stat_QC_labels {ggQC} | R Documentation |
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.
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"), ...)
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
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?
|
inherit.aes |
If |
n |
number, for
|
digits |
integer, indicating the number of decimal places |
method |
string, calling the following methods:
|
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)
|
... |
Other arguments passed on to |
data need to produce the mR plot in ggplot.
#########################
# 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