Data structures that allow missing values

arrays, julia, missing-data, statistics



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DataArrays DataArrays

The DataArrays package extends Julia by introducing data structures that can contain missing data. In particular, the package introduces three new data types to Julia:

  • NA: A singleton type that represents a single missing value.
  • DataArray{T}: An array-like data structure that can contain values of type T, but can also contain missing values.
  • PooledDataArray{T}: A variant of DataArray{T} optimized for representing arrays that contain many repetitions of a small number of unique values -- as commonly occurs when working with categorical data.

The NA Value

Many languages represent missing values using a reserved value like NULL or NA. A missing integer value, for example, might be represented as a NULL value in SQL or as an NA value in R.

Julia takes its conception of NA from R, where NA denotes missingness based on lack of information. If, for example, we were to measure people's heights as integers, an NA might reflect our ignorance of a specific person's height.

Conceptualizing the use of NA as a signal of uncertainty will help you understand how NA interacts with other values. For example, it explains why NA + 1 is NA, but NA & false is false. In general, NA corrupts any computation whose results cannot be determined without knowledge of the value that is NA.


Most Julian arrays cannot contain NA values: only Array{NAtype} and heterogeneous Arrays can contain NA values. Of these, only heterogeneous arrays could contain values of any type other than NAtype.

The generic use of heterogeneous Arrays is discouraged in Julia because it is inefficient: accessing any value requires dereferencing a pointer. The DataArray type allows one to work arond this inefficiency by providing tightly-typed arrays that can contain values of exactly one type, but can also contain NA values.

For example, a DataArray{Int} can contain integers and NA values. We can construct one as follows:

da = @data([1, 2, NA, 4])


As noted above, the DataArray type provides an efficient array-like data structure that contain potentially missing values. When working with categorical data sets in which a large number of data points occur, but only take on a limited set of unique values, we provide an analog to DataArray that is optimized for efficient memory usage: PooledDataArray.

For example, we can use a PooledDataArray to represent the following data efficiently:

d = repeat(["Group A", "Group B"], inner = [1000, 1000])
pda = PooledDataArray(d)

Instead of maintaining an array of 2,000 distinct strings, the PooledDataArray will maintain an array of 2,000 distinct references to the 2 unique values: "Group A" and "Group B".

In addition to using less memory, PooledDataArray's also maintain a cached record of the unique values in an array: for applications, like the creation of dummy variables, in which the unique values are frequently needed, this allows computations to avoid recalculating the unique values.