# Introduction

Several months ago, I promised to write an updated version of my old post, “The State of Statistics in Julia”, that would describe how Julia’s support for statistical computing has evolved since December 2012.

I’ve kept putting off writing that post for several reasons, but the most important reason is that all of my attention for the last few months has been focused on what’s wrong with how Julia handles statistical computing. As such, the post I’ve decided to write isn’t a review of what’s already been done in Julia, but a summary of what’s being done right now to improve Julia’s support for statistical computing.

In particular, this post focuses on several big changes to the core data structures that are used in Julia to represent statistical data. These changes should all ship when Julia 0.4 is released.

# What’s Wrong with Statistics in Julia Today?

The primary problem with statistical computing in Julia is that the current tools were all designed to emulate R. Unfortunately, R’s approach to statistical computing isn’t amenable to the kinds of static analysis techniques that Julia uses to produce efficient machine code.

In particular, the following differences between R and Julia have repeatedly created problems for developers:

• In Julia, computations involving scalars are at least as important as computations involving vectors. In particular, iterative computations are first-class citizens in Julia. This implies that statistical libraries must allow developers to write efficient code that iterates over the elements of a vector in pure Julia. Because Julia’s compiler can only produce efficient machine code for computations that are type-stable, the representations of missing values, categorical values and ordinal values in Julia programs must all be type-stable. Whether a value is missing or not, its type must remain the same.
• In Julia, almost all end-users will end up creating their own types. As such, any tools for statistical computing must be generic enough that they can be extended to arbitrary types with little to no effort. In contrast to R, which can heavily optimize its algorithms for a very small number of primitive types, Julia developers must ensure that their libraries are both highly performant and highly abstract.
• Julia, like most mainstream languages, eagerly evaluates the arguments passed to functions. This implies that idioms from R which depend upon non-standard evaluation are not appropriate for Julia, although it is possible to emulate some forms of non-standard evaluation using macros. In addition, Julia doesn’t allow programmers to reify scope. This implies that idioms from R that require access to the caller’s scope are not appropriate for Julia.

The most important way in which these issues came up in the first generation of statistical libraries was in the representation of a single scalar missing value. In Julia 0.3, this concept is represented by the value NA, but that representation will be replaced when 0.4 is released. Most of this post will focus on the problems created by NA.

In addition to problems involving NA, there were also problems with how expressions were being passed to some functions. These problems have been resolved by removing the function signatures for statistical functions that involved passing expressions as arguments to those functions. A prototype package called DataFramesMeta, which uses macros to emulate some kinds of non-standard evaluation, is being developed by Tom Short.

# Representing Missing Values

In Julia 0.3, missing values are represented by a singleton object, NA, of type NAtype. Thus, a variable x, which might be either a Float64 value or a missing value encoded as NA, will end up with type Union(Float64, NAtype). This Union type is a source of performance problems because it defeats Julia’s compiler’s attempts to assign a unique concrete type to every variable.

We could remove this type-instability by ensuring that every type has a specific value, such as NaN, that signals missingness. This is the approach that both R and pandas take. It offers acceptable performance, but does so at the expense of generic handling of non-primitive types. Given Julia’s rampant usage of custom types, the sentinel values approach is not viable.

As such, we’re going to represent missing values in Julia 0.4 by borrowing some ideas from functional languages. In particular, we’ll be replacing the singleton object NA with a new parametric type Nullable{T}. Unlike NA, a Nullable object isn’t a direct scalar value. Rather, a Nullable object is a specialized container type that either contains one value or zero values. An empty Nullable container is taken to represent a missing value.

The Nullable approach to representing a missing scalar value offers two distinct improvements:

• Nullable{T} provides radically better performance than Union(T, NA). In some benchmarks, I find that iterative constructs can be as much as 100x faster when using Nullable{Float64} instead of Union(Float64, NA). Alternatively, I’ve found that Nullable{Float64} is about 60% slower than using NaN to represent missing values, but involves a generic approach that trivially extends to arbitrary new types, including integers, dates, complex numbers, quaternions, etc…
• Nullable{T} provides more type safety by requiring that all attempts to interact with potentially missing values explicitly indicate how missing values should be treated.

In a future blog post, I’ll describe how Nullable works in greater detail.

# Categorical Values

In addition to revising the representation of missing values, I’ve also been working on revising our representation of categorical values. Working with categorical data in Julia has always been a little strange, because the main tool for representing categorical data, the PooledDataArray, has always occupied an awkward intermediate position between two incompatible objectives:

• A container that keeps track of the unique values present in the container and uses this information to efficiently represent values as pointers to a pool of unique values.
• A container that contains values of a categorical variable drawn from a well-defined universe of possible values. The universe can include values that are not currently present in the container.

These two goals come into severe tension when considering subsets of a PooledDataArray. The uniqueness constraint suggests that the pool should shrink, whereas the categorical variable definition suggests that the pool should be maintained without change. In Julia 0.4, we’re going to commit completely to the latter behavior and leave the problem of efficiently representing highly compressible data for another data structure.

We’ll also begin representing scalar values of categorical variables using custom types. The new CategoricalVariable and OrdinalVariable types that will ship with Julia 0.4 will further the efforts to put scalar computations on an equal footing with vector computations. This will be particularly notable for dealing with ordinal variables, which are not supported at all in Julia 0.3.

# Metaprogramming

Many R functions employ non-standard evaluation as a mechanism for augmenting the current scope with the column names of a data.frame. In Julia, it’s often possible to emulate this behavior using macros. The in-progress DataFramesMeta package explores this alternative to non-standard evaluation. We will also be exploring other alternatives to non-standard evaluation in the future.

# What’s Next

In the long-term future, I’m hoping to improve several other parts of Julia’s core statistical infrastructure. In particular, I’d like to replace DataFrames with a new type that no longer occupies a strange intermediate position between matrices and relational tables. I’ll write another post about those issues later.