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Tiffany Timbers

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Last week I wrote a post on how to build a basic database from a .csv files using SQLite, and today I finally got around to writing this short post on what to do next (i.e., how do your data out of the database and into R).

This task requires the R packages DBI and RSQLite (use install.packages("PackageName") to install). After opening R you must load these libraries:


You must connect R to the SQLite database file using dbConnect(). The extension can be either .db or .sqlite, whichever you named the database file when you created it via SQLite. Note - for this to work you must either set R’s working directory to the directory where your database lives or provide the path to the database.

con = dbConnect(SQLite(), dbname="mydatabase.db")

Next, you build your query (i.e., describe what you want to grab from the SQLite database). In the query below, we will be grabbing the entire column named “strain” from the table named “behaviour” from the database we just connected to.

myQuery <- dbSendQuery(con, "SELECT strain FROM behaviour")

After building your query, you can use that query to fetch the data from the database and assign that to an R object. The n = -1 argument in dbFetch() must be used if you want to retrieve all records in the database, otherwise the default is to stop at 500 records.

my_data <- dbFetch(myQuery, n = -1)

Now that the data is assigned the data to an object in R, it is advisable to clear the query. With very large datasets, this can be import for avoiding the exhaustion of resources (memory, file descriptors, etc.).


And that’s it! Now a subset of the data you want is in an R object. When you are done analyzing that aspect of the data, you can remove that R object and load the next subset of the database you need to analyze.