stat_QC_labels: Write QC Line Labels to ggplot QC Charts.

In ggQC: Quality Control Charts for 'ggplot'

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.

Usage

stat_QC_labels(mapping = NULL, data = NULL, geom = "label",
  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"), ...)

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.
digits	integer, indicating the number of decimal places
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 mR control limits.
color.qc_center	color, used to colorize the plot's center line.
text.size	number, size of the text label
physical.limits	vector, specify lower physical boundary and upper physical boundary
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.
...	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

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

Example output

Loading required package: ggplot2

ggQC documentation built on May 1, 2019, 10:26 p.m.