The DBI package helps connecting R to database management systems (DBMS). DBI separates the connectivity to the DBMS into a “front-end” and a “back-end”. The package defines an interface that is implemented by DBI backends such as:
and many more, see the list of backends. R scripts and packages use DBI to access various databases through their DBI backends.
The interface defines a small set of classes and methods similar in spirit to Perl’s DBI, Java’s JDBC, Python’s DB-API, and Microsoft’s ODBC. It supports the following operations:
Most users who want to access a database do not need to install DBI directly. It will be installed automatically when you install one of the database backends:
You can install the released version of DBI from CRAN with:
install.packages("DBI")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("r-dbi/DBI")The following example illustrates some of the DBI capabilities:
library(DBI)
# Create an ephemeral in-memory RSQLite database
con <- dbConnect(RSQLite::SQLite(), dbname = ":memory:")
dbListTables(con)
#> character(0)
dbWriteTable(con, "mtcars", mtcars)
dbListTables(con)
#> [1] "mtcars"
dbListFields(con, "mtcars")
#> [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
#> [11] "carb"
dbReadTable(con, "mtcars")
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> [ reached 'max' / getOption("max.print") -- omitted 23 rows ]
# You can fetch all results:
res <- dbSendQuery(con, "SELECT * FROM mtcars WHERE cyl = 4")
dbFetch(res)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 2 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 3 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 4 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 5 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 6 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 7 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 8 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 9 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> [ reached 'max' / getOption("max.print") -- omitted 2 rows ]
dbClearResult(res)
# Or a chunk at a time
res <- dbSendQuery(con, "SELECT * FROM mtcars WHERE cyl = 4")
while(!dbHasCompleted(res)){
chunk <- dbFetch(res, n = 5)
print(nrow(chunk))
}
#> [1] 5
#> [1] 5
#> [1] 1
dbClearResult(res)
dbDisconnect(con)There are four main DBI classes. Three which are each extended by individual database backends:
DBIObject: a common base class for all DBI.
DBIDriver: a base class representing overall DBMS
properties. Typically generator functions instantiate the driver objects
like RSQLite(), RPostgreSQL(),
RMySQL() etc.
DBIConnection: represents a connection to a specific
database
DBIResult: the result of a DBMS query or
statement.
All classes are virtual: they cannot be instantiated directly and instead must be subclassed.
Databases using R describes the tools and best practices in this ecosystem.
The DBI project site hosts a blog where recent developments are presented.
A history of DBI by David James, the driving force behind the development of DBI, and many of the packages that implement it.
Please note that the DBI project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.