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How to implement Control Charts in R?

Description

To know about the data types in R programming.

Process
Need for Control Charts?
• Control charts, also known as Shewhart charts or process- behavior charts, are a statistical process control tool used to determine if a manufacturing or business process is in a state of control.
Interpretation of Control Charts: If it is in Control:
• If all the points lie within the control limits, the process is said to be “in control”
• If analysis of the control chart indicates that the process is currently under control, then no corrections or changes to process control parameters are needed.
• Moreover, data from the method can be used to predict the future performance of the process.
If it is not in Control:
• If the control chart indicates that the process is not in control, analysis of the chart can help determine the sources of variation, as this willresult in degradation of process performance.
Need of detecting Outliers:
• Missing data are a common occurrence and can have a significanteffect on the conclusions that can be drawn from the data.
Package and Function:
• R Package : qcc
• R Function : groups(x) -- For Grouping the data
• R Function : qcc(x, type=, nsigmas= ,newdata= )
• x --training data
• type = “R”,” xbar” -- Type of Control Chart
• nsigmas -- Default value is 3, means ±3 standard deviation
• Newdata -- new sample data from the same process
Sapmle Code

#Control Charts
#install.packages(“qcc”)
library(“qcc”)
data(pistonrings)
attach(pistonrings)

#Grouping the data
dia<-qcc.groups(diameter,sample)

#R Chart
obj<-qcc(dia[1:30,],type = “R”)
summary(obj)

#X- Bar Chart
obj<-qcc(dia[1:30,], type=”xbar”)
summary(obj)

#Changing the Values of nsigma
obj<-qcc(dia[1:30,],type=”R”,nsigmas = 1)
summary(obj)

#Testing new Data
obj<-qcc(dia[1:30,],type = “xbar”,newdata = dia[31:40,])