NOTE: This repository is no longer maintained.
Control Charts made easy. ccharter focus on calculating control systems in a series and plotting them as an overlay to it.
-
You can install from github with:
if ( !("devtools" %in% installed.packages()) ) { install.packages("devtools") } devtools::install_github("jbaxx/ccharter")
The ccharter package comes with two main functions:
ccpointsto calculate Control Systems points from a series data framecc2plotto plot the Control Systems points (Control Chart)
Loading the package
library(ccharter)Calculating the Control Systems points
- An example usage of
ccpoints:
# Create a random time series
set.seed(154)
time.series <- data.frame(t.dates = seq.Date(as.Date("2014-02-01"), as.Date("2016-08-01"), "month"),
t.values = c(
seq(0.1, 0.8, by = 0.1) * runif(8) + 3,
seq(0.1, 0.7, by = 0.1) * runif(7) + 4,
seq(0.1, 0.7, by = 0.1) * runif(7) + 5,
seq(0.1, 0.4, by = 0.1) * runif(4) + 4,
seq(0.1, 0.5, by = 0.1) * runif(5) + 4)
)
# Execute function
control.chart.data <- ccpoints(time.series, "t.dates", "t.values")If we inspect the object we may find it's a list consisting in the following elements:
- The input time series along with the calculated mean and sd for each system
- The name of the dates column
- The name of the values column
- The count of systems result
- The summary of missing values removed
- The reference point of last break
- The weeks since last break (if date values were provided)
- The next break expectation, above or below of current system
- The points remaining for a new system break
# Inspecting cchart object content
lapply(control.chart.data, head)
#> $data
#> t.dates t.values data.mean data.ll data.ul
#> 1 2014-02-01 3.071129 3.217276 2.996493 3.438059
#> 2 2014-03-01 3.140564 3.217276 2.996493 3.438059
#> 3 2014-04-01 3.008430 3.217276 2.996493 3.438059
#> 4 2014-05-01 3.243547 3.217276 2.996493 3.438059
#> 5 2014-06-01 3.442773 3.217276 2.996493 3.438059
#> 6 2014-07-01 3.029862 3.217276 2.996493 3.438059
#>
#> $dates.name
#> [1] "t.dates"
#>
#> $values.name
#> [1] "t.values"
#>
#> $systems_count
#> [1] 4
#>
#> $missing_values
#> [1] NA
#>
#> $point_last_break
#> [1] "2015-12-01"
#>
#> $weeks_since_last_break
#> Time difference of 57 weeks
#>
#> $next_break
#> [1] "Next system expected to break negative"
#>
#> $next_break_values
#> Continous points vs mean:
#> 6To extract the data frame, we can access it from the list in the following way:
control.chart.data[["data"]]Plotting the Control Systems points
This is how the example series looks like:
- An example usage of
cc2plot:
# Passing the cchart object to the cc2plot function
cc2plot(control.chart.data)
For detailed functions documentation you can access the help files in R with ?ccpoints and ?cc2plot.
Here you may find a brief guide on how to integrate the package in a KNIME workflow.