Forum Discussion

tglah's avatar
tglah
Frequent Visitor
9 years ago

R Control Chart

HelloPBI Community,   I havent found too much info on R Control Charts in this forum and am wondering if there is anyone out there that could copy&paste one?     Cheers
TedPattison's avatar
TedPattison
Microsoft Employee
9 years ago

Here's is an example of creating a histogram with a density distribution line.

 

hist(dataset$Age, 
     main = "Customer Count by Age",
     ylab="Customer Count", 
     xlab="Customer Age",
     xlim = c(18, 100),
     border="black",
     breaks=20,
     col=c("lightyellow", "lightblue"), 
     las=1,
     probability = TRUE
)

lines(density(dataset$Age),lty="dotdash", lwd=4, col="red")

Here's an example of creating a barblot

 

barplot(dataset$'Sales Revenue',        
        names.arg = dataset$'Age Group', 
        main = "Sales Revenue by Customer Age Group",
        col = c("red","yellow","orange","blue", "green")
)

minValue <- 0
maxValue <- max(as.vector(dataset$'Sales Revenue'))
yTicks <- seq(from=minValue, to = maxValue, length.out = 10)
yTicks <- pretty(yTicks)
yTickLabels <- paste("$",format(yTicks/1000, , big.mark=","), "K",sep="")
axis(2, at=yTicks, labels = yTickLabels, lty = 1, las=1, cex.axis=0.7 )

Of course, these are simple examples using the built-in R graphics functionality. You can also use a richer graphics package such as lattice or ggplot2 to create some really detailed charts and graphs.

 

Is this what you are looking for?

  • tglah's avatar
    tglah
    Frequent Visitor
    9 years ago

    Thanks Ted, this is along the same lines, but I am interested more in an "xbar" chart and also looking for a simple R script to create a Pareto chart for my customers.

     

    Thank you for the scripts for the Histogram and Bar Plot, I will also be adding this to my arsenal.

    • MawashiKid's avatar
      MawashiKid
      Resolver II
      9 years ago

      RE:  also looking for a simple R script to create a Pareto chart

      In order to use any chart -that is part of a R Visualization package library - it's logical to first make sure it's installed.

      install.packages('<yourPackage>')


      Now considering the huge amount of R package visualization libraries, there may be a few ways to reproduce what you want. Note that I don't consider myself an R guru at this stage, still here's a couple of basic samples.

      qcc library...



      library(qcc)
    defect <- c(80, 27, 66, 94, 33)
    names(defect) <- c("price code", "schedule date", "supplier code", "contact num.", "part num.")
    pareto.chart(defect, ylab = "Error frequency", col=heat.colors(length(defect)))

      ggplot2 library...

      library(ggplot2)

counts  <- c(80, 27, 66, 94, 33)
defects <- c("price code", "schedule date", "supplier code", "contact num.", "part num.")

dat <- data.frame(
  count = counts,
  defect = defects,
  stringsAsFactors=FALSE
)

dat <- dat[order(dat$count, decreasing=TRUE), ]
dat$defect <- factor(dat$defect, levels=dat$defect)
dat$**bleep** <- cumsum(dat$count)
dat

ggplot(dat, aes(x=defect)) +
  geom_bar(aes(y=count), fill="blue", stat="identity") +
  geom_point(aes(y=**bleep**)) +
  geom_path(aes(y=**bleep**, group=1))


      I haven't played much with x-Bar chart though I'll check if I can find something... Anyway hope this helps