Selecting a database interface

DBI support is key

Connecting to a database with a method that supports the DBI package provides the following advantages:

  • A consistent set of functions that work across all connections
  • Makes using dplyr as a front-end possible

Mostly based on how difficult is to setup, the order of preference of connection methods is:

  1. Native database driver implemented in database package (e.g. RPostgresSQL). To see the list of known database packages check out the Databases page.
  2. ODBC as implemented in odbc package
  3. JDBC as implemented in RJDBC package

If there is a need to connect to several databases in one or several R projects, then it would be preferable to use ODBC for all connections because it increases consistency.

Additionally, using the odbc package inside the RStudio IDE will enable the use of the Connections Pane


Do I need to replace all of my existing JDBC connections, in favor of ODBC connections?

Not necessarily, if there are existing R jobs that successfully run on a regular basis then there’s no need to introduce risk, in other words if it ain’t broke, don’t fix it.

For new projects, if using ODBC is still not a viable option, then using an R package that implements a DBI back-end for the JDBC connection will be advisable, like RJDBC. There are other packages that implement both a DBI back-end for a JDBC connection and a dplyr translation, such as implyr for Impala connections.

Currently, I use RODBC, should I consider switching to odbc?

For new projects, yes. Switching will enable dplyr interactivity, and the Connections Pane inside the RStudio IDE (see the previous section for more info).

Existing projects may need to be individually vetted to see if the possible increase in performance that the odbc package may provide is worth making coding changes.