RSQLite

RSQLite is the easiest way to use a database from R because the package itself contains SQLite; no external software is needed. This vignette will walk you through the basics of using a SQLite database.

RSQLite is a DBI-compatible interface which means you primarily use functions defined in the DBI package, so you should always start by loading DBI, not RSQLite:

library(DBI)

Creating a new database

To create a new SQLite database, you simply supply the filename to dbConnect():

mydb <- dbConnect(RSQLite::SQLite(), "my-db.sqlite")
dbDisconnect(mydb)

If you just need a temporary database, use either "" (for an on-disk database) or ":memory:" or "file::memory:" (for a in-memory database). This database will be automatically deleted when you disconnect from it.

mydb <- dbConnect(RSQLite::SQLite(), "")
dbDisconnect(mydb)

Loading data

You can easily copy an R data frame into a SQLite database with dbWriteTable():

mydb <- dbConnect(RSQLite::SQLite(), "")
dbWriteTable(mydb, "mtcars", mtcars)
dbWriteTable(mydb, "iris", iris)
dbListTables(mydb)
#> [1] "iris"   "mtcars"

Queries

Issue a query with dbGetQuery():

dbGetQuery(mydb, 'SELECT * FROM mtcars LIMIT 5')
#>    mpg cyl disp  hp drat    wt  qsec vs am gear carb
#> 1 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
#> 2 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
#> 3 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
#> 4 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
#> 5 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2

Not all R variable names are valid SQL variable names, so you may need to escape them with ":

dbGetQuery(mydb, 'SELECT * FROM iris WHERE "Sepal.Length" < 4.6')
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1          4.4         2.9          1.4         0.2  setosa
#> 2          4.3         3.0          1.1         0.1  setosa
#> 3          4.4         3.0          1.3         0.2  setosa
#> 4          4.5         2.3          1.3         0.3  setosa
#> 5          4.4         3.2          1.3         0.2  setosa

If you need to insert the value from a user into a query, don’t use paste()! That makes it easy for a malicious attacker to insert SQL that might damage your database or reveal sensitive information. Instead, use a parameterised query:

dbGetQuery(mydb, 'SELECT * FROM iris WHERE "Sepal.Length" < :x',
  params = list(x = 4.6))
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1          4.4         2.9          1.4         0.2  setosa
#> 2          4.3         3.0          1.1         0.1  setosa
#> 3          4.4         3.0          1.3         0.2  setosa
#> 4          4.5         2.3          1.3         0.3  setosa
#> 5          4.4         3.2          1.3         0.2  setosa

This is a little more typing, but much much safer.

Batched queries

If you run a query and the results don’t fit in memory, you can use dbSendQuery(), dbFetch() and dbClearResults() to retrieve the results in batches. By default dbFetch() will retrieve all available rows: use n to set the maximum number of rows to return.

rs <- dbSendQuery(mydb, 'SELECT * FROM mtcars')
while (!dbHasCompleted(rs)) {
  df <- dbFetch(rs, n = 10)
  print(nrow(df))
}
#> [1] 10
#> [1] 10
#> [1] 10
#> [1] 2
dbClearResult(rs)

Multiple parameterised queries

You can use the same approach to run the same parameterised query with different parameters. Call dbBind() to set the parameters:

rs <- dbSendQuery(mydb, 'SELECT * FROM iris WHERE "Sepal.Length" < :x')
dbBind(rs, params = list(x = 4.5))
nrow(dbFetch(rs))
#> [1] 4
dbBind(rs, params = list(x = 4))
nrow(dbFetch(rs))
#> [1] 0
dbClearResult(rs)

You can also pass multiple parameters in one call to dbBind():

rs <- dbSendQuery(mydb, 'SELECT * FROM iris WHERE "Sepal.Length" = :x')
dbBind(rs, params = list(x = seq(4, 4.4, by = 0.1)))
nrow(dbFetch(rs))
#> [1] 4
dbClearResult(rs)

Statements

DBI has new functions dbSendStatement() and dbExecute(), which are the counterparts of dbSendQuery() and dbGetQuery() for SQL statements that do not return a tabular result, such as inserting records into a table, updating a table, or setting engine parameters. It is good practice, although currently not enforced, to use the new functions when you don’t expect a result.