# 7QC_TOOL ----------------------------------------------------------------
library(SixSigma)
library(quicharts)
library(plotrix)
library(e1071) #ML
# 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"
# detail data include
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 = "TOPIC",
  sub = "vice-topic")


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

par(bg = "gray95")
hist(robo_data,
     main = "TOPIC subject",
     sub = "vice-topic",
     xlab = expression("Rowname"),
     col = "steelblue",
     border = "white",
     lwd = 2,
     las = 1,
     bg = "gray")

## other way to prove Lattice

library(lattice)
histogram(robo_data,
          xlab = expression("RowName"),
          ylab = "Colnames",
          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 rule told us : cause20%/ problem 80%
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)


library(qualityTools)
paretoChart(x = data_pareto2, main = "Out-of-control causes")

library(qicharts)
spreadvector <- rep(names(data_pareto2),times = data_pareto2)
paretochart(x=spreadvector)





# ScatterChart ------------------------------------------------------------
set.seed(1234)
robo_data2 <-  15*robo_data + rnorm(100)

plot(robo_data ~ robo_data2,
     col = "gray40",  #which mean color of points
     pch = 20,
     main = "TOPIC",
     xlab = "RowName",
     ylab = expression("ColName"))

# Grantt & Shewhart.rules -------------------------------------------------


head(cars)
list(robo_data,robo_data2)
shewhart.rules(ss1, limits = ss1$limits, run.length = qcc.options("run.length"))
beyond.limits(cars, limits = cars$limits)
violating.runs(cars, run.length = qcc.options("run.length"))

library(plotrix)
require(plotrix)
Ymd.format <- "%Y/%m/%d"
gantt.info <- list(labels =
                     c("First task"," Second task", "Third task",
                       "Fourth task", "Fifth task"),
                   starts = as.POSIXct(strptime(
                     c("2012/01/01", "2012/02/02", "2012/03/03",
                       "2012/05/05", "2012/09/09"),
                     format = Ymd.format)),
                   ends = as.POSIXct(strptime(
                     c("2012/03/03", "2012/05/05", "2012/05/05",
                       "2012/08/08", "2012/12/12"),
                     format = Ymd.format)),
                   priorities = c(1, 2, 3, 4, 5))
vgridpos <- as.POSIXct(strptime(paste("2017/7/",(seq(1,12))),
                                format = Ymd.format))
vgridpos

vgridlab <- month.abb # All months

gantt.chart(gantt.info, main = "Topic subject",
            priority.legend = TRUE, vgridpos = vgridpos,
            vgridlab = vgridlab, hgrid = TRUE)
?gantt.chart





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

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

## Normality Test
shapiro.test(robo_data)
#QQplot Normality confirm by plot
qqnorm(robo_data , pch = 16, col = gray(0.4))
grid()
qqline(robo_data)