XMR control charts are useful when determining if there are significant trends in data. XMR charts have two key assumptions: one is that the measurements of value happen over time, and the other is that each measurement of time has exactly one measurement of value.

Take careful thought about what you are trying to measure with XMR. Proportions work great, headcount is okay, costs over time don't work well.


The arguments for xmr() are:

The data required for XMR charts take a specific format, with at least two columns of data - one for the time variable and another for the measurement.

Like so:

Time Measure
2000 54
2001 56
2002 59
2003 65
2004 53
2005 64
2006 65
2007 61
2008 60
2009 51
2010 53
2011 53
2012 80
2013 76
2014 82
2015 77
2016 81
2017 85

If we wanted to use xmr() on this data would be written like this:

xmr_data <- xmr(df = example_data, measure = "Measure")

And if we wanted the bounds to recalculate, we'd use this.

xmr_data <- xmr(df = example_data, measure = "Measure", recalc = T)

Output data looks like this:

The only mandatory arguments are df, because the function needs to operate on a dataframe, and measure because the function needs to be told which column contains the measurements. Everything else has been set to what I believe is a safe and sensible default.

In our shop, we typically run the following rules. Since they are the default, there is no need to specify them directly:

xmr_data <- xmr(example_data,  "Measure", 
                recalc = T,
                interval = 5,
                shortrun = c(3,4),
                longrun = c(5,8))

Feel free to play around with your own definitions of what a shortrun or longrun is.

xmr_data <- xmr(df = example_data, 
                measure = "Measure", 
                recalc = T,
                #change the rule like so:,
                interval = 4,
                shortrun = c(2,3))

The statistical differences between rules are slight, but each user will have different needs and it's useful to be able to tune the function to those needs.

It is important to use a consistent definition of what a long/short run are. It wouldn't be appropriate in one report to use one set of definitions for one dataset, and another set for a different dataset.


The xmr() function is handy for generating chart data as the output can be saved and used in other applications. But what about visualization within R?

xmr_chart() takes the output from xmr() and generates a ggplot graphic. This works well for reporting, but it also works great for quick diagnostics of your data.

The arguments for xmr_chart() are:

There are defaults set for most arguments, so all the user needs to supply are the column names for the Time and Measurement column unless they want some slight modification of the default chart.

          time = "Time", 
          measure = "Measure",
          line_width = 0.75, text_size = 12, point_size = 2.5)

A work-flow that I use is to 'pipe' the output of xmr() directly into xmr_chart():

example_data %>% 
  xmr("Measure", recalc = T) %>% 
  xmr_chart("Time", "Measure")

Tidyverse - dplyr & ggplot2

Simple datasets like those illustrated above are common, but how could we work with large datasets that have multiple factors?

Consider the following data. How would xmr() benefit the user in this case?

Year Variable Measure
2004 A 38
2005 A 78
2006 A 93
2007 A 21
2008 A 65
2009 A 13
2010 A 27
2011 A 39
2012 A 1
2013 A 38
2014 A 87
2015 A 34
2016 A 48
2017 A 60
2004 B 49
2005 B 19
2006 B 83
2007 B 67
2008 B 79
2009 B 11
2010 B 72
2011 B 41
2012 B 82
2013 B 65
2014 B 78
2015 B 55
2016 B 53
2017 B 79

The answer is by leveraging other R packages, namely the tidyverse.

You can install and load the tidyverse with:

#this installs many useful packages

#this just loads the ones we need

With dplyr, we can make use of powerful data-wrangling verbs without writing them into xmrr's functions specifically:

Also loaded with dplyr is a powerful operator to chain functions together, called a pipe %>%.

With ggplot2, we take a modern approach to visualizing data. An up-to-date reference list of functions can be found here

This enables a number of verb-type functions for tidying, wrangling, and plotting data. This is how to use them alongside the xmr() and xmr_chart() functions.

Grouping and Faceting

Take our multiple factor data MFD - here is how to apply the xmr() function to certain groups within that data.

MFD_xmr <- MFD %>% 
  group_split(Variable)  %>% 
  map(xmr, measure = "Measure", recalc = T) %>%

To obtain the following:

And as you may be able to see in the data, the xmr() calculated on Measure BY Variable in one chained function instead of having to manually split the data and run the function multiple times. This is possible with an arbitrary number of factors, and leverages the speed of dplyr verbs.

Similarly, ggplot2 can be leveraged in plotting. Note that since xmr_chart() outputs a ggplot object, we can apply the regular ggplot2 functions to it and return a faceted chart rather than filtering the chart and making two.

MFD_xmr %>% 
  xmr_chart("Year", "Measure", line_width = 0.75, text_size = 12) + 
  facet_wrap(~Variable) + 
  scale_x_discrete(breaks = seq(2004, 2017, 4))