README.md

DBI

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Status CRAN_Status_Badge CII Best
Practices

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:

Installation

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")

Example

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)

Class structure

There are four main DBI classes. Three which are each extended by individual database backends:

All classes are virtual: they cannot be instantiated directly and instead must be subclassed.

Further Reading

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.



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DBI documentation built on Dec. 20, 2021, 9:07 a.m.