Statistical analysis construction: The mason R package
Written on August 4, 2016
Most analyses follow a similar pattern to how construction/engineering projects
are developed: design -> add specifications -> construction -> (optional) add to
the design and specs and continue construction -> cleaning, scrubbing, and
polishing. I created the mason package to try to emulate this process and make
it easier to do analyses in a consistent and ‘tidy’ format.
Basic command flow
The general command flow for using mason is:
Start the design of a blueprint for the analysis by specifying which
statistical technique to use in your analysis (design()).
Add settings/options to the blueprint for the methods of the statistics
Add the variables you want to run the statistics on (add_variables()).
These variables include the $y$ variables (outcomes), the $x$ variables
(predictors), covariates, and interaction variables.
Using the blueprint, construct the ‘mason project’ (stats analysis) so that
the results are generated (construct()).
Sometimes analyses are too big for one first pass, from blueprint to
construction, and needs to add more to the blueprint. Use add_variables() or
add_settings() after the construct() to add to the existing
When you are ready, make the ‘mason project’ cleaned up by scrubbing it down
and polishing it up (scrub() and polish_*() commands). The results are now
ready for further presentation in a figure or table!
Let’s go over an example analysis. We’ll use glm for a simple linear
regression. Let’s use the built-in swiss dataset. A quick peek at it shows:
Ok, let’s say we want to several models. We are interested in Fertility and
Infant.Mortality as outcomes and Education and Agriculture as potential
predictors. We also want to control for Catholic. This setup means we have
four potential models to analyze. With mason this is relatively easy. Analyses
in mason are essentially separated into a blueprint phase and a construction
phase. Since any structure or building always needs a blueprint, let’s get that
So far, all we’ve done is created a blueprint of the analysis, but it doesn’t
contain much. Let’s add some settings to the blueprint. mason was designed to
make use of the %>% pipes from the package magrittr (also found in dplyr),
so let’s load up magrittr!
You’ll notice that each time, the only thing that is printed to the console is
the dataset. That’s because we haven’t constructed the analysis yet! We are
still in the blueprint phase, so nothing new has been added! Since we have two
outcomes and two predictors, we have a total of four models to analysis.
Normally we would need to run each of the models separately. However, if we simply
list the outcomes and the predictors in mason, it will compute a model for each
combination! Next, we can construct the analysis using construct().
Cool! This is the unadjusted model, without any covariates. We said we wanted to
adjust for Catholic. But let’s say we want to keep the unadjusted analysis
too. Since we have ‘finished’ the analysis by cleaning it up, we can still add
to the blueprint.
We now have two models in the results. We’re happy with them, so let’s clean it
up using the scrub() function.
All scrub() does is remove any extra specs in the attributes and sets the
results as the main dataset. We can put it all as a single pipe chain:
There are also additional polish_* type commands that are more or less simply
wrappers around commands that you may do on the results dataset, like filtering
or renaming. The list of polish commands can be found in ?mason::polish.
Note: This is a slightly modified version of the introduction vignette for
from version 0.2.5). I also created this package to make
my analyses easier. So most of the statistics that have been implemented were chosen
because that’s what I use. If you would like a particular statistical method
included, please fill out an Issue
and I will try to implement it!