Recommendations for database with R [closed]

Question

I am using R to run simulations using time series data. I have been using arrays to store data but I need a less memory intensive solution for storing data at intermediate steps in order to document the process. I am not a programmer so I am looking for something relatively easy to setup on multiple platforms if possible (Windows, Mac, Linux). I also need to be able to directly call the database from R since learning another language is not feasible now. Ideally, I would like to be able to read and write frequently to the database in a manner similar to an array though I don't know if that is realistic. I will gladly sacrifice speed for ease of use but I am willing to work to learn open source solutions. Any suggestions would be appreciated.

Answer 1

Quick comments:

• R is good at this, as a language for programming with data, there are plenty of interfaces
• There is an entire manual devoted to data import/export, and it has a section on relational databases, so start there.
• R has the widely-used DBI package which provides a unified interface for many backends, among them SQLite, MySQL, PostgreSQL, Oracle, ... Use that, maybe with RSQLite to get something going quickly. You can still switch backends afterwards.
• There is also RODBC but I find ODBC tedious to work with.
• R also has a specialised variant in the TSdbi package by Paul Gilbert which brings the DBI-alike abstraction to timeseries databases. It also supports multiple backends.
• The data.table package was written for this and is very fast on indexing and aggregation.