R語(yǔ)言NOAA風(fēng)暴數(shù)據(jù)數(shù)據(jù)分析可視化報(bào)告(附代碼數(shù)據(jù))

【原創(chuàng)】【原創(chuàng)】R語(yǔ)言案例數(shù)據(jù)分析報(bào)告論文(附代碼數(shù)據(jù))有問(wèn)題到淘寶找“大數(shù)據(jù)部落”就可以了NOAA風(fēng)暴數(shù)據(jù)數(shù)據(jù)分析可視化報(bào)告概要風(fēng)暴和其他惡劣的天氣事件可能導(dǎo)致社區(qū)和市政的公共衛(wèi)生和經(jīng)濟(jì)問(wèn)題。許多嚴(yán)重的事件可能導(dǎo)致死亡，受傷和財(cái)產(chǎn)損失，并盡可能地防止這些結(jié)果是一個(gè)關(guān)鍵問(wèn)題。作為數(shù)據(jù)科學(xué)專(zhuān)業(yè)的可重復(fù)性研究項(xiàng)目2作業(yè)的一部分，該項(xiàng)目將探索美國(guó)NOAA的風(fēng)暴數(shù)據(jù)庫(kù)。該數(shù)據(jù)庫(kù)跟蹤美國(guó)主要風(fēng)暴和天氣事件的特征，包括發(fā)生的時(shí)間和地點(diǎn)，以及死亡，受傷和財(cái)產(chǎn)損失的估計(jì)。數(shù)據(jù)處理NOAA的Storm數(shù)據(jù)庫(kù)位于以下URL地址：/repdata%2Fdata%2FStormData.csv.bz2有關(guān)如何收集數(shù)據(jù)以及有關(guān)變量的說(shuō)明的信息，請(qǐng)參閱/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf加載數(shù)據(jù)：數(shù)據(jù)壓縮在bz2文件中，但可以使用read.csv函數(shù)直接從中讀取數(shù)據(jù)。這將需要一些時(shí)間來(lái)加載：FILENAME<-"repdata%2Fdata%2FStormData.csv.bz2"NOAAS_DT<-read.csv(FILENAME,header=TRUE,sep=",",quote="\"")初始分析如何看起來(lái)將有助于確定是否需要數(shù)據(jù)轉(zhuǎn)換，所以我們先做一個(gè)總結(jié):summary(NOAASDT)##STATEBGNDATEBGNTIME##Min.:1.05/25/20110:00:00:120212:00:00AM:10163##1stQu.：19?04/27/20110:00:00:119306:00:00PM:7350##Median:30.06/9/20110:00:00:103004:00:00PM:7261##Mean:31.25/30/20040:00:00:101605:00:00PM:6891##3rdQu.:45?04/4/20110:00:00:100912:00:00PM:6703##Max.:95?04/2/20060:00:00:98103:00:00PM:6700##(Other):895866(Other):857229##TIMEZONECOUNTYCOUNTYNAMESTATE##CST:547493Min.:0.0JEFFERSON:7840TX:83728##EST:2455581stQu.:31.0WASHINGTON:7603KS:53440##MST:68390Median:75.0JACKSON:6660OK:46802

##PST:28302Mean100.6FRANKLIN:6256MO:35648##AST:63603rdQu.131.0LINCOLN:5937IA:31069##HST:2563Max.873.0MADISON:5632NE:30271##(Other):3631(Other):862369(Other):621339##EVTYPEBGNRANGEBGNAZI##HAIL:288661Min.:0.000:547332##TSTMWIND:2199401stQu.:0.000N:86752##THUNDERSTORMWIND:82563Median:0.000W:38446##TORNADO:60652Mean:1.484S:37558##FLASHFLOOD:542773rdQu.:1.000E:33178##FLOOD:25326Max.:3749.000NW:24041##(Other):170878(Other):134990##BGNLOCATIENDDATEENDTIME##:287743:243411:238978##COUNTYWIDE:196804/27/20110:00:00:121406:00:00PM:9802##Countywide:9935/25/20110:00:00:119605:00:00PM:8314##SPRINGFIELD:8436/9/20110:00:00:102104:00:00PM:8104##SOUTHPORTION:8104/4/20110:00:00:100712:00:00PM:7483##NORTHPORTION:7845/30/20040:00:00:99811:59:00PM:7184##(Other):591444(Other):653450(Other):622432

100444-EVTYPEEXCESSIVEHEATFLASHFLOODFLOOD100444-EVTYPEEXCESSIVEHEATFLASHFLOODFLOODHEATICESTORMLIGHTNINGTHUNDERSTORMWINDTORNADOT$TMWINDWINTERSTORMEVTYPE從輸出結(jié)果可以看出，不需要特殊的轉(zhuǎn)換來(lái)準(zhǔn)備數(shù)據(jù)，所以繼續(xù)進(jìn)行分析。這個(gè)分析重點(diǎn)是回答以下問(wèn)題：1?在美國(guó)，哪些類(lèi)型的事件（如EVTYPE變量所示）對(duì)人群健康危害最大？2?在美國(guó)，哪類(lèi)事件的經(jīng)濟(jì)后果最大？以下部分將涵蓋有關(guān)數(shù)據(jù)分析的詳細(xì)信息，需要找到答案1?在美國(guó)各地，哪些類(lèi)型的事件（如EVTYPE變量所示）在人群健康方面是最有害的？考慮到以死亡或受傷為終點(diǎn)的人口健康危害事件，為了回答這個(gè)問(wèn)題，需要計(jì)算每次事件的死亡和傷害總數(shù)。讓我們?cè)谝粋€(gè)名為total_victims的新專(zhuān)欄里總結(jié)死傷：library(dplyr)####Attachingpackage:'dplyr##Thefollowingobjectsaremaskedfrom'package:stats':####filter,lag##Thefollowingobjectsaremaskedfrom'package:base':####intersect,setdiff,setequal,unionlibrary(grDevices)

library(ggplot2)NOAAS_DT$TOTAL_VICTIMS<-NOAAS_DT$FATALITIES+NOAAS_DT$INJURIESNow,itisrequiredtogroupbyeventthetotalnumberofvictims,sothatwayitcanbeseenwhicheventisthemostharmful.Toachievethis,theuseofthegroup_bydyplr'sfunctionpipelinedwithsummarisewilldothework.Afterthatlet'sshowthe10mostharmfulevents#ThetrickisdonebygroupingbyEventTypeanddoasummarycountingthenumberofvictimsnoaas_evtype_victims<-NOAAS_DT%>%group_by(EVTYPE)%>%summarise(VICTIMS=sum(TOTAL_VICTIMS))%>%arrange(desc(VICTIMS))#getthetoptenmostharmfuleventsnoaasevtypevictims<-noaasevtypevictims[1:10,]ggplot(noaas_evtype_victims,aes(x=EVTYPE,y=VICTIMS))+geombar(stat="identity",aes(fill=EVTYPE))+scalexdiscretelabels=c())lOOOOC-75000-EVTYPELIGHTNINGTl-UNDERSTORMWINDTORNADOlOOOOC-75000-EVTYPELIGHTNINGTl-UNDERSTORMWINDTORNADOTSTMWINDWIN'TEFtSTORMEVTYPEEXCESSIVEHEATFLASHFLOODHLOOD|HEATIICESTORMQothereventsTheplotaboveclearlyshowsthatTornadosleadmorevictimswithahugedifferencetootherevents【原創(chuàng)】R語(yǔ)言案例數(shù)據(jù)分析報(bào)告論文（附代碼數(shù)據(jù)）有問(wèn)題到淘寶找“大數(shù)據(jù)部落”就可以了2.AcrosstheUnitedStates,whichtypesofeventshavethegreatesteconomicconsequences?FromthestructureoftheNOAASDatasetitcanbeseenthatdamagesaredividedinto2bigcategories:#####PropertyDamages:Thoseocurredtobuildings,insfrastructuressupportingdevices.DescribedinNOAASDatasetPROPDMGvariable.#####CultiveDamages:Thoseoccurredaggriculturalactivities.DescribedinNOAASDatasetCROPDMGvariableAdditionally,NOAASDatasetdoesspecifydamagesmagnitudeusinganacronym:h:100factorK:1000factorM:1000000factorB:1000000000factorNOAASDatasetPROPDMGEXPandNOAASDatasetCROPDMGEXPspecifythemagnitudeforeachobservationaccordingly.Again,groupingisthekeytasktopresentthedataforthegivenquestion.Thefollowingcodespippetdoesthework:#definethefactors:H_FACTOR<-100K_FACTOR<-1000M_FACTOR<-K_FACTOR*K_FACTORB_FACTOR<-M_FACTOR*K_FACTORNOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP==""]<-NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP==""]*1NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="0"]<-NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="0"]*1NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="1"]<-NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="1"]*1NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="2"]<-NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="2"]*1NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="3"]<-NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="3"]*1NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="4"]<-NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="4"]*1NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="5"]<-NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="5"]*1NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="6"]<-NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="6"]*1NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="7"]<-NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="7"]*1NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="8"]<-NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="8"]*1【原創(chuàng)】【原創(chuàng)】R語(yǔ)言案例數(shù)據(jù)分析報(bào)告論文（附代碼數(shù)據(jù)）有問(wèn)題到淘寶找“大數(shù)據(jù)部落”就可以了NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="+"]<-NOAAS_DT$PROPDMG[N0AAS_DT$PR0PDMGEXP=="+"]*1NOAAS_DT$PROPDMG[N0AAS_DT$PR0PDMGEXP=="8"]<-N0AAS_DT$PR0PDMG[N0AAS_DT$PR0PDMGEXP=="-"]*1NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="K"]<-NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="K"]*K_FACTORNOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="M"]<-NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="M"]*M_FACTORNOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="B"]<-NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="B"]*B_FACTORNOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="h"]<-NOAAS_DT$PROPDMG[NOAAS_DT$PROPDMGEXP=="h"]*H_FACTORNOAAS_DT$CROPDMG[NOAAS_DT$CROPDMGEXP==""]<-NOAAS_DT$CROPDMG[NOAAS_DT$CROPDMGEXP==""]*1NOAAS_DT$CROPDMG[NOAAS_DT$CROPDMGEXP=="0"]<-NOAAS_DT$CROPDMG[NOAAS_DT$CROPDMGEXP=="0"]*1NOAAS_DT$CROPDMG[NOAAS_DT$CROPDMGEXP=="2"]<-NOAAS_DT$CROPDMG[NOAAS_DT$CROPDMGEXP=="2"]*1NOAAS_DT$CROPDMG[NOAAS_DT$CROPDMGEXP=="?"]<-NOAAS_DT$CROPDMG[NOAAS_DT$CROPDMGEXP=="?"]*1NOAAS_DT$CROPDMG[NOAAS_DT$CROPDMGEXP=="B"]<-NOAAS_DT$CROPDMG[NOAAS_DT$CROPDMGEXP=="B"]*B_FACTORNOAAS_DT$CROPDMG[NOAAS_DT$CROPDMGEXP=="K"]<-NOAAS_DT$CROPDMG[NOAAS_DT$CROPDMGEXP=="K"]*K_FACTORNOAAS_DT$CROPDMG[NOAAS_DT$CROPD