SPC_proj.v1

v1

library(qcc)
library(readr)
library(ggQC)
library(ggplot2)
# x2fr_data ----------------------------------------------------------------

X2fr_2 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr_2.txt")
X2fr_3 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr_3.txt")
X2fr_4 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr_4.txt")
X2fr_5 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr_5.txt")
X2fr_6 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr_6.txt")
X2fr_7 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr_7.txt")
X2fr_8 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr_8.txt")
X2fr_9 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr_9.txt")
X2fr_10 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr_10.txt")




head(x2frData_v1)
summary(X2fr2_1)
summary(X2fr2_2)
summary(X2fr2_3)
summary(X2fr2_4)
summary(X2fr2_5)
summary(X2fr2_6)
summary(X2fr2_7)
summary(X2fr2_8)
summary(X2fr2_9)
summary(X2fr2_10)
#data x2fr2_ each mean list
x2frMean<- c(-0.08387,-0.07136,-0.1439,-0.1990,-0.1210,-0.1978,-0.2397,-0.1766,-0.0957,-0.2030)
library(qcc)
as.data.frame(x2frMean) #data transformation
qccx2fr2<-qcc(x2frMean, type = c("xbar.one"))
#INDEX limit set -0.05/-.026
process.capability(object= qcc(x2frMean, type = c("xbar.one")),spec.limits = c(-0.05,-0.26))
ewma(x2frMean) 
lines(ewmaSmooth(x= x2frMean,y=1:10), col="red")

#lambda	<the smoothing parameter> where 0 <= lambda <= 1 is the parameter which controls the weights applied.
summary(ewma(x2frMean))
cusum(x2frMean)
summary(x2frMean)

#x <- 1:50
#y <- rnorm(50, sin(x/5), 0.5)
#plot(x,y)
#lines(ewmaSmooth(x,y,lambda=0.1), col="red")

# 2fr2_data ---------------------------------------------------------------

X2fr2_1 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr2_1.txt")
X2fr2_2 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr2_2.txt")
X2fr2_3 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr2_3.txt")
X2fr2_4 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr2_4.txt")
X2fr2_5 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr2_5.txt")
X2fr2_6 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr2_6.txt")
X2fr2_7 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr2_7.txt")
X2fr2_8 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr2_8.txt")
X2fr2_9 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr2_9.txt")
X2fr2_10 <- read_csv("C:/Users/koukiliu/Music/pca_result/2fr2_10.txt")

summary(X2fr2_1)
summary(X2fr2_2)
summary(X2fr2_3)
summary(X2fr2_4)
summary(X2fr2_5)
summary(X2fr2_6)
summary(X2fr2_7)
summary(X2fr2_8)
summary(X2fr2_9)
summary(X2fr2_10)
x2frMean2<-c(-0.15884,-0.07136,-0.1439,-0.1990,-0.1210,-0.1978,-0.2397,-0.1766,-0.0957,-0.2030)
ewma(x2frMean2)
cusum(x2frMean2)
qcc(x2frMean2, type = c("xbar.one"))
#xbar_chart diagram
data.frame(v1 = x2frMean2,v2 =rep("A",10)) # 10 data as one group
x2frMean2_1<-data.frame(v1 = x2frMean2,v2 =rep(c(sample(letters,10))))
x2frMean2_1<-data.frame(v1 = x2frMean2,v2 =rep("A",10)) # each pt assign a letter as one group
qcc(x2frMean2_1, type = c("S"))
#S chart diagram
stats.S(x2frMean2_1) 
#the stat value of s
process.capability(object = qcc(x2frMean2, type = c("xbar.one")),spec.limits = c(-0.05,-0.2))
#the Process capabiltiy (CPk) of above
require(ggQC)
require(ggQC)
require(plyr)
require(ggplot2)

# EX_data -----------------------------------------------------------------

ExtF_1 <- read_csv("C:/Users/koukiliu/Music/pca_result/ExtF_1.txt")
ExtF_2 <- read_csv("C:/Users/koukiliu/Music/pca_result/ExtF_2.txt")
ExtF_3 <- read_csv("C:/Users/koukiliu/Music/pca_result/ExtF_3.txt")
ExtF_4 <- read_csv("C:/Users/koukiliu/Music/pca_result/ExtF_4.txt")
ExtF_5 <- read_csv("C:/Users/koukiliu/Music/pca_result/ExtF_5.txt")
ExtF_6 <- read_csv("C:/Users/koukiliu/Music/pca_result/ExtF_6.txt")
ExtF_7 <- read_csv("C:/Users/koukiliu/Music/pca_result/ExtF_7.txt")
ExtF_8 <- read_csv("C:/Users/koukiliu/Music/pca_result/ExtF_8.txt")
ExtF_9 <- read_csv("C:/Users/koukiliu/Music/pca_result/ExtF_9.txt")
ExtF_10 <- read_csv("C:/Users/koukiliu/Music/pca_result/ExtF_10.txt")

ExtF_Data<- list(ExtF_1,ExtF_2,ExtF_3,ExtF_4,ExtF_5,ExtF_6,ExtF_7,ExtF_8,ExtF_9,ExtF_10)
ExtFData_v1 <-as.data.frame(ExtF_Data)
summary(ExtFData_v1)
ExtF_mean<- c(0.2788,0.6511,0.4214,0.8186,0.4092,0.6549,0.3408,0.6314,0.3871,0.5940)
qccExtF_<-qcc(ExtF_mean, type = c("xbar.one"))
#xbar_chart diagram
data.frame(v1 = ExtF_mean,v2 =rep("A",10)) # 10 data as one group
ExtF_mean2_1<-data.frame(v1 = ExtF_mean,v2 =rep(c(sample(letters,10))))
ExtF_mean2_1<-data.frame(v1 = ExtF_mean,v2 =rep("A",10)) # each pt assign a letter as one group
qcc(ExtF_mean2_1, type = c("S"))
#S chart diagram
stats.S(ExtF_mean2_1) 
#the stat value of s
process.capability(object = qcc(ExtF_mean, type = c("xbar.one")),spec.limits = c(0.05,0.5))
#the Process capabiltiy (CPk) of above
ewma(ExtF_mean)
summary(ewma(ExtF_mean))
cusum(ExtF_mean)
summary(cusum(ExtF_mean))
# Normal_data -------------------------------------------------------------

normal_1 <- read_csv("C:/Users/koukiliu/Music/pca_result/normal_1.txt")
normal_2 <- read_csv("C:/Users/koukiliu/Music/pca_result/normal_2.txt")
normal_3 <- read_csv("C:/Users/koukiliu/Music/pca_result/normal_3.txt")
normal_4 <- read_csv("C:/Users/koukiliu/Music/pca_result/normal_4.txt")
normal_5 <- read_csv("C:/Users/koukiliu/Music/pca_result/normal_5.txt")
normal_6 <- read_csv("C:/Users/koukiliu/Music/pca_result/normal_6.txt")
normal_7 <- read_csv("C:/Users/koukiliu/Music/pca_result/normal_7.txt")
normal_8 <- read_csv("C:/Users/koukiliu/Music/pca_result/normal_8.txt")
normal_9 <- read_csv("C:/Users/koukiliu/Music/pca_result/normal_9.txt")
normal_10 <- read_csv("C:/Users/koukiliu/Music/pca_result/normal_10.txt")

normal_Data<-list(normal_1,normal_2,normal_3,normal_4,normal_5,normal_6,normal_7,normal_8,normal_9,normal_10)
normalData_v1<-as.data.frame(normal_Data) # data transformation
summary(normalData_v1)
Normal_mean<-c(-0.07747,-0.0925,-0.03626,0.06373,0.1030,-0.01115,0.04799,0.00762,0.1430,0.03866)

qccnormal<-qcc(Normal_mean, type = c("xbar.one"))
#xbar_chart diagram
data.frame(v1 = Normal_mean,v2 =rep("A",10)) # 10 data as one group
Normal_mean_1<-data.frame(v1 = Normal_mean,v2 =rep(c(sample(letters,10))))
Normal_mean_1<-data.frame(v1 = Normal_mean,v2 =rep("A",10)) # each pt assign a letter as one group
qcc(Normal_mean_1, type = c("S"))
#S chart diagram
stats.S(Normal_mean_1) 
#the stat value of s
process.capability(object = qcc(Normal_mean, type = c("xbar.one")),spec.limits = c(-0.02,0.05))
#the Process capabiltiy (CPk) of above

ewma(Normal_mean)
summary(ewma(Normal_mean))
cusum(Normal_mean)
summary(cusum(Normal_mean))
#x2frData<- list(X2fr_2,X2fr_3,X2fr_4,X2fr_5,X2fr2_6,X2fr2_7,X2fr2_8,X2fr2_9,X2fr2_10)
#x2frData_v1<-as.data.frame(x2frData)
#Question:
#cusum why always > 0 in texbook but actaully include < 0