R/04_stat_qc.R
In ggmtech/ggQC: Quality Control Charts for 'ggplot'
Defines functions
stat_QC_labels
stat_QC
Documented in
stat_QC
stat_QC_labels
##############################
# Copyright 2018 Kenith Grey #
##############################
# Copyright Notice --------------------------------------------------------
# This file is part of ggQC.
#
# ggQC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ggQC is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ggQC. If not, see <http://www.gnu.org/licenses/>.
STAT_QC <- ggplot2::ggproto("STAT_QC", ggplot2::Stat,
compute_group = function(data, scales, n=NULL, digits=1,
method=NULL, draw.line=draw.line,
physical.limits=c(NA,NA),
call.from = "QC.NULL",
show.1n2.sigma = FALSE, limit.txt.label=c("LCL","UCL")){
temp <- aggregate(data=data, y~x, mean)
# print("---NEW---")
# print(call.from)
# print(limit.txt.label)
LCL <- limit.txt.label[1]
UCL <- limit.txt.label[2]
if(method %in% c("mR", "XmR", "c")){
qcline <- if(draw.line == "center") c(2) else c(1,3)
dflines <- ylines_indv(temp$y, n=n, method = method)
sigma <- dflines$sigma
center <- dflines[2]
limits_txt_lbl <- limit.txt.label
#print(limits_txt_lbl)
if(!all(is.na(physical.limits)) & method %in% c("XmR", "c")){
if(!any(is.na(physical.limits))){
if(physical.limits[1] > dflines[1] & physical.limits[2] < dflines[3]){
dflines[1] <- physical.limits[1]
dflines[3] <- physical.limits[2]
limits_txt_lbl <- c("LB", "UB")
}else if(physical.limits[1] > dflines[1]){
dflines[1] <- physical.limits[1]
limits_txt_lbl <- c("LB", UCL)
}else if(physical.limits[2] < dflines[3]){
dflines[3] <- physical.limits[2]
limits_txt_lbl <- c(LCL, "UB")
}
}else if(!is.na(physical.limits[1])){
if(physical.limits[1] > dflines[1]){
dflines[1] <- physical.limits[1]
limits_txt_lbl <- c("LB", UCL)
}
}else if(!is.na(physical.limits[2])){
if(physical.limits[2] < dflines[3]){
dflines[3] <- physical.limits[2]
limits_txt_lbl <- c(LCL, "UB")
}
}
}
#print(dflines)
limits_df <- data.frame(yintercept = t(dflines)[qcline])
#print(limits_df)
}else if(method == "np"){
if (is.null(n)){
warning("**np-chart Error**\n For np chart, specify value for n in stat_QC", call. = FALSE)
return(NULL)
}else if(!binCheck_pChart(temp$y/n, n)){
warning("**np-chart Error**\n Items of Opportunity 'n' < Item Nonconforming\n check value of 'n' in stat_QC.", call. = FALSE)
return(NULL)
}
#print(temp$y/n)
qcline <- if(draw.line == "center") c(2) else c(1,3)
dflines <- ylines_indv(temp$y, n=n, method = method)
sigma <- dflines$sigma
center <- dflines[2]
limits_txt_lbl <- limit.txt.label
limits_df <- data.frame(yintercept = t(dflines)[qcline])
}else if(method == "p"){
if (is.null(data$n)){
warning("**p-chart Error**\n supply vector of opportunity, 'n', in ggplot aes", call. = FALSE)
return(NULL)
}
qcline <- if(draw.line == "center") c(2,4) else c(1,3,4)
pdata <- QC_Lines(data = data$y, n=data$n, method = "p")
pdata$x <- data$x
pchart_data <- reshape2::melt(pdata[,qcline], id.vars=c("x"))
colnames(pchart_data) <- c("x", "group", "y")
return(pchart_data)
}else if(method == "u"){
if (is.null(data$n)){
warning("**u-chart Error**\n supply vector of opportunity, 'n', in ggplot aes", call. = FALSE)
return(NULL)
}
qcline <- if(draw.line == "center") c(2,4) else c(1,3,4)
udata <- QC_Lines(data = data$y, n=data$n, method = "u")
udata$x <- data$x
uchart_data <- reshape2::melt(udata[,qcline], id.vars=c("x"))
colnames(uchart_data) <- c("x", "group", "y")
return(uchart_data)
}else{
#Studentized charts Xbar-Rbar etc.
qcline <- if(draw.line == "center") c(6) else c(5,7)
dflines <- QC_Lines(data = data, value = "y", grouping = "x", n=n, method = method)
sigma <- dflines$sigma
center <- dflines[6]
limits_txt_lbl <- limit.txt.label
if(!all(is.na(physical.limits)) & !method %in% c("rBar", "rMedian", "sBar")){
if(!any(is.na(physical.limits))){
if(physical.limits[1] > dflines[5] & physical.limits[2] < dflines[7]){
dflines[5] <- physical.limits[1]
dflines[7] <- physical.limits[2]
limits_txt_lbl <- c("LB", "UB")
}else if(physical.limits[1] > dflines[5]){
dflines[5] <- physical.limits[1]
limits_txt_lbl <- c("LB", UCL)
}else if(physical.limits[2] < dflines[7]){
dflines[7] <- physical.limits[2]
limits_txt_lbl <- c(LCL, "UB")
}
}else if(!is.na(physical.limits[1])){
if(physical.limits[1] > dflines[5]){
dflines[5] <- physical.limits[1]
limits_txt_lbl <- c("LB", UCL)
}
}else if(!is.na(physical.limits[2])){
if(physical.limits[2] < dflines[7]){
dflines[7] <- physical.limits[2]
limits_txt_lbl <- c(LCL, "UB")
}
}
}
limits_df <- data.frame(yintercept = t(dflines[,qcline]))
}
#print(qcline)
limits_df$y <- limits_df$yintercept
limits_df$x <- Inf
limits_df$label <- paste0(round(limits_df$yintercept,digits))
limits_df$hjust <- 1.1
limits_df$alpha <- 1
# Sigma Limits ------------------------------------------------------------
if(grepl(pattern = "c|XmR|xBar|xMedian|np", method) &
!call.from == "QC.Label" & show.1n2.sigma){
sigma_df <- data.frame(
yintercept = c(center[[1]] + 1*sigma, center[[1]] - 1*sigma,
center[[1]] + 2*sigma, center[[1]] - 2*sigma),
y = c(center[[1]] + 1*sigma, center[[1]] - 1*sigma,
center[[1]] + 2*sigma, center[[1]] - 2*sigma),
x = rep(NA, 4),
label = rep(NA, 4),
hjust = rep(0, 4),
alpha = c(.20,.20,.50,.50)
)
}else if(!grepl(pattern = "c|XmR|xBar|xMedian|np", method) &
!call.from == "QC.Label" & show.1n2.sigma){
warning(paste(method, " method: does not support drawing sigma lines"),call. = F)
sigma_df <- data.frame(
yintercept = integer(),
y = integer(),
x = integer(),
label = integer(),
hjust = integer(),
alpha = integer()
)
}else{
sigma_df <- data.frame(
yintercept = integer(),
y = integer(),
x = integer(),
label = integer(),
hjust = integer(),
alpha = integer()
)
}
#print(sigma_df)
#sigma_df <- ifelse(call.from == "QC.Label", na.omit(sigma_df), sigma_df)
#limits_df$sigma <- sigma
#limits_df$center <- center[[1]]
#limits_df$center <- center
if(!any(qcline %in% c(2,6))){
#print(limit.txt.label)
if(!any(is.na(limit.txt.label))){
limits_df_txt_lbl <- limits_df
limits_df_txt_lbl$x <- -Inf
limits_df_txt_lbl$label <- limits_txt_lbl
limits_df_txt_lbl$hjust <- 0
}else{
limits_df_txt_lbl <- NULL
}
if(call.from == "QC.Label"){
limits_df <- rbind(limits_df_txt_lbl, limits_df)
# print(limits_txt_lbl)
# print(limits_df)
}else{
if(show.1n2.sigma){
limits_df <- rbind(limits_df_txt_lbl, limits_df, sigma_df)
}else{
limits_df <- rbind(limits_df_txt_lbl, limits_df)
}
}
#print(limits_df)
#print(limits_df)
}
#print(data$group)
limits_df
}
)
#' @export
#' @title Produce QC Charts with ggplot Framework.
#' @description Produce QC charts with ggplot framework. Support for faceting and
#' layering of multiple QC chart lines on a single plot. Charts supported (see method argument for call):
#' \itemize{
#' \item \bold{Individuals Charts}: mR, XmR,
#' \item \bold{Attribute Charts}: c, np, p, u,
#' \item \bold{Studentized Charts}: xBar.rBar, xBar.rMedian, xBar.sBar, xMedian.rBar,
#' xMedian.rMedian,
#' \item \bold{Dispersion Charts}: rBar, rMedian, sBar.
#' }
#'To label chart lines see \link[ggQC]{stat_QC_labels}
#' @inheritParams ggplot2::stat_identity
#' @param na.rm a logical value indicating whether NA values should be
#' stripped before the computation proceeds.
#' @param n number, for
#' \itemize{
#' \item \bold{Studentized Charts}, used for custom or hypothetical subgroup size.
#' \item \bold{np Charts}, used to specify a fixed area of opportunity.
#' }
#' @param method string, calling the following methods:
#' \itemize{
#' \item \bold{Individuals Charts}: mR, XmR,
#' \item \bold{Attribute Charts}: c, np, p, u,
#' \item \bold{Studentized Charts}: xBar.rBar, xBar.rMedian, xBar.sBar, xMedian.rBar,
#' xMedian.rMedian
#' \item \bold{Dispersion Charts}: rBar, rMedian, sBar.
#' }
#' @param color.qc_limits color, used to colorize the plot's upper and lower control limits.
#' @param physical.limits vector, specify lower physical boundary and upper physical boundary
#' @param color.qc_center color, used to colorize the plot's center line.
#' @param color.point color, used to colorize points in studentized plots. You will need geom_point() for C, P, U, NP, and XmR charts.
#' @param color.line color, used to colorize lines connecting points in studentized plots. You will need geom_line() for C, P, U, NP, and XmR charts.
#' @param auto.label boolean setting, if T labels graph with control limits.
#' @param label.digits integer, number of decimal places to display.
#' @param show.1n2.sigma boolean setting, if T labels graph 1 and 2 sigma lines. Line color is set by color.qc_limits
#' @param limit.txt.label vector, provides option for naming or not showing the limit text labels (e.g., UCL, LCL)
#' \itemize{
#' \item \bold{limit.txt.label = c("LCL", "UCL")}: default
#' \item \bold{limit.txt.label = c("Low", "High")}: changes the label text to low and high
#' \item \bold{limit.txt.label = NA}: does not show label text.
#' }
#' @return ggplot control charts.
#' @examples
#'# Load Libraries ----------------------------------------------------------
#' require(ggQC)
#' 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(5556)
#' 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)
#'
#'#############################
#'# Example 1: XmR Chart #
#'#############################
#'
#'
#' EX1.1 <- ggplot(Both_Processes, aes(x=Process_run_id, y = metric_value)) +
#' geom_point() + geom_line() + stat_QC(method="XmR") +
#' stat_QC_labels(method="XmR", digits = 2) +
#' facet_grid(.~processID, scales = "free_x")
#' #EX1.1
#'
#' EX1.2 <- ggplot(Both_Processes, aes(x=Process_run_id, y = metric_value)) +
#' stat_mR() + ylab("Moving Range") +
#' stat_QC_labels(method="mR", digits = 2) +
#' facet_grid(.~processID, scales = "free_x")
#' #EX1.2
#'
#'#############################
#'# Example 2: XbarR Chart #
#'#############################
# XbarR Plot and rBar Plot ------------------------------------------------
#'
#' EX2.1 <- ggplot(Both_Processes, aes(x = subgroup_sample,
#' y = metric_value,
#' group = processID)) +
#' stat_summary(fun.y = "mean", color = "blue", geom = c("point")) +
#' stat_summary(fun.y = "mean", color = "blue", geom = c("line")) +
#' stat_QC(method = "xBar.rBar") + facet_grid(.~processID, scales = "free_x")
#' #EX2.1
#'
#' EX2.2 <- ggplot(Both_Processes, aes(x = subgroup_sample,
#' y = metric_value,
#' group = processID)) +
#' stat_summary(fun.y = "QCrange", color = "blue", geom = "point") +
#' stat_summary(fun.y = "QCrange", color = "blue", geom = "line") +
#' stat_QC(method = "rBar") +
#' ylab("Range") +
#' facet_grid(.~processID, scales = "free_x")
#' #EX2.2
#'
#'#############################
#'# Example 3: p Chart #
#'#############################
#'# p chart Setup -----------------------------------------------------------
#' set.seed(5556)
#' bin_data <- data.frame(
#' trial=1:30,
#' Num_Incomplete_Items = rpois(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
#'
#'# Plot p chart ------------------------------------------------------------
#' EX3.1 <- ggplot(data = bin_data, aes(x=trial,
#' y=Proportion_Incomplete,
#' n=Num_Items_in_Set)) +
#' geom_point() + geom_line() +
#' stat_QC(method = "p")
#' #EX3.1
#'
#'#############################
#'# Example 4: u Chart #
#'#############################
#'# u chart Setup -----------------------------------------------------------
#' set.seed(5555)
#' bin_data <- data.frame(
#' trial=1:30,
#' Num_of_Blemishes = rpois(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
#'
#'# Plot u chart ------------------------------------------------------------
#' EX4.1 <- ggplot(data = bin_data, aes(x=trial,
#' y=Blemish_Rate,
#' n=Num_Items_Inspected)) +
#' geom_point() + geom_line() +
#' stat_QC(method = "u")
#' #EX4.1
#'
#'#############################
#'# Example 5: np Chart #
#'#############################
#'# np chart Setup -----------------------------------------------------------
#' set.seed(5555)
#' bin_data <- data.frame(
#' trial=1:30,
#' NumNonConforming = rbinom(30, 30, prob = .50))
#' Units_Tested_Per_Batch <- 60
#'
#'# Plot np chart ------------------------------------------------------------
#' EX5.1 <- ggplot(data = bin_data, aes(trial, NumNonConforming)) +
#' geom_point() +
#' stat_QC(method = "np", n = Units_Tested_Per_Batch)
#' #EX5.1
#'
#'#############################
#'# Example 6: c Chart #
#'#############################
#'# c chart Setup -----------------------------------------------------------
#' set.seed(5555)
#' Process1 <- data.frame(Process_run_id = 1:30,
#' Counts=rpois(n = 30, lambda = 25),
#' Group = "A")
#' Process2 <- data.frame(Process_run_id = 1:30,
#' Counts = rpois(n = 30, lambda = 5),
#' Group = "B")
#'
#' all_processes <- rbind(Process1, Process2)
#'# Plot C Chart ------------------------------------------------------------
#'
#' EX6.1 <- ggplot(all_processes, aes(x=Process_run_id, y = Counts)) +
#' geom_point() + geom_line() +
#' stat_QC(method = "c", auto.label = TRUE, label.digits = 2) +
#' scale_x_continuous(expand = expand_scale(mult = .25)) +
#' facet_grid(.~Group)
#'# EX6.1
stat_QC <- function(mapping = NULL,
data = NULL,
geom = "hline",
#yintercept = NULL,
position = "identity",
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE,
n=NULL,
#digits=1,
method="xBar.rBar",
color.qc_limits = "red",
color.qc_center = "blue",
color.point="black",
color.line="black",
physical.limits=c(NA,NA),
auto.label = FALSE,
limit.txt.label=c("LCL","UCL"),
label.digits = 1,
show.1n2.sigma = FALSE,
#color.point="black",
#color.line="black",
...) {
if(method %in% c("p", "u")){
Limits <- ggplot2::layer(
stat = STAT_QC, data = data, mapping = mapping,
geom = "step",
position = position, show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm, n=n, digits=1, method=method,
color= color.qc_limits, direction="vh",
draw.line = "limit",
...)
)
Centerline <- ggplot2::layer(
stat = STAT_QC, data = data, mapping = mapping,
geom = "step",
position = position, show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm, n=n,
digits=1, method=method,
color=color.qc_center, draw.line = "center",
...)
)
#n_p_points <- ggplot2::layer(geom = "point", color=color.point)
#n_p_line <- ggplot2::layer(geom="line", color=color.line)
}else if(method %in% c("mR")){
MR <- stat_mR(mapping = mapping,
data = data,
geom = "point",
#yintercept = NULL,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
na.rm = FALSE,
color.mr_point=color.point,
color.mr_line=color.line,
color.qc_limits = color.qc_limits,
color.qc_center = color.qc_center,
...)
}else{
if(method %in% c("xBar.rBar", "xBar.rMedian", "xBar.sBar", "xMedian.rBar", "xMedian.rMedian")){
meanOmedian <- ifelse(grepl("xBar|XmR|c|np", method), "mean", "median")
Summary_Stat_Point <-
ggplot2::stat_summary(fun.y = meanOmedian, color = color.point, geom = c("point"))
Summary_Stat_Line <-
ggplot2::stat_summary(fun.y = meanOmedian, color = color.line, geom = c("line"))
}
if(method %in% c("np", "c","XmR")){
#do nothing
}
if(method %in% c("rBar", "rMedian", "sBar")){
rangeOsd <- ifelse(grepl("rBar|rMedian", method), "QCrange", "sd")
Summary_Stat_Point <-
ggplot2::stat_summary(fun.y = rangeOsd, color = color.point, geom = c("point"))
Summary_Stat_Line <-
ggplot2::stat_summary(fun.y = rangeOsd, color = color.line, geom = c("line"))
}
Limits <- ggplot2::layer(
stat = STAT_QC, data = data, mapping = mapping,
geom = geom, position = position, show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm, n=n, digits=1, method=method,
color= color.qc_limits, draw.line = "limit",
physical.limits=physical.limits,
show.1n2.sigma=show.1n2.sigma,
#limit.txt.label=limit.txt.label,
...)
)
Centerline <- ggplot2::layer(
stat = STAT_QC, data = data, mapping = mapping,
geom = geom, position = position, show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm, n=n,
digits=1, method=method,
color=color.qc_center, draw.line = "center",
physical.limits=c(NA,NA),
limit.txt.label=limit.txt.label,
...)
)
}
#return(list(Limits, Centerline))
if(!method %in% c("p", "u")){
QC_Labels <- stat_QC_labels(mapping = mapping,
data = data,
geom = "label",
#yintercept = NULL,
position = position,
na.rm = na.rm,
show.legend = show.legend,
inherit.aes = TRUE,
n=n, digits=label.digits,
method=method,
color.qc_limits = color.qc_limits,
color.qc_center = color.qc_center,
text.size=3,
physical.limits=physical.limits,
limit.txt.label = limit.txt.label,
...)
}
if(auto.label){
if(method %in% c("p", "u")){
return(list(Limits, Centerline))
}
if(method == "mR"){
return(list(MR, QC_Labels))
}
if(method %in% c("np", "c","XmR")){
return(list(Limits, Centerline, QC_Labels))
}
return(list(Limits, Centerline, Summary_Stat_Line,
Summary_Stat_Point, QC_Labels))
}else{
if(method == "mR"){
return(list(MR))
}
if(method %in% c("p", "u", "np", "c","XmR")){
return(list(Limits, Centerline))
}
return(list(Limits, Centerline,
Summary_Stat_Line, Summary_Stat_Point
))
}
}
#' @export
#' @title Write QC Line Labels to ggplot QC Charts.
#' @description Write QC line labels to ggplot QC Charts. Useful if you want
#' to see the value of the center line and QC limits. see method argument
#' for methods supported.
#' @inheritParams ggplot2::stat_identity
#' @param na.rm a logical value indicating whether NA values should be
#' stripped before the computation proceeds.
#' @param n number, for
#' \itemize{
#' \item \bold{Studentized Charts}, used for custom or hypothetical subgroup size.
#' \item \bold{np Charts}, used to specify a fixed area of opportunity.
#' }
#' @param digits integer, indicating the number of decimal places
#' @param text.size number, size of the text label
#' @param method string, calling the following methods:
#' \itemize{
#' \item \bold{Individuals Charts}: mR, XmR,
#' \item \bold{Attribute Charts}: c, np, p, u,
#' \item \bold{Studentized Charts}: xBar.rBar, xBar.rMedian, xBar.sBar, xMedian.rBar,
#' xMedian.rMedian
#' \item \bold{Dispersion Charts}: rBar, rMedian, sBar.
#' }
#' @param color.qc_limits color, used to colorize the plot's upper and lower mR control limits.
#' @param physical.limits vector, specify lower physical boundary and upper physical boundary
#' @param color.qc_center color, used to colorize the plot's center line.
#' @param limit.txt.label vector, provides option for naming or not showing the limit text labels (e.g., UCL, LCL)
#' \itemize{
#' \item \bold{limit.txt.label = c("LCL", "UCL")}: default
#' \item \bold{limit.txt.label = c("Low", "High")}: changes the label text to low and high
#' \item \bold{limit.txt.label = NA}: does not show label text.
#' }
#' @return data need to produce the mR plot in ggplot.
#' @examples
#' #########################
#' # Example 1: mR Chart #
#' #########################
#'
#'# Load Libraries ----------------------------------------------------------
#' require(ggQC)
#' 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(5556)
#' 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)
#'
#'# Facet Plot - Both Processes ---------------------------------------------
#' EX1.1 <- ggplot(Both_Processes, aes(x=Process_run_id, y = metric_value)) +
#' geom_point() + geom_line() + stat_QC(method="XmR") +
#' stat_QC_labels(method="XmR", digits = 2) +
#' facet_grid(.~processID, scales = "free_x")
#' #EX1.1
#'
#' EX1.2 <- ggplot(Both_Processes, aes(x=Process_run_id, y = metric_value)) +
#' stat_mR() + ylab("Moving Range") +
#' stat_QC_labels(method="mR", digits = 2) +
#' facet_grid(.~processID, scales = "free_x")
#' #EX1.2
#'
#'#############################
#'# Example 2: XbarR Chart #
#'#############################
#'# Facet Plot - Studentized Process ----------------------------------------
#'
#' EX2.1 <- ggplot(Both_Processes, aes(x=subgroup_sample,
#' y = metric_value,
#' group = processID)) +
#' geom_point(alpha=.2) +
#' stat_summary(fun.y = "mean", color="blue", geom=c("point")) +
#' stat_summary(fun.y = "mean", color="blue", geom=c("line")) +
#' stat_QC() + facet_grid(.~processID, scales = "free_x") +
#' stat_QC_labels(text.size =3, label.size=.1)
#' #EX2.1
#'
#' EX2.2 <- ggplot(Both_Processes, aes(x=subgroup_sample,
#' y = metric_value,
#' group = processID)) +
#' stat_summary(fun.y = "QCrange", color="blue", geom = "point") +
#' stat_summary(fun.y = "QCrange", color="blue", geom = "line") +
#' stat_QC(method="rBar") +
#' stat_QC_labels(digits=2, method="rBar") +
#' ylab("Range") +
#' facet_grid(.~processID, scales = "free_x")
#' #EX2.2
stat_QC_labels <- function(mapping = NULL,
data = NULL,
geom = "label",
#yintercept = NULL,
position = "identity",
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE,
n=NULL,digits=1, method="xBar.rBar",
color.qc_limits = "red",
color.qc_center = "black",
text.size=3,
physical.limits=c(NA,NA), limit.txt.label=c("LCL","UCL"),
...) {
Center <- ggplot2::layer(
stat = STAT_QC,
data = data,
mapping = mapping,
geom = geom,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm,
vjust=.5, size=text.size,n=n,
digits=digits,method=method,
color="black",
draw.line = "center",
call.from = "QC.Label",
limit.txt.label=limit.txt.label, ...)
)
Limits <- ggplot2::layer(
stat = STAT_QC,
data = data,
mapping = mapping,
geom = geom,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm,
vjust=.5, size=text.size,n=n,
digits=digits,method=method,
color="black",
draw.line = "limit",
physical.limits=physical.limits,
call.from = "QC.Label",
limit.txt.label=limit.txt.label,
...)
)
if(method %in% c("p", "u")){
return(warning("Stat_QC_Label does not support u or p charts"))
}else{return(list(Center, Limits))}
}
ggmtech/ggQC documentation built on Dec. 20, 2021, 10:43 a.m.
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Defines functions stat_QC_labels stat_QC
Documented in stat_QC stat_QC_labels
##############################
# Copyright 2018 Kenith Grey #
##############################
# Copyright Notice --------------------------------------------------------
# This file is part of ggQC.
#
# ggQC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ggQC is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ggQC. If not, see <http://www.gnu.org/licenses/>.
STAT_QC <- ggplot2::ggproto("STAT_QC", ggplot2::Stat,
compute_group = function(data, scales, n=NULL, digits=1,
method=NULL, draw.line=draw.line,
physical.limits=c(NA,NA),
call.from = "QC.NULL",
show.1n2.sigma = FALSE, limit.txt.label=c("LCL","UCL")){
temp <- aggregate(data=data, y~x, mean)
# print("---NEW---")
# print(call.from)
# print(limit.txt.label)
LCL <- limit.txt.label[1]
UCL <- limit.txt.label[2]
if(method %in% c("mR", "XmR", "c")){
qcline <- if(draw.line == "center") c(2) else c(1,3)
dflines <- ylines_indv(temp$y, n=n, method = method)
sigma <- dflines$sigma
center <- dflines[2]
limits_txt_lbl <- limit.txt.label
#print(limits_txt_lbl)
if(!all(is.na(physical.limits)) & method %in% c("XmR", "c")){
if(!any(is.na(physical.limits))){
if(physical.limits[1] > dflines[1] & physical.limits[2] < dflines[3]){
dflines[1] <- physical.limits[1]
dflines[3] <- physical.limits[2]
limits_txt_lbl <- c("LB", "UB")
}else if(physical.limits[1] > dflines[1]){
dflines[1] <- physical.limits[1]
limits_txt_lbl <- c("LB", UCL)
}else if(physical.limits[2] < dflines[3]){
dflines[3] <- physical.limits[2]
limits_txt_lbl <- c(LCL, "UB")
}
}else if(!is.na(physical.limits[1])){
if(physical.limits[1] > dflines[1]){
dflines[1] <- physical.limits[1]
limits_txt_lbl <- c("LB", UCL)
}
}else if(!is.na(physical.limits[2])){
if(physical.limits[2] < dflines[3]){
dflines[3] <- physical.limits[2]
limits_txt_lbl <- c(LCL, "UB")
}
}
}
#print(dflines)
limits_df <- data.frame(yintercept = t(dflines)[qcline])
#print(limits_df)
}else if(method == "np"){
if (is.null(n)){
warning("**np-chart Error**\n For np chart, specify value for n in stat_QC", call. = FALSE)
return(NULL)
}else if(!binCheck_pChart(temp$y/n, n)){
warning("**np-chart Error**\n Items of Opportunity 'n' < Item Nonconforming\n check value of 'n' in stat_QC.", call. = FALSE)
return(NULL)
}
#print(temp$y/n)
qcline <- if(draw.line == "center") c(2) else c(1,3)
dflines <- ylines_indv(temp$y, n=n, method = method)
sigma <- dflines$sigma
center <- dflines[2]
limits_txt_lbl <- limit.txt.label
limits_df <- data.frame(yintercept = t(dflines)[qcline])
}else if(method == "p"){
if (is.null(data$n)){
warning("**p-chart Error**\n supply vector of opportunity, 'n', in ggplot aes", call. = FALSE)
return(NULL)
}
qcline <- if(draw.line == "center") c(2,4) else c(1,3,4)
pdata <- QC_Lines(data = data$y, n=data$n, method = "p")
pdata$x <- data$x
pchart_data <- reshape2::melt(pdata[,qcline], id.vars=c("x"))
colnames(pchart_data) <- c("x", "group", "y")
return(pchart_data)
}else if(method == "u"){
if (is.null(data$n)){
warning("**u-chart Error**\n supply vector of opportunity, 'n', in ggplot aes", call. = FALSE)
return(NULL)
}
qcline <- if(draw.line == "center") c(2,4) else c(1,3,4)
udata <- QC_Lines(data = data$y, n=data$n, method = "u")
udata$x <- data$x
uchart_data <- reshape2::melt(udata[,qcline], id.vars=c("x"))
colnames(uchart_data) <- c("x", "group", "y")
return(uchart_data)
}else{
#Studentized charts Xbar-Rbar etc.
qcline <- if(draw.line == "center") c(6) else c(5,7)
dflines <- QC_Lines(data = data, value = "y", grouping = "x", n=n, method = method)
sigma <- dflines$sigma
center <- dflines[6]
limits_txt_lbl <- limit.txt.label
if(!all(is.na(physical.limits)) & !method %in% c("rBar", "rMedian", "sBar")){
if(!any(is.na(physical.limits))){
if(physical.limits[1] > dflines[5] & physical.limits[2] < dflines[7]){
dflines[5] <- physical.limits[1]
dflines[7] <- physical.limits[2]
limits_txt_lbl <- c("LB", "UB")
}else if(physical.limits[1] > dflines[5]){
dflines[5] <- physical.limits[1]
limits_txt_lbl <- c("LB", UCL)
}else if(physical.limits[2] < dflines[7]){
dflines[7] <- physical.limits[2]
limits_txt_lbl <- c(LCL, "UB")
}
}else if(!is.na(physical.limits[1])){
if(physical.limits[1] > dflines[5]){
dflines[5] <- physical.limits[1]
limits_txt_lbl <- c("LB", UCL)
}
}else if(!is.na(physical.limits[2])){
if(physical.limits[2] < dflines[7]){
dflines[7] <- physical.limits[2]
limits_txt_lbl <- c(LCL, "UB")
}
}
}
limits_df <- data.frame(yintercept = t(dflines[,qcline]))
}
#print(qcline)
limits_df$y <- limits_df$yintercept
limits_df$x <- Inf
limits_df$label <- paste0(round(limits_df$yintercept,digits))
limits_df$hjust <- 1.1
limits_df$alpha <- 1
# Sigma Limits ------------------------------------------------------------
if(grepl(pattern = "c|XmR|xBar|xMedian|np", method) &
!call.from == "QC.Label" & show.1n2.sigma){
sigma_df <- data.frame(
yintercept = c(center[[1]] + 1*sigma, center[[1]] - 1*sigma,
center[[1]] + 2*sigma, center[[1]] - 2*sigma),
y = c(center[[1]] + 1*sigma, center[[1]] - 1*sigma,
center[[1]] + 2*sigma, center[[1]] - 2*sigma),
x = rep(NA, 4),
label = rep(NA, 4),
hjust = rep(0, 4),
alpha = c(.20,.20,.50,.50)
)
}else if(!grepl(pattern = "c|XmR|xBar|xMedian|np", method) &
!call.from == "QC.Label" & show.1n2.sigma){
warning(paste(method, " method: does not support drawing sigma lines"),call. = F)
sigma_df <- data.frame(
yintercept = integer(),
y = integer(),
x = integer(),
label = integer(),
hjust = integer(),
alpha = integer()
)
}else{
sigma_df <- data.frame(
yintercept = integer(),
y = integer(),
x = integer(),
label = integer(),
hjust = integer(),
alpha = integer()
)
}
#print(sigma_df)
#sigma_df <- ifelse(call.from == "QC.Label", na.omit(sigma_df), sigma_df)
#limits_df$sigma <- sigma
#limits_df$center <- center[[1]]
#limits_df$center <- center
if(!any(qcline %in% c(2,6))){
#print(limit.txt.label)
if(!any(is.na(limit.txt.label))){
limits_df_txt_lbl <- limits_df
limits_df_txt_lbl$x <- -Inf
limits_df_txt_lbl$label <- limits_txt_lbl
limits_df_txt_lbl$hjust <- 0
}else{
limits_df_txt_lbl <- NULL
}
if(call.from == "QC.Label"){
limits_df <- rbind(limits_df_txt_lbl, limits_df)
# print(limits_txt_lbl)
# print(limits_df)
}else{
if(show.1n2.sigma){
limits_df <- rbind(limits_df_txt_lbl, limits_df, sigma_df)
}else{
limits_df <- rbind(limits_df_txt_lbl, limits_df)
}
}
#print(limits_df)
#print(limits_df)
}
#print(data$group)
limits_df
}
)
#' @export
#' @title Produce QC Charts with ggplot Framework.
#' @description Produce QC charts with ggplot framework. Support for faceting and
#' layering of multiple QC chart lines on a single plot. Charts supported (see method argument for call):
#' \itemize{
#' \item \bold{Individuals Charts}: mR, XmR,
#' \item \bold{Attribute Charts}: c, np, p, u,
#' \item \bold{Studentized Charts}: xBar.rBar, xBar.rMedian, xBar.sBar, xMedian.rBar,
#' xMedian.rMedian,
#' \item \bold{Dispersion Charts}: rBar, rMedian, sBar.
#' }
#'To label chart lines see \link[ggQC]{stat_QC_labels}
#' @inheritParams ggplot2::stat_identity
#' @param na.rm a logical value indicating whether NA values should be
#' stripped before the computation proceeds.
#' @param n number, for
#' \itemize{
#' \item \bold{Studentized Charts}, used for custom or hypothetical subgroup size.
#' \item \bold{np Charts}, used to specify a fixed area of opportunity.
#' }
#' @param method string, calling the following methods:
#' \itemize{
#' \item \bold{Individuals Charts}: mR, XmR,
#' \item \bold{Attribute Charts}: c, np, p, u,
#' \item \bold{Studentized Charts}: xBar.rBar, xBar.rMedian, xBar.sBar, xMedian.rBar,
#' xMedian.rMedian
#' \item \bold{Dispersion Charts}: rBar, rMedian, sBar.
#' }
#' @param color.qc_limits color, used to colorize the plot's upper and lower control limits.
#' @param physical.limits vector, specify lower physical boundary and upper physical boundary
#' @param color.qc_center color, used to colorize the plot's center line.
#' @param color.point color, used to colorize points in studentized plots. You will need geom_point() for C, P, U, NP, and XmR charts.
#' @param color.line color, used to colorize lines connecting points in studentized plots. You will need geom_line() for C, P, U, NP, and XmR charts.
#' @param auto.label boolean setting, if T labels graph with control limits.
#' @param label.digits integer, number of decimal places to display.
#' @param show.1n2.sigma boolean setting, if T labels graph 1 and 2 sigma lines. Line color is set by color.qc_limits
#' @param limit.txt.label vector, provides option for naming or not showing the limit text labels (e.g., UCL, LCL)
#' \itemize{
#' \item \bold{limit.txt.label = c("LCL", "UCL")}: default
#' \item \bold{limit.txt.label = c("Low", "High")}: changes the label text to low and high
#' \item \bold{limit.txt.label = NA}: does not show label text.
#' }
#' @return ggplot control charts.
#' @examples
#'# Load Libraries ----------------------------------------------------------
#' require(ggQC)
#' 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(5556)
#' 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)
#'
#'#############################
#'# Example 1: XmR Chart #
#'#############################
#'
#'
#' EX1.1 <- ggplot(Both_Processes, aes(x=Process_run_id, y = metric_value)) +
#' geom_point() + geom_line() + stat_QC(method="XmR") +
#' stat_QC_labels(method="XmR", digits = 2) +
#' facet_grid(.~processID, scales = "free_x")
#' #EX1.1
#'
#' EX1.2 <- ggplot(Both_Processes, aes(x=Process_run_id, y = metric_value)) +
#' stat_mR() + ylab("Moving Range") +
#' stat_QC_labels(method="mR", digits = 2) +
#' facet_grid(.~processID, scales = "free_x")
#' #EX1.2
#'
#'#############################
#'# Example 2: XbarR Chart #
#'#############################
# XbarR Plot and rBar Plot ------------------------------------------------
#'
#' EX2.1 <- ggplot(Both_Processes, aes(x = subgroup_sample,
#' y = metric_value,
#' group = processID)) +
#' stat_summary(fun.y = "mean", color = "blue", geom = c("point")) +
#' stat_summary(fun.y = "mean", color = "blue", geom = c("line")) +
#' stat_QC(method = "xBar.rBar") + facet_grid(.~processID, scales = "free_x")
#' #EX2.1
#'
#' EX2.2 <- ggplot(Both_Processes, aes(x = subgroup_sample,
#' y = metric_value,
#' group = processID)) +
#' stat_summary(fun.y = "QCrange", color = "blue", geom = "point") +
#' stat_summary(fun.y = "QCrange", color = "blue", geom = "line") +
#' stat_QC(method = "rBar") +
#' ylab("Range") +
#' facet_grid(.~processID, scales = "free_x")
#' #EX2.2
#'
#'#############################
#'# Example 3: p Chart #
#'#############################
#'# p chart Setup -----------------------------------------------------------
#' set.seed(5556)
#' bin_data <- data.frame(
#' trial=1:30,
#' Num_Incomplete_Items = rpois(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$Num_Items_in_Set
#'
#'# Plot p chart ------------------------------------------------------------
#' EX3.1 <- ggplot(data = bin_data, aes(x=trial,
#' y=Proportion_Incomplete,
#' n=Num_Items_in_Set)) +
#' geom_point() + geom_line() +
#' stat_QC(method = "p")
#' #EX3.1
#'
#'#############################
#'# Example 4: u Chart #
#'#############################
#'# u chart Setup -----------------------------------------------------------
#' set.seed(5555)
#' bin_data <- data.frame(
#' trial=1:30,
#' Num_of_Blemishes = rpois(30, lambda = 30),
#' Num_Items_Inspected = runif(n = 30, min = 50, max = 100)
#' )
#' bin_data$Blemish_Rate <- bin_data$Num_of_Blemishes/bin_data$Num_Items_Inspected
#'
#'# Plot u chart ------------------------------------------------------------
#' EX4.1 <- ggplot(data = bin_data, aes(x=trial,
#' y=Blemish_Rate,
#' n=Num_Items_Inspected)) +
#' geom_point() + geom_line() +
#' stat_QC(method = "u")
#' #EX4.1
#'
#'#############################
#'# Example 5: np Chart #
#'#############################
#'# np chart Setup -----------------------------------------------------------
#' set.seed(5555)
#' bin_data <- data.frame(
#' trial=1:30,
#' NumNonConforming = rbinom(30, 30, prob = .50))
#' Units_Tested_Per_Batch <- 60
#'
#'# Plot np chart ------------------------------------------------------------
#' EX5.1 <- ggplot(data = bin_data, aes(trial, NumNonConforming)) +
#' geom_point() +
#' stat_QC(method = "np", n = Units_Tested_Per_Batch)
#' #EX5.1
#'
#'#############################
#'# Example 6: c Chart #
#'#############################
#'# c chart Setup -----------------------------------------------------------
#' set.seed(5555)
#' Process1 <- data.frame(Process_run_id = 1:30,
#' Counts=rpois(n = 30, lambda = 25),
#' Group = "A")
#' Process2 <- data.frame(Process_run_id = 1:30,
#' Counts = rpois(n = 30, lambda = 5),
#' Group = "B")
#'
#' all_processes <- rbind(Process1, Process2)
#'# Plot C Chart ------------------------------------------------------------
#'
#' EX6.1 <- ggplot(all_processes, aes(x=Process_run_id, y = Counts)) +
#' geom_point() + geom_line() +
#' stat_QC(method = "c", auto.label = TRUE, label.digits = 2) +
#' scale_x_continuous(expand = expand_scale(mult = .25)) +
#' facet_grid(.~Group)
#'# EX6.1
stat_QC <- function(mapping = NULL,
data = NULL,
geom = "hline",
#yintercept = NULL,
position = "identity",
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE,
n=NULL,
#digits=1,
method="xBar.rBar",
color.qc_limits = "red",
color.qc_center = "blue",
color.point="black",
color.line="black",
physical.limits=c(NA,NA),
auto.label = FALSE,
limit.txt.label=c("LCL","UCL"),
label.digits = 1,
show.1n2.sigma = FALSE,
#color.point="black",
#color.line="black",
...) {
if(method %in% c("p", "u")){
Limits <- ggplot2::layer(
stat = STAT_QC, data = data, mapping = mapping,
geom = "step",
position = position, show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm, n=n, digits=1, method=method,
color= color.qc_limits, direction="vh",
draw.line = "limit",
...)
)
Centerline <- ggplot2::layer(
stat = STAT_QC, data = data, mapping = mapping,
geom = "step",
position = position, show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm, n=n,
digits=1, method=method,
color=color.qc_center, draw.line = "center",
...)
)
#n_p_points <- ggplot2::layer(geom = "point", color=color.point)
#n_p_line <- ggplot2::layer(geom="line", color=color.line)
}else if(method %in% c("mR")){
MR <- stat_mR(mapping = mapping,
data = data,
geom = "point",
#yintercept = NULL,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
na.rm = FALSE,
color.mr_point=color.point,
color.mr_line=color.line,
color.qc_limits = color.qc_limits,
color.qc_center = color.qc_center,
...)
}else{
if(method %in% c("xBar.rBar", "xBar.rMedian", "xBar.sBar", "xMedian.rBar", "xMedian.rMedian")){
meanOmedian <- ifelse(grepl("xBar|XmR|c|np", method), "mean", "median")
Summary_Stat_Point <-
ggplot2::stat_summary(fun.y = meanOmedian, color = color.point, geom = c("point"))
Summary_Stat_Line <-
ggplot2::stat_summary(fun.y = meanOmedian, color = color.line, geom = c("line"))
}
if(method %in% c("np", "c","XmR")){
#do nothing
}
if(method %in% c("rBar", "rMedian", "sBar")){
rangeOsd <- ifelse(grepl("rBar|rMedian", method), "QCrange", "sd")
Summary_Stat_Point <-
ggplot2::stat_summary(fun.y = rangeOsd, color = color.point, geom = c("point"))
Summary_Stat_Line <-
ggplot2::stat_summary(fun.y = rangeOsd, color = color.line, geom = c("line"))
}
Limits <- ggplot2::layer(
stat = STAT_QC, data = data, mapping = mapping,
geom = geom, position = position, show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm, n=n, digits=1, method=method,
color= color.qc_limits, draw.line = "limit",
physical.limits=physical.limits,
show.1n2.sigma=show.1n2.sigma,
#limit.txt.label=limit.txt.label,
...)
)
Centerline <- ggplot2::layer(
stat = STAT_QC, data = data, mapping = mapping,
geom = geom, position = position, show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm, n=n,
digits=1, method=method,
color=color.qc_center, draw.line = "center",
physical.limits=c(NA,NA),
limit.txt.label=limit.txt.label,
...)
)
}
#return(list(Limits, Centerline))
if(!method %in% c("p", "u")){
QC_Labels <- stat_QC_labels(mapping = mapping,
data = data,
geom = "label",
#yintercept = NULL,
position = position,
na.rm = na.rm,
show.legend = show.legend,
inherit.aes = TRUE,
n=n, digits=label.digits,
method=method,
color.qc_limits = color.qc_limits,
color.qc_center = color.qc_center,
text.size=3,
physical.limits=physical.limits,
limit.txt.label = limit.txt.label,
...)
}
if(auto.label){
if(method %in% c("p", "u")){
return(list(Limits, Centerline))
}
if(method == "mR"){
return(list(MR, QC_Labels))
}
if(method %in% c("np", "c","XmR")){
return(list(Limits, Centerline, QC_Labels))
}
return(list(Limits, Centerline, Summary_Stat_Line,
Summary_Stat_Point, QC_Labels))
}else{
if(method == "mR"){
return(list(MR))
}
if(method %in% c("p", "u", "np", "c","XmR")){
return(list(Limits, Centerline))
}
return(list(Limits, Centerline,
Summary_Stat_Line, Summary_Stat_Point
))
}
}
#' @export
#' @title Write QC Line Labels to ggplot QC Charts.
#' @description Write QC line labels to ggplot QC Charts. Useful if you want
#' to see the value of the center line and QC limits. see method argument
#' for methods supported.
#' @inheritParams ggplot2::stat_identity
#' @param na.rm a logical value indicating whether NA values should be
#' stripped before the computation proceeds.
#' @param n number, for
#' \itemize{
#' \item \bold{Studentized Charts}, used for custom or hypothetical subgroup size.
#' \item \bold{np Charts}, used to specify a fixed area of opportunity.
#' }
#' @param digits integer, indicating the number of decimal places
#' @param text.size number, size of the text label
#' @param method string, calling the following methods:
#' \itemize{
#' \item \bold{Individuals Charts}: mR, XmR,
#' \item \bold{Attribute Charts}: c, np, p, u,
#' \item \bold{Studentized Charts}: xBar.rBar, xBar.rMedian, xBar.sBar, xMedian.rBar,
#' xMedian.rMedian
#' \item \bold{Dispersion Charts}: rBar, rMedian, sBar.
#' }
#' @param color.qc_limits color, used to colorize the plot's upper and lower mR control limits.
#' @param physical.limits vector, specify lower physical boundary and upper physical boundary
#' @param color.qc_center color, used to colorize the plot's center line.
#' @param limit.txt.label vector, provides option for naming or not showing the limit text labels (e.g., UCL, LCL)
#' \itemize{
#' \item \bold{limit.txt.label = c("LCL", "UCL")}: default
#' \item \bold{limit.txt.label = c("Low", "High")}: changes the label text to low and high
#' \item \bold{limit.txt.label = NA}: does not show label text.
#' }
#' @return data need to produce the mR plot in ggplot.
#' @examples
#' #########################
#' # Example 1: mR Chart #
#' #########################
#'
#'# Load Libraries ----------------------------------------------------------
#' require(ggQC)
#' 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(5556)
#' 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)
#'
#'# Facet Plot - Both Processes ---------------------------------------------
#' EX1.1 <- ggplot(Both_Processes, aes(x=Process_run_id, y = metric_value)) +
#' geom_point() + geom_line() + stat_QC(method="XmR") +
#' stat_QC_labels(method="XmR", digits = 2) +
#' facet_grid(.~processID, scales = "free_x")
#' #EX1.1
#'
#' EX1.2 <- ggplot(Both_Processes, aes(x=Process_run_id, y = metric_value)) +
#' stat_mR() + ylab("Moving Range") +
#' stat_QC_labels(method="mR", digits = 2) +
#' facet_grid(.~processID, scales = "free_x")
#' #EX1.2
#'
#'#############################
#'# Example 2: XbarR Chart #
#'#############################
#'# Facet Plot - Studentized Process ----------------------------------------
#'
#' EX2.1 <- ggplot(Both_Processes, aes(x=subgroup_sample,
#' y = metric_value,
#' group = processID)) +
#' geom_point(alpha=.2) +
#' stat_summary(fun.y = "mean", color="blue", geom=c("point")) +
#' stat_summary(fun.y = "mean", color="blue", geom=c("line")) +
#' stat_QC() + facet_grid(.~processID, scales = "free_x") +
#' stat_QC_labels(text.size =3, label.size=.1)
#' #EX2.1
#'
#' EX2.2 <- ggplot(Both_Processes, aes(x=subgroup_sample,
#' y = metric_value,
#' group = processID)) +
#' stat_summary(fun.y = "QCrange", color="blue", geom = "point") +
#' stat_summary(fun.y = "QCrange", color="blue", geom = "line") +
#' stat_QC(method="rBar") +
#' stat_QC_labels(digits=2, method="rBar") +
#' ylab("Range") +
#' facet_grid(.~processID, scales = "free_x")
#' #EX2.2
stat_QC_labels <- function(mapping = NULL,
data = NULL,
geom = "label",
#yintercept = NULL,
position = "identity",
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE,
n=NULL,digits=1, method="xBar.rBar",
color.qc_limits = "red",
color.qc_center = "black",
text.size=3,
physical.limits=c(NA,NA), limit.txt.label=c("LCL","UCL"),
...) {
Center <- ggplot2::layer(
stat = STAT_QC,
data = data,
mapping = mapping,
geom = geom,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm,
vjust=.5, size=text.size,n=n,
digits=digits,method=method,
color="black",
draw.line = "center",
call.from = "QC.Label",
limit.txt.label=limit.txt.label, ...)
)
Limits <- ggplot2::layer(
stat = STAT_QC,
data = data,
mapping = mapping,
geom = geom,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm,
vjust=.5, size=text.size,n=n,
digits=digits,method=method,
color="black",
draw.line = "limit",
physical.limits=physical.limits,
call.from = "QC.Label",
limit.txt.label=limit.txt.label,
...)
)
if(method %in% c("p", "u")){
return(warning("Stat_QC_Label does not support u or p charts"))
}else{return(list(Center, Limits))}
}
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