.Rhistory

q()
q()
q()
Data1
Data1<- read.csv("testDataset.csv", header=T) # load the datasets
View(Data1)
View(Data1)
Data1.X1
Data1
Data1.X1
CumSum(Data1.X1)
duration = Data1$X1
duration
cumsum(duration)
testDataset <- read.csv("C:/Workspace/PomWalker/testDataset.csv", header=F)
View(testDataset)
testDataset
testDataset$V3
plot(testDataset)
myDates <- as.Dates(testDataset$V1)
myDates <- as.Date(testDataset$V1)
myDates
myDates <- as.Date(testDataset$V1, "%m/%d/%Y")
myDates
myDates <- as.Date(testDataset$V1, "%d/%m/%Y")
myDates
plot(myDates, testDataset$V3)
plot(myDates, cumsum(testDataset$V3))
lines(myDates, cumsum(testDataset$V3))
set.seed(3)
library(data.table)
ct <- data.table(id=sample(1:10,15,replace=TRUE),item=round(rnorm(15),3))
st <- ct[,countid:=.N,by=id]
CT <- data.frame( value = runif(10) , id = sample(5,10,repl=T) )
#  sort on ID when calculating rle
Count <- rle( sort( CT$id ) )
#  match values
CT$Count <- Count[[1]][ match( CT$id , Count[[2]] ) ]
CT
CT$ID
CT$id
CT$count
CT$Count
CT <- data.frame( value = runif(10) , id = sample(5,10,repl=T) )
CT
Count <- rle( sort( CT$id ) )
Count
CT
Count[[1]]
Count
Count <- rle( sort( testDataset$V2 ) )
Count <- rle( sort( testDataset$V1 ) )
Count <- rle( sort( testDataset$V3 ) )
testDataset$myDates
testDataset$myDates <-myDates
testDataset
testDataset$V2
Count(testDataset$V2)
testDataset$V2
unique(testDataset$V2)
unique(testDataset$V1)
library(plyr)
install.packages("library(plyr)")