Description Usage Arguments Value Examples
Draws Vertical Capability Stats
1 2 |
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 |
vertical lines for histogram and density plots.
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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