Is there another control chart library for R that I should be trying instead?
I looked around and could not find an R package for Shewhart control charts. I'll post the function I wrote for my own needs, but note that I am not a statistician, nor am I particularly experienced with R -- so use at your own risk ;-)
It only does X-bar, R, and geometric moving average. I used the "traditional" calculations (i.e. use constants based on sample group size, and range, to calculate UCL/LCL) instead of a more rigorous approach.
Below is the function and an example of how to use it (if anyone has suggestions for improvement, I'd love to hear them).
HTH,
Joe
8<-------------------------
controlChart <- function(xdata, ssize, CLnumGroups = 0)
{
if (!is.vector(xdata))
stop("Data must be a vector") if (!is.numeric(xdata))
stop("Data vector must be numeric") xdatalen <- length(xdata)
xdataresid <- xdatalen %% ssize
newxdatalen <- xdatalen - xdataresid
if (xdataresid != 0)
xdata <- xdata[1:newxdatalen] if (ssize < 1 | ssize > 10)
{
stop("Sample size must be in the range of 1 to 10")
}
else if (ssize > 1 & ssize < 11)
{
# Xbar/R factors
ng <- c(2:10)
D3 <- c(0,0,0,0,0,0.08,0.14,0.18,0.22)
D4 <- c(3.27,2.57,2.28,2.11,2.00,1.92,1.86,1.82,1.78)
A2 <- c(1.88,1.02,0.73,0.58,0.48,0.42,0.37,0.34,0.31)
d2 <- c(1.13,1.69,2.06,2.33,2.53,2.70,2.85,2.97,3.08)
v <- data.frame(ng, D3, D4, A2, d2) # put into sample groups
m <- matrix(xdata, ncol = ssize, byrow = TRUE) # number of groups
numgroups <- nrow(m) # Adjust number of points used to calculate control limits.
if (numgroups < CLnumGroups | CLnumGroups == 0)
CLnumGroups = numgroups # range for each group
r <- apply(m, 1, range)
r <- r[2,] - r[1,] # Rbar
rb <- mean(r[1:CLnumGroups])
rb <- rep(rb, numgroups) # R UCL and LCL
rucl <- v$D4[match(ssize,v$ng) + 1] * rb
rlcl <- v$D3[match(ssize,v$ng) + 1] * rb # Xbar
xb <- apply(m, 1, mean) # Xbarbar
xbb <- mean(xb[1:numgroups])
xbb <- rep(xbb, numgroups) # X UCL and LCL
xucl <- xbb + (v$A2[match(ssize,v$ng) + 1] * rb)
xlcl <- xbb - (v$A2[match(ssize,v$ng) + 1] * rb)
}
else #sample size is 1
{
m <- xdata # number of groups
numgroups <- length(m) # Adjust number of points used to calculate control limits.
if (numgroups < CLnumGroups | CLnumGroups == 0)
CLnumGroups = numgroups # set range for each group to 0
r <- rep(0, numgroups) # Rbar
rb <- rep(0, numgroups) # R UCL and LCL
rucl <- rep(0, numgroups)
rlcl <- rep(0, numgroups) # Xbar is a copy of the individual data points
xb <- m # Xbarbar is mean over the data
xbb <- mean(xb[1:CLnumGroups])
xbb <- rep(xbb, numgroups) # standard deviation over the data
xsd <- sd(xb[1:CLnumGroups]) # X UCL and LCL
xucl <- xbb + 3 * xsd
xlcl <- xbb - 3 * xsd
} # geometric moving average
if (numgroups > 1)
{
rg <- 0.25
gma = c(xb[1])
for(i in 2:numgroups)
gma[i] = (rg * xb[i]) + ((1 - rg) * gma[i - 1])
}
else
{
gma <- rep(0, numgroups)
}# create a single dataframe with all the plot data
controlChartSummary <- data.frame(1:numgroups, xb, xbb, xucl, xlcl, r, rb, rucl, rlcl, gma)
return(controlChartSummary) }
# sample data xdata <- 12 + 4 * rnorm(90) # sample size ssize <- 3 # get control chart data cc <- controlChart(xdata, ssize) # get number of sample groups numgroups <- length(cc$xb)
# X Bar chart plotxrange <- range(c(1:numgroups)) plotyrange <- range(cc$xb, cc$xucl, cc$xlcl) plotyrange[1] <- plotyrange[1] - (plotyrange[2] - plotyrange[1]) * 0.1 plotyrange[2] <- plotyrange[2] + (plotyrange[2] - plotyrange[1]) * 0.1
plot(c(1:numgroups), xlim = plotxrange, ylim = plotyrange, cc$xb, type = "b", lty = 1)
lines(c(1:numgroups), cc$xbb, lty = 1)
lines(c(1:numgroups), cc$xucl, lty = 1)
lines(c(1:numgroups), cc$xlcl, lty = 1)
# R chart plotxrange <- range(c(1:numgroups)) plotyrange <- range(cc$r, cc$rucl, cc$rlcl) plotyrange[1] <- plotyrange[1] - (plotyrange[2] - plotyrange[1]) * 0.1 plotyrange[2] <- plotyrange[2] + (plotyrange[2] - plotyrange[1]) * 0.1
plot(c(1:numgroups), xlim = plotxrange, ylim = plotyrange, cc$r, type = "b", lty = 1)
lines(c(1:numgroups), cc$rb, lty = 1)
lines(c(1:numgroups), cc$rucl, lty = 1)
lines(c(1:numgroups), cc$rlcl, lty = 1)
# Geometric Moving Average chart plotxrange <- range(c(1:numgroups)) plotyrange <- range(cc$gma) plotyrange[1] <- plotyrange[1] - (plotyrange[2] - plotyrange[1]) * 0.1 plotyrange[2] <- plotyrange[2] + (plotyrange[2] - plotyrange[1]) * 0.1
plot(c(1:numgroups), xlim = plotxrange, ylim = plotyrange, cc$gma, type = "b", lty = 1)
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