RRRRRRRRRRRRRRRRR

SPC

install.packages("MASS","spc","qcc")
install.packages("qcc")
install.packages("SixSigma")
install.packages("lattice")
install.packages("AcceptanceSampling","Dodge","edcc","gplot2","graphics")
install.packages("grid","IQCC","knitr","mpcv","MSQC","qcr")
qcc(data = pdensity, type = "xbar.one")
install.packages("qicharts")
install.packages("qualityTools")


## ---------------------------bar{x}圖-----------------------------------
library(qcc)
require(qcc)

robo_data <- sample(seq(0,1, 0.01), 100, replace = FALSE)#輸入DATA
robo_data2<- sample(seq(0,1, 0.01), 100, replace = FALSE)#輸入DATA
robot_xbar<-qcc(robo_data,
    type = "xbar.one", #獨特xbar圖
    restore.par = FALSE,
    data.name = "資料名稱",
    xlab = "橫坐標值",
    ylab = expression("縱坐標值"),
    axes.las = 1)
abline(h = 10.5,col = "red",lwd = 2)
text(x = 12,y = 10.5,labels = "線上字幕",pos = 3)
robot_xbar
summary(robot_xbar) #歸納圖表
robot_xbar$violations #get OC points
summary(hist(robo_data))

write.csv('data', file="選一個",row.names=FALSE)

# 7QC_TOOL ----------------------------------------------------------------

# Fishbone ----------------------------------------------------------------



cManpower <- c("Recepcionist", "Record. Operator","Storage operators")
cMaterials <- c("Supplier", "Transport agency","Packing")
cMachines <- c("Compressor type","Operation conditions","Machine adjustment")
cMethods <- c("Reception", "Transport method")
cMeasurements <- c("Recording method","Measurement appraisal")
cGroups <- c("Manpower", "Materials", "Machines","Methods", "Measurements")
cEffect <- "Too high density"
#參造上面因素構成因素群。
cause.and.effect( cause = list(Manpower = cManpower,
               Materials = cMaterials,
               Machines = cMachines,
               Methods = cMethods,
               Measurements = cMeasurements),
  effect = cEffect)

library(SixSigma)
library(e1071)
ss.ceDiag(
  effect = cEffect,
  causes.gr <- cGroups,
  causes = list(cManpower, cMaterials, cMachines,
                cMethods, cMeasurements),
  main = "主標題",
  sub = "副標題")


# Histogram ---------------------------------------------------------------

par(bg = "gray95")
hist(robo_data,
     main = "主標題",
     sub = "副標題",
     xlab = expression("橫坐標"),
     col = "steelblue",
     border = "white",
     lwd = 2,
     las = 1,
     bg = "gray")

##另種寫法使用LATTICE

library(lattice)
histogram(robo_data,
          xlab = expression("橫坐標"),
          ylab = "縱坐標",
          type = "density",
          panel = function(x, ...) {
            panel.histogram(x, ...)
            panel.mathdensity(dmath = dnorm,
                              col = "black",
                              lwd = 0.3,
                              args = list(mean = mean(x),
                                          sd = sd(x)))
          } )


# Pareto Chart ------------------------------------------------------------

##80&20法則告訴我們: cause20/problem80
data_checkSheet <- rbind(
  data.frame(Group = "Manpower",
             Cause = cManpower),
  data.frame(Group = "Machines",
             Cause = cMachines),
  data.frame(Group = "Materials",
             Cause = cMaterials),
  data.frame(Group = "Methods",
             Cause = cMethods),
  data.frame(Group = "Measurements",
             Cause = cMeasurements)
)


data_checkSheet$Factor_1 <- c(2, 0, 0, 2, 1, 7, 1, 3, 6, 0, 1, 2, 0)
data_checkSheet$Factor_2 <- c(0, 0, 1, 1, 2, 1, 12, 1, 2, 1, 0, 0, 1)
data_checkSheet$Factor_3 <- c(0, 1, 0, 6, 0, 2, 2, 4, 3, 0, 1, 0, 2)
data_checkSheet$Total <- data_checkSheet$Factor_1 + data_checkSheet$Factor_2 +data_checkSheet$Factor_3
data_checkSheet

barplot(height = data_checkSheet$Total,names.arg = data_checkSheet$Cause)

data_pareto <- data_checkSheet[order(data_checkSheet$Total,decreasing = TRUE), ]
par(mar = c(8, 4, 4, 2) + 0.1)

barplot(height = data_pareto$Total,
        names.arg = data_pareto$Cause,
        las = 2,
        main = "Pareto chart for total causes")
par(mar = c(8, 4, 4, 2) + 0.1)


data_pareto2<- data_pareto$Total
names(data_pareto2) <- data_pareto$Cause
pareto.chart(data = data_pareto2,main = "Out-of-control causes")

#also can summary(pareto.chart)

#柏拉圖2
library(qualityTools)
paretoChart(x = data_pareto2, main = "Out-of-control causes")
#柏拉圖3
library(qicharts)
spreadvector <- rep(names(data_pareto2),times = data_pareto2)
paretochart(x=spreadvector)





# ScatterChart ------------------------------------------------------------



# Hypotthesis Testing -----------------------------------------------------

t.test(robo_data) #mean test
var.test(robo_data,sample(1:10,10,replace = TRUE)) #var test
prop.test() #比例test

## Normality Test
shapiro.test(robo_data)
#QQplot 常態檢測
qqnorm(robo_data , pch = 16, col = gray(0.4))
grid()
qqline(robo_data)


# Control Chart -----------------------------------------------------------

aggregate(thickness ~ ushift, 
          data = ss.data.thickness2,
          FUN = mean)
#引用SixSigma資料
head(ss.data.thickness)


samples.thick <- qcc.groups(data = ss.data.thickness2$thickness, sample = ss.data.thickness2$ushift)
head(samples.thick)
xbar.thick <- qcc(data = samples.thick, type = "xbar")
summary(xbar.thick)
plot(xbar.thick)