Analysing disctrete correlation

survey <- cleme="" esktop="" read.csv="" sers="" stringsasfactors="FALSE)<br" survey-covid.csv="">surveyFinalized<-survey br="" esponse="" finalized="" survey="" tatus="=">
names(surveyFinalized)[names(surveyFinalized) == "What.is.your.home.situation..you.are.in.living...."] <- br="" latorhouse="">names(surveyFinalized)[names(surveyFinalized) == "What.is.your.family.situation."] <- br="" situation="">names(surveyFinalized)[names(surveyFinalized) == "How.worried.are.you.about.the.impact.of.coronavirus.on.you.personally."] <- anxiety="" br="">
View(surveyFinalized)
surveyFinalized$situation[grepl( "without child", surveyFinalized$situation,fixed = TRUE)]<- br="" lone="">surveyFinalized$situation[grepl( "with child", surveyFinalized$situation,fixed = TRUE)]<- amily="" br="">
surveyFinalized$FlatOrHouse[grepl( "ppart", surveyFinalized$FlatOrHouse,fixed = TRUE)]<- br="" ppartment="">surveyFinalized$FlatOrHouse[grepl( "Apart", surveyFinalized$FlatOrHouse,fixed = TRUE)]<- br="" ppartment="">surveyFinalized$FlatOrHouse[grepl( "flat", surveyFinalized$FlatOrHouse,fixed = TRUE)]<- br="" ppartment="">surveyFinalized$FlatOrHouse[grepl( "city", surveyFinalized$FlatOrHouse,fixed = TRUE)]<- br="" ppartment="">surveyFinalized$FlatOrHouse[grepl( "country", surveyFinalized$FlatOrHouse,fixed = TRUE)]<- br="" ouse="">surveyFinalized$FlatOrHouse[grepl( "house", surveyFinalized$FlatOrHouse,fixed = TRUE)]<- br="" ouse="">

library("ggplot2")

surveyFinalized$anxiety<-factor all="" anxiety="" at="" br="" ery="" levels="c(" omewhat="" ot="" so="" surveyfinalized="" worried="" xtremely="">relevel(surveyFinalized$anxiety,"Very worried")

(d <- aes="" anxiety="" br="" depending="" eneral="" ggplot="" ggtitle="" on="" situation="" surveyfinalized="">    geom_jitter())

(d <- aes="" anxiety="" br="" depending="" eneral="" ggplot="" ggtitle="" latorhouse="" on="" setup="" surveyfinalized="">    geom_jitter())

R rough guide

Working directory

getwd()

setwd("D:\\data\\Core kpi\\workspace\\tekpi-service\\src\\test\\resources\\data\\performance")

Loading data from external files

testplan.1_users.20171228 <- data="" ore="" read.csv="" u="">kpi

/workspace/tekpi-service/src/test/resources/data/performance/testplan-per-query-aggregate-1_users-20171228.csv")

Converting from Posix Date

In jmeter, the date are stored in POSIX format (number of millis since 1970), in order to generate propely the date. Use the following commande.

.1_users.20171228$timeStamp/1000,origin ="1970-01-01", tz = "GMT"))

View a table

View(testplan.1_users.20171228)

Install a package

install.packages("ggplot2")

load a library

library("ggplot2")

dataTestFiles<- u="">dir

(pattern = "testplan-per-query-aggregate-.*-20180125.*")

Process a batch of file

testplan<-null span="">

for (filename in dataTestFiles) {

  print(filename);

  nbUserStr<-sub u="">testplan

-per-query-aggregate-","",filename)

  nbUserStr<-sub nbuserstr="" span="" users="">

  nbUserStr<- u="">strsplit

(nbUserStr,"-")[[1]][1]

  nbUser<-as .numeric="" nbuserstr="" span="">

  print(substr(filename,30,30));

  temptestplan <- filename="" read.csv="" span="">

  temptestplan$nbuser=nbUser;

  splittedData <- data.frame="" do.call="" u="">rbind

', strsplit(as.character(temptestplan$label),'-',fixed=TRUE)))

  temptestplan<- u="">cbind

(temptestplan,splittedData)

  temptestplan$session=filename;

  testplan<- u="">rbind

(testplan,temptestplan)

}

Agregate on function

aggregate(x=testplanclean$elapsed,by=list(testplanclean$session),FUN='mean')

aggregate(x=testplanclean$elapsed,by=list(testplanclean$session),FUN='var')

aggregate(x=testplanclean$elapsed,by=list(testplanclean$session),FUN='max')

nbReqSession<-aggregate x="<u">testplanclean

$elapsed,by=list(testplanclean$session),FUN='length')

startSession<-aggregate x="<u">testplanclean

$timeStamp,by=list(testplanclean$session),FUN='min')

endSession<-aggregate x="<u">testplanclean

$timeStamp,by=list(testplanclean$session),FUN='max')

intensite<- u="">cbind

(nbReqSession[1],nbReqSession[2]/(endSession[2]-startSession[2])*60000)

testplanclean$nbuserlabel<-paste u="">testplanclean

$nbuser, " user: ",testplanclean$X1)

# Time elapsed per User and type of graph

timeElapsedGraph <- u="">ggplot

(testplanclean, aes(nbuserlabel,elapsed/1000,color=X1))

timeElapsedGraph + geom_boxplot() + labs(y="Temps de r�ponse (s)",x="Utilisateurs simultan�s")+ scale_x_discrete(labels=c(timeElapsedGraph$nbuser))

# Time elapsed per Filter

timeElapsedPerFilterGraph <- u="">ggplot

(testplanclean, aes(X3,elapsed/1000))

timeElapsedPerFilterGraph + stat_boxplot() + labs(y="Temps de r�ponse (s)",x="Type de filtre")

# Time elapsed per aircraft

timeElapsedPerAircraftGraph <- u="">ggplot

(testplanclean, aes(X2,elapsed/1000))

timeElapsedPerAircraftGraph + stat_boxplot() + labs(y="Temps de r�ponse (s)",x="Aircraft")

# Time elapsed  type of graph

timeElapsedGraph <- u="">ggplot

(testplanclean, aes(X1,elapsed/1000))

timeElapsedGraph + geom_boxplot() + labs(y="Temps de r�ponse (s)",x="Type of Graph")

hist(testplanclean$timeStamp/60000)

aggregate(x=testplan$responseCode,by=list(testplan$label,testplan$responseCode),FUN='length')

intensiteChart<- u="">ggplot

(intensite,aes(Group.1,x))+theme(axis.text.x=element_blank(),  axis.ticks.x=element_blank())

intensiteChart+geom_point()+geom_smooth(method="lm",color="Red")+labs(y="Request per minutes",x="Stress of the scenario")

intensite

Package pour traiter les dates

install.packages('lubridate')

library(lubridate)

access_log$Date<-dmy_hms access_log="" span="">