stat_QC_cap_vlabels: Vertical Label Capability Stat
In kenithgrey/rQC: Quality Control Charts for 'ggplot'
Description
Usage
Arguments
Value
Examples
View source: R/04_stat_qc_capability.R
Description
Draws Vertical Lables on Vertical Capability lines
Usage
1
2
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
vertical lines for histogram and density plots.
Examples
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# 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
kenithgrey/rQC documentation built on May 20, 2019, 9:04 a.m.
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Description Usage Arguments Value Examples
View source: R/04_stat_qc_capability.R
Description
Draws Vertical Lables on Vertical Capability lines
Usage
1 2 |
Arguments
LSL |
numeric, Customer's lower specification limit |
USL |
numeric, Customer's Upper specification limit |
method |
string, calling the following methods:
|
show |
vector, indicating which lines to draw ie., c("LCL", "LSL", "X", "USL", "UCL")
|
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
inherit.aes |
If |
... |
Other arguments passed on to |
Value
vertical lines for histogram and density plots.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | # 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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