stat_QC_cap_hlines: horizontal Line Capability Stat

Description

Draws horizontal Capability Lines

Usage

stat_QC_cap_hlines(LSL, USL, method = "xBar.rBar", show = c("LSL",
  "USL"), mapping = NULL, data = NULL, inherit.aes = TRUE, ...)

Arguments

LSL

numeric, Customer's lower specification limit

USL

numeric, Customer's Upper specification limit

method

string, calling the following methods:

Individuals Charts: XmR,
Studentized Charts: xBar.rBar, xBar.rMedian, xBar.sBar, xMedian.rBar, xMedian.rMedian

show

vector, indicating which lines to draw ie., c("LCL", "LSL", "X", "USL", "UCL")

LCL: Lower Control Limit
LSL: Lower Specification Limit
X: Process Center
USL: Upper Specification Limit
UCL: Upper Control Limit

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.

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().

...

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

horizontal lines for histogram and density plots.

Examples

Run this code

# NOT RUN {
# Load Libraries ----------------------------------------------------------
require(ggQC)
require(ggplot2)


# Setup Data --------------------------------------------------------------
set.seed(5555)
Process1 <- data.frame(ProcessID = as.factor(rep(1,100)),
                       Value = rnorm(100,10,1),
                       Subgroup = rep(1:20, each=5),
                       Process_run_id = 1:100)
set.seed(5556)
Process2 <- data.frame(ProcessID = as.factor(rep(2,100)),
                       Value = rnorm(100,20, 1),
                       Subgroup = rep(1:10, each=10),
                       Process_run_id = 101:200)

df <- rbind(Process1, Process2)

######################
## Example 1 XmR    ##
######################
##You may need to use the r-studio Zoom for these plots or make the size of the
##stat_QC_cap_summary smaller with size = some number"

method <- "XmR"

# Normal Histogram XmR --------------------------------------------------------

EX1.1 <-  ggplot(df[df$ProcessID == 1,], aes(x=Value, QC.Subgroup=Subgroup)) +
  geom_histogram(binwidth = 1, color="purple") +
  geom_hline(yintercept=0, color="grey") +
  stat_QC_cap_vlines(LSL = 5, USL = 15, show=c("X", "LSL", "USL"), method=method) +
  stat_QC_cap_vlabels(LSL = 5, USL = 15, show=c("X", "LSL", "USL"), method=method) +
  stat_QC_cap_summary(LSL = 5, USL = 15, method=method,
                      #show="ALL",
                      #show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk",
                      #       "LCL", "X", "UCL", "Sig"),
                      #show=c("Sig","TOL", "DNS"),
                      show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk"),
                      color="black", digits=2, size=4) +
  scale_x_continuous(expand =  expand_scale(mult = c(0.15,.8))) +
  ylim(0,45)
#Ex1.1

# Facet Histogram XmR -----------------------------------------------------

EX1.2 <- ggplot(df[order(df$Process_run_id),],
                aes(x=Value, QC.Subgroup=Subgroup, color=ProcessID)) +
  geom_histogram(binwidth = 1) +
  geom_hline(yintercept=0, color="grey") +
  stat_QC_cap_vlines(LSL = 5, USL = 15, method=method) +
  stat_QC_cap_vlabels(LSL = 5, USL = 15, method=method) +
  stat_QC_cap_summary(LSL = 5, USL = 15, method=method,
                      #show="ALL",
                      #show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk",
                      #       "LCL", "X", "UCL", "Sig"),#show=c("Sig","TOL", "DNS"),
                      show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk"),
                      color="black", digits=4, size=4) +
  scale_x_continuous(expand =  ggplot2::expand_scale(mult = c(0.15,.8))) +
  facet_grid(.~ProcessID) + ylim(0,45)
#EX1.2

# Facet Density Plot XmR -------------------------------------------------

EX1.3 <- ggplot(df[df$ProcessID == 1,], aes(x=Value, QC.Subgroup=Subgroup)) +
  geom_density(bw = .4, fill="purple", trim=TRUE) +
  geom_hline(yintercept=0, color="grey") +
  stat_QC_cap_vlines(LSL = 5, USL = 15, show=c("X", "LSL", "USL"), method=method) +
  stat_QC_cap_vlabels(LSL = 5, USL = 15, show=c("X", "LSL", "USL"), method=method) +
  stat_QC_cap_summary(LSL = 5, USL = 15, method=method,
                      #show="ALL",
                      #show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk",
                      #       "LCL", "X", "UCL", "Sig"),
                      #show=c("Sig","TOL", "DNS"),
                      show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk"),
                      color="black", digits=2, size=4) +

  scale_x_continuous(expand =  expand_scale(mult = c(0.15,.8)))  + ylim(0,.5)
#EX1.3

# Facet Density Plot XmR --------------------------------------------------

EX1.4 <- ggplot(df[order(df$Process_run_id),],
                aes(x=Value, QC.Subgroup=Subgroup, color=ProcessID)) +
  geom_density(bw = .4, fill="grey", trim=TRUE ) +
  stat_QC_cap_vlines(LSL = 5, USL = 15, method=method) +
  stat_QC_cap_vlabels(LSL = 5, USL = 15, method=method) +
  stat_QC_cap_summary(LSL = 5, USL = 15, method=method, #py=.3,
                      #show="ALL",
                      #show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk",
                      #       "LCL", "X", "UCL", "Sig"),
                      #show=c("Sig","TOL", "DNS"),
                      show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk"),
                      color="black", digits=4, size=4) +
  scale_x_continuous(expand =  ggplot2::expand_scale(mult = c(0.15,.8))) +
  # geom_hline(yintercept=0, color="black") +
  facet_grid(.~ProcessID) + ylim(0,.5)
#EX1.4


########################################
##  Example 2: xBar.rBar or xBar.sBar ##
########################################

method <- "xBar.rBar" #Alternativly Use "xBar.sBar" if desired


# Single Histogram xBar.rBar ----------------------------------------------

EX2.1 <- ggplot(df[df$ProcessID==1,], aes(x=Value, QC.Subgroup=Subgroup)) +
  geom_histogram(binwidth = 1) +
  stat_QC_cap_vlines(LSL = 5, USL = 15, method=method) +
  stat_QC_cap_vlabels(LSL = 5, USL = 15, method=method) +
  stat_QC_cap_summary(LSL = 5, USL = 15, method=method, #py=.3,
                      #show="ALL",
                      #show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk",
                      #       "LCL", "X", "UCL", "Sig"),
                      #show=c("Sig","TOL", "DNS"),
                      show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk"),
                      color="black", digits=4, size=4) +
  scale_x_continuous(expand =  ggplot2::expand_scale(mult = c(0.15,.8))) #+
#EX2.1


# Faceted Histogram xBar.rBar ---------------------------------------------

EX2.2 <- ggplot(df, aes(x=Value, QC.Subgroup=Subgroup)) +
  geom_histogram(binwidth = 1) +
  stat_QC_cap_vlines(LSL = 5, USL = 15, method=method) +
  stat_QC_cap_vlabels(LSL = 5, USL = 15, method=method) +
  stat_QC_cap_summary(LSL = 5, USL = 15, method=method, #py=.3,
                      #show="ALL",
                      #show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk",
                      #       "LCL", "X", "UCL", "Sig"),
                      #show=c("Sig","TOL", "DNS"),
                      show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk"),
                      color="black", digits=4, size=4) +
  scale_x_continuous(expand =  ggplot2::expand_scale(mult = c(0.15,.8)))+
  facet_grid(.~ProcessID, scales="free_x")
#EX2.2

# Single Density xBar.rBar ----------------------------------------------

EX2.3 <- ggplot(df[df$ProcessID==1,], aes(x=Value, QC.Subgroup=Subgroup)) +
  geom_density(bw = .4, fill="grey", alpha=.4) +
  stat_QC_cap_vlines(LSL = 5, USL = 15, method=method) +
  stat_QC_cap_vlabels(LSL = 5, USL = 15, method=method) +
  stat_QC_cap_summary(LSL = 5, USL = 15, method=method, #py=.3,
                      #show="ALL",
                      #show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk",
                      #       "LCL", "X", "UCL", "Sig"),
                      #show=c("Sig","TOL", "DNS"),
                      show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk"),
                      color="black", digits=4, size=4) +
  scale_x_continuous(expand =  ggplot2::expand_scale(mult = c(0.15,.8))) #+
#EX2.3

# Faceted Density xBar.rBar ---------------------------------------------

EX2.4 <-  ggplot(df, aes(x=Value, QC.Subgroup=Subgroup)) +
  geom_density(bw = .4, fill="grey", alpha=.4) +
  stat_QC_cap_vlines(LSL = 5, USL = 15, method=method) +
  stat_QC_cap_vlabels(LSL = 5, USL = 15, method=method) +
  stat_QC_cap_summary(LSL = 5, USL = 15, method=method, #py=.3,
                      #show="ALL",
                      #show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk",
                      #       "LCL", "X", "UCL", "Sig"),
                      #show=c("Sig","TOL", "DNS"),
                      show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk"),
                      color="black", digits=4, size=4) +
  scale_x_continuous(expand =  ggplot2::expand_scale(mult = c(0.15,.8)))+
  facet_grid(.~ProcessID, scales="free_x")
#EX2.4


###############################
##  Example 3: xBar.rMedian  ##
###############################

## Plots involving medians should give warning: "median based QC methods represent
## at best *potential* process capability"

##These plot work the same as in examples 2.X; below is an example.

method <- "xBar.rMedian"
EX3.1 <- ggplot(df[order(df$Process_run_id),], aes(x=Value, QC.Subgroup=Run)) +
  geom_histogram(binwidth = 1) +
  stat_QC_cap_vlines(LSL = 5, USL = 15, method=method) +
  stat_QC_cap_vlabels(LSL = 5, USL = 15, method=method) +
  stat_QC_cap_summary(LSL = 5, USL = 15, method=method, #py=.3,
                      #show="ALL",
                      #show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk",
                      #       "LCL", "X", "UCL", "Sig"),
                      #show=c("Sig","TOL", "DNS"),
                      show=c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk"),
                      color="black", digits=4, size=4) +
  scale_x_continuous(expand =  ggplot2::expand_scale(mult = c(0.15,.8)))
#EX3.1
# }

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Description

Usage

Arguments

Value

Examples