A New ODBC Package For R

Kevin Feasel



David Smith looks at the odbc package in R:

The odbc package is a from-the-ground-up implementation of an ODBC interface for R that provides native support for additional data types (including dates, timestamps, raw binary, and 64-bit integers) and parameterized queries. The odbc package provides connections with any ODBC-compliant database, and has been comprehensively tested on SQL Server, PostgreSQL and MySQL. Benchmarks show that it’s also somewhat faster than RODBC: 3.2 times faster for reads, and 1.9 times faster for writes.

Sounds like odbc lets you run ad hoc queries and also lets you use dplyr as an ORM, similar to using Linq in C#.

Related Posts

Faster User-Defined Functions In SparkR

Liang Zhang and Hossein Falaki note a major performance improvement for functions in SparkR using the latest version of the Databricks Runtime: SparkR offers four APIs that run a user-defined function in R to a SparkDataFrame dapply() dapplyCollect() gapply() gapplyCollect() dapply() allows you to run an R function on each partition of the SparkDataFrame and returns […]

Read More

Subsetting Matrices In R

Dave Mason continues his look at matrices in R: We can extract an entire row from a matrix. To do this, specify the desired row only within the square brackets [ ]. The placeholder where you would otherwise specify the column is left empty. > #Points scored by Kendrick Perkins. > points_scored_by_quarter[1,] 1st 2nd 3rd 4th […]

Read More


August 2017
« Jul Sep »