Calculate QC Limits
Calculates QC chart lines for the following chart types and reports in a dataframe:
Individuals Charts: mR, XmR,
Attribute Charts: c, np, p, u,
Studentized Charts: xBar.rBar, xBar.rMedian, xBar.sBar, xMedian.rBar, xMedian.rMedian,
Dispersion Charts: rBar, rMedian, sBar.
QC_Lines(data = NULL, value = NULL, grouping = NULL, formula = NULL, n = NULL, method = "xBar.rBar", na.rm = FALSE)
data |
vector or dataframe, as indicated below for each chart type
|
value |
string, Studentized Charts and Dispersion Charts, numeric vector in dataframe with values of interest |
grouping |
string, Studentized Charts and Dispersion Charts: single factor/variable to split the dataframe "values" by |
formula |
Studentized Charts and Dispersion Charts: a formula, such as y ~ x1 + x2, where the y variable is numeric data to be split into groups according to the grouping x factors/variables |
n |
number or vector as indicated below for each chart type.
|
method |
string, calling the following methods:
|
na.rm |
a logical value indicating whether NA values should be stripped before the computation proceeds. |
a dataframe,
Attribute Data: (p and u) Center Line, Upper Control Limit and Lower Control limit for each point.
Other Data: single line dataframe, with relevant control limits noted in column headings.
If using the formula argument do not use value and group arguments.
Wheeler, DJ, and DS Chambers. Understanding Statistical Process Control, 2nd Ed. Knoxville, TN: SPC, 1992. Print.
#############################################
# Example 1: Charts other than "p" or "u" #
#############################################
# Load Libraries ----------------------------------------------------------
require(ggQC)
require(plyr)
require(ggplot2)
# Setup Data --------------------------------------------------------------
set.seed(5555)
Process1 <- data.frame(processID = as.factor(rep(1,100)),
metric_value = rnorm(100,0,1),
subgroup_sample=rep(1:20, each=5),
Process_run_id = 1:100)
set.seed(5555)
Process2 <- data.frame(processID = as.factor(rep(2,100)),
metric_value = rnorm(100,5, 1),
subgroup_sample=rep(1:10, each=10),
Process_run_id = 101:200)
Both_Processes <- rbind(Process1, Process2)
# QC Values For Individuals -----------------------------------------------
# All Together
QC_Lines(data = Both_Processes$metric_value, method = "XmR")
# For Each Process
ddply(Both_Processes, .variables = "processID",
.fun =function(df){
QC_Lines(data = df$metric_value, method = "XmR")
}
)
# QC Values For Studentized Runs-------------------------------------------
# All Together
QC_Lines(data = Both_Processes,
formula = metric_value ~ subgroup_sample)
# For Each Process
ddply(Both_Processes, .variables = "processID",
.fun =function(df){
QC_Lines(data = df, formula = metric_value ~ subgroup_sample)
}
)
########################
# Example 2 "p" data #
########################
# Setup p Data ------------------------------------------------------------
set.seed(5555)
bin_data <- data.frame(
trial = 1:30,
Num_Incomplete_Items = rpois(n = 30, lambda = 30),
Num_Items_in_Set = runif(n = 30, min = 50, max = 100))
bin_data$Proportion_Incomplete <- bin_data$Num_Incomplete_Items/bin_data$Num_Items_in_Set
# QC_Lines for "p" data ---------------------------------------------------
QC_Lines(data = bin_data$Proportion_Incomplete,
n = bin_data$Num_Items_in_Set, method="p")
########################
# Example 3 "u" data #
########################
# Setup u Data ------------------------------------------------------------
set.seed(5555)
bin_data <- data.frame(
trial=1:30,
Num_of_Blemishes = rpois(n = 30, lambda = 30),
Num_Items_Inspected = runif(n = 30, min = 50, max = 100))
bin_data$Blemish_Rate <- bin_data$Num_of_Blemishes/bin_data$Num_Items_Inspected
# QC Lines for "u" data ---------------------------------------------------
QC_Lines(data = bin_data$Blemish_Rate,
n = bin_data$Num_Items_Inspected, method="u")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.