Effects of natural events on public health and economy
Vishal
08/06/2020
Synopsis
This document represents data analysis based on 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.
With the given analysis we are trying to find out which events have caused more damage to the public health as well as to the economic consequences it has produced.
Most harmful events w.r.t population health
1. Data Processing
- If the unzipped file is not present then unzip the data from zipped file.
- Read the CSV file using read.csv function.
- Print out the head of data to see what it contains.
if(!file.exists('repdata_data_StormData.csv')){
unzip('repdata_data_StormData.csv.bz2')
}
df <- read.csv('repdata_data_StormData.csv')
head(df)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE EVTYPE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL TORNADO
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL TORNADO
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL TORNADO
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL TORNADO
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL TORNADO
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL TORNADO
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1 0 0 NA
## 2 0 0 NA
## 3 0 0 NA
## 4 0 0 NA
## 5 0 0 NA
## 6 0 0 NA
## END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES INJURIES PROPDMG
## 1 0 14.0 100 3 0 0 15 25.0
## 2 0 2.0 150 2 0 0 0 2.5
## 3 0 0.1 123 2 0 0 2 25.0
## 4 0 0.0 100 2 0 0 2 2.5
## 5 0 0.0 150 2 0 0 2 2.5
## 6 0 1.5 177 2 0 0 6 2.5
## PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES LATITUDE LONGITUDE
## 1 K 0 3040 8812
## 2 K 0 3042 8755
## 3 K 0 3340 8742
## 4 K 0 3458 8626
## 5 K 0 3412 8642
## 6 K 0 3450 8748
## LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3051 8806 1
## 2 0 0 2
## 3 0 0 3
## 4 0 0 4
## 5 0 0 5
## 6 0 0 6- Select only the features(columns) that are required for the given analysis.
- For first dataframe choose the columns to perform analysis based on population health.
- For the second dataframe choose the columns to perform analysis based on the economical consequences.
dfHealth <- df[, c(8, 23:24)]
head(dfHealth)## EVTYPE FATALITIES INJURIES
## 1 TORNADO 0 15
## 2 TORNADO 0 0
## 3 TORNADO 0 2
## 4 TORNADO 0 2
## 5 TORNADO 0 2
## 6 TORNADO 0 62. Results
- Dataframe from the above code is grouped based on the event type.
- Then it is summarised to produce the sum of the columns of INJURIES and FATALITIES.
- Then df is arranged in descending order of INJURIES and FATALITIES.
- Top 10 records were chosen to plot.
dfHealth1 <- dfHealth %>%
group_by(EVTYPE) %>%
summarise(FATALITIES = sum(FATALITIES), INJURIES = sum(INJURIES))%>%
arrange(desc(INJURIES + FATALITIES))
dfHealth1 <- dfHealth1[1:10, ]
dfHealth1$EVTYPE <- factor(dfHealth1$EVTYPE, levels = dfHealth1$EVTYPE)
dfHealth2 <- melt(dfHealth1[, c('EVTYPE', 'FATALITIES', 'INJURIES')], id.vars = 1)- 2 plots are plotted showing the comparison between injuries and fatalities.
- Both plots are arranged in a single figure using grid.arrange function.
p1 <- ggplot(dfHealth2, aes(x = EVTYPE, y = value)) +
geom_bar(aes(fill = variable), stat = "identity", position = "dodge") +
theme(axis.text.x = element_text(angle = 30, hjust = 1)) +
xlab("Event type") +
ggtitle("Harmful events w.r.t population health")
p2 <- ggplot(dfHealth2, aes(x = EVTYPE, y = value)) +
geom_bar(aes(fill = variable), stat = "identity", position = "stack") +
theme(axis.text.x = element_text(angle = 30, hjust = 1)) +
xlab("Event type") +
ggtitle("Harmful events w.r.t population health(stack)") +
scale_fill_manual(values = c("yellow","black"))
grid.arrange(p1, p2, ncol = 2)
Most harmful events w.r.t economical consequences
1. Data Processing
- The dataframe is created accoring to the required columns from the original dataset.
dfEco <- df[, c(8, 25:28)]
head(dfEco)## EVTYPE PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 25.0 K 0
## 2 TORNADO 2.5 K 0
## 3 TORNADO 25.0 K 0
## 4 TORNADO 2.5 K 0
## 5 TORNADO 2.5 K 0
## 6 TORNADO 2.5 K 0- To find out the factors in PROPDMGEXP and RROPDMGEXP table function is used.
table(dfEco$PROPDMGEXP)##
## - ? + 0 1 2 3 4 5 6
## 465934 1 8 5 216 25 13 4 4 28 4
## 7 8 B h H K m M
## 5 1 40 1 6 424665 7 11330table(dfEco$CROPDMGEXP)##
## ? 0 2 B k K m M
## 618413 7 19 1 9 21 281832 1 1994- Levels are set for the factors we got from the table function.
dfEco$propFac <- factor(dfEco$PROPDMGEXP, levels = c("H", "K", "M", "B", "h", "m", "O"))
dfEco$propFac[is.na(dfEco$propFac)] <- "O"
dfEco$cropFac <- factor(dfEco$CROPDMGEXP, levels = c("K", "M", "B", "k", "m", "O"))
dfEco$cropFac[is.na(dfEco$cropFac)] <- "O"- New columns are created in the same df.
dfEco <- mutate(dfEco, property = 0, crop = 0)- Based on the literals for values, corresponding values are assigned. i.e O = 1, H = 100, etc.
dfEco$property[dfEco$propFac == "H" | dfEco$propFac == 'h'] <- 100
dfEco$property[dfEco$propFac == "K"] <- 1000
dfEco$property[dfEco$propFac == "M" | dfEco$propFac == 'm'] <- 1e6
dfEco$property[dfEco$propFac == "B"] <- 1e9
dfEco$property[dfEco$propFac == "O"] <- 1
dfEco$crop[dfEco$cropFac == "K" | dfEco$cropFac == "k"] <- 1000
dfEco$crop[dfEco$cropFac == "M" | dfEco$cropFac == "m"] <- 1e6
dfEco$crop[dfEco$cropFac == "B"] <- 1e9
dfEco$crop[dfEco$cropFac == "O"] <- 1- Values are converted into million.
dfEco <- mutate(dfEco, propertyVal = PROPDMG * property / 1e6, cropVal = CROPDMG * crop / 1e6)- Dataframe is grouped based on the event and new columns are created.
dfEco2 <- dfEco %>%
group_by(EVTYPE) %>%
summarise(propVal = sum(propertyVal, na.rm = TRUE), cropVal = sum(cropVal, na.rm = TRUE)) %>%
arrange(desc(propVal + cropVal))Results
- Top 10 events which cased most economical consequences are found out
dfEco2 <- dfEco2[1:10, ]- Data is melted for visualization purpose.
dfEco2 <- dfEco2[1:10, ]
dfEco2$EVTYPE <- factor(dfEco2$EVTYPE, levels = dfEco2$EVTYPE)
dfEco3 <- melt(dfEco2[, c('EVTYPE', 'propVal', 'cropVal')], id.vars = 1)- Plots are plotted using ggplot and combined using grid.arrange() function.
p3 <- ggplot(dfEco3, aes(EVTYPE, value)) +
geom_bar(aes(fill = variable), stat = "identity", position = "dodge") +
theme(axis.text.x = element_text(angle = 30, hjust = 1)) +
xlab("Event type") +
ggtitle("Harmful events w.r.t economic consequences") +
scale_fill_manual(values = c("red","black"))
p4 <- ggplot(dfEco3, aes(EVTYPE, value)) +
geom_bar(aes(fill = variable), stat = "identity", position = "stack") +
theme(axis.text.x = element_text(angle = 30, hjust = 1)) +
xlab("Event type") +
ggtitle("Harmful events w.r.t economic consequences") +
scale_fill_manual(values = c("magenta","blue"))
grid.arrange(p3, p4, ncol = 2)