5  Core dependencies

The Tidy R OMOP CDM packages rely on dplyr and the tidyverse packages to manipulate the data from the cdm object. The cdm object is an object that contains all the tables available and is central for the data manipulation. This cdm object is defined in omopgenerics.

5.1 omopgenerics

5.1.1 Classes

5.1.2 Methods

5.1.3 Input validation functions

5.1.4 Manipulate a cdm_reference object

5.1.5 Manipulate a summarised_result object

5.2 Other useful packages

The omopverse has some core packages that will be useful to simplify the scope of your package:

  • PatientProfiles: this package is very useful for data manipulation, you can find extract basic demographic information, intersections of cohorts and summarise data in a standard format. Take a look at the list of functions in PatientProfiles to prevent you from duplicating code.

  • visOmopResults: this package is very useful to create data visualisations (tables and plots) from standardised data. There are many different supported types of tables (r visOmopResults::tableType()) and with little customisation you can get quite pretty tables. For plots the support is a bit more limited but it creates some simple ggplot2 visualisations with very little effort. Take a look at the list of functions in visOmopResults to prevent you from duplicating code.

  • CodelistGenerator: this package can be useful to query the vocabularies. Take a look at the list of functions in CodelistGenerator to prevent you from duplicating code.

  • omock: this package can be very useful for testing. With very few lines of code you can create a mock CDM object with your desired specifications, specially useful can be the function mockCdmFromTables() that will create a viable CDM object from the supplied information. Take a look at the list of functions in omock to prevent you from duplicating code. This package is quite useful, but usually listed in Suggests as you will only need this package for testing purposes.