Extending diseasystore

This vignette gives you the knowledge you need to create your own diseasystore.

The diseasy data model

To begin, we go through the data model used within the diseasystores. It is this data model that enables the automatic coupling of features and powers the package.

A bitemporal data model

The data created by diseasystores are so-called “bitemporal” data. This means we have two temporal dimensions. One representing the validity of the record, and one representing the availability of the record.

valid_from and valid_until

The validity dimension indicates when a given data point is “valid”, e.g. a hospitalisation is valid between admission and discharge date. This temporal dimension should be familiar to you is simply “regular” time.

We encode the validity information into the columns valid_from and valid_until such that a record is valid for any time t which satisfies valid_from <= t < valid_until. For many features, the validity is a single day (such as a test result) and the valid_until column will be the day after valid_from.

By convention, we place these column as the last columns of the table1.

from_ts and until_ts

diseasystore uses {SCDB} in the background to store the computed features. SCDB implements the second temporal dimension which indicates when a record was present in the data. This information is encoded in the columns from_ts and until_ts. Normally, you don’t see these columns when working with diseasystore since they are masked by SCDB. However, if you inspect the tables created in the database by diseasystore, you will find they are present. For our purposes, it is sufficient to know that these column gives a time-versioned data base where we can extract previous versions through the slice_ts argument. By supplying any time τ as slice_ts, we get the data as they were available on that date. This allows us to build continuous integration of our features while preserving previously computed features.

Automatic data-coupling

A primary feature of diseasystore is its ability to automatically couple and aggregate features. This coupling requires common “key_*” columns between the features. Any feature in a diseasystore therefore must have at least one “key_*” column. By convention, we place these column as the first columns of the table.


Finally, we come to the main data of the diseasystore, namely the features. First, a reminder that “feature” here comes from machine learning and is any individual piece of information.

We subdivide features into two categories: “observables” and “stratifications”. On most levels, these are indistinguishable, but their purposes differ and hence we need to handle them individually.


In diseasystore any feature whose name either starts with “n_” or ends with “_temperature” are treated as “observables”. From a modelling perspective, these observables are typically the metrics you want to model or take as inputs to inform your model.


Conversely, any other feature is a “stratification” feature. These features are the variables used to subdivide your analysis to match the structure of your model (hence why they are called stratification features).

A prominent example for most disease models would be a stratification feature like “age_group”, since most diseases show a strong dependency on the age of the affected individuals.

Naming convention

While there is no formal requirement for the naming of the observables or stratifications, it is considered best practice to use the same names as other diseasystores for features where possible2. This simplifies the process of adapting analyses and disease models to new diseasystores.

Creating FeatureHandlers

To facilitate the automatic coupling and aggregation of features, we use the ?FeatureHandler class. Each feature3 in the diseasystore has an associated FeatureHandler which implements the computation, retrieval and aggregation of the feature.

Computing features

The FeatureHandler defines a compute function which must be on the form:

compute = function(start_date, end_date, slice_ts, source_conn)

The arguments start_date and end_date indicates the period for which features should be computed. The diseasystores are dynamically expanded, so feature computation is often restricted to limited time intervals as indicated by start_date and end_date.

As mentioned above slice_ts specifies what date the should be computed for. E.g. if slice_ts is the current date, the current features should be computed. Conversely, if slice_ts is some past date, features corresponding to this date should be computed.

Lastly, the source_conn is a flexible argument passed to the FeatureHandler indicating where the source data needed to compute the features is stored (e.g. a database connection or directory).

Note that multiple features can be computed by a single FeatureHandler. For example, you may decide that it is more convenient for compute multiple different features simultaneously (e.g. a hospitalisation and the classification of said hospitalisation or a test and the associated test result).

Retrieving features

The FeatureHandler defines a $get() function which must be in the form:

get = function(target_table, slice_ts, target_conn)

Typically, you do not need to specify this function since the default (a variant of SCDB::get_table()) always works.

However, in the case that you do need to specify it, the target_table argument will be a DBI::Id specifying the location of the data base table where the features are stored. target_conn is connection to the database. And as above, slice_ts is the time-keeping variable.


The FeatureHandler defines a key_join function which must be on the form:

key_join = function(.data, feature)

In most cases, you should be able to use the bundled key_join_* functions (see ?aggregators for a full list).

In the event, that you need to create your own aggregator the arguments are as follows:

Your aggregator should return a dplyr::summarise() call that operates on all columns specified in the feature argument.

Putting it all together

By now, you should know the basics of creating your own FeatureHandlers. To see some FeatureHandlers in action, you can consult a few of those bundled with the diseasystore package.

For example:

Creating a diseasystore

With the knowledge of how to build custom FeatureHandlers, we turn our attention to the remaining parts of the diseasystore’s anatomy.

The diseasystores are R6 classes which is a implementation of object-oriented (OO) programming. To those unfamiliar with OO programming, the diseasystores are single “objects” with a number of “public” and “private” functions and variables. The public functions and variables are visible to the user of the diseasystore with the private functions and variables are visible only to us (the developers).

When extending diseasystore, we are only writing private functions and variables. The public functions and variables are handled elsewhere4.


The ds_map field of the diseasystore tells the diseasystore which FeatureHandler is responsible for each feature, thus allowing the diseasystore to retrieve the features specified in the observable and stratification arguments of calls to $get_feature().

In other words, it maps the names of features to their corresponding FeatureHandlers.

As we saw above, a FeatureHandler may compute more than a single feature. Each feature should be mapped to the FeatureHandler here or else the diseasystore will not be able to automatically interact with it.

By convention, the name of the FeatureHandler should be snake_case and contain a diseasystore specific prefix (e.g. for DiseasystoreGoogleCovid19, all FeatureHandlers are named “google_covid_19_”).

These names are used as the table names when storing the features in the database, and the prefix helps structure the database accordingly.

This latter part becomes important when clean up for the data base needs to be performed.

Key join filter

The diseasystore are made to be as flexible as possible which means that it can incorporate both individual level data and semi-aggregated data. For semi-aggregated data, it is often the case that the data includes aggregations at different levels, nested within the data.

For example, the Google COVID-19 data repository contains information on both country-level and region-level in the same data files. When the user of DiseasystoreGoogleCovid19 asks to get a feature stratified by, for example, “country_id”, we need to filter out the data aggregated at the region level.

This is the purpose of $key_join_filter(). It takes as input the requested stratifications and filters the data accordingly after the features have been joined inside the diseasystore.

For an example, you can consult DiseasystoreGoogleCovid19: key_join_filter

Testing your diseasystore

The diseasystore package includes the function test_diseasystore() to test the diseasystores. You can see how to call the testing suite in action with DiseasystoreGoogleCovid19 as an example here.

  1. The SCDB package places checksum, from_ts, and until_ts as the last columns. But valid_from and valid_until should be the last columns in the output passed to SCDB.↩︎

  2. In practice, this means that the names of features should be in snake_case.↩︎

  3. Or “coupled” set of features as we will soon see.↩︎

  4. By the DiseasystoreBase class.↩︎