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

I also need to be able to directly
call the database from R

I suggest setting up MySQL with RMySQL interface.

Once the DB connection is open, you can query the database and get the the data into R, example:

# Run an SQL statement by creating first a resultSet object
rs <- dbSendQuery(con, statement = paste(
                      "SELECT w.laser_id, w.wavelength, p.cut_off",
                      "FROM WL w, PURGE P",
                      "WHERE w.laser_id = p.laser_id",
                      "SORT BY w.laser_id")
# we now fetch records from the resultSet into a data.frame
data <- fetch(rs, n = -1)   # extract all rows

RMySQL: R interface to the MySQL database

Database interface and MySQL driver
for R. This version complies with the
database interface definition as
implemented in the package DBI 0.2-2.

MySQL Database:

Available for all the platforms you cited in the question, and more, download here.