• control charts,  ggQC

    ggQC | ggplot Quality Control Charts – New Release

    The ggQC package is a quality control extension for ggplot. Use it to create XmR, XbarR, C and many other highly customizable Control Charts. Additional statistical process control functions include Shewart violation checks as well as capability analysis. If your process is running smoothly, visualize the potential impacted of your next process improvement with a Pareto chart. To learn more, read on! To get started with ggQC, install it from CRAN by running the following code: 1 install.packages("ggQC")install.packages("ggQC") ggQC Control Charts Control charts are a great way to monitor process outputs, drive improvement, and evaluate measurement systems. The types of control chart types supported by ggQC include: Individuals Charts :…

  • ggQC

    Control Charts with ggQC: XmR

    Preparing an XmR plot is common when dealing with processes where a single product/item is made or measured and there is a significant time gap between the next production or observation. XmR plots can also be useful when dealing with outputs from a batch process rather than a continuous one. In this post, we will show how to make quick QC XmR plots with the ggQC package available on cran or github. cran: install.package("ggQC") To get us started, let’s simulate some data on the diameter of a golden egg produced monthly by a golden goose. set.seed(5555) Golden_Egg_df <- data.frame(month=1:12, egg_diameter = rnorm(n = 12, mean = 1.5, sd = 0.2) )…

  • ggQC

    Control Charts with ggQC: XbarR

    XbarR charts are useful when monitoring a continuous process over time and your taking multiple samples in a given period. Some examples might include, the first, middle, and last parts coming off an assembly line, subgroups of molded parts produced several at a time over several cycles, batch uniformity of continuously produced chemical / material. In this post, we will show how to make quick QC XbarR plots with the ggQC package available on cran or github. cran: install.package("ggQC") Generating an Xbar or XbarR plot with ggQC is simple. To get us started, let’s simulate some production line data on candles. The candles are shaped using a mold capable of producing…