synopsis

The goal of this work is determination of main reasons (climate disasters) for American population’s injuries and fatalities and for negative economic consequences.
Firstly, I’ve read the data set with necessary information about injuries, fatalities, suffers and externalities in USA. Then saw some basic information like summaries. The next step is grouping database by type and calculating total events with injuries and fatalities. Then I’ve chosen top 10 types by the number of fatalitites and injuries and created two barplots to illustrate the results. Next I moved on to the second question and again grouped database by types of disasters and calculated the sum of dollars like negative economic consequences. Then I’ve chosen top 10 again and finally created a barplot for better illustration.

Data Processing

Firstly, i’d like to read the data and see what data i have.

df <- read.csv('C:/Users/dmitr/Downloads/coursera/repdata_data_StormData.csv')
print(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
summary(df$FATALITIES)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##   0.0000   0.0000   0.0000   0.0168   0.0000 583.0000
summary(df$INJURIES)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##    0.0000    0.0000    0.0000    0.1557    0.0000 1700.0000

Results

Analysis of consequences for economics and population health by storms in USA

Population health

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

I’d like to start with grouping data of injuries and fatalities by type of disaster.

library(dplyr)
## 
## Присоединяю пакет: 'dplyr'
## Следующие объекты скрыты от 'package:stats':
## 
##     filter, lag
## Следующие объекты скрыты от 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tidyr)
filtered_data <- df %>%
  group_by(EVTYPE) %>%
  summarise(total_injuries = sum(INJURIES, na.rm = TRUE),
            total_fatalities = sum(FATALITIES, na.rm = TRUE)) %>%
  filter(total_injuries > 0 & total_fatalities > 0)

Then i want to show top 10 disasters by injuries and fatalities.

top_10_injuries <- head(filtered_data$total_injuries[order(-filtered_data$total_injuries)], 10)

top_10_fatalities <- head(filtered_data$total_fatalities[order(-filtered_data$total_fatalities)], 10)

And finally let’s create barplots.

barplot(top_10_injuries, names.arg = tolower(filtered_data$EVTYPE[order(-filtered_data$total_injuries)][1:10]), 
        col = "skyblue", main = "Top 10 Disasters by Total Injuries", 
        xlab = "Type of Disaster", ylab = "Total Injuries")

barplot(top_10_fatalities, names.arg = tolower(filtered_data$EVTYPE[order(-filtered_data$total_fatalities)][1:10]), 
        col = "skyblue", main = "Top 10 Disatsters by Total Fatalities", 
        xlab = "Type of Disaster", ylab = "Total Fatalities")

So, we can say that tornado, wind and flood are the most important reasons of injuries and tornado again, heat and flood are the most frequent reasons for fatalities.

Economic consequences

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

I’m going to group economic consequences by type of disaster and calculate total sum of missed dollars.

df$PROPDMGEXP <- recode(df$PROPDMGEXP,'H' = 100, 'K' = 1000, 'M' = 1000000, 'B' = 1000000000, .default = 1)
df$CROPDMGEXP <- recode(df$CROPDMGEXP,'H' = 100, 'K' = 1000, 'M' = 1000000, 'B' = 1000000000, .default = 1)

df$PROP <- df$PROPDMG * df$PROPDMGEXP
df$CROP <- df$CROPDMG * df$CROPDMGEXP

economics_all <- df %>% 
  group_by(EVTYPE) %>% 
  summarise(total_prop = sum(PROP, na.rm = T), 
            total_crop = sum(CROP, na.rm = T),
            total_cons = total_prop + total_crop) %>% 
  filter(total_crop > 0 & total_prop > 0)

Again calculate top 10s

top_10_cons <- head(economics_all$total_cons[order(-economics_all$total_cons)], 10)

And create a barplot.

barplot(top_10_cons, names.arg = tolower(economics_all$EVTYPE[order(-economics_all$total_cons)][1:10]), 
        col = "skyblue", main = "Top 10 Disasters by Total Economic Consequences", 
        xlab = "Type of Disaster", ylab = "Total Economic Consequences")

So, floods have made a significant impact on economic consequences.