Cat 4 July 22

tasking <- read.csv("C:/Users/Rinor/Downloads/sgt/july22/tasking4.csv")
taskingTS <- ts(tasking$Average.taskingPerformance, frequency=1, start=c(0,1))

elset <- read.csv("C:/Users/Rinor/Downloads/sgt/july22/elset4.csv")
elsetTS <- ts(elset$Average.elsetAge, frequency=1, start=c(0,1))

rcs <- read.csv("C:/Users/Rinor/Downloads/sgt/july22/rcs4.csv")
rcsTS <- ts(rcs$Average.rcs, frequency=1, start=c(0,1))

rrc <- read.csv("C:/Users/Rinor/Downloads/sgt/july22/rrc4.csv")
rrcTS <- ts(rrc$rrc, frequency=1, start=c(0,1))

Outlier Detection Tasking Performance Category 4

library(tsoutliers)
outliers <- tso(taskingTS)
plot(outliers)

Arima of Tasking Priority 4

library(forecast)
library(timeDate)
fit <- auto.arima(taskingTS)
plot(forecast(fit, 14), ylab="Tasking Performance 14 Day Prediction", xlab="Day")

Arima of Adjusted Graph (Outliers Taken Out)

fit <- auto.arima(outliers$yadj)
plot(forecast(fit, 14), ylab="Tasking Performance 14 Day Prediction", xlab="Day")

Correlation of Tasking Performance Priority 4 vs RCS

cor(taskingTS, rcsTS)
## [1] 0.4478105
library(clusterSim)
z1 <- data.Normalization(tasking$Average.taskingPerformance,type="n1",normalization="column")
z2 <- data.Normalization(rcs$Average.rcs,type="n1",normalization="column")
timeSeries1 <- ts(z1, frequency=1, start=c(0,1))
timeSeries2 <- ts(z2, frequency=1, start=c(0,1))
ts.plot(timeSeries1, timeSeries2, col=1:2, main="Tasking Perfomance Priority 4 (Black) vs RCS (Red)", xlab="Day", ylab="# of Std Dev from the Mean")

x <- unlist(tasking$Average.taskingPerformance)
y <- unlist(rcs$Average.rcs)
plot(x,y, pch = 16, cex = 1.3, col = "blue", main="Tasking Performance Priority 4 vs RCS",xlab="task", ylab="rrc")
abline(lm(y~x), col="red")

Correlation of Tasking Performance Priority 4 vs RRC

cor(taskingTS, rrcTS)
## [1] 0.6164239
library(clusterSim)
z1 <- data.Normalization(tasking$Average.taskingPerformance,type="n1",normalization="column")
z2 <- data.Normalization(rrc$rrc,type="n1",normalization="column")
timeSeries1 <- ts(z1, frequency=1, start=c(0,1))
timeSeries2 <- ts(z2, frequency=1, start=c(0,1))
ts.plot(timeSeries1, timeSeries2, col=1:2, main="Tasking Perfomance Priority 4 (Black) vs RRC (Red)", xlab="Day", ylab="# of Std Dev from the Mean")

x <- unlist(tasking$Average.taskingPerformance)
y <- unlist(rrc$rrc)
plot(x,y, pch = 16, cex = 1.3, col = "blue", main="Tasking Performance Priority 4 vs RRC",xlab="task", ylab="rrc")
abline(lm(y~x), col="red")

Correlation of Tasking Performance Priority 4 vs Elset

cor(taskingTS, elsetTS)
## [1] 0.1983045
library(clusterSim)
z1 <- data.Normalization(tasking$Average.taskingPerformance,type="n1",normalization="column")
z2 <- data.Normalization(elset$Average.elsetAge,type="n1",normalization="column")
timeSeries1 <- ts(z1, frequency=1, start=c(0,1))
timeSeries2 <- ts(z2, frequency=1, start=c(0,1))
ts.plot(timeSeries1, timeSeries2, col=1:2, main="Tasking Perfomance Priority 4 (Black) vs Elset (Red)", xlab="Day", ylab="# of Std Dev from the Mean")

x <- unlist(tasking$Average.taskingPerformance)
y <- unlist(elset$Average.elsetAge)
plot(x,y, pch = 16, cex = 1.3, col = "blue", main="Tasking Performance Priority 4 vs Elset",xlab="task", ylab="rrc")
abline(lm(y~x), col="red")

Corr Plot 4

library(corrplot)
cat4 <- read.csv("C:/Users/Rinor/Downloads/sgt/july22/corr4.csv")
M <- cor(cat4)
corrplot(M, method="number", type="lower")