Assignment2
Haoyi Wei
2022-12-18
Explore the NOAA Storm Database
Synopsis
The basic goal of this assignment is to expore the NOAA Storm Database and answer some basic questions about severe weather events. The data analyssi aims to addres two questions: (1) Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health? (2) Across the US,, which types of events have the greatest economic consequences?
Loading and Processing the Raw Data
From the Coursera website, we obtained the storm dataset.
Read the data
library(readr)
storm_data <- read_csv("repdata-data-StormData.csv")## Rows: 902297 Columns: 37
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (18): BGN_DATE, BGN_TIME, TIME_ZONE, COUNTYNAME, STATE, EVTYPE, BGN_AZI,...
## dbl (18): STATE__, COUNTY, BGN_RANGE, COUNTY_END, END_RANGE, LENGTH, WIDTH, ...
## lgl (1): COUNTYENDN
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.After reading in the data, we check the first few rows in the datasets
dim(storm_data)## [1] 902297 37head(storm_data)## # A tibble: 6 × 37
## STATE__ BGN_DATE BGN_T…¹ TIME_…² COUNTY COUNT…³ STATE EVTYPE BGN_R…⁴ BGN_AZI
## <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 1 4/18/1950… 0130 CST 97 MOBILE AL TORNA… 0 <NA>
## 2 1 4/18/1950… 0145 CST 3 BALDWIN AL TORNA… 0 <NA>
## 3 1 2/20/1951… 1600 CST 57 FAYETTE AL TORNA… 0 <NA>
## 4 1 6/8/1951 … 0900 CST 89 MADISON AL TORNA… 0 <NA>
## 5 1 11/15/195… 1500 CST 43 CULLMAN AL TORNA… 0 <NA>
## 6 1 11/15/195… 2000 CST 77 LAUDER… AL TORNA… 0 <NA>
## # … with 27 more variables: BGN_LOCATI <chr>, END_DATE <chr>, END_TIME <chr>,
## # COUNTY_END <dbl>, COUNTYENDN <lgl>, END_RANGE <dbl>, END_AZI <chr>,
## # END_LOCATI <chr>, LENGTH <dbl>, WIDTH <dbl>, F <dbl>, MAG <dbl>,
## # FATALITIES <dbl>, INJURIES <dbl>, PROPDMG <dbl>, PROPDMGEXP <chr>,
## # CROPDMG <dbl>, CROPDMGEXP <chr>, WFO <chr>, STATEOFFIC <chr>,
## # ZONENAMES <chr>, LATITUDE <dbl>, LONGITUDE <dbl>, LATITUDE_E <dbl>,
## # LONGITUDE_ <dbl>, REMARKS <chr>, REFNUM <dbl>, and abbreviated variable …
## # ℹ Use `colnames()` to see all variable namesclean the data
tidy_storm <- storm_data[,c('EVTYPE','FATALITIES','INJURIES', 'PROPDMG', 'PROPDMGEXP', 'CROPDMG', 'CROPDMGEXP')]
head(tidy_storm)## # A tibble: 6 × 7
## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## <chr> <dbl> <dbl> <dbl> <chr> <dbl> <chr>
## 1 TORNADO 0 15 25 K 0 <NA>
## 2 TORNADO 0 0 2.5 K 0 <NA>
## 3 TORNADO 0 2 25 K 0 <NA>
## 4 TORNADO 0 2 2.5 K 0 <NA>
## 5 TORNADO 0 2 2.5 K 0 <NA>
## 6 TORNADO 0 6 2.5 K 0 <NA>str(tidy_storm)## tibble [902,297 × 7] (S3: tbl_df/tbl/data.frame)
## $ EVTYPE : chr [1:902297] "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ FATALITIES: num [1:902297] 0 0 0 0 0 0 0 0 1 0 ...
## $ INJURIES : num [1:902297] 15 0 2 2 2 6 1 0 14 0 ...
## $ PROPDMG : num [1:902297] 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
## $ PROPDMGEXP: chr [1:902297] "K" "K" "K" "K" ...
## $ CROPDMG : num [1:902297] 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr [1:902297] NA NA NA NA ...Results
Question 1
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
# check number of missing values
sum(is.na(tidy_storm[,c('FATALITIES','INJURIES')]))## [1] 0# make the health demage dataset
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)
tidy_q1 <- tidy_storm %>%
group_by(EVTYPE) %>%
summarize(fatalities = sum(FATALITIES), injuries= sum(INJURIES), .groups='drop') %>%
mutate(total = fatalities + injuries) %>%
select(EVTYPE, total,fatalities, injuries) %>%
arrange(-total,-fatalities,-injuries) %>%
slice(1:10) %>% # select top ten events
mutate(evtype=factor(EVTYPE, levels=EVTYPE)) %>%
gather(key = Type, value = Value,total,fatalities,injuries)# make the plot
library(ggplot2)
ggplot(tidy_q1, aes(evtype, Value, fill=Type)) +
geom_bar(position="dodge",stat="identity") +
theme_bw() +
theme(axis.text.x= element_text(angle=20, vjust=1, hjust=1)) +
xlab("Event Type") +
ylab("Fatalities and Injuries") +
ggtitle("Number of population health outcomes by top 10 Weather Events") +
scale_fill_manual(values=c("yellow","orange","red"))
Tornadoes are most harmful with respect to population health
Question 2
unique(storm_data$PROPDMGEXP)## [1] "K" "M" NA "B" "m" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "H" "-" "1" "8"unique(storm_data$CROPDMGEXP)## [1] NA "M" "K" "m" "B" "?" "0" "k" "2"create a function to clean the property damage data
cost <- function(x) {
if (x == "H")
1E-4
else if (x == "K")
1E-3
else if (x == "M")
1
else if (x == "B")
1E3
else
1-6
}make the plot
tidy_q2 <- tidy_storm %>%
filter(is.na(CROPDMGEXP)!=1 & is.na(PROPDMGEXP)!=1) %>%
mutate(prop_dmg = PROPDMG*sapply(PROPDMGEXP, FUN = cost),
crop_dmg = CROPDMG*sapply(CROPDMGEXP, FUN = cost), .keep="unused") %>%
group_by(EVTYPE) %>%
summarize(property = sum(prop_dmg), crop = sum(crop_dmg), .groups='drop') %>%
arrange(desc(property), desc(crop)) %>%
mutate(total=property+crop) %>%
slice(1:10) %>%
gather(key = type, value = value, total, property, crop)
ggplot(data=tidy_q2, aes(reorder(EVTYPE, -value), value, fill=type)) +
geom_bar(position = "dodge", stat="identity") +
labs(x="Event Type", y="Count (millions)") +
theme_bw() +
theme(axis.text.x = element_text(angle = 25, vjust=0.5)) +
ggtitle("Total Cost of Property and Crop Damage by top 10 storm event types") +
scale_fill_manual(values=c("red", "blue","yellow"))
Across the United States, Flood have the greatest economic consequence.
Conclusion
Baed on the analysis the government should consider implementing projects to relief tornado and flood’s impact.