Identifying severe weather events with most impact on human health and the economy based on the data from NOAA Storm Database Your document should have a title that briefly summarizes your data analysis
Petr Ya
11 июля 2016 г
𝚌𝚊𝚌𝚑𝚎 = 𝚃𝚁𝚄𝙴
sessionInfo()## R version 3.3.1 (2016-06-21)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.11.6 (El Capitan)
##
## locale:
## [1] ru_RU.UTF-8/ru_RU.UTF-8/ru_RU.UTF-8/C/ru_RU.UTF-8/ru_RU.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] magrittr_1.5 formatR_1.4 tools_3.3.1 htmltools_0.3.5
## [5] yaml_2.1.13 Rcpp_0.12.5 stringi_1.1.1 rmarkdown_1.0
## [9] knitr_1.13 stringr_1.0.0 digest_0.6.9 evaluate_0.9Загружаем данные
setwd("~/Documents/Документы/Самообразование/Data Science/R5 Reproducible Research/Course Project 2")
# dir.create("data")
# download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", "data/StormData.csv.bz2", method = "curl")
# data <- read.csv("data/StormData.csv.bz2", na.strings = "")Сохраняем фрейм в виде объекта для ускорения последующей загрузки.
# save(data, file = "data/StormData.RData")
load("data/StormData.RData")проверяем вид таблицы.
head(data)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## 3 TORNADO 0 0
## 4 TORNADO 0 0
## 5 TORNADO 0 0
## 6 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## 4 NA 0 0.0 100 2 0 0
## 5 NA 0 0.0 150 2 0 0
## 6 NA 0 1.5 177 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## 4 2 2.5 K 0
## 5 2 2.5 K 0
## 6 6 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3
## 4 3458 8626 0 0 4
## 5 3412 8642 0 0 5
## 6 3450 8748 0 0 6Сколько раз встречаются каждый из типов явлений в таблице? Какие из них самые частые.
evtypenumber <- table(data$EVTYPE)
head(sort(evtypenumber, decreasing = T), 20)##
## HAIL TSTM WIND THUNDERSTORM WIND
## 288661 219940 82563
## TORNADO FLASH FLOOD FLOOD
## 60652 54277 25326
## THUNDERSTORM WINDS HIGH WIND LIGHTNING
## 20843 20212 15754
## HEAVY SNOW HEAVY RAIN WINTER STORM
## 15708 11723 11433
## WINTER WEATHER FUNNEL CLOUD MARINE TSTM WIND
## 7026 6839 6175
## MARINE THUNDERSTORM WIND WATERSPOUT STRONG WIND
## 5812 3796 3566
## URBAN/SML STREAM FLD WILDFIRE
## 3392 2761Что в конце таблицы? Какие события редки?
head(sort(evtypenumber, decreasing = F), 20)##
## HIGH SURF ADVISORY COASTAL FLOOD
## 1 1
## FLASH FLOOD LIGHTNING
## 1 1
## TSTM WIND (G45) WATERSPOUT
## 1 1
## WIND ?
## 1 1
## ABNORMALLY WET APACHE COUNTY
## 1 1
## AVALANCE BEACH EROSIN
## 1 1
## Beach Erosion BEACH EROSION/COASTAL FLOOD
## 1 1
## BITTER WIND CHILL BLIZZARD AND HEAVY SNOW
## 1 1
## Blizzard Summary BLIZZARD WEATHER
## 1 1
## BLIZZARD/FREEZING RAIN BLIZZARD/HIGH WIND
## 1 1Есть неизвестные события (“?”), а есть те, которые повторяют частотнче. Но есть строки со словом “summary”
names(evtypenumber[1:20])## [1] " HIGH SURF ADVISORY" " COASTAL FLOOD"
## [3] " FLASH FLOOD" " LIGHTNING"
## [5] " TSTM WIND" " TSTM WIND (G45)"
## [7] " WATERSPOUT" " WIND"
## [9] "?" "ABNORMAL WARMTH"
## [11] "ABNORMALLY DRY" "ABNORMALLY WET"
## [13] "ACCUMULATED SNOWFALL" "AGRICULTURAL FREEZE"
## [15] "APACHE COUNTY" "ASTRONOMICAL HIGH TIDE"
## [17] "ASTRONOMICAL LOW TIDE" "AVALANCE"
## [19] "AVALANCHE" "BEACH EROSIN"Первые 8 элементов (в алфавитном порядке) начинаются с пробелов. Их меньше 10, так что этим можно пренебречь.
Суммируем количество травм по каждому типу. Выводим первые 20
injuriessum <- tapply(data$INJURIES, data$EVTYPE, sum)
head(sort(injuriessum, decreasing = T), 20)## TORNADO TSTM WIND FLOOD
## 91346 6957 6789
## EXCESSIVE HEAT LIGHTNING HEAT
## 6525 5230 2100
## ICE STORM FLASH FLOOD THUNDERSTORM WIND
## 1975 1777 1488
## HAIL WINTER STORM HURRICANE/TYPHOON
## 1361 1321 1275
## HIGH WIND HEAVY SNOW WILDFIRE
## 1137 1021 911
## THUNDERSTORM WINDS BLIZZARD FOG
## 908 805 734
## WILD/FOREST FIRE DUST STORM
## 545 440Количество жертв:
fatalitiessum <- tapply(data$FATALITIES, data$EVTYPE, sum)
head(sort(fatalitiessum, decreasing = T), 20)## TORNADO EXCESSIVE HEAT FLASH FLOOD
## 5633 1903 978
## HEAT LIGHTNING TSTM WIND
## 937 816 504
## FLOOD RIP CURRENT HIGH WIND
## 470 368 248
## AVALANCHE WINTER STORM RIP CURRENTS
## 224 206 204
## HEAT WAVE EXTREME COLD THUNDERSTORM WIND
## 172 160 133
## HEAVY SNOW EXTREME COLD/WIND CHILL STRONG WIND
## 127 125 103
## BLIZZARD HIGH SURF
## 101 101Просто график
hist(evtypenumber)