Storm_Data_Documentation.Rmd

---
title: "Storm_Data_Documentation"
author: "msagastya"
date: "`r Sys.Date()`"
output: html_document
---
## Synopsis
Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern.

This project involves exploring the U.S. National Oceanic and Atmospheric Administration's (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage.

The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. You must use the database to answer the questions below and show the code for your entire analysis. Your analysis can consist of tables, figures, or other summaries. You may use any R package you want to support your analysis.

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r}
dataset<-download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2","/cloud/project/storm_dataset")
```

```{r, echo=FALSE, results='hide', warning=FALSE, message=FALSE}
library(ggplot2)
library(dplyr)
library(reshape2)
```

## Data Processing

```{r}
dataset <- read.csv(bzfile("repdata_data_StormData.csv.bz2"))
head(dataset)
str(dataset)
names(dataset)
```


### 1) Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?

For Events we have to compare with 2 health problems i.e injuries and fatalities.  
We have to aggregate both one by one the compare

### a) aggregating EVTYPE wrt injuries

```{r}
total_injuries <- aggregate(INJURIES~EVTYPE, dataset, sum)
total_injuries <- arrange(total_injuries, desc(INJURIES))
total_injuries <- total_injuries[1:20, ]
total_injuries
```

### b) aggregating EVTYPE wrt fatalities

```{r}
total_fatalities <- aggregate(FATALITIES~EVTYPE,dataset, sum)
total_fatalities <- arrange(total_fatalities, desc(FATALITIES))
total_fatalities <- total_fatalities[1:20, ]
total_fatalities
```

### c) ploting

```{r}
par(mfrow = c(1, 2), mar = c(15, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(total_fatalities$FATALITIES, las = 3, names.arg = total_fatalities$EVTYPE, main = "Weather Events With\n The Top 10 Highest Fatalities", ylab = "Number of Fatalities", col = total_fatalities$FATALITIES)
```

## Creating double bar graphs

### d) merging both

```{r}
totals<- merge(total_fatalities, total_injuries, by.x = "EVTYPE", by.y = "EVTYPE")
totals<-arrange(totals,desc(FATALITIES+INJURIES))


bad_stuff <- melt(totals, id.vars="EVTYPE", variable.name = "bad_thing")
tail(bad_stuff, 5)

```

### e) ploting

```{r}
# Create chart
healthChart <- ggplot(bad_stuff, aes(x=reorder(EVTYPE, -value), y=value))

# Plot data as bar chart
healthChart = healthChart + geom_bar(stat="identity", aes(fill=bad_thing), position="dodge")

# Set x-axis label
healthChart = healthChart + xlab("Event Type") 

# Rotate x-axis tick labels 
healthChart = healthChart + theme(axis.text.x = element_text(angle=45, hjust=1))

# Set chart title and center it
healthChart = healthChart + ggtitle("Top 10 US Killers") + theme(plot.title = element_text(hjust = 0.5))

healthChart

```

## 2. Across the United States, which types of events have the greatest economic consequences?

We have property Damage and crop damage

## Results

### a) Aggregate Data for Property Damage

```{r}
propdmg <- aggregate(PROPDMG ~ EVTYPE, data = dataset, FUN = sum)
propdmg <- propdmg[order(propdmg$PROPDMG, decreasing = TRUE), ]
# 10 most harmful causes of injuries
propdmgMax <- propdmg[1:10, ]
print(propdmgMax)
```

### b) Aggregate Data for Crop Damage

```{r}
cropdmg <- aggregate(CROPDMG ~ EVTYPE, data = dataset, FUN = sum)
cropdmg <- cropdmg[order(cropdmg$CROPDMG, decreasing = TRUE), ]
# 10 most harmful causes of injuries
cropdmgMax <- cropdmg[1:10, ]
print(cropdmgMax)
```

### c)ploting

```{r}
par(mfrow = c(1, 2), mar = c(15, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)

barplot(propdmgMax$PROPDMG, las = 3, names.arg = propdmgMax$EVTYPE, 
        main = "Top 10 Events with\n Greatest Property Damages", 
        ylab = "Number of Injuries", col = propdmgMax$PROPDMG)

barplot(cropdmgMax$CROPDMG, las = 3, names.arg = cropdmgMax$EVTYPE, 
        main = "Top 10 Events with\n Greatest Crop Damages", 
        ylab = "Number of Injuries", col = cropdmgMax$CROPDMG)

```
## MAKING DOUBLE BAR GRAPH

### d)merging both

```{r}
totalDamage<- merge(propdmgMax,cropdmgMax,by.x = "EVTYPE", by.y = "EVTYPE")
totalDamage<-arrange(totalDamage,desc(PROPDMG + CROPDMG))


top_10_damages <- melt(totalDamage, id.vars="EVTYPE", variable.name = "Damage_Types")
head(top_10_damages, 5)

```

### e)ploting

```{r}
# Create chart
DamageChart <- ggplot(top_10_damages, aes(x=reorder(EVTYPE, -value/100000), y=value/100000))

# Plot data as bar chart
DamageChart = DamageChart + geom_bar(stat="identity", aes(fill=Damage_Types), position="dodge")

# Set x-axis label
DamageChart = DamageChart + xlab("Event Type") +ylab("Cost of damage in $(billions)")

# Rotate x-axis tick labels 
DamageChart = DamageChart + theme(axis.text.x = element_text(angle=45, hjust=1))

# Set chart title and center it
DamageChart = DamageChart + ggtitle("Top 10 greatest economic consequences") + theme(plot.title = element_text(hjust = 0.5))

DamageChart

```

## MAKING TRIPLE BAR GRAPH

### f)merging both and melting

```{r}
#merging both
totalDamage<- merge(propdmgMax,cropdmgMax,by.x = "EVTYPE", by.y = "EVTYPE")
totalDamage$TOTALDMG <- totalDamage$PROPDMG + totalDamage$CROPDMG
totalDamage<-arrange(totalDamage,desc(TOTALDMG))
#totalDamage<-totalDamage[,c(totalDamage$EVTYPE,round(totalDamage$PROPDMG),round(totalDamage$CROPDMG),round(totalDamage$TOTALDMG))]

top_10_damages <- melt(totalDamage, id.vars="EVTYPE", variable.name = "Damage_Types")
tail(top_10_damages, 5)
```

### g)ploting

```{r}
# Create chart
DamageChart <- ggplot(top_10_damages, aes(x=reorder(EVTYPE, -value/1000), y=value/1000),fill=Damage_Types)

# Plot data as bar chart
DamageChart = DamageChart + geom_bar(stat="identity", aes(fill=Damage_Types), position="dodge")

# Set x-axis label
DamageChart = DamageChart + xlab("Event Type") + ylab("Cost of damage in $(billions)")

# Rotate x-axis tick labels 
DamageChart = DamageChart + theme(axis.text.x = element_text(angle=45, hjust=1))

# Set chart title and center it
DamageChart = DamageChart + ggtitle("Top 10 greatest economic consequences") + theme(plot.title = element_text(hjust = 0.5))

DamageChart

```