stat_QC {ggQC}

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):

• 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.

To label chart lines see stat_QC_labels

Usage

stat_QC(mapping = NULL, data = NULL, geom = "hline",
  position = "identity", na.rm = FALSE, show.legend = NA,
  inherit.aes = TRUE, n = NULL, 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, ...)

Arguments

mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data.

geom

The geometric object to use display the data

position

Position adjustment, either as a string, or the result of a call to a position adjustment function.

na.rm

a logical value indicating whether NA values should be stripped before the computation proceeds.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

n

number, for

• Studentized Charts, used for custom or hypothetical subgroup size.

• np Charts, used to specify a fixed area of opportunity.

method

string, calling the following methods:

• 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.

color.qc_limits

color, used to colorize the plot's upper and lower control limits.

color.qc_center

color, used to colorize the plot's center line.

color.point

color, used to colorize points in studentized plots. You will need geom_point() for C, P, U, NP, and XmR charts.

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.

physical.limits

vector, specify lower physical boundary and upper physical boundary

auto.label

boolean setting, if T labels graph with control limits.

limit.txt.label

vector, provides option for naming or not showing the limit text labels (e.g., UCL, LCL)

• limit.txt.label = c("LCL", "UCL"): default

• limit.txt.label = c("Low", "High"): changes the label text to low and high

• limit.txt.label = NA: does not show label text.

label.digits

integer, number of decimal places to display.

show.1n2.sigma

boolean setting, if T labels graph 1 and 2 sigma lines. Line color is set by color.qc_limits

...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.

Value

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  #
#############################

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