rquery is already one of the fastest and most teachable (due to deliberate conformity to Codd’s influential work) tools to wrangle data on databases and big data systems. And now rquery is also one of the fastest methods to wrangle data in-memory in R (thanks to data.table, via a thin adaption supplied by rqdatatable).
cdata is a data manipulation package that subsumes many higher order data manipulation operations including pivot/un-pivot, spread/gather, or cast/melt. The record to record transforms are specified by drawing a table that expresses the record structure (called the “control table” and also the link between the key concepts of row-records and block-records).
What can be quickly specified and achieved using these concepts and notations is amazing and quite teachable. These transforms can be run in-memory or in remote database or big-data systems (such as Spark).
The 0.7.0 update adds local versions of the operators in addition to the Spark and database implementations. These methods should now be a bit safer for in-memory complex/annotated types such as dates and times.
If you are working with predictive modeling or machine learning in R this is the R tip that is going to save you the most time and deliver the biggest improvement in your results.
R Tip: Use the vtreat package for data preparation in predictive analytics and machine learning projects.
When attempting predictive modeling with real-world data you quickly run into difficulties beyond what is typically emphasized in machine learning coursework:
Missing, invalid, or out of range values.
Categorical variables with large sets of possible levels.
Novel categorical levels discovered during test, cross-validation, or model application/deployment.
Large numbers of columns to consider as potential modeling variables (both statistically hazardous and time consuming).
Nested model bias poisoning results in non-trivial data processing pipelines.
Any one of these issues can add to project time and decrease the predictive power and reliability of a machine learning project. Many real world projects encounter all of these issues, which are often ignored leading to degraded performance in production.
vtreat systematically and correctly deals with all of the above issues in a documented, automated, parallel, and statistically sound manner.
vtreat can fix or mitigate these domain independent issues much more reliably and much faster than by-hand ad-hoc methods.
This leaves the data scientist or analyst more time to research and apply critical domain dependent (or knowledge based) steps and checks.
If you are attempting high-value predictive modeling in R, you should try out vtreat and consider adding it to your workflow.
In a previous article, we showed the use of partial pooling, or hierarchical/multilevel models, for level coding high-cardinality categorical variables in vtreat. In this article, we will discuss a little more about the how and why of partial pooling in R.
We will use the lme4 package to fit the hierarchical models. The acronym “lme” stands for “linear mixed-effects” models: models that combine so-called “fixed effects” and “random effects” in a single (generalized) linear model. The lme4 documentation uses the random/fixed effects terminology, but we are going to follow Gelman and Hill, and avoid the use of the terms “fixed” and “random” effects.
The varying coefficients [corresponding to the levels of a categorical variable] in a multilevel model are sometimes called random effects, a term that refers to the randomness in the probability model for the group-level coefficients….
The term fixed effects is used in contrast to random effects – but not in a consistent way! … Because of the conflicting definitions and advice, we will avoid the terms “fixed” and “random” entirely, and focus on the description of the model itself…
– Gelman and Hill 2007, Chapter 11.4
We will also restrict ourselves to the case that vtreat considers: partially pooled estimates of conditional group expectations, with no other predictors considered.
One of the services that the R package vtreat provides is level coding (what we sometimes call impact coding): converting the levels of a categorical variable to a meaningful and concise single numeric variable, rather than coding them as indicator variables (AKA "one-hot encoding"). Level coding can be computationally and statistically preferable to one-hot encoding for variables that have an extremely large number of possible levels.
By default, vtreat level codes to the difference between the conditional means and the grand mean (catN variables) when the outcome is numeric, and to the difference between the conditional log-likelihood and global log-likelihood of the target class (catB variables) when the outcome is categorical. These aren’t the only possible level codings. For example, the ranger package can encode categorical variables as ordinals, sorted by the conditional expectations/means. While this is not a completely faithful encoding for all possible models (it is not completely faithful for linear or logistic regression, for example), it is often invertible for tree-based methods, and has the advantage of keeping the original levels distinct, which impact coding may not. That is, two levels with the same conditional expectation would be conflated by vtreat‘s coding. This often isn’t a problem — but sometimes, it may be.
So the data scientist may want to use a level coding different from what vtreat defaults to. In this article, we will demonstrate how to implement custom level encoders in vtreat. We assume you are familiar with the basics of vtreat: the types of derived variables, how to create and apply a treatment plan, etc.