot(totalsPlotNEW3, aes(x=reorder(EVTYPE, -totalDollarLost), y=dollarDamage/1000000000, fill=dollarHarm, order=desc(dollarHarm))) + #works
     geom_bar(stat="identity") +
     theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
     #geom_text(aes(label=totalDeathInjury)) +
     ggtitle(paste("Most Costly Weather Events ")) +
     xlab("Weather Event") + ylab("Total Economic Cost - Billions $") +
     theme(legend.position=c(.9,.9))  +
     scale_fill_brewer(palette = "Dark2", labels=c("Property", "Crop")) + #http://www.cookbook-r.com/Graphs/Colors_(ggplot2)
     labs(fill="Cost")


################MY CODE######################################