• control charts

    Estimating Control Chart Constants with R

    In this post, I will show you how a very basic R code can be used to estimate quality control constants needed to construct X-Individuals, X-Bar, and R-Bar charts. The value of this approach is that it gives you a mechanical sense of where these constants come from and some reinforcement on their application. If you work in a production or quality control environment, chances are you've made or seen a control chart. If you're new to control charting or need a refresher check out Understanding Statistical Process Control, Wheeler et. al. If you want to dive in and start making control charts with R, check out R packages ggQC:…

  • rlang

    Infamous Inf – Part II

    R’s Inf keyword – Have you’ve ever wondered what to do with it? If so, this is the second in series of posts that explore how we can exploit the keyword’s interesting properties to get the answers we need and improve code robustness. If you want to catch up on the first post where we look at Inf and the cut() function, please see Infamous Inf – Part I For those unfamiliar with R’s Inf keyword, it is defined as a positive or negative number divided by zero yielding positive or negative infinity, respectively. c(plus_inf = 1/0, minus_inf = -1/0) # plus_inf minus_inf # Inf -Inf Sounds very theoretical. So…

  • rlang

    Infamous Inf – Part I

    R’s Inf keyword – Have you’ve ever wondered what to do with it? If so, this is the first in series of posts that explore how we can exploit the keyword’s interesting properties to get the answers we need and improve code robustness. For those unfamiliar with R’s Inf keyword, it is defined as a positive or negative number divided by zero yielding positive or negative infinity, respectively. c(plus_inf = 1/0, minus_inf = -1/0) # plus_inf minus_inf # Inf -Inf Sounds very theoretical. So how we can make practical use of infinity in R? In this first post, we’ll be discussing how Inf can make binning data with cut() a…

  • 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…