Final remarks

Tidy R programming with the OMOP Common Data Model aims to (1) explain the main principles for working with databases from R and (2) how to apply these principles and use them with the OMOP CDM. Hopefully, after reading this book, you can understand how the dplyr and dbplyr packages interact with the databases, in particular with data formatted to the OMOP CDM; how the cdm_reference object can be used to extract and identify your population of interest; and add the desired features to your dataset. Note that in this book we always worked with relatively small synthetic data with unrealistic performance. Any analysis conducted with real-world data and big databases will take more time, that’s why we would always recommend you test your code against synthetic data or subsets of your data to ensure good performance. Be careful, especially while writing custom code using join functions that can create some ugly SQL.

Learning more

If you find this book useful then joining the Tidy R in OMOP OHDSI working group will likely be of interest. Building on many of the concepts and tools seen in this book, the Tidy R in OMOP OHDSI working group aims to (1) create and promote a unified set of resources to guide Tidy R development in OMOP and support the OHDSI community, and (2) establish an overview of available packages relevant to Tidy R programming in OMOP (tidyverse style packages). If you are interested in joining the working group then please email Martí Català or Raivo Kolde.

Support us

We encourage you to support this work by either citing the book in your papers or documentation, recommending it to your colleagues, or starring the GitHub repository, or simply letting us know how it helped you. Most importantly, please use it in research that results in patient benefit.