stat_qc_violations: Inspect QC Violations

In ggmtech/ggQC: Quality Control Charts for 'ggplot'

Description

ggplot stat function that renders a faceted plot of QC violations based on the following 4 rules:

• Violation Same Side: 8 or more consecutive, same-side points

• Violation 1 Sigma: 4 or more consecutive, same-side points exceeding 1 sigma

• Violation 2 Sigma: 2 or more consecutive, same-side points exceeding 2 sigma

• Violation 3 Sigma: any points exceeding 3 sigma

Usage

stat_qc_violations(mapping = NULL, data = NULL, geom = "point",
  position = "identity", show.legend = NA, inherit.aes = TRUE,
  na.rm = FALSE, method = "xBar.rBar", geom_points = TRUE,
  geom_line = TRUE, point.size = 1.5, point.color = "black",
  violation_point.color = "red", line.color = NULL,
  rule.color = "darkgreen", show.facets = c(1:4), ...)

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.
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().
na.rm	a logical value indicating whether NA values should be stripped before the computation proceeds.
method	string, calling the following methods: Individuals Charts: XmR, Studentized Charts: xBar.rBar, xBar.rMedian, xBar.sBar, xMedian.rBar, xMedian.rMedian
geom_points	boolean, draw points
geom_line	boolean, draw line
point.size	number, size of points on chart
point.color	string, color of points on charts (e.g., "black")
violation_point.color	string, color of violation points on charts (e.g., "red")
line.color	string, color of lines connecting points
rule.color	string, color or horizontal rules indicating distribution center and sigma levels
show.facets	vector, selects violation facet 1 through 4. eg., c(1:4), c(1,4)
...	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

faceted plot.

Examples

#####################################
#  Example 1: XmR Check Violations  #
#####################################
# Load Libraries ----------------------------------------------------------
 require(ggQC)
 require(ggplot2)

# Setup Data --------------------------------------------------------------

    set.seed(5555)
    QC_XmR <- data.frame(
    data = c(c(-1, 2.3, 2.4, 2.5),                        #Outlier Data
          sample(c(rnorm(60),5,-5), 62, replace = FALSE), #Normal Data
          c(1,-.3, -2.4,-2.6,-2.5,-2.7, .3)),             #Outlier Data
    Run_Order = 1:73                                      #Run Order
    )


# Render QC Violation Plot ------------------------------------------------------

   EX1 <- ggplot(QC_XmR, aes(x = Run_Order, y = data)) +
     stat_qc_violations(method = "XmR")   #Makes facet graph with violations
   #EX1
#######################################
#  Example 2: Xbar Check Violations   #
#######################################

# Setup Some Data ------------------------------------------------------------
     QC_xBar.rBar <- do.call(rbind, lapply(1:3, function(X){
       set.seed(5555+X)                                   #Loop over 3 seeds
       data.frame(
         sub_group = rep(1:42),                           #Define Subgroups
         sub_class = letters[X],
         c(
          c(runif(n = 5, min = 2.0,3.2)),                 #Outlier Data
          sample(c(rnorm(30),5,-4), 32, replace = FALSE), #Normal Data
          c(runif(n = 5, min = -3.2, max = -2.0))         #Outlier Data
         )
      )
     }
   )
)

colnames(QC_xBar.rBar) <- c("sub_group","sub_class", "value")

# Render QC Violation Plot --------------------------------------------------
    EX2 <- ggplot(QC_xBar.rBar, aes(x = sub_group, y = value)) +
      stat_qc_violations(method = "xBar.rBar")
      #stat_qc_violations(method="xBar.rMedian")
      #stat_qc_violations(method="xBar.sBar")
      #stat_qc_violations(method="xMedian.rBar")
      #stat_qc_violations(method="xMedian.rMedian")
   #EX2

#######################################
#  Example 3: Selected Facets         #
#######################################

# Render QC Violation Plot --------------------------------------------------
    EX3 <- ggplot(QC_xBar.rBar, aes(x = sub_group, y = value)) +
      stat_qc_violations(method = "xBar.rBar", show.facets = c(4))

   #EX3


#######################################################
# Complete User Control - Bypass stat_qc_violation   #
#######################################################
#### The code below has two options if you are looking for complete
#### control over the look and feel of the graph. Use option 1 or option
#### 2 as appropriate. If you want something quick and easy use examples above.

##### Option 1: Setup for XmR Type Data
 # QC_XmR: Defined in Example 1
   QC_Vs <- QC_Violations(data  = QC_XmR$data, method = "XmR")
   QC_Stats <- QC_Lines(data  = QC_XmR$data, method = "XmR")
   MEAN <- QC_Stats$mean
   SIGMA <- QC_Stats$sigma

##### Option 2: Setup for xBar.rBar Type Data
 # QC_xBar.rBar: Defined in Example 2
   QC_Vs <- QC_Violations(data  = QC_xBar.rBar,
                          formula = value~sub_group,
                          method = "xBar.rBar")
   QC_Stats <- QC_Lines(data  = QC_xBar.rBar,
                        formula = value~sub_group,
                        method = "xBar.rBar")
   MEAN <- QC_Stats$xBar_Bar
   SIGMA <- QC_Stats$sigma

##### Setup second table for horizontal rules
 FacetNames <- c("Violation Same Side",
                 "Violation 1 Sigma",
                 "Violation 2 Sigma",
                 "Violation 3 Sigma")

 QC_Vs$Violation_Result <- ordered(QC_Vs$Violation_Result,
                                     levels=FacetNames)

 QC_Stats_df <- data.frame(
   Violation_Result = factor(x = FacetNames, levels = FacetNames),
   SigmaPlus = MEAN+SIGMA*0:3,
   MEAN = MEAN,
   SigmaMinus = MEAN-SIGMA*0:3
 )

##### Make the Plot
 ggplot(QC_Vs, aes(x=Index, y=data, color=Violation, group=1)) +
   geom_point() + geom_line() +
   facet_grid(.~Violation_Result) +
   geom_hline(data = QC_Stats_df, aes(yintercept = c(SigmaPlus))) +
   geom_hline(data = QC_Stats_df, aes(yintercept = c(SigmaMinus))) +
   geom_hline(data = QC_Stats_df, aes(yintercept = c(MEAN)))

ggmtech/ggQC documentation built on Dec. 20, 2021, 10:43 a.m.