Using R with different types of databases: An overview

R is a working environment and computer language used in statistical computing. It is widely used as a tool in knowledge discovery and big data analytics as it provides an environment in which different statistical methods can be applied efficiently. As the size of analyzed data grows different data sources may be added to enrich statistical models and improve analysis quality. However, with R there is no need to store those datasets into the database. At the same time, R as environment support different data structures that make design and development of the information systems even more complex. R data structures could be divided into two main categories, homogeneous data such as atomic vector, matrix or array, and heterogeneous such as list and data frame. Additionally, we are witnesses to new trends in database design such as NoSQL, New SQL, graph database, in memory data storage, columnar and different application of SQL. Aim of this paper is to give an overview of different databases types in the context of R, covering concepts from navigational databases, to rational and post relational databases and how they are suited to host data for the purpose of access, storage and manipulation by statistical procedures in R.