Auto QC Capability Stat Function
Draws lines, lables and summary statistics. Works best with histogram and density plots.
stat_QC_Capability(LSL, USL, method = "xBar.rBar",
show.lines = c("LSL", "USL"), line.direction = "v",
show.line.labels = TRUE, line.label.size = 3,
show.cap.summary = c("Cp", "Cpk", "Pp", "Ppk"), cap.summary.size = 4,
px = Inf, py = -Inf, digits = 3)LSL |
numeric, Customer's lower specification limit |
USL |
numeric, Customer's Upper specification limit |
method |
string, calling the following methods:
|
show.lines |
vector, indicating which lines to draw ie., c("LCL", "LSL", "X", "USL", "UCL")
|
line.direction |
string "v" or "h", specifies which direction to draw lines. |
show.line.labels |
boolean, if TRUE then draw. |
line.label.size |
numeric, control the size of the line labels. |
show.cap.summary |
vector, indicating which lines to draw ie., c("TOL","DNS", "Cp", "Cpk", "Pp", "Ppk", "LCL", "X", "UCL", "Sig"). The order given in the vector is the order presented in the graph.
|
cap.summary.size |
numeric, control the size/scale of the summary text box. |
px |
numeric, x position for summary text box. Use Inf to force label to x-limit. |
py |
numeric, y position for summary text box. Use Inf to force label to y-limits. May also need vjust parameter. |
digits |
integer, how many digits to report. |
capability layer for histogram and density plots.
for more control over lines, labels, and capability data see the following functions:
# 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"
# Normal Histogram XmR --------------------------------------------------------
EX1.1 <- ggplot(df[df$ProcessID == 1,], aes(x=Value, QC.Subgroup=Subgroup)) +
geom_histogram(binwidth = 1, color="purple") +
geom_hline(yintercept=0, color="grey") +
stat_QC_Capability(LSL=5, USL=15, show.cap.summary = "all", method="XmR") +
scale_x_continuous(expand = expand_scale(mult = c(0.15,.8))) +
ylim(0,45)
#Ex1.1
# Facet Histogram XmR -----------------------------------------------------
EX1.2 <- ggplot(df[order(df$Process_run_id),],
aes(x=Value, QC.Subgroup=Subgroup, color=ProcessID)) +
geom_histogram(binwidth = 1) +
geom_hline(yintercept=0, color="grey") +
stat_QC_Capability(LSL=5, USL=15, show.cap.summary = "all", method="XmR") +
scale_x_continuous(expand = ggplot2::expand_scale(mult = c(0.15,.8))) +
facet_grid(.~ProcessID, scales = "free_x") + ylim(0,45)
#EX1.2
# Normal Density XmR --------------------------------------------------------
EX1.3 <- ggplot(df[df$ProcessID == 1,], aes(x=Value, QC.Subgroup=Subgroup)) +
geom_density(bw = .4, fill="purple", trim=TRUE) +
geom_hline(yintercept=0, color="grey") +
stat_QC_Capability(LSL=5, USL=15, show.cap.summary = "all", method="XmR") +
scale_x_continuous(expand = expand_scale(mult = c(0.15,.8))) + ylim(0,.5)
#EX1.3
########################################
## Example 2: xBar.rBar or xBar.sBar ##
########################################
# Single Histogram xBar.rBar ----------------------------------------------
EX2.1 <- ggplot(df[df$ProcessID==1,], aes(x=Value, QC.Subgroup=Subgroup)) +
geom_histogram(binwidth = 1) +
stat_QC_Capability(LSL=5, USL=15, method="xBar.rBar") +
scale_x_continuous(expand = ggplot2::expand_scale(mult = c(0.15,.8))) #+
#EX2.1Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.